FlightGear wiki - User contributions [en]

Howto:Using QGIS to process NLCD data

2020-09-23T03:15:16Z

Montagdude: /* Available scenery using NLCD data - add PA, NJ, NYC scenery */

This tutorial will describe '''how to use data from the [https://www.usgs.gov/centers/eros/science/national-land-cover-database?qt-science_center_objects=0#qt-science_center_objects National Land Cover Database (NLCD)] to create US scenery''' that is on par with the existing European scenery.

The default scenery (World Scenery 2.0) in FlightGear is of higher quality in some areas than others. For example, European scenery is generally of very high quality due to the use of [https://land.copernicus.eu/pan-european/corine-land-cover CORINE land cover data]. Compared to the European scenery, the US scenery is much less detailed and less accurate. Thankfully, there is a wealth of freely-available geographical information for the US that can be used to improve the scenery.

== Getting set up for scenery generation ==
=== Prerequisites ===
Please read and understand the [[Using TerraGear|Using TerraGear wiki article]]. This tutorial will follow the same process but replace the "Landuse data" section with custom shapefiles generated from NLCD data.

=== Tools ===
This tutorial will make use of the following GIS software:
# QGIS (version 3.10)
# GRASS (version 7.8, used as a QGIS plugin)
# TerraGear

=== Elevation data ===
Elevation data for the entire world is available from the Shuttle Radar Topology Mission (SRTM). It is highly recommended to use the void-filled version of these datasets, which can be obtained from the [https://earthexplorer.usgs.gov/ USGS EarthExplorer] (note: it is required to create a login to download data from that page). The void-filled datasets reduce or eliminate the small gaps that can occur in scenery generated using the original SRTM datasets. The elevation data is in GeoTIFF format. To convert it to hgt format as expected by TerraGear, use gdal_translate as follows:

gdal_translate -of SRTMHGT input_file.tif output_file.hgt

For the output file, TerraGear might expect it to follow a certain naming convention, like N38W080.hgt. The data from USGS follows a naming convention like n38_w080_3arc_v2.tif. The following (sh/bash shell) script will batch convert all the files in the working directory following the expected naming convention:

for file in *.tif; do
base=$(echo $file | cut -f 1 -d '.')
NORTH=$(echo $base | cut -f 1 -d '_' | cut -f 2 -d 'n')
WEST=$(echo $base | cut -f 2 -d '_' | cut -f 2 -d 'w')
gdal_translate -of SRTMHGT $file N${NORTH}W${WEST}.hgt
done

== NLCD data ==
You can download the NLCD data [https://www.mrlc.gov/data?f%5B0%5D=category%3Aland%20cover here]. At the time of this writing, the latest dataset is from 2016. For the continental US, you should choose the dataset labeled "NLCD 2016 Land Cover (CONUS)." There is also a separate dataset for Alaska. The download is a large (>1 GB) zip file. After downloading, unzip the files.

=== Processing the data ===
* Fire up QGIS and select the WGS 84 template. If you don't have a template to choose from, don't worry about it, as we'll be setting the coordinate reference system for the project later. Just create a new project in that case.
* In the menu bar, select Project -> Properties -> CRS. Pick WGS 84 - EPSG:4326 as the project coordinate reference system.
<gallery>
49532654886 e94a32328d o.png|Setting the CRS
</gallery>
* Set a basemap. An easy way to do this is to double click on XYZ Tiles -> OpenStreetMap in the browser on the left. You can also get them via the QuickMapServices plugin.
* Browse to the location where you have unzipped the NLCD data in the QGIS project browser and double click the .img file to load it as a layer.
* Right-click on the NLCD layer -> Set CRS -> Set Layer CRS and select NAD83 / Conus Albers - EPSG:5070 -> Transformation 1. This will place the layer in the right location on top of the basemap. Note: it may not look like it's in the right spot until you zoom in on it.
<gallery>
49532655611 64dc27a6fd o.png|NLCD data loaded in QGIS
</gallery>
* Now we need to clip the data to the region of interest. The best way to do this is to by clicking the New Shapefile Layer button (see screenshot below). In the dialog, change Geometry type to Polygon and save it as a shapefile, and then click OK.
* Click the Toggle Editing button to edit the layer.
* Click the Add Polygon Feature button to create a polygon. Now you can click on the map to create a polygon covering the area of interest. When you are done, you can right-click to stop adding vertices.
* If you want to edit the vertex locations, you can click the Vertex Tool button, right-click on your polygon, and then edit the coordinates in the table on the bottom left of the GUI window.
* When you are done editing the layer, click the Save Layer Edits button (next to the Toggle Editing button).
'''Note''': when you are learning the process, start with a small polygon, say 1x1 degrees. Some of the steps take a very long time if processing very large sections of this data.
<gallery>
New layer.png|New shapefile layer
Shapefile dialog.png|Shapefile dialog
Toggle editing.png|Toggle editing
Add polygon.png|Add polygon
Vertex tool.png|Vertex tool
</gallery>
*Once the polygon is completed, the next step is to clip the NLCD layer using this polygon. Select the NLCD layer and choose Raster -> Extraction -> Clip Raster by Mask Layer. Make sure the input layer is your NLCD layer and the mask layer is the polygon you have created. For Source CRS choose the Conus Albers CRS of the original NLCD layer, and for Target CRS choose the Project CRS, EPSG:4326. Near the bottom of the dialog box, where under Clipped (mask) it says [Save to temporary file], click the button to the right and choose Save to File. Select IMG files as the filter and save it somewhere in your working directory with a descriptive name like NLCD_clip.img. Click Run. Once it completes, if you hide the original NLCD layer and the polygon, you should see the new clipped layer. As long as it worked correctly, you can now safely remove the big original NLCD layer. The image below shows a small sample section in Idaho that has been clipped using this method.
<gallery>
49532154383 b437eb21b7 o.png|Clipped NLCD data
</gallery>
*If you zoom in on the data, you will see that it is highly detailed but also pixelated due to the raster format. It needs to be simplified and smoothed out to look good in FlightGear and also to reduce the polygon count on the resulting shapefiles. We will first do some smoothing of the raster itself, and then we'll do additional smoothing after converting to vector format. In the Processing Toolbox, find GRASS -> r.neighbors. The clipped NLCD layer should be selected for both of the top input dropdowns. For Neighborhood Operation, select median with a Neighborhood size of 5. As before, you may want to choose an actual IMG file to save the output to instead of a temporary file. Click Run.
'''Note''': never use an averaging neighborhood operation, as the raster values are uniquely associated with a specific type of landcover. The averaging operation will introduce new values that do not correspond to a landcover type.
*Now we'll run r.neighbors once more on the resulting layer using the mode Neighborhood Operation with a Neighborhood of 3. The result should look a lot smoother and a bit less detailed than the original dataset (but still very highly detailed compared to the default scenery). An image from the sample dataset is below. This area shows McCall, Idaho and the south end of Payette Lake.
<gallery>
49539367447 cde1c6666a o.png|After r.neighbors operations
</gallery>
*We're now ready to convert the raster data to vector. In the Processing Toolbox, find Grass -> r.to.vect. Choose the output layer from the last step (median neighbors filter), choose area as the feature type, and check the option "Smooth corners of area features." This will make the output vector a little less pixely than the input. As before, you may want to save to a real shapefile instead of a temporary one. Click Run. This step will take longer than any of the previous ones to process.
*We could use this vector data as is, but it's still a bit blocky and unnatural looking. Instead, we'll do a couple processing steps on the vector data. GRASS -> v.generalize. Choose the default douglas algorithm with a tolerance of 0.0003 (this is in degrees, since our CRS is in degrees). You may want to play around with different values of tolerance until you find something that works best. Click Run. If errors result, it will create a new layer, which can be deleted. '''Note''': this step will take a very long time for large areas.
*Now we'll use v.generalize one more to smooth the simplified layer. This time, choose the chaiken algorithm with the same tolerance. Click Run. '''Note''': this step will take a very long time for large areas.
<gallery>
Vectorized.png|Vectorized NLCD data
Simplified.png|Simplified NLCD data
Smoothed.png|Smoothed NLCD data
</gallery>
*At this point, it is a good idea to compare the smoothed vector layer to the original NLCD raster data and make sure the smoothing steps have not introduced too much error in the shape of coastlines and other features. If they have, you may need to redo some or all of the last several steps (r.neighbors, r.to.vect, and v.generalize) adjusting the number of neighbors and tolerance until you get satisfactory results. However, I've found these parameters to produce quite good results.
*We now have a high-resolution vectorized dataset that can be used to define landclasses in FlightGear. We just need to save individual shape files representing each landclass. At this point, it is a good idea to have the NLCD map with the legend displayed to match to values in the shapefiles. That map can be found here:
https://www.mrlc.gov/viewer/
*You will also want to have default_priorities.txt from your TerraGear installation open to map NLCD landclass types to available materials in FlightGear. Some artistic license is possible here, but I use this mapping:
{| class="wikitable"
|-
! NLCD Value !! Legend label !! FlightGear material
|-
| 11 || Open Water || Lake or Ocean
|-
| 12 || Perennial Ice/Snow || SnowCover or Glacier
|-
| 21 || Developed, Open Space || Greenspace
|-
| 22 || Developed, Low Intensity || Town
|-
| 23 || Developed, Medium Intensity || SubUrban
|-
| 24 || Developed, High Intensity || Urban
|-
| 31 || Barren Land (Rock/Sand/Clay) || Rock or Sand (depends on location)
|-
| 32 || Unconsolidated Shore || Sand
|-
| 41 || Deciduous Forest || DeciduousForest
|-
| 42 || Evergreen Forest || EvergreenForest
|-
| 43 || Mixed Forest || MixedForest
|-
| 51,52 || Dwarf Scrub/Shrub/Scrub || Scrub
|-
| 71 || Grasslands/Herbaceous || Grassland
|-
| 72-74 || Herbaceous/Moss/Lichens (AK only) || I haven't used these, so check default_priorities.txt if you find them in your data
|-
| 81 || Pasture/Hay || DryCrop
|-
| 82 || Cultivated Crops || IrrCrop
|-
| 90 || Woody Wetlands || Marsh
|-
| 95 || Emergent Herbaceous Wetlands || Bog
|}
*To actually select and export individual shapefiles for each type, do the following:
**Select the final smoothed vector data layer. Click the Deselect Features from All Layers button near the top middle of the QGIS toolbar. Then click the button right next to it, Select Features by Value. In the dialog, enter the number from the NLCD legend in the value field, make sure the filter says Equal to (=), and then click Select Features and Close. Alternatively, if you want to combine multiple NLCD values into a single output shapefile, rather than clicking Close, you could enter in another value and then choose Add to Current Selection in the dropdown next to Select Features.
**Once you have the desired features selected, right click on the smoothed vector layer, click Export -> Save Selected Features As, choose the ESRI shapefile format, and save it somewhere under the project directory with a name corresponding to the appropriate area type in default_priorities.txt (e.g. data/shapefiles/Lake.shp). For CRS, choose the project CRS EPSG:4326 - WGS 84.
**Repeat this process until you've gone over all the values in the NLCD data legend. When finished, you should have individual layers representing different area types covering the entire area of interest. The result for the sample area is shown in the image below.
<gallery>
Nlcd shapefiles.jpg|Shapefiles from NLCD data
</gallery>

=== Converting with ogr-decode ===

If you have saved the shapefiles with a name corresponding to a material in default_priorities.txt, then they can be batch-converted with ogr-decode as follows:

# work and shapefiles directories
WORK=./work
DATADIR=./data/shapefiles/NLCD
# Process landcover
for file in $DATADIR/*.shp; do
fname=$(basename $file)
area_type=$(echo $fname | cut -f 1 -d '.')
if [ -d $WORK/$area_type ]; then
rm -rf $WORK/$area_type
fi
ogr-decode --area-type $area_type work/$area_type $file
done

(The script above is a bash/sh script for Linux. Something similar could probably done with a .bat script on Windows. The locations of the work and data directories will need to be edited if you use a different directory structure.)

=== Building with tg-construct ===

The final step in building the terrain is to run tg-construct. This process is described in the [[Using TerraGear|Using TerraGear wiki article]]. This should be done after adding an supplementary shapefiles, as described in the next section. When running tg-construct, you will want to use the '''--ignore-landmass''' flag so that lakes and other waterbodies are not assumed to be at sea level.

== Supplementary data ==

You will probably want to supplement your custom scenery with some other data. Sources include:

* National Hydrography Data for waterways and waterbodies. Though NLCD includes water landcover, it is not as accurate or complete as NHD, and the smoothing process described above will cause thin rivers to turn into "islands." I recommend using the NHDArea and NHDWaterbody datasets but skipping the line data. The data is in shapefile format, which can be viewed and manipulated with QGIS. Be sure to filter the data to remove extraneous elements. It includes things like marshes, dams, and intermittent streams that probably should not be included. The NHD data can be accessed here: [https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products Access National Hydrography Products]. To filter the data, you will want to refer to this page: [https://nhd.usgs.gov/userGuide/Robohelpfiles/NHD_User_Guide/Feature_Catalog/Hydrography_Dataset/Complete_FCode_List.htm NHD FCodes].

* [https://www.openstreetmap.org OpenStreetMap] for roads. QGIS can open .osm and .pbf files extracted from OpenStreetMap and edit them just like all the other datasets that have been discussed here. These can be "baked into" the terrain using TerraGear, or you can use:

* [https://osm2city.readthedocs.io/en/latest/index.html osm2city], an autogen tool for FlightGear that creates buildings, roads, bridges, pylons, docks, and other details from OpenStreetMap data. This tool has a bit of a learning curve and does not flow through QGIS, but it can really make your project shine.

== Sample results ==
<gallery>
McCall, Idaho, default WS 2.0 scenery.png|McCall, Idaho with default WS2.0 scenery
McCall, Idaho custom scenery.png|McCall, Idaho with NLCD + osm2city scenery
Cape Cod.png|Cape Cod with NLCD and osm2city
Laurel Bed Lake.png|Laurel Bed Lake in western Virginia
Ec135-manhattan.jpg|EC135 near Central Park in Manhattan, New York
C182s-atlantic-city.jpg|Cessna 182S over Atlantic City, New Jersey
</gallery>

== Available scenery using NLCD data ==

* Pennsylvania, New Jersey, and New York City by montagdude: [https://github.com/montagdude/FGScenery-PA_NJ_NYC project page], [https://forum.flightgear.org/viewtopic.php?f=5&t=38041 forum topic]
* Virginia, Maryland, Delaware, and the District of Columbia by montagdude: [https://github.com/montagdude/FGScenery-VA_MD_DE project page], [https://forum.flightgear.org/viewtopic.php?f=5&t=37322 forum topic]
* Rhode Island and Southeastern Massachusetts by montagdude: [https://github.com/montagdude/FGScenery-RIandSEMass project page], [https://forum.flightgear.org/viewtopic.php?f=5&t=36977 forum topic]
* Kansas and Ohio scenery by wlbragg: [[User:Wlbragg]]
* Tennessee scenery by xDraconian: [[US-Tennessee_Custom_Scenery]], [https://forum.flightgear.org/viewtopic.php?f=5&t=35521 forum topic]

== Related content ==
=== Forum topic ===
* {{forum link|t=36913|title=How to create US scenery with NLCD land cover data and QGIS}}

[[Category:Scenery enhancement]]

Howto:Using QGIS to process NLCD data

2020-09-23T03:13:33Z

Montagdude: /* Sample results - added a couple more images */

This tutorial will describe '''how to use data from the [https://www.usgs.gov/centers/eros/science/national-land-cover-database?qt-science_center_objects=0#qt-science_center_objects National Land Cover Database (NLCD)] to create US scenery''' that is on par with the existing European scenery.

The default scenery (World Scenery 2.0) in FlightGear is of higher quality in some areas than others. For example, European scenery is generally of very high quality due to the use of [https://land.copernicus.eu/pan-european/corine-land-cover CORINE land cover data]. Compared to the European scenery, the US scenery is much less detailed and less accurate. Thankfully, there is a wealth of freely-available geographical information for the US that can be used to improve the scenery.

== Getting set up for scenery generation ==
=== Prerequisites ===
Please read and understand the [[Using TerraGear|Using TerraGear wiki article]]. This tutorial will follow the same process but replace the "Landuse data" section with custom shapefiles generated from NLCD data.

=== Tools ===
This tutorial will make use of the following GIS software:
# QGIS (version 3.10)
# GRASS (version 7.8, used as a QGIS plugin)
# TerraGear

=== Elevation data ===
Elevation data for the entire world is available from the Shuttle Radar Topology Mission (SRTM). It is highly recommended to use the void-filled version of these datasets, which can be obtained from the [https://earthexplorer.usgs.gov/ USGS EarthExplorer] (note: it is required to create a login to download data from that page). The void-filled datasets reduce or eliminate the small gaps that can occur in scenery generated using the original SRTM datasets. The elevation data is in GeoTIFF format. To convert it to hgt format as expected by TerraGear, use gdal_translate as follows:

gdal_translate -of SRTMHGT input_file.tif output_file.hgt

For the output file, TerraGear might expect it to follow a certain naming convention, like N38W080.hgt. The data from USGS follows a naming convention like n38_w080_3arc_v2.tif. The following (sh/bash shell) script will batch convert all the files in the working directory following the expected naming convention:

for file in *.tif; do
base=$(echo $file | cut -f 1 -d '.')
NORTH=$(echo $base | cut -f 1 -d '_' | cut -f 2 -d 'n')
WEST=$(echo $base | cut -f 2 -d '_' | cut -f 2 -d 'w')
gdal_translate -of SRTMHGT $file N${NORTH}W${WEST}.hgt
done

== NLCD data ==
You can download the NLCD data [https://www.mrlc.gov/data?f%5B0%5D=category%3Aland%20cover here]. At the time of this writing, the latest dataset is from 2016. For the continental US, you should choose the dataset labeled "NLCD 2016 Land Cover (CONUS)." There is also a separate dataset for Alaska. The download is a large (>1 GB) zip file. After downloading, unzip the files.

=== Processing the data ===
* Fire up QGIS and select the WGS 84 template. If you don't have a template to choose from, don't worry about it, as we'll be setting the coordinate reference system for the project later. Just create a new project in that case.
* In the menu bar, select Project -> Properties -> CRS. Pick WGS 84 - EPSG:4326 as the project coordinate reference system.
<gallery>
49532654886 e94a32328d o.png|Setting the CRS
</gallery>
* Set a basemap. An easy way to do this is to double click on XYZ Tiles -> OpenStreetMap in the browser on the left. You can also get them via the QuickMapServices plugin.
* Browse to the location where you have unzipped the NLCD data in the QGIS project browser and double click the .img file to load it as a layer.
* Right-click on the NLCD layer -> Set CRS -> Set Layer CRS and select NAD83 / Conus Albers - EPSG:5070 -> Transformation 1. This will place the layer in the right location on top of the basemap. Note: it may not look like it's in the right spot until you zoom in on it.
<gallery>
49532655611 64dc27a6fd o.png|NLCD data loaded in QGIS
</gallery>
* Now we need to clip the data to the region of interest. The best way to do this is to by clicking the New Shapefile Layer button (see screenshot below). In the dialog, change Geometry type to Polygon and save it as a shapefile, and then click OK.
* Click the Toggle Editing button to edit the layer.
* Click the Add Polygon Feature button to create a polygon. Now you can click on the map to create a polygon covering the area of interest. When you are done, you can right-click to stop adding vertices.
* If you want to edit the vertex locations, you can click the Vertex Tool button, right-click on your polygon, and then edit the coordinates in the table on the bottom left of the GUI window.
* When you are done editing the layer, click the Save Layer Edits button (next to the Toggle Editing button).
'''Note''': when you are learning the process, start with a small polygon, say 1x1 degrees. Some of the steps take a very long time if processing very large sections of this data.
<gallery>
New layer.png|New shapefile layer
Shapefile dialog.png|Shapefile dialog
Toggle editing.png|Toggle editing
Add polygon.png|Add polygon
Vertex tool.png|Vertex tool
</gallery>
*Once the polygon is completed, the next step is to clip the NLCD layer using this polygon. Select the NLCD layer and choose Raster -> Extraction -> Clip Raster by Mask Layer. Make sure the input layer is your NLCD layer and the mask layer is the polygon you have created. For Source CRS choose the Conus Albers CRS of the original NLCD layer, and for Target CRS choose the Project CRS, EPSG:4326. Near the bottom of the dialog box, where under Clipped (mask) it says [Save to temporary file], click the button to the right and choose Save to File. Select IMG files as the filter and save it somewhere in your working directory with a descriptive name like NLCD_clip.img. Click Run. Once it completes, if you hide the original NLCD layer and the polygon, you should see the new clipped layer. As long as it worked correctly, you can now safely remove the big original NLCD layer. The image below shows a small sample section in Idaho that has been clipped using this method.
<gallery>
49532154383 b437eb21b7 o.png|Clipped NLCD data
</gallery>
*If you zoom in on the data, you will see that it is highly detailed but also pixelated due to the raster format. It needs to be simplified and smoothed out to look good in FlightGear and also to reduce the polygon count on the resulting shapefiles. We will first do some smoothing of the raster itself, and then we'll do additional smoothing after converting to vector format. In the Processing Toolbox, find GRASS -> r.neighbors. The clipped NLCD layer should be selected for both of the top input dropdowns. For Neighborhood Operation, select median with a Neighborhood size of 5. As before, you may want to choose an actual IMG file to save the output to instead of a temporary file. Click Run.
'''Note''': never use an averaging neighborhood operation, as the raster values are uniquely associated with a specific type of landcover. The averaging operation will introduce new values that do not correspond to a landcover type.
*Now we'll run r.neighbors once more on the resulting layer using the mode Neighborhood Operation with a Neighborhood of 3. The result should look a lot smoother and a bit less detailed than the original dataset (but still very highly detailed compared to the default scenery). An image from the sample dataset is below. This area shows McCall, Idaho and the south end of Payette Lake.
<gallery>
49539367447 cde1c6666a o.png|After r.neighbors operations
</gallery>
*We're now ready to convert the raster data to vector. In the Processing Toolbox, find Grass -> r.to.vect. Choose the output layer from the last step (median neighbors filter), choose area as the feature type, and check the option "Smooth corners of area features." This will make the output vector a little less pixely than the input. As before, you may want to save to a real shapefile instead of a temporary one. Click Run. This step will take longer than any of the previous ones to process.
*We could use this vector data as is, but it's still a bit blocky and unnatural looking. Instead, we'll do a couple processing steps on the vector data. GRASS -> v.generalize. Choose the default douglas algorithm with a tolerance of 0.0003 (this is in degrees, since our CRS is in degrees). You may want to play around with different values of tolerance until you find something that works best. Click Run. If errors result, it will create a new layer, which can be deleted. '''Note''': this step will take a very long time for large areas.
*Now we'll use v.generalize one more to smooth the simplified layer. This time, choose the chaiken algorithm with the same tolerance. Click Run. '''Note''': this step will take a very long time for large areas.
<gallery>
Vectorized.png|Vectorized NLCD data
Simplified.png|Simplified NLCD data
Smoothed.png|Smoothed NLCD data
</gallery>
*At this point, it is a good idea to compare the smoothed vector layer to the original NLCD raster data and make sure the smoothing steps have not introduced too much error in the shape of coastlines and other features. If they have, you may need to redo some or all of the last several steps (r.neighbors, r.to.vect, and v.generalize) adjusting the number of neighbors and tolerance until you get satisfactory results. However, I've found these parameters to produce quite good results.
*We now have a high-resolution vectorized dataset that can be used to define landclasses in FlightGear. We just need to save individual shape files representing each landclass. At this point, it is a good idea to have the NLCD map with the legend displayed to match to values in the shapefiles. That map can be found here:
https://www.mrlc.gov/viewer/
*You will also want to have default_priorities.txt from your TerraGear installation open to map NLCD landclass types to available materials in FlightGear. Some artistic license is possible here, but I use this mapping:
{| class="wikitable"
|-
! NLCD Value !! Legend label !! FlightGear material
|-
| 11 || Open Water || Lake or Ocean
|-
| 12 || Perennial Ice/Snow || SnowCover or Glacier
|-
| 21 || Developed, Open Space || Greenspace
|-
| 22 || Developed, Low Intensity || Town
|-
| 23 || Developed, Medium Intensity || SubUrban
|-
| 24 || Developed, High Intensity || Urban
|-
| 31 || Barren Land (Rock/Sand/Clay) || Rock or Sand (depends on location)
|-
| 32 || Unconsolidated Shore || Sand
|-
| 41 || Deciduous Forest || DeciduousForest
|-
| 42 || Evergreen Forest || EvergreenForest
|-
| 43 || Mixed Forest || MixedForest
|-
| 51,52 || Dwarf Scrub/Shrub/Scrub || Scrub
|-
| 71 || Grasslands/Herbaceous || Grassland
|-
| 72-74 || Herbaceous/Moss/Lichens (AK only) || I haven't used these, so check default_priorities.txt if you find them in your data
|-
| 81 || Pasture/Hay || DryCrop
|-
| 82 || Cultivated Crops || IrrCrop
|-
| 90 || Woody Wetlands || Marsh
|-
| 95 || Emergent Herbaceous Wetlands || Bog
|}
*To actually select and export individual shapefiles for each type, do the following:
**Select the final smoothed vector data layer. Click the Deselect Features from All Layers button near the top middle of the QGIS toolbar. Then click the button right next to it, Select Features by Value. In the dialog, enter the number from the NLCD legend in the value field, make sure the filter says Equal to (=), and then click Select Features and Close. Alternatively, if you want to combine multiple NLCD values into a single output shapefile, rather than clicking Close, you could enter in another value and then choose Add to Current Selection in the dropdown next to Select Features.
**Once you have the desired features selected, right click on the smoothed vector layer, click Export -> Save Selected Features As, choose the ESRI shapefile format, and save it somewhere under the project directory with a name corresponding to the appropriate area type in default_priorities.txt (e.g. data/shapefiles/Lake.shp). For CRS, choose the project CRS EPSG:4326 - WGS 84.
**Repeat this process until you've gone over all the values in the NLCD data legend. When finished, you should have individual layers representing different area types covering the entire area of interest. The result for the sample area is shown in the image below.
<gallery>
Nlcd shapefiles.jpg|Shapefiles from NLCD data
</gallery>

=== Converting with ogr-decode ===

If you have saved the shapefiles with a name corresponding to a material in default_priorities.txt, then they can be batch-converted with ogr-decode as follows:

# work and shapefiles directories
WORK=./work
DATADIR=./data/shapefiles/NLCD
# Process landcover
for file in $DATADIR/*.shp; do
fname=$(basename $file)
area_type=$(echo $fname | cut -f 1 -d '.')
if [ -d $WORK/$area_type ]; then
rm -rf $WORK/$area_type
fi
ogr-decode --area-type $area_type work/$area_type $file
done

(The script above is a bash/sh script for Linux. Something similar could probably done with a .bat script on Windows. The locations of the work and data directories will need to be edited if you use a different directory structure.)

=== Building with tg-construct ===

The final step in building the terrain is to run tg-construct. This process is described in the [[Using TerraGear|Using TerraGear wiki article]]. This should be done after adding an supplementary shapefiles, as described in the next section. When running tg-construct, you will want to use the '''--ignore-landmass''' flag so that lakes and other waterbodies are not assumed to be at sea level.

== Supplementary data ==

You will probably want to supplement your custom scenery with some other data. Sources include:

* National Hydrography Data for waterways and waterbodies. Though NLCD includes water landcover, it is not as accurate or complete as NHD, and the smoothing process described above will cause thin rivers to turn into "islands." I recommend using the NHDArea and NHDWaterbody datasets but skipping the line data. The data is in shapefile format, which can be viewed and manipulated with QGIS. Be sure to filter the data to remove extraneous elements. It includes things like marshes, dams, and intermittent streams that probably should not be included. The NHD data can be accessed here: [https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products Access National Hydrography Products]. To filter the data, you will want to refer to this page: [https://nhd.usgs.gov/userGuide/Robohelpfiles/NHD_User_Guide/Feature_Catalog/Hydrography_Dataset/Complete_FCode_List.htm NHD FCodes].

* [https://www.openstreetmap.org OpenStreetMap] for roads. QGIS can open .osm and .pbf files extracted from OpenStreetMap and edit them just like all the other datasets that have been discussed here. These can be "baked into" the terrain using TerraGear, or you can use:

* [https://osm2city.readthedocs.io/en/latest/index.html osm2city], an autogen tool for FlightGear that creates buildings, roads, bridges, pylons, docks, and other details from OpenStreetMap data. This tool has a bit of a learning curve and does not flow through QGIS, but it can really make your project shine.

== Sample results ==
<gallery>
McCall, Idaho, default WS 2.0 scenery.png|McCall, Idaho with default WS2.0 scenery
McCall, Idaho custom scenery.png|McCall, Idaho with NLCD + osm2city scenery
Cape Cod.png|Cape Cod with NLCD and osm2city
Laurel Bed Lake.png|Laurel Bed Lake in western Virginia
Ec135-manhattan.jpg|EC135 near Central Park in Manhattan, New York
C182s-atlantic-city.jpg|Cessna 182S over Atlantic City, New Jersey
</gallery>

== Available scenery using NLCD data ==

* Virginia, Maryland, Delaware, and the District of Columbia by montagdude: [https://github.com/montagdude/FGScenery-VA_MD_DE project page], [https://forum.flightgear.org/viewtopic.php?f=5&t=37322 forum topic]
* Rhode Island and Southeastern Massachusetts by montagdude: [https://github.com/montagdude/FGScenery-RIandSEMass project page], [https://forum.flightgear.org/viewtopic.php?f=5&t=36977 forum topic]
* Kansas and Ohio scenery by wlbragg: [[User:Wlbragg]]
* Tennessee scenery by xDraconian: [[US-Tennessee_Custom_Scenery]], [https://forum.flightgear.org/viewtopic.php?f=5&t=35521 forum topic]

== Related content ==
=== Forum topic ===
* {{forum link|t=36913|title=How to create US scenery with NLCD land cover data and QGIS}}

[[Category:Scenery enhancement]]

FlightGear Newsletter September 2020

2020-09-23T03:10:50Z

Montagdude: /* Scenery corner - added note about custom PA, NJ, and NYC scenery */

{{User:Skybike/Template:Newsletter-header-translate|2020-09}}
{{TOC_right|limit=2}}
''We would like to emphasize that the monthly newsletter can not live without the contributions of FlightGear users and developers. Everyone with a wiki account (free to register) is welcome to contribute to the newsletter. If you know about any FlightGear related news or projects such as for example updated scenery or aircraft, please do feel invited to add such news to the newsletter.''

== Development news ==


== New software tools and projects ==


=== FGCom-mumble ===
[https://github.com/hbeni/fgcom-mumble FGCom-Mumble] aims to provide a mumble based FGCom implementation. This will simulate radio communications in a seamless frequency spectrum.

The project aims to be easy to use: Pilots just install the plugin, open mumble, join a channel on a mumble server and start using their radio stack in Flightgear.

The development is discussed in the {{forum link|title=FGCom-mumble|f=32|t=37644}}. Releases can be downloaded from [https://github.com/hbeni/fgcom-mumble github].

==== Current state ====
As the development of the mumble plugin framework goes into its final phase, it introduced lots of changes to the API, so the FGCom-mumble release 0.4.0 does not work with recent compiles of mumble and murmur anymore.

After the 0.4.0 release, work still did go on for FGCom-mumble. The new API changes were implemented, bugs were again found and fixed and some new features introduced and contained in the 0.5.0 release:
* There is better support for distributed setup of the server side
* Callsign and frequencies are now reported to the users mumble comment, so its easier to detect if the client has all needed data, and also to match callsigns to mumble user names
* The plugin now can be configured using an optional ini file
* The detection of the "special channel" is now configurable, the default value matches all channels starting with "fgcom-mumble"; so a single server can host several "worlds" now
* A new optional "raw" mode in the Statuspage can display JSON encoded user info, that can easily be digested by third party applications (mp online map?)
* The Statuspage optionally displays a highscore now (when was the last user peak, and how much users where connected?).
* Usage statistics can now generated by the Statusbot, which can also be displayed graphically trough the Statuspage (using gnuplot)
* The plugin updater was reworked so it works more reliable
* The audio effects have been tuned a bit

==== Open tasks ====
The plugin is principally functional with the latest API. As the mumble project introduces further changes to the API, i will integrate them.

To conduct further and broader testing, we probably need to wait for mumble 1.4.0. As this is probably still a long time out, early testing under linux is welcome.
Also, I still have no idea if the windows plugin loads, so someone with a windows build environment is most appreciated to compile mumble and try the plugin.

The [https://github.com/hbeni/fgcom-mumble/issues issue tracker] still lists some points, but those are either minor ones or are not implementable at the current time. Nonetheless, please see trough the items, maybe you can lend a hand. Also, some code review is welcome!
Especially the memory management needs some more eyes as there still may be memory leaks.

==== Trying it out yourself ====
As mumble 1.4.0 is still in the pipeline, you need a self compiled mumble (and murmur) from the [https://github.com/Krzmbrzl/mumble/tree/plugin-API Krzmbrzl:plugin-API branch] to test this out. If you additionally want to run the server side bots, you also need [https://github.com/bkacjios/lua-mumble lua-mumble].
Detailed installation descriptions are laid out in the readmes.

It would be cool if someone with a public server could try to setup that, so I can verify the readme files are correct and we get an online test bed.

== In the hangar ==












== Help wanted ==
{{Help wanted}}

== AI ==


The AI team makes FlightGear more realistic, colorful and lively every month. You can support the important development of ''Interactive Traffic'' and contribute at the FlightGear {{forum link|title=AI|f=23}}.



== Scenery corner ==
=== Scenery development news ===
montagdude has released custom scenery covering the states of Pennsylvania and New Jersey as well as New York City in the United States. This scenery includes custom terrain, materials, and Osm2city. Please see the [https://forum.flightgear.org/viewtopic.php?f=5&t=38041 forum thread] for instructions on how to download and install it. Some screenshots are below.

<gallery mode="packed">
File:Ec135-manhattan.jpg
File:Pa28-wilkes-barre.jpg
File:C182s-atlantic-city.jpg
File:Dr400-philadelphia.jpg
</gallery>













== Community news ==







=== FlightGear on Facebook ===
Since early December 2010, FlightGear has an [http://www.facebook.com/FlightGear official Facebook page]. If you have a Facebook account please feel free to join the page.

=== FlightGear on Instagram ===
In January 2018 the [https://www.instagram.com/flightgear_sim/ @flightgear_sim Instagram account] was brought back to life. If you've got nice screenshots to be featured, feel free to {{forum link|text=contact the maintainer|t=33636}}.

=== FlightGear on FlightSim.com ===
Flightgear has also a [https://www.flightsim.com/vbfs/forumdisplay.php?102-FlightGear sub-forum] on flightsim.com - just like the commercial flightsims. It is an opportunity to showcase what FG can do, get people curious and answer any questions they may have with regard to the software or the project.

== Multiplayer events ==









== Contributing ==

=== Translators needed ===
{|
| [[File:en.gif]]
| The FlightGear Wiki still needs help for translating it into various languages. If you are interested in making the FlightGear Wiki multilingual, you can start by looking at [[Help:Translate]].
|-
| [[File:fr.gif]]
| Le wiki de FlightGear a toujours besoin d'aide pour être traduit en différentes langues. Si vous êtes intéressé par le rendre multilingue, commencez par lire [[:fr:Help:Traduire|Help:Traduire]].
|-
| [[File:de.gif]]
| Das FlightGear Wiki benötigt immer noch Hilfe bei der Übersetzung in verschiedene Sprachen. Wenn Du Interesse daran hast, das FlightGear Wiki mehrsprachig zu machen, dann fang mit dem [[:de:Help:Übersetzen|Help:Übersetzen]] an.
|-
| [[File:nl.gif]]
| De FlightGear Wiki kan nog steed hulp gebruiken bij het vertalen van artikelen. Als je interesse hebt om de wiki meertalig te maken, raden we je aan om een kijkje te nemen bij [[:nl:Help:Vertalen|Help:Vertalen]].
|-
| [[File:es.gif]]
| La wiki de FlightGear todavía necesita ayuda para traducirla a varios lenguajes. Si estás interesado en hacer la FlightGear wiki multilingüe, entonces comienza en [[:es:Help:Traducir|Help:Traducir]].
|-
| [[File:cat.gif]]
| La wiki de FlightGear encara necessita ajuda per traduir-la a diverses llengües. Si esteu interessat en fer la wiki de FlightGear multilingüe, llavors comenceu a [[:ca:Help:Traduir|Help:Traduir]].
|-
| [[File:pt.gif]]
| A wiki de FlightGear ainda necessita de ajuda para traduzi-la em vários idiomas. Se estás interessado em tornar a wiki de FlightGear multi-lingual, por favor começa em [[:pt:Help:Traduzir|Help:Traduzir]].
|-
| [[File:zh.gif]]
| FlightGear 百科仍然需要志愿者将其翻译为各种语言。如果你有兴趣让FlightGear百科支持更多语言, 你可以查看 [[Help:Translate]].
|}

=== FlightGear logos ===
If you want some graphic elements for your FlightGear-related site (such as a hangar or YouTube channel), please feel free to visit [[FlightGear logos]] for a repository of logos. And if you have some art skills, please don't hesitate to contribute with your own design creations.

=== Screenshots ===
The FlightGear project always needs screenshots, which show features that were added since the last release. These should be of good quality, especially in content and technical image properties. It is therefore recommended to use the best viable filter settings ([[anti-aliasing]], texture sharpening, etc.). More info at [[Howto:Make nice screenshots]].

==== Screenshot of the Month ====


If you want to participate in the screenshot contest , you can submit your candidate to {{forum link|title=this|f=19|t= }}. Be sure to see the first post for participation rules. For purposes of convenience and organization, after all the entries have been submitted, a new forum topic will be started containing all shots in an easy-to-view layout. The voting will then take place there. Once the voting has finished, the best screenshot will be presented in the Newsletter.

''Thanks for reading {{PAGENAME}}!''

{{Appendix}}

[[Category:Changes after 2020.1]]
[[Category:FlightGear Newsletter|2020 09]]

[[de:FlightGear Newsletter {{#time: F Y | 2020-09 | de }}]]

Suggested custom scenery

2020-09-23T03:04:46Z

Montagdude: /* Add screenshots for PA, NJ, NYC scenery */

Besides the official TerraSync scenery that is automatically synced while Flightgear is running, there are a number of custom sceneries available, created by users for beautifying some specific areas of the scenery. This page is a list of some '''suggested custom scenery''' for downloading and visiting. This list can not be complete as custom sceneries might come and go. It's worth to search the forum for additional custom sceneries.

'''Quick update, March 2020/May 2020:'''

* See '''[[Suggested_airports]]''' for a quick list of featured airports situated in well developed regions, with links to OSM2City scenery. It's suitable for newcomers to start at to explore surrounding regions. (March 2020)

* '''[[Project3000]]''' adds a lot of clutter and buildings to airports which do not have models or details. It's a must have. It is planned to be integrated into the FG core eventually. [https://forum.flightgear.org/viewtopic.php?f=5&t=27688 Forum thread]. (March 2020)

* '''OSM2City scenery''' buildings, roads, pylons, and so on, for entire countries and cities [[Areas_populated_with_osm2city_scenery|'''here''']]. There is a current ongoing [[Osm2city_worldbuild|world build of OSM2City]] scenery ([https://forum.flightgear.org/viewtopic.php?f=5&t=35581 forum thread]) for Flightgear 2020.x. Most of the world has already been completed - see OSM2City downloads. (March 2020)

* '''[[:Category:Scenery_enhancement_projects|Custom terrain & region definition projects]]''': There are ongoing scenery enhancement projects to update both regional definitions and terrain in large areas like the [[Australia_Custom_Scenery|Australia]] (regional definitions are planned to be added to FG core). This a partial list, search the '''[https://forum.flightgear.org/viewforum.php?f=5 scenery forum]''' for details. Some of the output of projects like Hawaii terrain has already been integrated into Terrasync. (March 2020)

* Status of terrain in Terrasync: Terrasync scenery which is automatically downloaded has high resolution terrain. All of Europe currently has high resolution terrain from CORINE data. A lot of old pre-world scenery 2 should be obsolete. (March 2020)

* ''Status of next world-build of scenery: World Scenery 2.x'': There is ongoing work being done towards a build using the Terragear toolchain. It is intended for there to be a way to contribute improvements back to a central source. For example, a landcover shapefile server. This should make the large majority, if not all, of custom sceneries obsolete. Clipped landcover shapefiles from various sources used for WS 2.0 have been re-generated in preparation for a new world scenery build, see [https://sourceforge.net/p/flightgear/mailman/message/36730169/ this] mailinglist post. The sources do not include the high quality NLCD data for USA, and contributing those to a (future) central server will be an improvement to the build. When a central repository is put up, people who have generated higher quality landcover and terrain for custom sceneries should get in contact to get their work included in the world build. (May 2020).

* ''Status of next generation of scenery technology: World Scenery 3.0'': There is ongoing work being done towards WS 3.0 which will use [[Virtual Planet Builder]] (VPB) toolchain from the Open Scene Graph project, whose scenegraph is already used in Flightgear. For more information see the [[World_Scenery_3.0_roadmap|World Scenery 3.0 Roadmap]]. To help with the WS 3.0 effort contact the team. Contributing land cover and terrain data currently used in custom sceneries to the central repository when it's established in future will help get work into WS 3.0.(May 2020)

* Status of terrain in custom sceneries: The page as it stands is missing almost all custom terrain. Some old island custom sceneries have been merged into the automatically downloaded sceneries terrasync. [[User:Wlbragg#Kansas_Custom_Scenery|Kansas/Midwest]] and [[US-Tennessee_Custom_Scenery|Tennessee]] are two examples of custom sceneries that aren't obsolete (March 2020).

:: ''If you are the creator of an island scenery you can add it to [[TerraSync]] by contacting the flightgear-devel [[Mailing_lists|mailing list]]''.

* Status of regional definitions in FG core: Areas with regional definitions and materials which use recent engine features, that are in areas with reasonably high resolution terrain, have ''extremely'' high fidelity at high settings. ''The following list is not comprehensive''. It's just off the top of my head.
::''Areas with regional definitions and materials that were updated with recent features like overlays and have high resolution terrain'':
:::Hawaii, Norway, Iceland, Sweden, Mediterranean landscapes (Spain, Sicily, Portugal, southern Italy, Greece).
::''Areas with partial overlays and developed scenery with high resolution terrain'':
:::Swizerland, Alps (in many countries), central Europe & England, Corsica.
::''Well developed areas with regional definitions that predate overlays'':
:::Alaska, Caribbean Islands, Bermuda, South America (lower res terrain), Grand Canyon, Mojave Desert (lower res terrain than Europe), Madagascar (lower res terrain than Europe).
::''Somewhat recently developed areas:''
:::South Africa in places (lower res terrain), Dubai.

:: ''If you are the author of an existing scenery that has regional definitions and/or GPL compatible textures that are an improvement to what is present in [[FGData]] ([[Howto:Regional_texturing#Guidelines|Checklist]]), you can add it to FGData by having it reviewed and merged. Regional definitions can be merged even when the terrain part cannot be merged until a rebuild of the complete landmass is done.''

== Custom scenery that is not obsolete (March 2020) ==

''This is only a fraction of available scenery (March 2020). Search the [https://forum.flightgear.org/viewforum.php?f=5 scenery forum] for more.''

''See [[Howto:Install_scenery|How to install custom scenery packages]].''

* '''USA, Tennessee:''' custom scenery for Tennessee by [[User:Scttgs|Scott Giese]](xDraconianx). This is very high resolution scenery created to test potential resolution & performance for the next world scenery build. See the [https://forum.flightgear.org/viewtopic.php?f=5&t=35521 forum thread] for feedback and more info. See the [[US-Tennessee_Custom_Scenery|wiki page]] for downloads. (March 2020)

<gallery mode="packed">
File:US-Tennessee-EastCentral.jpg
File:US-Tennessee-West.jpg
File:US-Tennessee-East.jpg
File:US-Tennessee-WestCentral.jpg
</gallery>

* '''USA, Kansas & Ohio:''' custom scenery for the entire states of Kansas and Ohio created by [[User:Wlbragg|Wlbragg]]. This high resolution scenery includes textures. See the [[User:Wlbragg#Kansas_Custom_Scenery|wiki page]] for downloads and info. (March 2020)

<gallery mode="packed">
File:Kansas_Scenery_Two.jpg
File:KDAY_James_M._Cox_Dayton_International_Airport.jpg
File:Kansas_Scenery_One.jpg
File:Kansas_Scenery_Three.jpg
</gallery>

* '''Scandinavia''': custom scenery covering Scandinavian countries such as Norway, Sweden, and Finland by [[User:D-ECHO|D-ECHO]]. Norway and Sweden have some of the most detailed regional definitions using recent engine features. This custom scenery project further improves it by mainly adding more detailed land type classification, digital elevation data, as well as updated airports. As of March 2020 it updates parts of these countries. See the [[Scandinavia Custom Scenery]] page for download and details.

* '''USA, Pennsylvania, New Jersey, and New York City''': custom scenery for these Eastern US states by montagdude. See [https://forum.flightgear.org/viewtopic.php?f=5&t=38041 forum thread]. Contains custom terrain, OSM2City, and custom materials. (September 2020)

<gallery mode="packed">
File:Ec135-manhattan.jpg
File:Pa28-wilkes-barre.jpg
File:C182s-atlantic-city.jpg
File:Dr400-philadelphia.jpg
</gallery>

* '''USA, Virginia, Maryland, Delaware, and the District of Columbia''': custom scenery for these Mid-Atlantic US states by montagdude. See [https://forum.flightgear.org/viewtopic.php?f=5&t=37322 forum thread]. Contains custom terrain, OSM2City, and custom materials. (April 2020)

* '''USA, Rhode Island and southeastern Massachusetts''': custom scenery for Rhode Island and southeastern parts of the state of Massachusetts by montagdude. See [https://forum.flightgear.org/viewtopic.php?f=5&t=36977 forum thread]. Contains OSM2City. (March 2020)

* '''Venezuela: [[Project Venezuela]]:''' custom scenery for Venezuela by [[User:Legoboyvdlp|J. Redpath]], with improved airport layouts, and objects at several airports. Highly improved over TerraSync status. (March 2020)

* '''Canary Islands:''' custom scenery for the Canary Islands by [[User:D-ECHO|D-ECHO]] with improved terrain, objects, vegetation, building, reworked airports, mostly with buildings and furniture. Check out the [[Canary_Islands_Custom_Scenery|wiki page]]. (March 2020)

<gallery mode="packed">
GCLP-1.jpg
GCLP-2.jpg
GCLP-3.jpg
</gallery>

* '''Argentina''': OSM-based custom scenery for Argentina and parts of Chile and Uruguay by Barta. Updated airports, landcover from OSM polygons, line data and objects using Osm2City. Updated frequently! Downloads:
:: All output: [http://scenery.bartatech.net/ http://scenery.bartatech.net/] . As of July 2020 these areas are available: Santiago (including Valparaiso), Mendoza, Buenos Aires, complete Rosario area. The other parts only have terrain (no roads, buildings etc). The server will eventually contain all of the sceneries in the southern cone of South America: Argentina, Chile, Uruguay.
::: It's possible to use the [http://scenery.bartatech.net/ scenery.bartatech.net] server directly as a Terrasync server. Just use the following in the [[FlightGear_Qt_launcher|launcher]] or .fgfsrc file:
::: ''--prop:/sim/terrasync/http-server=<nowiki>http://scenery.bartatech.net/</nowiki>''
:: Buenos Aires province: https://github.com/bartacruz/scenery-6040
:: Cuyo Area + Santiago: https://github.com/bartacruz/scenery-cuyo

* '''USA, Florida''': custom scenery for Florida by [[User:Legoboyvdlp|J. Redpath]] with both terrain and OSM2City. It also covers the [[Space Shuttle]] landing site at [[Shuttle_Landing_Facility|KTTS]]. See the [https://github.com/legoboyvdlp/Florida-fg-CustomScenery repository] downloads section for the [https://github.com/legoboyvdlp/Florida-fg-CustomScenery/archive/master.zip zip-file].

* '''London, England''': custom scenery by [[User:Legoboyvdlp|J. Redpath]] for the London area including an improved Gatwick Airport (ICAO: EGKK). See the [https://forum.flightgear.org/viewtopic.php?f=5&t=37444 forum thread] and [https://github.com/legoboyvdlp/London-fg-CustomScenery repository] downloads section for the [https://github.com/legoboyvdlp/London-fg-CustomScenery/archive/master.zip zip-file].

* '''Moscow area, Russia''': custom scenery by [[User:Legoboyvdlp|J. Redpath]] for the Moscow area covering 14 tiles. See the [https://forum.flightgear.org/viewtopic.php?f=5&t=37887 forum thread] for the github [https://github.com/legoboyvdlp/Moscow-fg-CustomScenery/archive/master.zip download]. (August 2020)

* '''Valdivostock area, Russia''': custom scenery by [[User:Legoboyvdlp|J. Redpath]] for the Vadivostock area. See the [https://forum.flightgear.org/viewtopic.php?f=5&t=37887 forum thread] for the github [https://github.com/legoboyvdlp/Vladivostok-fg-CustomScenery/archive/master.zip download]. (August 2020)

* '''Eastern China''': custom scenery by [[User:Legoboyvdlp|J. Redpath]] for the eastern portion of China covering the area around ZSAM and ZSNJ airports. See the [https://forum.flightgear.org/viewtopic.php?f=5&t=37887 forum thread] for the github [https://github.com/legoboyvdlp/EastChina-fg-CustomScenery/archive/master.zip download]. (August 2020)

* '''Beijing area, China''': custom scenery by [[User:Legoboyvdlp|J. Redpath]] for the area around Beijing covering 15 tiles. See the [https://forum.flightgear.org/viewtopic.php?f=5&t=37887 forum thread] for the github [https://github.com/legoboyvdlp/Beijing-fg-CustomScenery/archive/master.zip download]. (August 2020)

* '''Sri Lanka''': 2019 custom scenery by [[User:Legoboyvdlp|J. Redpath]] for the island of Sri Lanka in the Indian ocean including rebuilt terrain, OSM2City output, and improved airports. See the [https://forum.flightgear.org/viewtopic.php?f=5&t=37887 forum thread] for github [https://codeload.github.com/legoboyvdlp/SriLanka-fg-CustomScenery/zip/master download].

== Custom scenery set to be merged into TerraSync and FGData ==

* New Zealand scenery ([https://forum.flightgear.org/viewtopic.php?f=5&t=36892 forum thread]). (March 2020)
* [[Australia_Custom_Scenery|Australian custom scenery project]] output - Regional definitions, and textures. See these forum threads for download and details: [https://forum.flightgear.org/viewtopic.php?f=5&t=36145 official thread], demo scenery [https://forum.flightgear.org/viewtopic.php?f=19&t=36956 install details (March 2020)]. A partially completed Sydney airport (YSSY) is also available for download. The terrain in the demo scenery release is likely to be merged when a re-build of Australia is done.
* All Channel Islands (Jersey, Guernsey, Alderney, Sark, etc) ([https://github.com/legoboyvdlp/Jersey-fg-CustomScenery github]). (March 2020).
* Sri Lanka, [https://forum.flightgear.org/viewtopic.php?f=5&t=37887 forum thread] and [https://github.com/legoboyvdlp/SriLanka-fg-CustomScenery Github]

== Custom scenery which has been merged into TerraSync and FGData ==

''You can remove old scenery folders containing known merged sceneries from your [[Howto:Install_scenery|scenery folder list]].''

* Islands merged recently (2018-2019): Hawaii, Iceland, Island of Jan Mayen (Norway)

== Old and undetermined custom scenery ==

''Some of these may have been obsoleted by the release of the [[FlightGear World Scenery 2.0|new world scenery in November 2013]].''

''Please move sceneries that aren't obsolete into other categories.''

* '''France''': Custom scenery for France with improved landcover information and more detailed elevation information (no improvements on the objects) is under development. See the [[Custom France Scenery|Custom France Scenery page]]. (Pre WS 2.0)

<gallery>
Image:High_textures_transitions3.png|High detail France scenery
Image:High_textures_transitions4_1.png|High detail France scenery
Image:High_textures_transitions6_2.png|High detail France scenery
Image:High_textures_transitions10_1.png|Mont Blanc
Image:High_textures_transitions11_1.png|Aguille du midi?
Image:High_textures_transitions13_1.png|Matterhorn without landclass, just slope transitions
</gallery>

* '''Innsbruck (Austria) area''': The scenery currently covers a 1x1 degree area containing parts of Austria and Germany, and makes use of accurate data from the Corine Land Cover, and Open Street Map projects. See [http://forum.flightgear.org/viewtopic.php?f=5&t=5350 this forum post] and [[FlightGear Newsletter August 2009#Innsbruck Gets A Face Lift|the August 2009 newsletter]] for details. (Pre WS 2.0)

* '''LOWI & Austria Scenery Preview''': This scenery includes the Innsbruck scenario and is much more complete than the previous. It is still experimental, but should try it as having been really good. Info and download can be found [[Innsbruck Airport|here]].

* '''Iberian Peninsula: Spain and Portugal''': custom scenery for Spain and Portugal with improved landcover and additional airports and objects. See the [[Custom Spain and Portugal Scenery|custom Spain and Portugal scenery page]].

* '''EDDK''': Custom scenery for the Cologne/Bonn airport in Germany with very nice and realistic terminal buildings. Available from https://github.com/mherweg/EDDK-fg-CustomScenery.

* '''Argentina''': OSM-based custom scenery for Argentina and parts of Chile and Uruguay. Updated airports, landcover from OSM polygons, line data and objects using Osm2City. Updated frequently! Downloads:
** Buenos Aires province: https://github.com/bartacruz/scenery-6040
** Cuyo Area + Santiago: https://github.com/bartacruz/scenery-cuyo

{{suggested}}

[[Category:Scenery]]

File:Pa28-wilkes-barre.jpg

2020-09-23T03:01:45Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=PA-28 over Wilkes-Barre, Pennsylvania}}
|date=2020-09-22
|source={{own}}
|author=[[User:Montagdude|Montagdude]]
|permission=
|other versions=
}}

=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

[[Category:Scenery enhancement]]
[[Category:Osm2city]]

File:C182s-atlantic-city.jpg

2020-09-23T03:01:45Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=Cessna 182S over Atlantic City, New Jersey}}
|date=2020-09-22
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|author=[[User:Montagdude|Montagdude]]
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=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

[[Category:Scenery enhancement]]
[[Category:Osm2city]]

File:Ec135-manhattan.jpg

2020-09-23T03:01:45Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=EC135 over Manhattan, New York}}
|date=2020-09-22
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|author=[[User:Montagdude|Montagdude]]
|permission=
|other versions=
}}

=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

[[Category:Scenery enhancement]]
[[Category:Osm2city]]

File:Dr400-philadelphia.jpg

2020-09-23T03:01:44Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=DR-400 near Philadelphia, Pennsylvania}}
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|permission=
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[[Category:Scenery enhancement]]
[[Category:Osm2city]]

Suggested custom scenery

2020-09-23T02:56:30Z

Montagdude: /* Added PA, NJ, NYC custom scenery bullet */

Besides the official TerraSync scenery that is automatically synced while Flightgear is running, there are a number of custom sceneries available, created by users for beautifying some specific areas of the scenery. This page is a list of some '''suggested custom scenery''' for downloading and visiting. This list can not be complete as custom sceneries might come and go. It's worth to search the forum for additional custom sceneries.

'''Quick update, March 2020/May 2020:'''

* See '''[[Suggested_airports]]''' for a quick list of featured airports situated in well developed regions, with links to OSM2City scenery. It's suitable for newcomers to start at to explore surrounding regions. (March 2020)

* '''[[Project3000]]''' adds a lot of clutter and buildings to airports which do not have models or details. It's a must have. It is planned to be integrated into the FG core eventually. [https://forum.flightgear.org/viewtopic.php?f=5&t=27688 Forum thread]. (March 2020)

* '''OSM2City scenery''' buildings, roads, pylons, and so on, for entire countries and cities [[Areas_populated_with_osm2city_scenery|'''here''']]. There is a current ongoing [[Osm2city_worldbuild|world build of OSM2City]] scenery ([https://forum.flightgear.org/viewtopic.php?f=5&t=35581 forum thread]) for Flightgear 2020.x. Most of the world has already been completed - see OSM2City downloads. (March 2020)

* '''[[:Category:Scenery_enhancement_projects|Custom terrain & region definition projects]]''': There are ongoing scenery enhancement projects to update both regional definitions and terrain in large areas like the [[Australia_Custom_Scenery|Australia]] (regional definitions are planned to be added to FG core). This a partial list, search the '''[https://forum.flightgear.org/viewforum.php?f=5 scenery forum]''' for details. Some of the output of projects like Hawaii terrain has already been integrated into Terrasync. (March 2020)

* Status of terrain in Terrasync: Terrasync scenery which is automatically downloaded has high resolution terrain. All of Europe currently has high resolution terrain from CORINE data. A lot of old pre-world scenery 2 should be obsolete. (March 2020)

* ''Status of next world-build of scenery: World Scenery 2.x'': There is ongoing work being done towards a build using the Terragear toolchain. It is intended for there to be a way to contribute improvements back to a central source. For example, a landcover shapefile server. This should make the large majority, if not all, of custom sceneries obsolete. Clipped landcover shapefiles from various sources used for WS 2.0 have been re-generated in preparation for a new world scenery build, see [https://sourceforge.net/p/flightgear/mailman/message/36730169/ this] mailinglist post. The sources do not include the high quality NLCD data for USA, and contributing those to a (future) central server will be an improvement to the build. When a central repository is put up, people who have generated higher quality landcover and terrain for custom sceneries should get in contact to get their work included in the world build. (May 2020).

* ''Status of next generation of scenery technology: World Scenery 3.0'': There is ongoing work being done towards WS 3.0 which will use [[Virtual Planet Builder]] (VPB) toolchain from the Open Scene Graph project, whose scenegraph is already used in Flightgear. For more information see the [[World_Scenery_3.0_roadmap|World Scenery 3.0 Roadmap]]. To help with the WS 3.0 effort contact the team. Contributing land cover and terrain data currently used in custom sceneries to the central repository when it's established in future will help get work into WS 3.0.(May 2020)

* Status of terrain in custom sceneries: The page as it stands is missing almost all custom terrain. Some old island custom sceneries have been merged into the automatically downloaded sceneries terrasync. [[User:Wlbragg#Kansas_Custom_Scenery|Kansas/Midwest]] and [[US-Tennessee_Custom_Scenery|Tennessee]] are two examples of custom sceneries that aren't obsolete (March 2020).

:: ''If you are the creator of an island scenery you can add it to [[TerraSync]] by contacting the flightgear-devel [[Mailing_lists|mailing list]]''.

* Status of regional definitions in FG core: Areas with regional definitions and materials which use recent engine features, that are in areas with reasonably high resolution terrain, have ''extremely'' high fidelity at high settings. ''The following list is not comprehensive''. It's just off the top of my head.
::''Areas with regional definitions and materials that were updated with recent features like overlays and have high resolution terrain'':
:::Hawaii, Norway, Iceland, Sweden, Mediterranean landscapes (Spain, Sicily, Portugal, southern Italy, Greece).
::''Areas with partial overlays and developed scenery with high resolution terrain'':
:::Swizerland, Alps (in many countries), central Europe & England, Corsica.
::''Well developed areas with regional definitions that predate overlays'':
:::Alaska, Caribbean Islands, Bermuda, South America (lower res terrain), Grand Canyon, Mojave Desert (lower res terrain than Europe), Madagascar (lower res terrain than Europe).
::''Somewhat recently developed areas:''
:::South Africa in places (lower res terrain), Dubai.

:: ''If you are the author of an existing scenery that has regional definitions and/or GPL compatible textures that are an improvement to what is present in [[FGData]] ([[Howto:Regional_texturing#Guidelines|Checklist]]), you can add it to FGData by having it reviewed and merged. Regional definitions can be merged even when the terrain part cannot be merged until a rebuild of the complete landmass is done.''

== Custom scenery that is not obsolete (March 2020) ==

''This is only a fraction of available scenery (March 2020). Search the [https://forum.flightgear.org/viewforum.php?f=5 scenery forum] for more.''

''See [[Howto:Install_scenery|How to install custom scenery packages]].''

* '''USA, Tennessee:''' custom scenery for Tennessee by [[User:Scttgs|Scott Giese]](xDraconianx). This is very high resolution scenery created to test potential resolution & performance for the next world scenery build. See the [https://forum.flightgear.org/viewtopic.php?f=5&t=35521 forum thread] for feedback and more info. See the [[US-Tennessee_Custom_Scenery|wiki page]] for downloads. (March 2020)

<gallery mode="packed">
File:US-Tennessee-EastCentral.jpg
File:US-Tennessee-West.jpg
File:US-Tennessee-East.jpg
File:US-Tennessee-WestCentral.jpg
</gallery>

* '''USA, Kansas & Ohio:''' custom scenery for the entire states of Kansas and Ohio created by [[User:Wlbragg|Wlbragg]]. This high resolution scenery includes textures. See the [[User:Wlbragg#Kansas_Custom_Scenery|wiki page]] for downloads and info. (March 2020)

<gallery mode="packed">
File:Kansas_Scenery_Two.jpg
File:KDAY_James_M._Cox_Dayton_International_Airport.jpg
File:Kansas_Scenery_One.jpg
File:Kansas_Scenery_Three.jpg
</gallery>

* '''Scandinavia''': custom scenery covering Scandinavian countries such as Norway, Sweden, and Finland by [[User:D-ECHO|D-ECHO]]. Norway and Sweden have some of the most detailed regional definitions using recent engine features. This custom scenery project further improves it by mainly adding more detailed land type classification, digital elevation data, as well as updated airports. As of March 2020 it updates parts of these countries. See the [[Scandinavia Custom Scenery]] page for download and details.

* '''USA, Pennsylvania, New Jersey, and New York City''': custom scenery for these Eastern US states by montagdude. See [https://forum.flightgear.org/viewtopic.php?f=5&t=38041 forum thread]. Contains custom terrain, OSM2City, and custom materials. (September 2020)

* '''USA, Virginia, Maryland, Delaware, and the District of Columbia''': custom scenery for these Mid-Atlantic US states by montagdude. See [https://forum.flightgear.org/viewtopic.php?f=5&t=37322 forum thread]. Contains custom terrain, OSM2City, and custom materials. (April 2020)

* '''USA, Rhode Island and southeastern Massachusetts''': custom scenery for Rhode Island and southeastern parts of the state of Massachusetts by montagdude. See [https://forum.flightgear.org/viewtopic.php?f=5&t=36977 forum thread]. Contains OSM2City. (March 2020)

* '''Venezuela: [[Project Venezuela]]:''' custom scenery for Venezuela by [[User:Legoboyvdlp|J. Redpath]], with improved airport layouts, and objects at several airports. Highly improved over TerraSync status. (March 2020)

* '''Canary Islands:''' custom scenery for the Canary Islands by [[User:D-ECHO|D-ECHO]] with improved terrain, objects, vegetation, building, reworked airports, mostly with buildings and furniture. Check out the [[Canary_Islands_Custom_Scenery|wiki page]]. (March 2020)

<gallery mode="packed">
GCLP-1.jpg
GCLP-2.jpg
GCLP-3.jpg
</gallery>

* '''Argentina''': OSM-based custom scenery for Argentina and parts of Chile and Uruguay by Barta. Updated airports, landcover from OSM polygons, line data and objects using Osm2City. Updated frequently! Downloads:
:: All output: [http://scenery.bartatech.net/ http://scenery.bartatech.net/] . As of July 2020 these areas are available: Santiago (including Valparaiso), Mendoza, Buenos Aires, complete Rosario area. The other parts only have terrain (no roads, buildings etc). The server will eventually contain all of the sceneries in the southern cone of South America: Argentina, Chile, Uruguay.
::: It's possible to use the [http://scenery.bartatech.net/ scenery.bartatech.net] server directly as a Terrasync server. Just use the following in the [[FlightGear_Qt_launcher|launcher]] or .fgfsrc file:
::: ''--prop:/sim/terrasync/http-server=<nowiki>http://scenery.bartatech.net/</nowiki>''
:: Buenos Aires province: https://github.com/bartacruz/scenery-6040
:: Cuyo Area + Santiago: https://github.com/bartacruz/scenery-cuyo

* '''USA, Florida''': custom scenery for Florida by [[User:Legoboyvdlp|J. Redpath]] with both terrain and OSM2City. It also covers the [[Space Shuttle]] landing site at [[Shuttle_Landing_Facility|KTTS]]. See the [https://github.com/legoboyvdlp/Florida-fg-CustomScenery repository] downloads section for the [https://github.com/legoboyvdlp/Florida-fg-CustomScenery/archive/master.zip zip-file].

* '''London, England''': custom scenery by [[User:Legoboyvdlp|J. Redpath]] for the London area including an improved Gatwick Airport (ICAO: EGKK). See the [https://forum.flightgear.org/viewtopic.php?f=5&t=37444 forum thread] and [https://github.com/legoboyvdlp/London-fg-CustomScenery repository] downloads section for the [https://github.com/legoboyvdlp/London-fg-CustomScenery/archive/master.zip zip-file].

* '''Moscow area, Russia''': custom scenery by [[User:Legoboyvdlp|J. Redpath]] for the Moscow area covering 14 tiles. See the [https://forum.flightgear.org/viewtopic.php?f=5&t=37887 forum thread] for the github [https://github.com/legoboyvdlp/Moscow-fg-CustomScenery/archive/master.zip download]. (August 2020)

* '''Valdivostock area, Russia''': custom scenery by [[User:Legoboyvdlp|J. Redpath]] for the Vadivostock area. See the [https://forum.flightgear.org/viewtopic.php?f=5&t=37887 forum thread] for the github [https://github.com/legoboyvdlp/Vladivostok-fg-CustomScenery/archive/master.zip download]. (August 2020)

* '''Eastern China''': custom scenery by [[User:Legoboyvdlp|J. Redpath]] for the eastern portion of China covering the area around ZSAM and ZSNJ airports. See the [https://forum.flightgear.org/viewtopic.php?f=5&t=37887 forum thread] for the github [https://github.com/legoboyvdlp/EastChina-fg-CustomScenery/archive/master.zip download]. (August 2020)

* '''Beijing area, China''': custom scenery by [[User:Legoboyvdlp|J. Redpath]] for the area around Beijing covering 15 tiles. See the [https://forum.flightgear.org/viewtopic.php?f=5&t=37887 forum thread] for the github [https://github.com/legoboyvdlp/Beijing-fg-CustomScenery/archive/master.zip download]. (August 2020)

* '''Sri Lanka''': 2019 custom scenery by [[User:Legoboyvdlp|J. Redpath]] for the island of Sri Lanka in the Indian ocean including rebuilt terrain, OSM2City output, and improved airports. See the [https://forum.flightgear.org/viewtopic.php?f=5&t=37887 forum thread] for github [https://codeload.github.com/legoboyvdlp/SriLanka-fg-CustomScenery/zip/master download].

== Custom scenery set to be merged into TerraSync and FGData ==

* New Zealand scenery ([https://forum.flightgear.org/viewtopic.php?f=5&t=36892 forum thread]). (March 2020)
* [[Australia_Custom_Scenery|Australian custom scenery project]] output - Regional definitions, and textures. See these forum threads for download and details: [https://forum.flightgear.org/viewtopic.php?f=5&t=36145 official thread], demo scenery [https://forum.flightgear.org/viewtopic.php?f=19&t=36956 install details (March 2020)]. A partially completed Sydney airport (YSSY) is also available for download. The terrain in the demo scenery release is likely to be merged when a re-build of Australia is done.
* All Channel Islands (Jersey, Guernsey, Alderney, Sark, etc) ([https://github.com/legoboyvdlp/Jersey-fg-CustomScenery github]). (March 2020).
* Sri Lanka, [https://forum.flightgear.org/viewtopic.php?f=5&t=37887 forum thread] and [https://github.com/legoboyvdlp/SriLanka-fg-CustomScenery Github]

== Custom scenery which has been merged into TerraSync and FGData ==

''You can remove old scenery folders containing known merged sceneries from your [[Howto:Install_scenery|scenery folder list]].''

* Islands merged recently (2018-2019): Hawaii, Iceland, Island of Jan Mayen (Norway)

== Old and undetermined custom scenery ==

''Some of these may have been obsoleted by the release of the [[FlightGear World Scenery 2.0|new world scenery in November 2013]].''

''Please move sceneries that aren't obsolete into other categories.''

* '''France''': Custom scenery for France with improved landcover information and more detailed elevation information (no improvements on the objects) is under development. See the [[Custom France Scenery|Custom France Scenery page]]. (Pre WS 2.0)

<gallery>
Image:High_textures_transitions3.png|High detail France scenery
Image:High_textures_transitions4_1.png|High detail France scenery
Image:High_textures_transitions6_2.png|High detail France scenery
Image:High_textures_transitions10_1.png|Mont Blanc
Image:High_textures_transitions11_1.png|Aguille du midi?
Image:High_textures_transitions13_1.png|Matterhorn without landclass, just slope transitions
</gallery>

* '''Innsbruck (Austria) area''': The scenery currently covers a 1x1 degree area containing parts of Austria and Germany, and makes use of accurate data from the Corine Land Cover, and Open Street Map projects. See [http://forum.flightgear.org/viewtopic.php?f=5&t=5350 this forum post] and [[FlightGear Newsletter August 2009#Innsbruck Gets A Face Lift|the August 2009 newsletter]] for details. (Pre WS 2.0)

* '''LOWI & Austria Scenery Preview''': This scenery includes the Innsbruck scenario and is much more complete than the previous. It is still experimental, but should try it as having been really good. Info and download can be found [[Innsbruck Airport|here]].

* '''Iberian Peninsula: Spain and Portugal''': custom scenery for Spain and Portugal with improved landcover and additional airports and objects. See the [[Custom Spain and Portugal Scenery|custom Spain and Portugal scenery page]].

* '''EDDK''': Custom scenery for the Cologne/Bonn airport in Germany with very nice and realistic terminal buildings. Available from https://github.com/mherweg/EDDK-fg-CustomScenery.

* '''Argentina''': OSM-based custom scenery for Argentina and parts of Chile and Uruguay. Updated airports, landcover from OSM polygons, line data and objects using Osm2City. Updated frequently! Downloads:
** Buenos Aires province: https://github.com/bartacruz/scenery-6040
** Cuyo Area + Santiago: https://github.com/bartacruz/scenery-cuyo

{{suggested}}

[[Category:Scenery]]

Suggested custom scenery

2020-05-27T13:58:30Z

Montagdude: Add link to VA, MD, DE, DC scenery

Howto:Using QGIS to process NLCD data

2020-05-27T13:54:58Z

Montagdude: Added link to VA, MD, DE, DC scenery

This tutorial will describe '''how to use data from the [https://www.usgs.gov/centers/eros/science/national-land-cover-database?qt-science_center_objects=0#qt-science_center_objects National Land Cover Database (NLCD)] to create US scenery''' that is on par with the existing European scenery.

The default scenery (World Scenery 2.0) in FlightGear is of higher quality in some areas than others. For example, European scenery is generally of very high quality due to the use of [https://land.copernicus.eu/pan-european/corine-land-cover CORINE land cover data]. Compared to the European scenery, the US scenery is much less detailed and less accurate. Thankfully, there is a wealth of freely-available geographical information for the US that can be used to improve the scenery.

== Getting set up for scenery generation ==
=== Prerequisites ===
Please read and understand the [[Using TerraGear|Using TerraGear wiki article]]. This tutorial will follow the same process but replace the "Landuse data" section with custom shapefiles generated from NLCD data.

=== Tools ===
This tutorial will make use of the following GIS software:
# QGIS (version 3.10)
# GRASS (version 7.8, used as a QGIS plugin)
# TerraGear

=== Elevation data ===
Elevation data for the entire world is available from the Shuttle Radar Topology Mission (SRTM). It is highly recommended to use the void-filled version of these datasets, which can be obtained from the [https://earthexplorer.usgs.gov/ USGS EarthExplorer] (note: it is required to create a login to download data from that page). The void-filled datasets reduce or eliminate the small gaps that can occur in scenery generated using the original SRTM datasets. The elevation data is in GeoTIFF format. To convert it to hgt format as expected by TerraGear, use gdal_translate as follows:

gdal_translate -of SRTMHGT input_file.tif output_file.hgt

For the output file, TerraGear might expect it to follow a certain naming convention, like N38W080.hgt. The data from USGS follows a naming convention like n38_w080_3arc_v2.tif. The following (sh/bash shell) script will batch convert all the files in the working directory following the expected naming convention:

for file in *.tif; do
base=$(echo $file | cut -f 1 -d '.')
NORTH=$(echo $base | cut -f 1 -d '_' | cut -f 2 -d 'n')
WEST=$(echo $base | cut -f 2 -d '_' | cut -f 2 -d 'w')
gdal_translate -of SRTMHGT $file N${NORTH}W${WEST}.hgt
done

== NLCD data ==
You can download the NLCD data [https://www.mrlc.gov/data?f%5B0%5D=category%3Aland%20cover here]. At the time of this writing, the latest dataset is from 2016. For the continental US, you should choose the dataset labeled "NLCD 2016 Land Cover (CONUS)." There is also a separate dataset for Alaska. The download is a large (>1 GB) zip file. After downloading, unzip the files.

=== Processing the data ===
* Fire up QGIS and select the WGS 84 template. If you don't have a template to choose from, don't worry about it, as we'll be setting the coordinate reference system for the project later. Just create a new project in that case.
* In the menu bar, select Project -> Properties -> CRS. Pick WGS 84 - EPSG:4326 as the project coordinate reference system.
<gallery>
49532654886 e94a32328d o.png|Setting the CRS
</gallery>
* Set a basemap. An easy way to do this is to double click on XYZ Tiles -> OpenStreetMap in the browser on the left. You can also get them via the QuickMapServices plugin.
* Browse to the location where you have unzipped the NLCD data in the QGIS project browser and double click the .img file to load it as a layer.
* Right-click on the NLCD layer -> Set CRS -> Set Layer CRS and select NAD83 / Conus Albers - EPSG:5070 -> Transformation 1. This will place the layer in the right location on top of the basemap. Note: it may not look like it's in the right spot until you zoom in on it.
<gallery>
49532655611 64dc27a6fd o.png|NLCD data loaded in QGIS
</gallery>
* Now we need to clip the data to the region of interest. The best way to do this is to by clicking the New Shapefile Layer button (see screenshot below). In the dialog, change Geometry type to Polygon and save it as a shapefile, and then click OK.
* Click the Toggle Editing button to edit the layer.
* Click the Add Polygon Feature button to create a polygon. Now you can click on the map to create a polygon covering the area of interest. When you are done, you can right-click to stop adding vertices.
* If you want to edit the vertex locations, you can click the Vertex Tool button, right-click on your polygon, and then edit the coordinates in the table on the bottom left of the GUI window.
* When you are done editing the layer, click the Save Layer Edits button (next to the Toggle Editing button).
'''Note''': when you are learning the process, start with a small polygon, say 1x1 degrees. Some of the steps take a very long time if processing very large sections of this data.
<gallery>
New layer.png|New shapefile layer
Shapefile dialog.png|Shapefile dialog
Toggle editing.png|Toggle editing
Add polygon.png|Add polygon
Vertex tool.png|Vertex tool
</gallery>
*Once the polygon is completed, the next step is to clip the NLCD layer using this polygon. Select the NLCD layer and choose Raster -> Extraction -> Clip Raster by Mask Layer. Make sure the input layer is your NLCD layer and the mask layer is the polygon you have created. For Source CRS choose the Conus Albers CRS of the original NLCD layer, and for Target CRS choose the Project CRS, EPSG:4326. Near the bottom of the dialog box, where under Clipped (mask) it says [Save to temporary file], click the button to the right and choose Save to File. Select IMG files as the filter and save it somewhere in your working directory with a descriptive name like NLCD_clip.img. Click Run. Once it completes, if you hide the original NLCD layer and the polygon, you should see the new clipped layer. As long as it worked correctly, you can now safely remove the big original NLCD layer. The image below shows a small sample section in Idaho that has been clipped using this method.
<gallery>
49532154383 b437eb21b7 o.png|Clipped NLCD data
</gallery>
*If you zoom in on the data, you will see that it is highly detailed but also pixelated due to the raster format. It needs to be simplified and smoothed out to look good in FlightGear and also to reduce the polygon count on the resulting shapefiles. We will first do some smoothing of the raster itself, and then we'll do additional smoothing after converting to vector format. In the Processing Toolbox, find GRASS -> r.neighbors. The clipped NLCD layer should be selected for both of the top input dropdowns. For Neighborhood Operation, select median with a Neighborhood size of 5. As before, you may want to choose an actual IMG file to save the output to instead of a temporary file. Click Run.
'''Note''': never use an averaging neighborhood operation, as the raster values are uniquely associated with a specific type of landcover. The averaging operation will introduce new values that do not correspond to a landcover type.
*Now we'll run r.neighbors once more on the resulting layer using the mode Neighborhood Operation with a Neighborhood of 3. The result should look a lot smoother and a bit less detailed than the original dataset (but still very highly detailed compared to the default scenery). An image from the sample dataset is below. This area shows McCall, Idaho and the south end of Payette Lake.
<gallery>
49539367447 cde1c6666a o.png|After r.neighbors operations
</gallery>
*We're now ready to convert the raster data to vector. In the Processing Toolbox, find Grass -> r.to.vect. Choose the output layer from the last step (median neighbors filter), choose area as the feature type, and check the option "Smooth corners of area features." This will make the output vector a little less pixely than the input. As before, you may want to save to a real shapefile instead of a temporary one. Click Run. This step will take longer than any of the previous ones to process.
*We could use this vector data as is, but it's still a bit blocky and unnatural looking. Instead, we'll do a couple processing steps on the vector data. GRASS -> v.generalize. Choose the default douglas algorithm with a tolerance of 0.0003 (this is in degrees, since our CRS is in degrees). You may want to play around with different values of tolerance until you find something that works best. Click Run. If errors result, it will create a new layer, which can be deleted. '''Note''': this step will take a very long time for large areas.
*Now we'll use v.generalize one more to smooth the simplified layer. This time, choose the chaiken algorithm with the same tolerance. Click Run. '''Note''': this step will take a very long time for large areas.
<gallery>
Vectorized.png|Vectorized NLCD data
Simplified.png|Simplified NLCD data
Smoothed.png|Smoothed NLCD data
</gallery>
*At this point, it is a good idea to compare the smoothed vector layer to the original NLCD raster data and make sure the smoothing steps have not introduced too much error in the shape of coastlines and other features. If they have, you may need to redo some or all of the last several steps (r.neighbors, r.to.vect, and v.generalize) adjusting the number of neighbors and tolerance until you get satisfactory results. However, I've found these parameters to produce quite good results.
*We now have a high-resolution vectorized dataset that can be used to define landclasses in FlightGear. We just need to save individual shape files representing each landclass. At this point, it is a good idea to have the NLCD map with the legend displayed to match to values in the shapefiles. That map can be found here:
https://www.mrlc.gov/viewer/
*You will also want to have default_priorities.txt from your TerraGear installation open to map NLCD landclass types to available materials in FlightGear. Some artistic license is possible here, but I use this mapping:
{| class="wikitable"
|-
! NLCD Value !! Legend label !! FlightGear material
|-
| 11 || Open Water || Lake or Ocean
|-
| 12 || Perennial Ice/Snow || SnowCover or Glacier
|-
| 21 || Developed, Open Space || Greenspace
|-
| 22 || Developed, Low Intensity || Town
|-
| 23 || Developed, Medium Intensity || SubUrban
|-
| 24 || Developed, High Intensity || Urban
|-
| 31 || Barren Land (Rock/Sand/Clay) || Rock or Sand (depends on location)
|-
| 32 || Unconsolidated Shore || Sand
|-
| 41 || Deciduous Forest || DeciduousForest
|-
| 42 || Evergreen Forest || EvergreenForest
|-
| 43 || Mixed Forest || MixedForest
|-
| 51,52 || Dwarf Scrub/Shrub/Scrub || Scrub
|-
| 71 || Grasslands/Herbaceous || Grassland
|-
| 72-74 || Herbaceous/Moss/Lichens (AK only) || I haven't used these, so check default_priorities.txt if you find them in your data
|-
| 81 || Pasture/Hay || DryCrop
|-
| 82 || Cultivated Crops || IrrCrop
|-
| 90 || Woody Wetlands || Marsh
|-
| 95 || Emergent Herbaceous Wetlands || Bog
|}
*To actually select and export individual shapefiles for each type, do the following:
**Select the final smoothed vector data layer. Click the Deselect Features from All Layers button near the top middle of the QGIS toolbar. Then click the button right next to it, Select Features by Value. In the dialog, enter the number from the NLCD legend in the value field, make sure the filter says Equal to (=), and then click Select Features and Close. Alternatively, if you want to combine multiple NLCD values into a single output shapefile, rather than clicking Close, you could enter in another value and then choose Add to Current Selection in the dropdown next to Select Features.
**Once you have the desired features selected, right click on the smoothed vector layer, click Export -> Save Selected Features As, choose the ESRI shapefile format, and save it somewhere under the project directory with a name corresponding to the appropriate area type in default_priorities.txt (e.g. data/shapefiles/Lake.shp). For CRS, choose the project CRS EPSG:4326 - WGS 84.
**Repeat this process until you've gone over all the values in the NLCD data legend. When finished, you should have individual layers representing different area types covering the entire area of interest. The result for the sample area is shown in the image below.
<gallery>
Nlcd shapefiles.jpg|Shapefiles from NLCD data
</gallery>

=== Converting with ogr-decode ===

If you have saved the shapefiles with a name corresponding to a material in default_priorities.txt, then they can be batch-converted with ogr-decode as follows:

# work and shapefiles directories
WORK=./work
DATADIR=./data/shapefiles/NLCD
# Process landcover
for file in $DATADIR/*.shp; do
fname=$(basename $file)
area_type=$(echo $fname | cut -f 1 -d '.')
if [ -d $WORK/$area_type ]; then
rm -rf $WORK/$area_type
fi
ogr-decode --area-type $area_type work/$area_type $file
done

(The script above is a bash/sh script for Linux. Something similar could probably done with a .bat script on Windows. The locations of the work and data directories will need to be edited if you use a different directory structure.)

=== Building with tg-construct ===

The final step in building the terrain is to run tg-construct. This process is described in the [[Using TerraGear|Using TerraGear wiki article]]. This should be done after adding an supplementary shapefiles, as described in the next section. When running tg-construct, you will want to use the '''--ignore-landmass''' flag so that lakes and other waterbodies are not assumed to be at sea level.

== Supplementary data ==

You will probably want to supplement your custom scenery with some other data. Sources include:

* National Hydrography Data for waterways and waterbodies. Though NLCD includes water landcover, it is not as accurate or complete as NHD, and the smoothing process described above will cause thin rivers to turn into "islands." I recommend using the NHDArea and NHDWaterbody datasets but skipping the line data. The data is in shapefile format, which can be viewed and manipulated with QGIS. Be sure to filter the data to remove extraneous elements. It includes things like marshes, dams, and intermittent streams that probably should not be included. The NHD data can be accessed here: [https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products Access National Hydrography Products]. To filter the data, you will want to refer to this page: [https://nhd.usgs.gov/userGuide/Robohelpfiles/NHD_User_Guide/Feature_Catalog/Hydrography_Dataset/Complete_FCode_List.htm NHD FCodes].

* [https://www.openstreetmap.org OpenStreetMap] for roads. QGIS can open .osm and .pbf files extracted from OpenStreetMap and edit them just like all the other datasets that have been discussed here. These can be "baked into" the terrain using TerraGear, or you can use:

* [https://osm2city.readthedocs.io/en/latest/index.html osm2city], an autogen tool for FlightGear that creates buildings, roads, bridges, pylons, docks, and other details from OpenStreetMap data. This tool has a bit of a learning curve and does not flow through QGIS, but it can really make your project shine.

== Sample results ==
<gallery>
McCall, Idaho, default WS 2.0 scenery.png|McCall, Idaho with default WS2.0 scenery
McCall, Idaho custom scenery.png|McCall, Idaho with NLCD + osm2city scenery
Cape Cod.png|Cape Cod with NLCD and osm2city
Laurel Bed Lake.png|Laurel Bed Lake in western Virginia
</gallery>

== Available scenery using NLCD data ==

* Virginia, Maryland, Delaware, and the District of Columbia by montagdude: [https://github.com/montagdude/FGScenery-VA_MD_DE project page], [https://forum.flightgear.org/viewtopic.php?f=5&t=37322 forum topic]
* Rhode Island and Southeastern Massachusetts by montagdude: [https://github.com/montagdude/FGScenery-RIandSEMass project page], [https://forum.flightgear.org/viewtopic.php?f=5&t=36977 forum topic]
* Kansas and Ohio scenery by wlbragg: [[User:Wlbragg]]
* Tennessee scenery by xDraconian: [[US-Tennessee_Custom_Scenery]], [https://forum.flightgear.org/viewtopic.php?f=5&t=35521 forum topic]

== Related content ==
=== Forum topic ===
* {{forum link|t=36913|title=How to create US scenery with NLCD land cover data and QGIS}}

[[Category:Scenery enhancement]]

Howto:Using QGIS to process NLCD data

2020-04-07T00:12:46Z

Montagdude: /* Sample results */

This tutorial will describe '''how to use data from the [https://www.usgs.gov/centers/eros/science/national-land-cover-database?qt-science_center_objects=0#qt-science_center_objects National Land Cover Database (NLCD)] to create US scenery''' that is on par with the existing European scenery.

The default scenery (World Scenery 2.0) in FlightGear is of higher quality in some areas than others. For example, European scenery is generally of very high quality due to the use of [https://land.copernicus.eu/pan-european/corine-land-cover CORINE land cover data]. Compared to the European scenery, the US scenery is much less detailed and less accurate. Thankfully, there is a wealth of freely-available geographical information for the US that can be used to improve the scenery.

== Getting set up for scenery generation ==
=== Prerequisites ===
Please read and understand the [[Using TerraGear|Using TerraGear wiki article]]. This tutorial will follow the same process but replace the "Landuse data" section with custom shapefiles generated from NLCD data.

=== Tools ===
This tutorial will make use of the following GIS software:
# QGIS (version 3.10)
# GRASS (version 7.8, used as a QGIS plugin)
# TerraGear

=== Elevation data ===
Elevation data for the entire world is available from the Shuttle Radar Topology Mission (SRTM). It is highly recommended to use the void-filled version of these datasets, which can be obtained from the [https://earthexplorer.usgs.gov/ USGS EarthExplorer] (note: it is required to create a login to download data from that page). The void-filled datasets reduce or eliminate the small gaps that can occur in scenery generated using the original SRTM datasets. The elevation data is in GeoTIFF format. To convert it to hgt format as expected by TerraGear, use gdal_translate as follows:

gdal_translate -of SRTMHGT input_file.tif output_file.hgt

For the output file, TerraGear might expect it to follow a certain naming convention, like N38W080.hgt. The data from USGS follows a naming convention like n38_w080_3arc_v2.tif. The following (sh/bash shell) script will batch convert all the files in the working directory following the expected naming convention:

for file in *.tif; do
base=$(echo $file | cut -f 1 -d '.')
NORTH=$(echo $base | cut -f 1 -d '_' | cut -f 2 -d 'n')
WEST=$(echo $base | cut -f 2 -d '_' | cut -f 2 -d 'w')
gdal_translate -of SRTMHGT $file N${NORTH}W${WEST}.hgt
done

== NLCD data ==
You can download the NLCD data [https://www.mrlc.gov/data?f%5B0%5D=category%3Aland%20cover here]. At the time of this writing, the latest dataset is from 2016. For the continental US, you should choose the dataset labeled "NLCD 2016 Land Cover (CONUS)." There is also a separate dataset for Alaska. The download is a large (>1 GB) zip file. After downloading, unzip the files.

=== Processing the data ===
* Fire up QGIS and select the WGS 84 template. If you don't have a template to choose from, don't worry about it, as we'll be setting the coordinate reference system for the project later. Just create a new project in that case.
* In the menu bar, select Project -> Properties -> CRS. Pick WGS 84 - EPSG:4326 as the project coordinate reference system.
<gallery>
49532654886 e94a32328d o.png|Setting the CRS
</gallery>
* Set a basemap. An easy way to do this is to double click on XYZ Tiles -> OpenStreetMap in the browser on the left. You can also get them via the QuickMapServices plugin.
* Browse to the location where you have unzipped the NLCD data in the QGIS project browser and double click the .img file to load it as a layer.
* Right-click on the NLCD layer -> Set CRS -> Set Layer CRS and select NAD83 / Conus Albers - EPSG:5070 -> Transformation 1. This will place the layer in the right location on top of the basemap. Note: it may not look like it's in the right spot until you zoom in on it.
<gallery>
49532655611 64dc27a6fd o.png|NLCD data loaded in QGIS
</gallery>
* Now we need to clip the data to the region of interest. The best way to do this is to by clicking the New Shapefile Layer button (see screenshot below). In the dialog, change Geometry type to Polygon and save it as a shapefile, and then click OK.
* Click the Toggle Editing button to edit the layer.
* Click the Add Polygon Feature button to create a polygon. Now you can click on the map to create a polygon covering the area of interest. When you are done, you can right-click to stop adding vertices.
* If you want to edit the vertex locations, you can click the Vertex Tool button, right-click on your polygon, and then edit the coordinates in the table on the bottom left of the GUI window.
* When you are done editing the layer, click the Save Layer Edits button (next to the Toggle Editing button).
'''Note''': when you are learning the process, start with a small polygon, say 1x1 degrees. Some of the steps take a very long time if processing very large sections of this data.
<gallery>
New layer.png|New shapefile layer
Shapefile dialog.png|Shapefile dialog
Toggle editing.png|Toggle editing
Add polygon.png|Add polygon
Vertex tool.png|Vertex tool
</gallery>
*Once the polygon is completed, the next step is to clip the NLCD layer using this polygon. Select the NLCD layer and choose Raster -> Extraction -> Clip Raster by Mask Layer. Make sure the input layer is your NLCD layer and the mask layer is the polygon you have created. For Source CRS choose the Conus Albers CRS of the original NLCD layer, and for Target CRS choose the Project CRS, EPSG:4326. Near the bottom of the dialog box, where under Clipped (mask) it says [Save to temporary file], click the button to the right and choose Save to File. Select IMG files as the filter and save it somewhere in your working directory with a descriptive name like NLCD_clip.img. Click Run. Once it completes, if you hide the original NLCD layer and the polygon, you should see the new clipped layer. As long as it worked correctly, you can now safely remove the big original NLCD layer. The image below shows a small sample section in Idaho that has been clipped using this method.
<gallery>
49532154383 b437eb21b7 o.png|Clipped NLCD data
</gallery>
*If you zoom in on the data, you will see that it is highly detailed but also pixelated due to the raster format. It needs to be simplified and smoothed out to look good in FlightGear and also to reduce the polygon count on the resulting shapefiles. We will first do some smoothing of the raster itself, and then we'll do additional smoothing after converting to vector format. In the Processing Toolbox, find GRASS -> r.neighbors. The clipped NLCD layer should be selected for both of the top input dropdowns. For Neighborhood Operation, select median with a Neighborhood size of 5. As before, you may want to choose an actual IMG file to save the output to instead of a temporary file. Click Run.
'''Note''': never use an averaging neighborhood operation, as the raster values are uniquely associated with a specific type of landcover. The averaging operation will introduce new values that do not correspond to a landcover type.
*Now we'll run r.neighbors once more on the resulting layer using the mode Neighborhood Operation with a Neighborhood of 3. The result should look a lot smoother and a bit less detailed than the original dataset (but still very highly detailed compared to the default scenery). An image from the sample dataset is below. This area shows McCall, Idaho and the south end of Payette Lake.
<gallery>
49539367447 cde1c6666a o.png|After r.neighbors operations
</gallery>
*We're now ready to convert the raster data to vector. In the Processing Toolbox, find Grass -> r.to.vect. Choose the output layer from the last step (median neighbors filter), choose area as the feature type, and check the option "Smooth corners of area features." This will make the output vector a little less pixely than the input. As before, you may want to save to a real shapefile instead of a temporary one. Click Run. This step will take longer than any of the previous ones to process.
*We could use this vector data as is, but it's still a bit blocky and unnatural looking. Instead, we'll do a couple processing steps on the vector data. GRASS -> v.generalize. Choose the default douglas algorithm with a tolerance of 0.0003 (this is in degrees, since our CRS is in degrees). You may want to play around with different values of tolerance until you find something that works best. Click Run. If errors result, it will create a new layer, which can be deleted. '''Note''': this step will take a very long time for large areas.
*Now we'll use v.generalize one more to smooth the simplified layer. This time, choose the chaiken algorithm with the same tolerance. Click Run. '''Note''': this step will take a very long time for large areas.
<gallery>
Vectorized.png|Vectorized NLCD data
Simplified.png|Simplified NLCD data
Smoothed.png|Smoothed NLCD data
</gallery>
*At this point, it is a good idea to compare the smoothed vector layer to the original NLCD raster data and make sure the smoothing steps have not introduced too much error in the shape of coastlines and other features. If they have, you may need to redo some or all of the last several steps (r.neighbors, r.to.vect, and v.generalize) adjusting the number of neighbors and tolerance until you get satisfactory results. However, I've found these parameters to produce quite good results.
*We now have a high-resolution vectorized dataset that can be used to define landclasses in FlightGear. We just need to save individual shape files representing each landclass. At this point, it is a good idea to have the NLCD map with the legend displayed to match to values in the shapefiles. That map can be found here:
https://www.mrlc.gov/viewer/
*You will also want to have default_priorities.txt from your TerraGear installation open to map NLCD landclass types to available materials in FlightGear. Some artistic license is possible here, but I use this mapping:
{| class="wikitable"
|-
! NLCD Value !! Legend label !! FlightGear material
|-
| 11 || Open Water || Lake or Ocean
|-
| 12 || Perennial Ice/Snow || SnowCover or Glacier
|-
| 21 || Developed, Open Space || Greenspace
|-
| 22 || Developed, Low Intensity || Town
|-
| 23 || Developed, Medium Intensity || SubUrban
|-
| 24 || Developed, High Intensity || Urban
|-
| 31 || Barren Land (Rock/Sand/Clay) || Rock or Sand (depends on location)
|-
| 32 || Unconsolidated Shore || Sand
|-
| 41 || Deciduous Forest || DeciduousForest
|-
| 42 || Evergreen Forest || EvergreenForest
|-
| 43 || Mixed Forest || MixedForest
|-
| 51,52 || Dwarf Scrub/Shrub/Scrub || Scrub
|-
| 71 || Grasslands/Herbaceous || Grassland
|-
| 72-74 || Herbaceous/Moss/Lichens (AK only) || I haven't used these, so check default_priorities.txt if you find them in your data
|-
| 81 || Pasture/Hay || DryCrop
|-
| 82 || Cultivated Crops || IrrCrop
|-
| 90 || Woody Wetlands || Marsh
|-
| 95 || Emergent Herbaceous Wetlands || Bog
|}
*To actually select and export individual shapefiles for each type, do the following:
**Select the final smoothed vector data layer. Click the Deselect Features from All Layers button near the top middle of the QGIS toolbar. Then click the button right next to it, Select Features by Value. In the dialog, enter the number from the NLCD legend in the value field, make sure the filter says Equal to (=), and then click Select Features and Close. Alternatively, if you want to combine multiple NLCD values into a single output shapefile, rather than clicking Close, you could enter in another value and then choose Add to Current Selection in the dropdown next to Select Features.
**Once you have the desired features selected, right click on the smoothed vector layer, click Export -> Save Selected Features As, choose the ESRI shapefile format, and save it somewhere under the project directory with a name corresponding to the appropriate area type in default_priorities.txt (e.g. data/shapefiles/Lake.shp). For CRS, choose the project CRS EPSG:4326 - WGS 84.
**Repeat this process until you've gone over all the values in the NLCD data legend. When finished, you should have individual layers representing different area types covering the entire area of interest. The result for the sample area is shown in the image below.
<gallery>
Nlcd shapefiles.jpg|Shapefiles from NLCD data
</gallery>

=== Converting with ogr-decode ===

If you have saved the shapefiles with a name corresponding to a material in default_priorities.txt, then they can be batch-converted with ogr-decode as follows:

# work and shapefiles directories
WORK=./work
DATADIR=./data/shapefiles/NLCD
# Process landcover
for file in $DATADIR/*.shp; do
fname=$(basename $file)
area_type=$(echo $fname | cut -f 1 -d '.')
if [ -d $WORK/$area_type ]; then
rm -rf $WORK/$area_type
fi
ogr-decode --area-type $area_type work/$area_type $file
done

(The script above is a bash/sh script for Linux. Something similar could probably done with a .bat script on Windows. The locations of the work and data directories will need to be edited if you use a different directory structure.)

=== Building with tg-construct ===

The final step in building the terrain is to run tg-construct. This process is described in the [[Using TerraGear|Using TerraGear wiki article]]. This should be done after adding an supplementary shapefiles, as described in the next section. When running tg-construct, you will want to use the '''--ignore-landmass''' flag so that lakes and other waterbodies are not assumed to be at sea level.

== Supplementary data ==

You will probably want to supplement your custom scenery with some other data. Sources include:

* National Hydrography Data for waterways and waterbodies. Though NLCD includes water landcover, it is not as accurate or complete as NHD, and the smoothing process described above will cause thin rivers to turn into "islands." I recommend using the NHDArea and NHDWaterbody datasets but skipping the line data. The data is in shapefile format, which can be viewed and manipulated with QGIS. Be sure to filter the data to remove extraneous elements. It includes things like marshes, dams, and intermittent streams that probably should not be included. The NHD data can be accessed here: [https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products Access National Hydrography Products]. To filter the data, you will want to refer to this page: [https://nhd.usgs.gov/userGuide/Robohelpfiles/NHD_User_Guide/Feature_Catalog/Hydrography_Dataset/Complete_FCode_List.htm NHD FCodes].

* [https://www.openstreetmap.org OpenStreetMap] for roads. QGIS can open .osm and .pbf files extracted from OpenStreetMap and edit them just like all the other datasets that have been discussed here. These can be "baked into" the terrain using TerraGear, or you can use:

* [https://osm2city.readthedocs.io/en/latest/index.html osm2city], an autogen tool for FlightGear that creates buildings, roads, bridges, pylons, docks, and other details from OpenStreetMap data. This tool has a bit of a learning curve and does not flow through QGIS, but it can really make your project shine.

== Sample results ==
<gallery>
McCall, Idaho, default WS 2.0 scenery.png|McCall, Idaho with default WS2.0 scenery
McCall, Idaho custom scenery.png|McCall, Idaho with NLCD + osm2city scenery
Cape Cod.png|Cape Cod with NLCD and osm2city
Laurel Bed Lake.png|Laurel Bed Lake in western Virginia
</gallery>

== Available scenery using NLCD data ==

* Rhode Island and Southeastern Massachusetts by montagdude: [https://github.com/montagdude/FGScenery-RIandSEMass project page], [https://forum.flightgear.org/viewtopic.php?f=5&t=36977 forum topic]
* Kansas and Ohio scenery by wlbragg: [[User:Wlbragg]]
* Tennessee scenery by xDraconian: [[US-Tennessee_Custom_Scenery]], [https://forum.flightgear.org/viewtopic.php?f=5&t=35521 forum topic]

== Related content ==
=== Forum topic ===
* {{forum link|t=36913|title=How to create US scenery with NLCD land cover data and QGIS}}

[[Category:Scenery enhancement]]

File:McCall, Idaho custom scenery.png

2020-04-07T00:10:01Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=Also includes osm2city}}
|date=2020-04-06
|source={{own}}
|author=[[User:Montagdude|Montagdude]]
|permission=
|other versions=
}}

=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

[[Category:Scenery]]
[[Category:Scenery enhancement]]

File:Laurel Bed Lake.png

2020-04-07T00:10:01Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=Laurel Bed Lake in western Virginia}}
|date=2020-04-06
|source={{own}}
|author=[[User:Montagdude|Montagdude]]
|permission=
|other versions=
}}

=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

[[Category:Scenery]]
[[Category:Scenery enhancement]]

File:Cape Cod.png

2020-04-07T00:10:01Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=Cape Cod with NLCD and osm2city}}
|date=2020-04-06
|source={{own}}
|author=[[User:Montagdude|Montagdude]]
|permission=
|other versions=
}}

=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

[[Category:Scenery]]
[[Category:Scenery enhancement]]

File:McCall, Idaho, default WS 2.0 scenery.png

2020-04-07T00:10:01Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=McCall, Idaho with default WS2.0 scenery}}
|date=2020-04-06
|source={{own}}
|author=[[User:Montagdude|Montagdude]]
|permission=
|other versions=
}}

=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

[[Category:Scenery]]
[[Category:Scenery enhancement]]

Howto:Using QGIS to process NLCD data

2020-04-07T00:00:41Z

Montagdude: /* Available scenery using NLCD data */

This tutorial will describe '''how to use data from the [https://www.usgs.gov/centers/eros/science/national-land-cover-database?qt-science_center_objects=0#qt-science_center_objects National Land Cover Database (NLCD)] to create US scenery''' that is on par with the existing European scenery.

The default scenery (World Scenery 2.0) in FlightGear is of higher quality in some areas than others. For example, European scenery is generally of very high quality due to the use of [https://land.copernicus.eu/pan-european/corine-land-cover CORINE land cover data]. Compared to the European scenery, the US scenery is much less detailed and less accurate. Thankfully, there is a wealth of freely-available geographical information for the US that can be used to improve the scenery.

== Getting set up for scenery generation ==
=== Prerequisites ===
Please read and understand the [[Using TerraGear|Using TerraGear wiki article]]. This tutorial will follow the same process but replace the "Landuse data" section with custom shapefiles generated from NLCD data.

=== Tools ===
This tutorial will make use of the following GIS software:
# QGIS (version 3.10)
# GRASS (version 7.8, used as a QGIS plugin)
# TerraGear

=== Elevation data ===
Elevation data for the entire world is available from the Shuttle Radar Topology Mission (SRTM). It is highly recommended to use the void-filled version of these datasets, which can be obtained from the [https://earthexplorer.usgs.gov/ USGS EarthExplorer] (note: it is required to create a login to download data from that page). The void-filled datasets reduce or eliminate the small gaps that can occur in scenery generated using the original SRTM datasets. The elevation data is in GeoTIFF format. To convert it to hgt format as expected by TerraGear, use gdal_translate as follows:

gdal_translate -of SRTMHGT input_file.tif output_file.hgt

For the output file, TerraGear might expect it to follow a certain naming convention, like N38W080.hgt. The data from USGS follows a naming convention like n38_w080_3arc_v2.tif. The following (sh/bash shell) script will batch convert all the files in the working directory following the expected naming convention:

for file in *.tif; do
base=$(echo $file | cut -f 1 -d '.')
NORTH=$(echo $base | cut -f 1 -d '_' | cut -f 2 -d 'n')
WEST=$(echo $base | cut -f 2 -d '_' | cut -f 2 -d 'w')
gdal_translate -of SRTMHGT $file N${NORTH}W${WEST}.hgt
done

== NLCD data ==
You can download the NLCD data [https://www.mrlc.gov/data?f%5B0%5D=category%3Aland%20cover here]. At the time of this writing, the latest dataset is from 2016. For the continental US, you should choose the dataset labeled "NLCD 2016 Land Cover (CONUS)." There is also a separate dataset for Alaska. The download is a large (>1 GB) zip file. After downloading, unzip the files.

=== Processing the data ===
* Fire up QGIS and select the WGS 84 template. If you don't have a template to choose from, don't worry about it, as we'll be setting the coordinate reference system for the project later. Just create a new project in that case.
* In the menu bar, select Project -> Properties -> CRS. Pick WGS 84 - EPSG:4326 as the project coordinate reference system.
<gallery>
49532654886 e94a32328d o.png|Setting the CRS
</gallery>
* Set a basemap. An easy way to do this is to double click on XYZ Tiles -> OpenStreetMap in the browser on the left. You can also get them via the QuickMapServices plugin.
* Browse to the location where you have unzipped the NLCD data in the QGIS project browser and double click the .img file to load it as a layer.
* Right-click on the NLCD layer -> Set CRS -> Set Layer CRS and select NAD83 / Conus Albers - EPSG:5070 -> Transformation 1. This will place the layer in the right location on top of the basemap. Note: it may not look like it's in the right spot until you zoom in on it.
<gallery>
49532655611 64dc27a6fd o.png|NLCD data loaded in QGIS
</gallery>
* Now we need to clip the data to the region of interest. The best way to do this is to by clicking the New Shapefile Layer button (see screenshot below). In the dialog, change Geometry type to Polygon and save it as a shapefile, and then click OK.
* Click the Toggle Editing button to edit the layer.
* Click the Add Polygon Feature button to create a polygon. Now you can click on the map to create a polygon covering the area of interest. When you are done, you can right-click to stop adding vertices.
* If you want to edit the vertex locations, you can click the Vertex Tool button, right-click on your polygon, and then edit the coordinates in the table on the bottom left of the GUI window.
* When you are done editing the layer, click the Save Layer Edits button (next to the Toggle Editing button).
'''Note''': when you are learning the process, start with a small polygon, say 1x1 degrees. Some of the steps take a very long time if processing very large sections of this data.
<gallery>
New layer.png|New shapefile layer
Shapefile dialog.png|Shapefile dialog
Toggle editing.png|Toggle editing
Add polygon.png|Add polygon
Vertex tool.png|Vertex tool
</gallery>
*Once the polygon is completed, the next step is to clip the NLCD layer using this polygon. Select the NLCD layer and choose Raster -> Extraction -> Clip Raster by Mask Layer. Make sure the input layer is your NLCD layer and the mask layer is the polygon you have created. For Source CRS choose the Conus Albers CRS of the original NLCD layer, and for Target CRS choose the Project CRS, EPSG:4326. Near the bottom of the dialog box, where under Clipped (mask) it says [Save to temporary file], click the button to the right and choose Save to File. Select IMG files as the filter and save it somewhere in your working directory with a descriptive name like NLCD_clip.img. Click Run. Once it completes, if you hide the original NLCD layer and the polygon, you should see the new clipped layer. As long as it worked correctly, you can now safely remove the big original NLCD layer. The image below shows a small sample section in Idaho that has been clipped using this method.
<gallery>
49532154383 b437eb21b7 o.png|Clipped NLCD data
</gallery>
*If you zoom in on the data, you will see that it is highly detailed but also pixelated due to the raster format. It needs to be simplified and smoothed out to look good in FlightGear and also to reduce the polygon count on the resulting shapefiles. We will first do some smoothing of the raster itself, and then we'll do additional smoothing after converting to vector format. In the Processing Toolbox, find GRASS -> r.neighbors. The clipped NLCD layer should be selected for both of the top input dropdowns. For Neighborhood Operation, select median with a Neighborhood size of 5. As before, you may want to choose an actual IMG file to save the output to instead of a temporary file. Click Run.
'''Note''': never use an averaging neighborhood operation, as the raster values are uniquely associated with a specific type of landcover. The averaging operation will introduce new values that do not correspond to a landcover type.
*Now we'll run r.neighbors once more on the resulting layer using the mode Neighborhood Operation with a Neighborhood of 3. The result should look a lot smoother and a bit less detailed than the original dataset (but still very highly detailed compared to the default scenery). An image from the sample dataset is below. This area shows McCall, Idaho and the south end of Payette Lake.
<gallery>
49539367447 cde1c6666a o.png|After r.neighbors operations
</gallery>
*We're now ready to convert the raster data to vector. In the Processing Toolbox, find Grass -> r.to.vect. Choose the output layer from the last step (median neighbors filter), choose area as the feature type, and check the option "Smooth corners of area features." This will make the output vector a little less pixely than the input. As before, you may want to save to a real shapefile instead of a temporary one. Click Run. This step will take longer than any of the previous ones to process.
*We could use this vector data as is, but it's still a bit blocky and unnatural looking. Instead, we'll do a couple processing steps on the vector data. GRASS -> v.generalize. Choose the default douglas algorithm with a tolerance of 0.0003 (this is in degrees, since our CRS is in degrees). You may want to play around with different values of tolerance until you find something that works best. Click Run. If errors result, it will create a new layer, which can be deleted. '''Note''': this step will take a very long time for large areas.
*Now we'll use v.generalize one more to smooth the simplified layer. This time, choose the chaiken algorithm with the same tolerance. Click Run. '''Note''': this step will take a very long time for large areas.
<gallery>
Vectorized.png|Vectorized NLCD data
Simplified.png|Simplified NLCD data
Smoothed.png|Smoothed NLCD data
</gallery>
*At this point, it is a good idea to compare the smoothed vector layer to the original NLCD raster data and make sure the smoothing steps have not introduced too much error in the shape of coastlines and other features. If they have, you may need to redo some or all of the last several steps (r.neighbors, r.to.vect, and v.generalize) adjusting the number of neighbors and tolerance until you get satisfactory results. However, I've found these parameters to produce quite good results.
*We now have a high-resolution vectorized dataset that can be used to define landclasses in FlightGear. We just need to save individual shape files representing each landclass. At this point, it is a good idea to have the NLCD map with the legend displayed to match to values in the shapefiles. That map can be found here:
https://www.mrlc.gov/viewer/
*You will also want to have default_priorities.txt from your TerraGear installation open to map NLCD landclass types to available materials in FlightGear. Some artistic license is possible here, but I use this mapping:
{| class="wikitable"
|-
! NLCD Value !! Legend label !! FlightGear material
|-
| 11 || Open Water || Lake or Ocean
|-
| 12 || Perennial Ice/Snow || SnowCover or Glacier
|-
| 21 || Developed, Open Space || Greenspace
|-
| 22 || Developed, Low Intensity || Town
|-
| 23 || Developed, Medium Intensity || SubUrban
|-
| 24 || Developed, High Intensity || Urban
|-
| 31 || Barren Land (Rock/Sand/Clay) || Rock or Sand (depends on location)
|-
| 32 || Unconsolidated Shore || Sand
|-
| 41 || Deciduous Forest || DeciduousForest
|-
| 42 || Evergreen Forest || EvergreenForest
|-
| 43 || Mixed Forest || MixedForest
|-
| 51,52 || Dwarf Scrub/Shrub/Scrub || Scrub
|-
| 71 || Grasslands/Herbaceous || Grassland
|-
| 72-74 || Herbaceous/Moss/Lichens (AK only) || I haven't used these, so check default_priorities.txt if you find them in your data
|-
| 81 || Pasture/Hay || DryCrop
|-
| 82 || Cultivated Crops || IrrCrop
|-
| 90 || Woody Wetlands || Marsh
|-
| 95 || Emergent Herbaceous Wetlands || Bog
|}
*To actually select and export individual shapefiles for each type, do the following:
**Select the final smoothed vector data layer. Click the Deselect Features from All Layers button near the top middle of the QGIS toolbar. Then click the button right next to it, Select Features by Value. In the dialog, enter the number from the NLCD legend in the value field, make sure the filter says Equal to (=), and then click Select Features and Close. Alternatively, if you want to combine multiple NLCD values into a single output shapefile, rather than clicking Close, you could enter in another value and then choose Add to Current Selection in the dropdown next to Select Features.
**Once you have the desired features selected, right click on the smoothed vector layer, click Export -> Save Selected Features As, choose the ESRI shapefile format, and save it somewhere under the project directory with a name corresponding to the appropriate area type in default_priorities.txt (e.g. data/shapefiles/Lake.shp). For CRS, choose the project CRS EPSG:4326 - WGS 84.
**Repeat this process until you've gone over all the values in the NLCD data legend. When finished, you should have individual layers representing different area types covering the entire area of interest. The result for the sample area is shown in the image below.
<gallery>
Nlcd shapefiles.jpg|Shapefiles from NLCD data
</gallery>

=== Converting with ogr-decode ===

If you have saved the shapefiles with a name corresponding to a material in default_priorities.txt, then they can be batch-converted with ogr-decode as follows:

# work and shapefiles directories
WORK=./work
DATADIR=./data/shapefiles/NLCD
# Process landcover
for file in $DATADIR/*.shp; do
fname=$(basename $file)
area_type=$(echo $fname | cut -f 1 -d '.')
if [ -d $WORK/$area_type ]; then
rm -rf $WORK/$area_type
fi
ogr-decode --area-type $area_type work/$area_type $file
done

(The script above is a bash/sh script for Linux. Something similar could probably done with a .bat script on Windows. The locations of the work and data directories will need to be edited if you use a different directory structure.)

=== Building with tg-construct ===

The final step in building the terrain is to run tg-construct. This process is described in the [[Using TerraGear|Using TerraGear wiki article]]. This should be done after adding an supplementary shapefiles, as described in the next section. When running tg-construct, you will want to use the '''--ignore-landmass''' flag so that lakes and other waterbodies are not assumed to be at sea level.

== Supplementary data ==

You will probably want to supplement your custom scenery with some other data. Sources include:

* National Hydrography Data for waterways and waterbodies. Though NLCD includes water landcover, it is not as accurate or complete as NHD, and the smoothing process described above will cause thin rivers to turn into "islands." I recommend using the NHDArea and NHDWaterbody datasets but skipping the line data. The data is in shapefile format, which can be viewed and manipulated with QGIS. Be sure to filter the data to remove extraneous elements. It includes things like marshes, dams, and intermittent streams that probably should not be included. The NHD data can be accessed here: [https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products Access National Hydrography Products]. To filter the data, you will want to refer to this page: [https://nhd.usgs.gov/userGuide/Robohelpfiles/NHD_User_Guide/Feature_Catalog/Hydrography_Dataset/Complete_FCode_List.htm NHD FCodes].

* [https://www.openstreetmap.org OpenStreetMap] for roads. QGIS can open .osm and .pbf files extracted from OpenStreetMap and edit them just like all the other datasets that have been discussed here. These can be "baked into" the terrain using TerraGear, or you can use:

* [https://osm2city.readthedocs.io/en/latest/index.html osm2city], an autogen tool for FlightGear that creates buildings, roads, bridges, pylons, docks, and other details from OpenStreetMap data. This tool has a bit of a learning curve and does not flow through QGIS, but it can really make your project shine.

== Sample results ==

== Available scenery using NLCD data ==

* Rhode Island and Southeastern Massachusetts by montagdude: [https://github.com/montagdude/FGScenery-RIandSEMass project page], [https://forum.flightgear.org/viewtopic.php?f=5&t=36977 forum topic]
* Kansas and Ohio scenery by wlbragg: [[User:Wlbragg]]
* Tennessee scenery by xDraconian: [[US-Tennessee_Custom_Scenery]], [https://forum.flightgear.org/viewtopic.php?f=5&t=35521 forum topic]

== Related content ==
=== Forum topic ===
* {{forum link|t=36913|title=How to create US scenery with NLCD land cover data and QGIS}}

[[Category:Scenery enhancement]]

Howto:Using QGIS to process NLCD data

2020-04-06T23:47:08Z

Montagdude: Add building with tg-construct section

This tutorial will describe '''how to use data from the [https://www.usgs.gov/centers/eros/science/national-land-cover-database?qt-science_center_objects=0#qt-science_center_objects National Land Cover Database (NLCD)] to create US scenery''' that is on par with the existing European scenery.

The default scenery (World Scenery 2.0) in FlightGear is of higher quality in some areas than others. For example, European scenery is generally of very high quality due to the use of [https://land.copernicus.eu/pan-european/corine-land-cover CORINE land cover data]. Compared to the European scenery, the US scenery is much less detailed and less accurate. Thankfully, there is a wealth of freely-available geographical information for the US that can be used to improve the scenery.

== Getting set up for scenery generation ==
=== Prerequisites ===
Please read and understand the [[Using TerraGear|Using TerraGear wiki article]]. This tutorial will follow the same process but replace the "Landuse data" section with custom shapefiles generated from NLCD data.

=== Tools ===
This tutorial will make use of the following GIS software:
# QGIS (version 3.10)
# GRASS (version 7.8, used as a QGIS plugin)
# TerraGear

=== Elevation data ===
Elevation data for the entire world is available from the Shuttle Radar Topology Mission (SRTM). It is highly recommended to use the void-filled version of these datasets, which can be obtained from the [https://earthexplorer.usgs.gov/ USGS EarthExplorer] (note: it is required to create a login to download data from that page). The void-filled datasets reduce or eliminate the small gaps that can occur in scenery generated using the original SRTM datasets. The elevation data is in GeoTIFF format. To convert it to hgt format as expected by TerraGear, use gdal_translate as follows:

gdal_translate -of SRTMHGT input_file.tif output_file.hgt

For the output file, TerraGear might expect it to follow a certain naming convention, like N38W080.hgt. The data from USGS follows a naming convention like n38_w080_3arc_v2.tif. The following (sh/bash shell) script will batch convert all the files in the working directory following the expected naming convention:

for file in *.tif; do
base=$(echo $file | cut -f 1 -d '.')
NORTH=$(echo $base | cut -f 1 -d '_' | cut -f 2 -d 'n')
WEST=$(echo $base | cut -f 2 -d '_' | cut -f 2 -d 'w')
gdal_translate -of SRTMHGT $file N${NORTH}W${WEST}.hgt
done

== NLCD data ==
You can download the NLCD data [https://www.mrlc.gov/data?f%5B0%5D=category%3Aland%20cover here]. At the time of this writing, the latest dataset is from 2016. For the continental US, you should choose the dataset labeled "NLCD 2016 Land Cover (CONUS)." There is also a separate dataset for Alaska. The download is a large (>1 GB) zip file. After downloading, unzip the files.

=== Processing the data ===
* Fire up QGIS and select the WGS 84 template. If you don't have a template to choose from, don't worry about it, as we'll be setting the coordinate reference system for the project later. Just create a new project in that case.
* In the menu bar, select Project -> Properties -> CRS. Pick WGS 84 - EPSG:4326 as the project coordinate reference system.
<gallery>
49532654886 e94a32328d o.png|Setting the CRS
</gallery>
* Set a basemap. An easy way to do this is to double click on XYZ Tiles -> OpenStreetMap in the browser on the left. You can also get them via the QuickMapServices plugin.
* Browse to the location where you have unzipped the NLCD data in the QGIS project browser and double click the .img file to load it as a layer.
* Right-click on the NLCD layer -> Set CRS -> Set Layer CRS and select NAD83 / Conus Albers - EPSG:5070 -> Transformation 1. This will place the layer in the right location on top of the basemap. Note: it may not look like it's in the right spot until you zoom in on it.
<gallery>
49532655611 64dc27a6fd o.png|NLCD data loaded in QGIS
</gallery>
* Now we need to clip the data to the region of interest. The best way to do this is to by clicking the New Shapefile Layer button (see screenshot below). In the dialog, change Geometry type to Polygon and save it as a shapefile, and then click OK.
* Click the Toggle Editing button to edit the layer.
* Click the Add Polygon Feature button to create a polygon. Now you can click on the map to create a polygon covering the area of interest. When you are done, you can right-click to stop adding vertices.
* If you want to edit the vertex locations, you can click the Vertex Tool button, right-click on your polygon, and then edit the coordinates in the table on the bottom left of the GUI window.
* When you are done editing the layer, click the Save Layer Edits button (next to the Toggle Editing button).
'''Note''': when you are learning the process, start with a small polygon, say 1x1 degrees. Some of the steps take a very long time if processing very large sections of this data.
<gallery>
New layer.png|New shapefile layer
Shapefile dialog.png|Shapefile dialog
Toggle editing.png|Toggle editing
Add polygon.png|Add polygon
Vertex tool.png|Vertex tool
</gallery>
*Once the polygon is completed, the next step is to clip the NLCD layer using this polygon. Select the NLCD layer and choose Raster -> Extraction -> Clip Raster by Mask Layer. Make sure the input layer is your NLCD layer and the mask layer is the polygon you have created. For Source CRS choose the Conus Albers CRS of the original NLCD layer, and for Target CRS choose the Project CRS, EPSG:4326. Near the bottom of the dialog box, where under Clipped (mask) it says [Save to temporary file], click the button to the right and choose Save to File. Select IMG files as the filter and save it somewhere in your working directory with a descriptive name like NLCD_clip.img. Click Run. Once it completes, if you hide the original NLCD layer and the polygon, you should see the new clipped layer. As long as it worked correctly, you can now safely remove the big original NLCD layer. The image below shows a small sample section in Idaho that has been clipped using this method.
<gallery>
49532154383 b437eb21b7 o.png|Clipped NLCD data
</gallery>
*If you zoom in on the data, you will see that it is highly detailed but also pixelated due to the raster format. It needs to be simplified and smoothed out to look good in FlightGear and also to reduce the polygon count on the resulting shapefiles. We will first do some smoothing of the raster itself, and then we'll do additional smoothing after converting to vector format. In the Processing Toolbox, find GRASS -> r.neighbors. The clipped NLCD layer should be selected for both of the top input dropdowns. For Neighborhood Operation, select median with a Neighborhood size of 5. As before, you may want to choose an actual IMG file to save the output to instead of a temporary file. Click Run.
'''Note''': never use an averaging neighborhood operation, as the raster values are uniquely associated with a specific type of landcover. The averaging operation will introduce new values that do not correspond to a landcover type.
*Now we'll run r.neighbors once more on the resulting layer using the mode Neighborhood Operation with a Neighborhood of 3. The result should look a lot smoother and a bit less detailed than the original dataset (but still very highly detailed compared to the default scenery). An image from the sample dataset is below. This area shows McCall, Idaho and the south end of Payette Lake.
<gallery>
49539367447 cde1c6666a o.png|After r.neighbors operations
</gallery>
*We're now ready to convert the raster data to vector. In the Processing Toolbox, find Grass -> r.to.vect. Choose the output layer from the last step (median neighbors filter), choose area as the feature type, and check the option "Smooth corners of area features." This will make the output vector a little less pixely than the input. As before, you may want to save to a real shapefile instead of a temporary one. Click Run. This step will take longer than any of the previous ones to process.
*We could use this vector data as is, but it's still a bit blocky and unnatural looking. Instead, we'll do a couple processing steps on the vector data. GRASS -> v.generalize. Choose the default douglas algorithm with a tolerance of 0.0003 (this is in degrees, since our CRS is in degrees). You may want to play around with different values of tolerance until you find something that works best. Click Run. If errors result, it will create a new layer, which can be deleted. '''Note''': this step will take a very long time for large areas.
*Now we'll use v.generalize one more to smooth the simplified layer. This time, choose the chaiken algorithm with the same tolerance. Click Run. '''Note''': this step will take a very long time for large areas.
<gallery>
Vectorized.png|Vectorized NLCD data
Simplified.png|Simplified NLCD data
Smoothed.png|Smoothed NLCD data
</gallery>
*At this point, it is a good idea to compare the smoothed vector layer to the original NLCD raster data and make sure the smoothing steps have not introduced too much error in the shape of coastlines and other features. If they have, you may need to redo some or all of the last several steps (r.neighbors, r.to.vect, and v.generalize) adjusting the number of neighbors and tolerance until you get satisfactory results. However, I've found these parameters to produce quite good results.
*We now have a high-resolution vectorized dataset that can be used to define landclasses in FlightGear. We just need to save individual shape files representing each landclass. At this point, it is a good idea to have the NLCD map with the legend displayed to match to values in the shapefiles. That map can be found here:
https://www.mrlc.gov/viewer/
*You will also want to have default_priorities.txt from your TerraGear installation open to map NLCD landclass types to available materials in FlightGear. Some artistic license is possible here, but I use this mapping:
{| class="wikitable"
|-
! NLCD Value !! Legend label !! FlightGear material
|-
| 11 || Open Water || Lake or Ocean
|-
| 12 || Perennial Ice/Snow || SnowCover or Glacier
|-
| 21 || Developed, Open Space || Greenspace
|-
| 22 || Developed, Low Intensity || Town
|-
| 23 || Developed, Medium Intensity || SubUrban
|-
| 24 || Developed, High Intensity || Urban
|-
| 31 || Barren Land (Rock/Sand/Clay) || Rock or Sand (depends on location)
|-
| 32 || Unconsolidated Shore || Sand
|-
| 41 || Deciduous Forest || DeciduousForest
|-
| 42 || Evergreen Forest || EvergreenForest
|-
| 43 || Mixed Forest || MixedForest
|-
| 51,52 || Dwarf Scrub/Shrub/Scrub || Scrub
|-
| 71 || Grasslands/Herbaceous || Grassland
|-
| 72-74 || Herbaceous/Moss/Lichens (AK only) || I haven't used these, so check default_priorities.txt if you find them in your data
|-
| 81 || Pasture/Hay || DryCrop
|-
| 82 || Cultivated Crops || IrrCrop
|-
| 90 || Woody Wetlands || Marsh
|-
| 95 || Emergent Herbaceous Wetlands || Bog
|}
*To actually select and export individual shapefiles for each type, do the following:
**Select the final smoothed vector data layer. Click the Deselect Features from All Layers button near the top middle of the QGIS toolbar. Then click the button right next to it, Select Features by Value. In the dialog, enter the number from the NLCD legend in the value field, make sure the filter says Equal to (=), and then click Select Features and Close. Alternatively, if you want to combine multiple NLCD values into a single output shapefile, rather than clicking Close, you could enter in another value and then choose Add to Current Selection in the dropdown next to Select Features.
**Once you have the desired features selected, right click on the smoothed vector layer, click Export -> Save Selected Features As, choose the ESRI shapefile format, and save it somewhere under the project directory with a name corresponding to the appropriate area type in default_priorities.txt (e.g. data/shapefiles/Lake.shp). For CRS, choose the project CRS EPSG:4326 - WGS 84.
**Repeat this process until you've gone over all the values in the NLCD data legend. When finished, you should have individual layers representing different area types covering the entire area of interest. The result for the sample area is shown in the image below.
<gallery>
Nlcd shapefiles.jpg|Shapefiles from NLCD data
</gallery>

=== Converting with ogr-decode ===

If you have saved the shapefiles with a name corresponding to a material in default_priorities.txt, then they can be batch-converted with ogr-decode as follows:

# work and shapefiles directories
WORK=./work
DATADIR=./data/shapefiles/NLCD
# Process landcover
for file in $DATADIR/*.shp; do
fname=$(basename $file)
area_type=$(echo $fname | cut -f 1 -d '.')
if [ -d $WORK/$area_type ]; then
rm -rf $WORK/$area_type
fi
ogr-decode --area-type $area_type work/$area_type $file
done

(The script above is a bash/sh script for Linux. Something similar could probably done with a .bat script on Windows. The locations of the work and data directories will need to be edited if you use a different directory structure.)

=== Building with tg-construct ===

The final step in building the terrain is to run tg-construct. This process is described in the [[Using TerraGear|Using TerraGear wiki article]]. This should be done after adding an supplementary shapefiles, as described in the next section. When running tg-construct, you will want to use the '''--ignore-landmass''' flag so that lakes and other waterbodies are not assumed to be at sea level.

== Supplementary data ==

You will probably want to supplement your custom scenery with some other data. Sources include:

* National Hydrography Data for waterways and waterbodies. Though NLCD includes water landcover, it is not as accurate or complete as NHD, and the smoothing process described above will cause thin rivers to turn into "islands." I recommend using the NHDArea and NHDWaterbody datasets but skipping the line data. The data is in shapefile format, which can be viewed and manipulated with QGIS. Be sure to filter the data to remove extraneous elements. It includes things like marshes, dams, and intermittent streams that probably should not be included. The NHD data can be accessed here: [https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products Access National Hydrography Products]. To filter the data, you will want to refer to this page: [https://nhd.usgs.gov/userGuide/Robohelpfiles/NHD_User_Guide/Feature_Catalog/Hydrography_Dataset/Complete_FCode_List.htm NHD FCodes].

* [https://www.openstreetmap.org OpenStreetMap] for roads. QGIS can open .osm and .pbf files extracted from OpenStreetMap and edit them just like all the other datasets that have been discussed here. These can be "baked into" the terrain using TerraGear, or you can use:

* [https://osm2city.readthedocs.io/en/latest/index.html osm2city], an autogen tool for FlightGear that creates buildings, roads, bridges, pylons, docks, and other details from OpenStreetMap data. This tool has a bit of a learning curve and does not flow through QGIS, but it can really make your project shine.

== Sample results ==

== Available scenery using NLCD data ==

== Related content ==
=== Forum topic ===
* {{forum link|t=36913|title=How to create US scenery with NLCD land cover data and QGIS}}

[[Category:Scenery enhancement]]

Howto:Using QGIS to process NLCD data

2020-04-06T23:42:34Z

Montagdude: /* Supplementary data */

This tutorial will describe '''how to use data from the [https://www.usgs.gov/centers/eros/science/national-land-cover-database?qt-science_center_objects=0#qt-science_center_objects National Land Cover Database (NLCD)] to create US scenery''' that is on par with the existing European scenery.

The default scenery (World Scenery 2.0) in FlightGear is of higher quality in some areas than others. For example, European scenery is generally of very high quality due to the use of [https://land.copernicus.eu/pan-european/corine-land-cover CORINE land cover data]. Compared to the European scenery, the US scenery is much less detailed and less accurate. Thankfully, there is a wealth of freely-available geographical information for the US that can be used to improve the scenery.

== Getting set up for scenery generation ==
=== Prerequisites ===
Please read and understand the [[Using TerraGear|Using TerraGear wiki article]]. This tutorial will follow the same process but replace the "Landuse data" section with custom shapefiles generated from NLCD data.

=== Tools ===
This tutorial will make use of the following GIS software:
# QGIS (version 3.10)
# GRASS (version 7.8, used as a QGIS plugin)
# TerraGear

=== Elevation data ===
Elevation data for the entire world is available from the Shuttle Radar Topology Mission (SRTM). It is highly recommended to use the void-filled version of these datasets, which can be obtained from the [https://earthexplorer.usgs.gov/ USGS EarthExplorer] (note: it is required to create a login to download data from that page). The void-filled datasets reduce or eliminate the small gaps that can occur in scenery generated using the original SRTM datasets. The elevation data is in GeoTIFF format. To convert it to hgt format as expected by TerraGear, use gdal_translate as follows:

gdal_translate -of SRTMHGT input_file.tif output_file.hgt

For the output file, TerraGear might expect it to follow a certain naming convention, like N38W080.hgt. The data from USGS follows a naming convention like n38_w080_3arc_v2.tif. The following (sh/bash shell) script will batch convert all the files in the working directory following the expected naming convention:

for file in *.tif; do
base=$(echo $file | cut -f 1 -d '.')
NORTH=$(echo $base | cut -f 1 -d '_' | cut -f 2 -d 'n')
WEST=$(echo $base | cut -f 2 -d '_' | cut -f 2 -d 'w')
gdal_translate -of SRTMHGT $file N${NORTH}W${WEST}.hgt
done

== NLCD data ==
You can download the NLCD data [https://www.mrlc.gov/data?f%5B0%5D=category%3Aland%20cover here]. At the time of this writing, the latest dataset is from 2016. For the continental US, you should choose the dataset labeled "NLCD 2016 Land Cover (CONUS)." There is also a separate dataset for Alaska. The download is a large (>1 GB) zip file. After downloading, unzip the files.

=== Processing the data ===
* Fire up QGIS and select the WGS 84 template. If you don't have a template to choose from, don't worry about it, as we'll be setting the coordinate reference system for the project later. Just create a new project in that case.
* In the menu bar, select Project -> Properties -> CRS. Pick WGS 84 - EPSG:4326 as the project coordinate reference system.
<gallery>
49532654886 e94a32328d o.png|Setting the CRS
</gallery>
* Set a basemap. An easy way to do this is to double click on XYZ Tiles -> OpenStreetMap in the browser on the left. You can also get them via the QuickMapServices plugin.
* Browse to the location where you have unzipped the NLCD data in the QGIS project browser and double click the .img file to load it as a layer.
* Right-click on the NLCD layer -> Set CRS -> Set Layer CRS and select NAD83 / Conus Albers - EPSG:5070 -> Transformation 1. This will place the layer in the right location on top of the basemap. Note: it may not look like it's in the right spot until you zoom in on it.
<gallery>
49532655611 64dc27a6fd o.png|NLCD data loaded in QGIS
</gallery>
* Now we need to clip the data to the region of interest. The best way to do this is to by clicking the New Shapefile Layer button (see screenshot below). In the dialog, change Geometry type to Polygon and save it as a shapefile, and then click OK.
* Click the Toggle Editing button to edit the layer.
* Click the Add Polygon Feature button to create a polygon. Now you can click on the map to create a polygon covering the area of interest. When you are done, you can right-click to stop adding vertices.
* If you want to edit the vertex locations, you can click the Vertex Tool button, right-click on your polygon, and then edit the coordinates in the table on the bottom left of the GUI window.
* When you are done editing the layer, click the Save Layer Edits button (next to the Toggle Editing button).
'''Note''': when you are learning the process, start with a small polygon, say 1x1 degrees. Some of the steps take a very long time if processing very large sections of this data.
<gallery>
New layer.png|New shapefile layer
Shapefile dialog.png|Shapefile dialog
Toggle editing.png|Toggle editing
Add polygon.png|Add polygon
Vertex tool.png|Vertex tool
</gallery>
*Once the polygon is completed, the next step is to clip the NLCD layer using this polygon. Select the NLCD layer and choose Raster -> Extraction -> Clip Raster by Mask Layer. Make sure the input layer is your NLCD layer and the mask layer is the polygon you have created. For Source CRS choose the Conus Albers CRS of the original NLCD layer, and for Target CRS choose the Project CRS, EPSG:4326. Near the bottom of the dialog box, where under Clipped (mask) it says [Save to temporary file], click the button to the right and choose Save to File. Select IMG files as the filter and save it somewhere in your working directory with a descriptive name like NLCD_clip.img. Click Run. Once it completes, if you hide the original NLCD layer and the polygon, you should see the new clipped layer. As long as it worked correctly, you can now safely remove the big original NLCD layer. The image below shows a small sample section in Idaho that has been clipped using this method.
<gallery>
49532154383 b437eb21b7 o.png|Clipped NLCD data
</gallery>
*If you zoom in on the data, you will see that it is highly detailed but also pixelated due to the raster format. It needs to be simplified and smoothed out to look good in FlightGear and also to reduce the polygon count on the resulting shapefiles. We will first do some smoothing of the raster itself, and then we'll do additional smoothing after converting to vector format. In the Processing Toolbox, find GRASS -> r.neighbors. The clipped NLCD layer should be selected for both of the top input dropdowns. For Neighborhood Operation, select median with a Neighborhood size of 5. As before, you may want to choose an actual IMG file to save the output to instead of a temporary file. Click Run.
'''Note''': never use an averaging neighborhood operation, as the raster values are uniquely associated with a specific type of landcover. The averaging operation will introduce new values that do not correspond to a landcover type.
*Now we'll run r.neighbors once more on the resulting layer using the mode Neighborhood Operation with a Neighborhood of 3. The result should look a lot smoother and a bit less detailed than the original dataset (but still very highly detailed compared to the default scenery). An image from the sample dataset is below. This area shows McCall, Idaho and the south end of Payette Lake.
<gallery>
49539367447 cde1c6666a o.png|After r.neighbors operations
</gallery>
*We're now ready to convert the raster data to vector. In the Processing Toolbox, find Grass -> r.to.vect. Choose the output layer from the last step (median neighbors filter), choose area as the feature type, and check the option "Smooth corners of area features." This will make the output vector a little less pixely than the input. As before, you may want to save to a real shapefile instead of a temporary one. Click Run. This step will take longer than any of the previous ones to process.
*We could use this vector data as is, but it's still a bit blocky and unnatural looking. Instead, we'll do a couple processing steps on the vector data. GRASS -> v.generalize. Choose the default douglas algorithm with a tolerance of 0.0003 (this is in degrees, since our CRS is in degrees). You may want to play around with different values of tolerance until you find something that works best. Click Run. If errors result, it will create a new layer, which can be deleted. '''Note''': this step will take a very long time for large areas.
*Now we'll use v.generalize one more to smooth the simplified layer. This time, choose the chaiken algorithm with the same tolerance. Click Run. '''Note''': this step will take a very long time for large areas.
<gallery>
Vectorized.png|Vectorized NLCD data
Simplified.png|Simplified NLCD data
Smoothed.png|Smoothed NLCD data
</gallery>
*At this point, it is a good idea to compare the smoothed vector layer to the original NLCD raster data and make sure the smoothing steps have not introduced too much error in the shape of coastlines and other features. If they have, you may need to redo some or all of the last several steps (r.neighbors, r.to.vect, and v.generalize) adjusting the number of neighbors and tolerance until you get satisfactory results. However, I've found these parameters to produce quite good results.
*We now have a high-resolution vectorized dataset that can be used to define landclasses in FlightGear. We just need to save individual shape files representing each landclass. At this point, it is a good idea to have the NLCD map with the legend displayed to match to values in the shapefiles. That map can be found here:
https://www.mrlc.gov/viewer/
*You will also want to have default_priorities.txt from your TerraGear installation open to map NLCD landclass types to available materials in FlightGear. Some artistic license is possible here, but I use this mapping:
{| class="wikitable"
|-
! NLCD Value !! Legend label !! FlightGear material
|-
| 11 || Open Water || Lake or Ocean
|-
| 12 || Perennial Ice/Snow || SnowCover or Glacier
|-
| 21 || Developed, Open Space || Greenspace
|-
| 22 || Developed, Low Intensity || Town
|-
| 23 || Developed, Medium Intensity || SubUrban
|-
| 24 || Developed, High Intensity || Urban
|-
| 31 || Barren Land (Rock/Sand/Clay) || Rock or Sand (depends on location)
|-
| 32 || Unconsolidated Shore || Sand
|-
| 41 || Deciduous Forest || DeciduousForest
|-
| 42 || Evergreen Forest || EvergreenForest
|-
| 43 || Mixed Forest || MixedForest
|-
| 51,52 || Dwarf Scrub/Shrub/Scrub || Scrub
|-
| 71 || Grasslands/Herbaceous || Grassland
|-
| 72-74 || Herbaceous/Moss/Lichens (AK only) || I haven't used these, so check default_priorities.txt if you find them in your data
|-
| 81 || Pasture/Hay || DryCrop
|-
| 82 || Cultivated Crops || IrrCrop
|-
| 90 || Woody Wetlands || Marsh
|-
| 95 || Emergent Herbaceous Wetlands || Bog
|}
*To actually select and export individual shapefiles for each type, do the following:
**Select the final smoothed vector data layer. Click the Deselect Features from All Layers button near the top middle of the QGIS toolbar. Then click the button right next to it, Select Features by Value. In the dialog, enter the number from the NLCD legend in the value field, make sure the filter says Equal to (=), and then click Select Features and Close. Alternatively, if you want to combine multiple NLCD values into a single output shapefile, rather than clicking Close, you could enter in another value and then choose Add to Current Selection in the dropdown next to Select Features.
**Once you have the desired features selected, right click on the smoothed vector layer, click Export -> Save Selected Features As, choose the ESRI shapefile format, and save it somewhere under the project directory with a name corresponding to the appropriate area type in default_priorities.txt (e.g. data/shapefiles/Lake.shp). For CRS, choose the project CRS EPSG:4326 - WGS 84.
**Repeat this process until you've gone over all the values in the NLCD data legend. When finished, you should have individual layers representing different area types covering the entire area of interest. The result for the sample area is shown in the image below.
<gallery>
Nlcd shapefiles.jpg|Shapefiles from NLCD data
</gallery>

=== Converting with ogr-decode ===

If you have saved the shapefiles with a name corresponding to a material in default_priorities.txt, then they can be batch-converted with ogr-decode as follows:

# work and shapefiles directories
WORK=./work
DATADIR=./data/shapefiles/NLCD
# Process landcover
for file in $DATADIR/*.shp; do
fname=$(basename $file)
area_type=$(echo $fname | cut -f 1 -d '.')
if [ -d $WORK/$area_type ]; then
rm -rf $WORK/$area_type
fi
ogr-decode --area-type $area_type work/$area_type $file
done

(The script above is a bash/sh script for Linux. Something similar could probably done with a .bat script on Windows. The locations of the work and data directories will need to be edited if you use a different directory structure.)

== Supplementary data ==

You will probably want to supplement your custom scenery with some other data. Sources include:

* National Hydrography Data for waterways and waterbodies. Though NLCD includes water landcover, it is not as accurate or complete as NHD, and the smoothing process described above will cause thin rivers to turn into "islands." I recommend using the NHDArea and NHDWaterbody datasets but skipping the line data. The data is in shapefile format, which can be viewed and manipulated with QGIS. Be sure to filter the data to remove extraneous elements. It includes things like marshes, dams, and intermittent streams that probably should not be included. The NHD data can be accessed here: [https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products Access National Hydrography Products]. To filter the data, you will want to refer to this page: [https://nhd.usgs.gov/userGuide/Robohelpfiles/NHD_User_Guide/Feature_Catalog/Hydrography_Dataset/Complete_FCode_List.htm NHD FCodes].

* [https://www.openstreetmap.org OpenStreetMap] for roads. QGIS can open .osm and .pbf files extracted from OpenStreetMap and edit them just like all the other datasets that have been discussed here. These can be "baked into" the terrain using TerraGear, or you can use:

* [https://osm2city.readthedocs.io/en/latest/index.html osm2city], an autogen tool for FlightGear that creates buildings, roads, bridges, pylons, docks, and other details from OpenStreetMap data. This tool has a bit of a learning curve and does not flow through QGIS, but it can really make your project shine.

== Sample results ==

== Available scenery using NLCD data ==

== Related content ==
=== Forum topic ===
* {{forum link|t=36913|title=How to create US scenery with NLCD land cover data and QGIS}}

[[Category:Scenery enhancement]]

Howto:Using QGIS to process NLCD data

2020-03-16T02:11:48Z

Montagdude: Add the rest of the sections to be filled out

This tutorial will describe '''how to use data from the [https://www.usgs.gov/centers/eros/science/national-land-cover-database?qt-science_center_objects=0#qt-science_center_objects National Land Cover Database (NLCD)] to create US scenery''' that is on par with the existing European scenery.

The default scenery (World Scenery 2.0) in FlightGear is of higher quality in some areas than others. For example, European scenery is generally of very high quality due to the use of [https://land.copernicus.eu/pan-european/corine-land-cover CORINE land cover data]. Compared to the European scenery, the US scenery is much less detailed and less accurate. Thankfully, there is a wealth of freely-available geographical information for the US that can be used to improve the scenery.

== Getting set up for scenery generation ==
=== Prerequisites ===
Please read and understand the [[Using TerraGear|Using TerraGear wiki article]]. This tutorial will follow the same process but replace the "Landuse data" section with custom shapefiles generated from NLCD data.

=== Tools ===
This tutorial will make use of the following GIS software:
# QGIS (version 3.10)
# GRASS (version 7.8, used as a QGIS plugin)
# TerraGear

=== Elevation data ===
Elevation data for the entire world is available from the Shuttle Radar Topology Mission (SRTM). It is highly recommended to use the void-filled version of these datasets, which can be obtained from the [https://earthexplorer.usgs.gov/ USGS EarthExplorer] (note: it is required to create a login to download data from that page). The void-filled datasets reduce or eliminate the small gaps that can occur in scenery generated using the original SRTM datasets. The elevation data is in GeoTIFF format. To convert it to hgt format as expected by TerraGear, use gdal_translate as follows:

gdal_translate -of SRTMHGT input_file.tif output_file.hgt

For the output file, TerraGear might expect it to follow a certain naming convention, like N38W080.hgt. The data from USGS follows a naming convention like n38_w080_3arc_v2.tif. The following (sh/bash shell) script will batch convert all the files in the working directory following the expected naming convention:

for file in *.tif; do
base=$(echo $file | cut -f 1 -d '.')
NORTH=$(echo $base | cut -f 1 -d '_' | cut -f 2 -d 'n')
WEST=$(echo $base | cut -f 2 -d '_' | cut -f 2 -d 'w')
gdal_translate -of SRTMHGT $file N${NORTH}W${WEST}.hgt
done

== NLCD data ==
You can download the NLCD data [https://www.mrlc.gov/data?f%5B0%5D=category%3Aland%20cover here]. At the time of this writing, the latest dataset is from 2016. For the continental US, you should choose the dataset labeled "NLCD 2016 Land Cover (CONUS)." There is also a separate dataset for Alaska. The download is a large (>1 GB) zip file. After downloading, unzip the files.

=== Processing the data ===
* Fire up QGIS and select the WGS 84 template. If you don't have a template to choose from, don't worry about it, as we'll be setting the coordinate reference system for the project later. Just create a new project in that case.
* In the menu bar, select Project -> Properties -> CRS. Pick WGS 84 - EPSG:4326 as the project coordinate reference system.
<gallery>
49532654886 e94a32328d o.png|Setting the CRS
</gallery>
* Set a basemap. An easy way to do this is to double click on XYZ Tiles -> OpenStreetMap in the browser on the left. You can also get them via the QuickMapServices plugin.
* Browse to the location where you have unzipped the NLCD data in the QGIS project browser and double click the .img file to load it as a layer.
* Right-click on the NLCD layer -> Set CRS -> Set Layer CRS and select NAD83 / Conus Albers - EPSG:5070 -> Transformation 1. This will place the layer in the right location on top of the basemap. Note: it may not look like it's in the right spot until you zoom in on it.
<gallery>
49532655611 64dc27a6fd o.png|NLCD data loaded in QGIS
</gallery>
* Now we need to clip the data to the region of interest. The best way to do this is to by clicking the New Shapefile Layer button (see screenshot below). In the dialog, change Geometry type to Polygon and save it as a shapefile, and then click OK.
* Click the Toggle Editing button to edit the layer.
* Click the Add Polygon Feature button to create a polygon. Now you can click on the map to create a polygon covering the area of interest. When you are done, you can right-click to stop adding vertices.
* If you want to edit the vertex locations, you can click the Vertex Tool button, right-click on your polygon, and then edit the coordinates in the table on the bottom left of the GUI window.
* When you are done editing the layer, click the Save Layer Edits button (next to the Toggle Editing button).
'''Note''': when you are learning the process, start with a small polygon, say 1x1 degrees. Some of the steps take a very long time if processing very large sections of this data.
<gallery>
New layer.png|New shapefile layer
Shapefile dialog.png|Shapefile dialog
Toggle editing.png|Toggle editing
Add polygon.png|Add polygon
Vertex tool.png|Vertex tool
</gallery>
*Once the polygon is completed, the next step is to clip the NLCD layer using this polygon. Select the NLCD layer and choose Raster -> Extraction -> Clip Raster by Mask Layer. Make sure the input layer is your NLCD layer and the mask layer is the polygon you have created. For Source CRS choose the Conus Albers CRS of the original NLCD layer, and for Target CRS choose the Project CRS, EPSG:4326. Near the bottom of the dialog box, where under Clipped (mask) it says [Save to temporary file], click the button to the right and choose Save to File. Select IMG files as the filter and save it somewhere in your working directory with a descriptive name like NLCD_clip.img. Click Run. Once it completes, if you hide the original NLCD layer and the polygon, you should see the new clipped layer. As long as it worked correctly, you can now safely remove the big original NLCD layer. The image below shows a small sample section in Idaho that has been clipped using this method.
<gallery>
49532154383 b437eb21b7 o.png|Clipped NLCD data
</gallery>
*If you zoom in on the data, you will see that it is highly detailed but also pixelated due to the raster format. It needs to be simplified and smoothed out to look good in FlightGear and also to reduce the polygon count on the resulting shapefiles. We will first do some smoothing of the raster itself, and then we'll do additional smoothing after converting to vector format. In the Processing Toolbox, find GRASS -> r.neighbors. The clipped NLCD layer should be selected for both of the top input dropdowns. For Neighborhood Operation, select median with a Neighborhood size of 5. As before, you may want to choose an actual IMG file to save the output to instead of a temporary file. Click Run.
'''Note''': never use an averaging neighborhood operation, as the raster values are uniquely associated with a specific type of landcover. The averaging operation will introduce new values that do not correspond to a landcover type.
*Now we'll run r.neighbors once more on the resulting layer using the mode Neighborhood Operation with a Neighborhood of 3. The result should look a lot smoother and a bit less detailed than the original dataset (but still very highly detailed compared to the default scenery). An image from the sample dataset is below. This area shows McCall, Idaho and the south end of Payette Lake.
<gallery>
49539367447 cde1c6666a o.png|After r.neighbors operations
</gallery>
*We're now ready to convert the raster data to vector. In the Processing Toolbox, find Grass -> r.to.vect. Choose the output layer from the last step (median neighbors filter), choose area as the feature type, and check the option "Smooth corners of area features." This will make the output vector a little less pixely than the input. As before, you may want to save to a real shapefile instead of a temporary one. Click Run. This step will take longer than any of the previous ones to process.
*We could use this vector data as is, but it's still a bit blocky and unnatural looking. Instead, we'll do a couple processing steps on the vector data. GRASS -> v.generalize. Choose the default douglas algorithm with a tolerance of 0.0003 (this is in degrees, since our CRS is in degrees). You may want to play around with different values of tolerance until you find something that works best. Click Run. If errors result, it will create a new layer, which can be deleted. '''Note''': this step will take a very long time for large areas.
*Now we'll use v.generalize one more to smooth the simplified layer. This time, choose the chaiken algorithm with the same tolerance. Click Run. '''Note''': this step will take a very long time for large areas.
<gallery>
Vectorized.png|Vectorized NLCD data
Simplified.png|Simplified NLCD data
Smoothed.png|Smoothed NLCD data
</gallery>
*At this point, it is a good idea to compare the smoothed vector layer to the original NLCD raster data and make sure the smoothing steps have not introduced too much error in the shape of coastlines and other features. If they have, you may need to redo some or all of the last several steps (r.neighbors, r.to.vect, and v.generalize) adjusting the number of neighbors and tolerance until you get satisfactory results. However, I've found these parameters to produce quite good results.
*We now have a high-resolution vectorized dataset that can be used to define landclasses in FlightGear. We just need to save individual shape files representing each landclass. At this point, it is a good idea to have the NLCD map with the legend displayed to match to values in the shapefiles. That map can be found here:
https://www.mrlc.gov/viewer/
*You will also want to have default_priorities.txt from your TerraGear installation open to map NLCD landclass types to available materials in FlightGear. Some artistic license is possible here, but I use this mapping:
{| class="wikitable"
|-
! NLCD Value !! Legend label !! FlightGear material
|-
| 11 || Open Water || Lake or Ocean
|-
| 12 || Perennial Ice/Snow || SnowCover or Glacier
|-
| 21 || Developed, Open Space || Greenspace
|-
| 22 || Developed, Low Intensity || Town
|-
| 23 || Developed, Medium Intensity || SubUrban
|-
| 24 || Developed, High Intensity || Urban
|-
| 31 || Barren Land (Rock/Sand/Clay) || Rock or Sand (depends on location)
|-
| 32 || Unconsolidated Shore || Sand
|-
| 41 || Deciduous Forest || DeciduousForest
|-
| 42 || Evergreen Forest || EvergreenForest
|-
| 43 || Mixed Forest || MixedForest
|-
| 51,52 || Dwarf Scrub/Shrub/Scrub || Scrub
|-
| 71 || Grasslands/Herbaceous || Grassland
|-
| 72-74 || Herbaceous/Moss/Lichens (AK only) || I haven't used these, so check default_priorities.txt if you find them in your data
|-
| 81 || Pasture/Hay || DryCrop
|-
| 82 || Cultivated Crops || IrrCrop
|-
| 90 || Woody Wetlands || Marsh
|-
| 95 || Emergent Herbaceous Wetlands || Bog
|}
*To actually select and export individual shapefiles for each type, do the following:
**Select the final smoothed vector data layer. Click the Deselect Features from All Layers button near the top middle of the QGIS toolbar. Then click the button right next to it, Select Features by Value. In the dialog, enter the number from the NLCD legend in the value field, make sure the filter says Equal to (=), and then click Select Features and Close. Alternatively, if you want to combine multiple NLCD values into a single output shapefile, rather than clicking Close, you could enter in another value and then choose Add to Current Selection in the dropdown next to Select Features.
**Once you have the desired features selected, right click on the smoothed vector layer, click Export -> Save Selected Features As, choose the ESRI shapefile format, and save it somewhere under the project directory with a name corresponding to the appropriate area type in default_priorities.txt (e.g. data/shapefiles/Lake.shp). For CRS, choose the project CRS EPSG:4326 - WGS 84.
**Repeat this process until you've gone over all the values in the NLCD data legend. When finished, you should have individual layers representing different area types covering the entire area of interest. The result for the sample area is shown in the image below.
<gallery>
Nlcd shapefiles.jpg|Shapefiles from NLCD data
</gallery>

=== Converting with ogr-decode ===

If you have saved the shapefiles with a name corresponding to a material in default_priorities.txt, then they can be batch-converted with ogr-decode as follows:

# work and shapefiles directories
WORK=./work
DATADIR=./data/shapefiles/NLCD
# Process landcover
for file in $DATADIR/*.shp; do
fname=$(basename $file)
area_type=$(echo $fname | cut -f 1 -d '.')
if [ -d $WORK/$area_type ]; then
rm -rf $WORK/$area_type
fi
ogr-decode --area-type $area_type work/$area_type $file
done

(The script above is a bash/sh script for Linux. Something similar could probably done with a .bat script on Windows. The locations of the work and data directories will need to be edited if you use a different directory structure.)

== Supplementary data ==

== Sample results ==

== Available scenery using NLCD data ==

[[Category:Scenery enhancement]]

Howto:Using QGIS to process NLCD data

2020-03-16T00:48:12Z

Montagdude: Fix script formatting

This tutorial will describe '''how to use data from the [https://www.usgs.gov/centers/eros/science/national-land-cover-database?qt-science_center_objects=0#qt-science_center_objects National Land Cover Database (NLCD)] to create US scenery''' that is on par with the existing European scenery.

The default scenery (World Scenery 2.0) in FlightGear is of higher quality in some areas than others. For example, European scenery is generally of very high quality due to the use of [https://land.copernicus.eu/pan-european/corine-land-cover CORINE land cover data]. Compared to the European scenery, the US scenery is much less detailed and less accurate. Thankfully, there is a wealth of freely-available geographical information for the US that can be used to improve the scenery.

== Getting set up for scenery generation ==
=== Prerequisites ===
Please read and understand the [[Using TerraGear|Using TerraGear wiki article]]. This tutorial will follow the same process but replace the "Landuse data" section with custom shapefiles generated from NLCD data.

=== Tools ===
This tutorial will make use of the following GIS software:
# QGIS (version 3.10)
# GRASS (version 7.8, used as a QGIS plugin)
# TerraGear

=== Elevation data ===
Elevation data for the entire world is available from the Shuttle Radar Topology Mission (SRTM). It is highly recommended to use the void-filled version of these datasets, which can be obtained from the [https://earthexplorer.usgs.gov/ USGS EarthExplorer] (note: it is required to create a login to download data from that page). The void-filled datasets reduce or eliminate the small gaps that can occur in scenery generated using the original SRTM datasets. The elevation data is in GeoTIFF format. To convert it to hgt format as expected by TerraGear, use gdal_translate as follows:

gdal_translate -of SRTMHGT input_file.tif output_file.hgt

For the output file, TerraGear might expect it to follow a certain naming convention, like N38W080.hgt. The data from USGS follows a naming convention like n38_w080_3arc_v2.tif. The following (sh/bash shell) script will batch convert all the files in the working directory following the expected naming convention:

for file in *.tif; do
base=$(echo $file | cut -f 1 -d '.')
NORTH=$(echo $base | cut -f 1 -d '_' | cut -f 2 -d 'n')
WEST=$(echo $base | cut -f 2 -d '_' | cut -f 2 -d 'w')
gdal_translate -of SRTMHGT $file N${NORTH}W${WEST}.hgt
done

== NLCD data ==
You can download the NLCD data [https://www.mrlc.gov/data?f%5B0%5D=category%3Aland%20cover here]. At the time of this writing, the latest dataset is from 2016. For the continental US, you should choose the dataset labeled "NLCD 2016 Land Cover (CONUS)." There is also a separate dataset for Alaska. The download is a large (>1 GB) zip file. After downloading, unzip the files.

=== Processing the data ===
* Fire up QGIS and select the WGS 84 template. If you don't have a template to choose from, don't worry about it, as we'll be setting the coordinate reference system for the project later. Just create a new project in that case.
* In the menu bar, select Project -> Properties -> CRS. Pick WGS 84 - EPSG:4326 as the project coordinate reference system.
<gallery>
49532654886 e94a32328d o.png|Setting the CRS
</gallery>
* Set a basemap. An easy way to do this is to double click on XYZ Tiles -> OpenStreetMap in the browser on the left. You can also get them via the QuickMapServices plugin.
* Browse to the location where you have unzipped the NLCD data in the QGIS project browser and double click the .img file to load it as a layer.
* Right-click on the NLCD layer -> Set CRS -> Set Layer CRS and select NAD83 / Conus Albers - EPSG:5070 -> Transformation 1. This will place the layer in the right location on top of the basemap. Note: it may not look like it's in the right spot until you zoom in on it.
<gallery>
49532655611 64dc27a6fd o.png|NLCD data loaded in QGIS
</gallery>
* Now we need to clip the data to the region of interest. The best way to do this is to by clicking the New Shapefile Layer button (see screenshot below). In the dialog, change Geometry type to Polygon and save it as a shapefile, and then click OK.
* Click the Toggle Editing button to edit the layer.
* Click the Add Polygon Feature button to create a polygon. Now you can click on the map to create a polygon covering the area of interest. When you are done, you can right-click to stop adding vertices.
* If you want to edit the vertex locations, you can click the Vertex Tool button, right-click on your polygon, and then edit the coordinates in the table on the bottom left of the GUI window.
* When you are done editing the layer, click the Save Layer Edits button (next to the Toggle Editing button).
'''Note''': when you are learning the process, start with a small polygon, say 1x1 degrees. Some of the steps take a very long time if processing very large sections of this data.
<gallery>
New layer.png|New shapefile layer
Shapefile dialog.png|Shapefile dialog
Toggle editing.png|Toggle editing
Add polygon.png|Add polygon
Vertex tool.png|Vertex tool
</gallery>
*Once the polygon is completed, the next step is to clip the NLCD layer using this polygon. Select the NLCD layer and choose Raster -> Extraction -> Clip Raster by Mask Layer. Make sure the input layer is your NLCD layer and the mask layer is the polygon you have created. For Source CRS choose the Conus Albers CRS of the original NLCD layer, and for Target CRS choose the Project CRS, EPSG:4326. Near the bottom of the dialog box, where under Clipped (mask) it says [Save to temporary file], click the button to the right and choose Save to File. Select IMG files as the filter and save it somewhere in your working directory with a descriptive name like NLCD_clip.img. Click Run. Once it completes, if you hide the original NLCD layer and the polygon, you should see the new clipped layer. As long as it worked correctly, you can now safely remove the big original NLCD layer. The image below shows a small sample section in Idaho that has been clipped using this method.
<gallery>
49532154383 b437eb21b7 o.png|Clipped NLCD data
</gallery>
*If you zoom in on the data, you will see that it is highly detailed but also pixelated due to the raster format. It needs to be simplified and smoothed out to look good in FlightGear and also to reduce the polygon count on the resulting shapefiles. We will first do some smoothing of the raster itself, and then we'll do additional smoothing after converting to vector format. In the Processing Toolbox, find GRASS -> r.neighbors. The clipped NLCD layer should be selected for both of the top input dropdowns. For Neighborhood Operation, select median with a Neighborhood size of 5. As before, you may want to choose an actual IMG file to save the output to instead of a temporary file. Click Run.
'''Note''': never use an averaging neighborhood operation, as the raster values are uniquely associated with a specific type of landcover. The averaging operation will introduce new values that do not correspond to a landcover type.
*Now we'll run r.neighbors once more on the resulting layer using the mode Neighborhood Operation with a Neighborhood of 3. The result should look a lot smoother and a bit less detailed than the original dataset (but still very highly detailed compared to the default scenery). An image from the sample dataset is below. This area shows McCall, Idaho and the south end of Payette Lake.
<gallery>
49539367447 cde1c6666a o.png|After r.neighbors operations
</gallery>
*We're now ready to convert the raster data to vector. In the Processing Toolbox, find Grass -> r.to.vect. Choose the output layer from the last step (median neighbors filter), choose area as the feature type, and check the option "Smooth corners of area features." This will make the output vector a little less pixely than the input. As before, you may want to save to a real shapefile instead of a temporary one. Click Run. This step will take longer than any of the previous ones to process.
*We could use this vector data as is, but it's still a bit blocky and unnatural looking. Instead, we'll do a couple processing steps on the vector data. GRASS -> v.generalize. Choose the default douglas algorithm with a tolerance of 0.0003 (this is in degrees, since our CRS is in degrees). You may want to play around with different values of tolerance until you find something that works best. Click Run. If errors result, it will create a new layer, which can be deleted. '''Note''': this step will take a very long time for large areas.
*Now we'll use v.generalize one more to smooth the simplified layer. This time, choose the chaiken algorithm with the same tolerance. Click Run. '''Note''': this step will take a very long time for large areas.
<gallery>
Vectorized.png|Vectorized NLCD data
Simplified.png|Simplified NLCD data
Smoothed.png|Smoothed NLCD data
</gallery>
*At this point, it is a good idea to compare the smoothed vector layer to the original NLCD raster data and make sure the smoothing steps have not introduced too much error in the shape of coastlines and other features. If they have, you may need to redo some or all of the last several steps (r.neighbors, r.to.vect, and v.generalize) adjusting the number of neighbors and tolerance until you get satisfactory results. However, I've found these parameters to produce quite good results.
*We now have a high-resolution vectorized dataset that can be used to define landclasses in FlightGear. We just need to save individual shape files representing each landclass. At this point, it is a good idea to have the NLCD map with the legend displayed to match to values in the shapefiles. That map can be found here:
https://www.mrlc.gov/viewer/
*You will also want to have default_priorities.txt from your TerraGear installation open to map NLCD landclass types to available materials in FlightGear. Some artistic license is possible here, but I use this mapping:
{| class="wikitable"
|-
! NLCD Value !! Legend label !! FlightGear material
|-
| 11 || Open Water || Lake or Ocean
|-
| 12 || Perennial Ice/Snow || SnowCover or Glacier
|-
| 21 || Developed, Open Space || Greenspace
|-
| 22 || Developed, Low Intensity || Town
|-
| 23 || Developed, Medium Intensity || SubUrban
|-
| 24 || Developed, High Intensity || Urban
|-
| 31 || Barren Land (Rock/Sand/Clay) || Rock or Sand (depends on location)
|-
| 32 || Unconsolidated Shore || Sand
|-
| 41 || Deciduous Forest || DeciduousForest
|-
| 42 || Evergreen Forest || EvergreenForest
|-
| 43 || Mixed Forest || MixedForest
|-
| 51,52 || Dwarf Scrub/Shrub/Scrub || Scrub
|-
| 71 || Grasslands/Herbaceous || Grassland
|-
| 72-74 || Herbaceous/Moss/Lichens (AK only) || I haven't used these, so check default_priorities.txt if you find them in your data
|-
| 81 || Pasture/Hay || DryCrop
|-
| 82 || Cultivated Crops || IrrCrop
|-
| 90 || Woody Wetlands || Marsh
|-
| 95 || Emergent Herbaceous Wetlands || Bog
|}
*To actually select and export individual shapefiles for each type, do the following:
**Select the final smoothed vector data layer. Click the Deselect Features from All Layers button near the top middle of the QGIS toolbar. Then click the button right next to it, Select Features by Value. In the dialog, enter the number from the NLCD legend in the value field, make sure the filter says Equal to (=), and then click Select Features and Close. Alternatively, if you want to combine multiple NLCD values into a single output shapefile, rather than clicking Close, you could enter in another value and then choose Add to Current Selection in the dropdown next to Select Features.
**Once you have the desired features selected, right click on the smoothed vector layer, click Export -> Save Selected Features As, choose the ESRI shapefile format, and save it somewhere under the project directory with a name corresponding to the appropriate area type in default_priorities.txt (e.g. data/shapefiles/Lake.shp). For CRS, choose the project CRS EPSG:4326 - WGS 84.
**Repeat this process until you've gone over all the values in the NLCD data legend. When finished, you should have individual layers representing different area types covering the entire area of interest. The result for the sample area is shown in the image below.
<gallery>
Nlcd shapefiles.jpg|Shapefiles from NLCD data
</gallery>

=== Converting with ogr-decode ===

If you have saved the shapefiles with a name corresponding to a material in default_priorities.txt, then they can be batch-converted with ogr-decode as follows:

# work and shapefiles directories
WORK=./work
DATADIR=./data/shapefiles/NLCD
# Process landcover
for file in $DATADIR/*.shp; do
fname=$(basename $file)
area_type=$(echo $fname | cut -f 1 -d '.')
if [ -d $WORK/$area_type ]; then
rm -rf $WORK/$area_type
fi
ogr-decode --area-type $area_type work/$area_type $file
done

(The script above is a bash/sh script for Linux. Something similar could probably done with a .bat script on Windows. The locations of the work and data directories will need to be edited if you use a different directory structure.)

== Supplementary data ==

[[Category:Scenery enhancement]]

Howto:Using QGIS to process NLCD data

2020-03-16T00:47:41Z

Montagdude: Add Converting with ogr-decode and Supplementary Data sections

This tutorial will describe '''how to use data from the [https://www.usgs.gov/centers/eros/science/national-land-cover-database?qt-science_center_objects=0#qt-science_center_objects National Land Cover Database (NLCD)] to create US scenery''' that is on par with the existing European scenery.

The default scenery (World Scenery 2.0) in FlightGear is of higher quality in some areas than others. For example, European scenery is generally of very high quality due to the use of [https://land.copernicus.eu/pan-european/corine-land-cover CORINE land cover data]. Compared to the European scenery, the US scenery is much less detailed and less accurate. Thankfully, there is a wealth of freely-available geographical information for the US that can be used to improve the scenery.

== Getting set up for scenery generation ==
=== Prerequisites ===
Please read and understand the [[Using TerraGear|Using TerraGear wiki article]]. This tutorial will follow the same process but replace the "Landuse data" section with custom shapefiles generated from NLCD data.

=== Tools ===
This tutorial will make use of the following GIS software:
# QGIS (version 3.10)
# GRASS (version 7.8, used as a QGIS plugin)
# TerraGear

=== Elevation data ===
Elevation data for the entire world is available from the Shuttle Radar Topology Mission (SRTM). It is highly recommended to use the void-filled version of these datasets, which can be obtained from the [https://earthexplorer.usgs.gov/ USGS EarthExplorer] (note: it is required to create a login to download data from that page). The void-filled datasets reduce or eliminate the small gaps that can occur in scenery generated using the original SRTM datasets. The elevation data is in GeoTIFF format. To convert it to hgt format as expected by TerraGear, use gdal_translate as follows:

gdal_translate -of SRTMHGT input_file.tif output_file.hgt

For the output file, TerraGear might expect it to follow a certain naming convention, like N38W080.hgt. The data from USGS follows a naming convention like n38_w080_3arc_v2.tif. The following (sh/bash shell) script will batch convert all the files in the working directory following the expected naming convention:

for file in *.tif; do
base=$(echo $file | cut -f 1 -d '.')
NORTH=$(echo $base | cut -f 1 -d '_' | cut -f 2 -d 'n')
WEST=$(echo $base | cut -f 2 -d '_' | cut -f 2 -d 'w')
gdal_translate -of SRTMHGT $file N${NORTH}W${WEST}.hgt
done

== NLCD data ==
You can download the NLCD data [https://www.mrlc.gov/data?f%5B0%5D=category%3Aland%20cover here]. At the time of this writing, the latest dataset is from 2016. For the continental US, you should choose the dataset labeled "NLCD 2016 Land Cover (CONUS)." There is also a separate dataset for Alaska. The download is a large (>1 GB) zip file. After downloading, unzip the files.

=== Processing the data ===
* Fire up QGIS and select the WGS 84 template. If you don't have a template to choose from, don't worry about it, as we'll be setting the coordinate reference system for the project later. Just create a new project in that case.
* In the menu bar, select Project -> Properties -> CRS. Pick WGS 84 - EPSG:4326 as the project coordinate reference system.
<gallery>
49532654886 e94a32328d o.png|Setting the CRS
</gallery>
* Set a basemap. An easy way to do this is to double click on XYZ Tiles -> OpenStreetMap in the browser on the left. You can also get them via the QuickMapServices plugin.
* Browse to the location where you have unzipped the NLCD data in the QGIS project browser and double click the .img file to load it as a layer.
* Right-click on the NLCD layer -> Set CRS -> Set Layer CRS and select NAD83 / Conus Albers - EPSG:5070 -> Transformation 1. This will place the layer in the right location on top of the basemap. Note: it may not look like it's in the right spot until you zoom in on it.
<gallery>
49532655611 64dc27a6fd o.png|NLCD data loaded in QGIS
</gallery>
* Now we need to clip the data to the region of interest. The best way to do this is to by clicking the New Shapefile Layer button (see screenshot below). In the dialog, change Geometry type to Polygon and save it as a shapefile, and then click OK.
* Click the Toggle Editing button to edit the layer.
* Click the Add Polygon Feature button to create a polygon. Now you can click on the map to create a polygon covering the area of interest. When you are done, you can right-click to stop adding vertices.
* If you want to edit the vertex locations, you can click the Vertex Tool button, right-click on your polygon, and then edit the coordinates in the table on the bottom left of the GUI window.
* When you are done editing the layer, click the Save Layer Edits button (next to the Toggle Editing button).
'''Note''': when you are learning the process, start with a small polygon, say 1x1 degrees. Some of the steps take a very long time if processing very large sections of this data.
<gallery>
New layer.png|New shapefile layer
Shapefile dialog.png|Shapefile dialog
Toggle editing.png|Toggle editing
Add polygon.png|Add polygon
Vertex tool.png|Vertex tool
</gallery>
*Once the polygon is completed, the next step is to clip the NLCD layer using this polygon. Select the NLCD layer and choose Raster -> Extraction -> Clip Raster by Mask Layer. Make sure the input layer is your NLCD layer and the mask layer is the polygon you have created. For Source CRS choose the Conus Albers CRS of the original NLCD layer, and for Target CRS choose the Project CRS, EPSG:4326. Near the bottom of the dialog box, where under Clipped (mask) it says [Save to temporary file], click the button to the right and choose Save to File. Select IMG files as the filter and save it somewhere in your working directory with a descriptive name like NLCD_clip.img. Click Run. Once it completes, if you hide the original NLCD layer and the polygon, you should see the new clipped layer. As long as it worked correctly, you can now safely remove the big original NLCD layer. The image below shows a small sample section in Idaho that has been clipped using this method.
<gallery>
49532154383 b437eb21b7 o.png|Clipped NLCD data
</gallery>
*If you zoom in on the data, you will see that it is highly detailed but also pixelated due to the raster format. It needs to be simplified and smoothed out to look good in FlightGear and also to reduce the polygon count on the resulting shapefiles. We will first do some smoothing of the raster itself, and then we'll do additional smoothing after converting to vector format. In the Processing Toolbox, find GRASS -> r.neighbors. The clipped NLCD layer should be selected for both of the top input dropdowns. For Neighborhood Operation, select median with a Neighborhood size of 5. As before, you may want to choose an actual IMG file to save the output to instead of a temporary file. Click Run.
'''Note''': never use an averaging neighborhood operation, as the raster values are uniquely associated with a specific type of landcover. The averaging operation will introduce new values that do not correspond to a landcover type.
*Now we'll run r.neighbors once more on the resulting layer using the mode Neighborhood Operation with a Neighborhood of 3. The result should look a lot smoother and a bit less detailed than the original dataset (but still very highly detailed compared to the default scenery). An image from the sample dataset is below. This area shows McCall, Idaho and the south end of Payette Lake.
<gallery>
49539367447 cde1c6666a o.png|After r.neighbors operations
</gallery>
*We're now ready to convert the raster data to vector. In the Processing Toolbox, find Grass -> r.to.vect. Choose the output layer from the last step (median neighbors filter), choose area as the feature type, and check the option "Smooth corners of area features." This will make the output vector a little less pixely than the input. As before, you may want to save to a real shapefile instead of a temporary one. Click Run. This step will take longer than any of the previous ones to process.
*We could use this vector data as is, but it's still a bit blocky and unnatural looking. Instead, we'll do a couple processing steps on the vector data. GRASS -> v.generalize. Choose the default douglas algorithm with a tolerance of 0.0003 (this is in degrees, since our CRS is in degrees). You may want to play around with different values of tolerance until you find something that works best. Click Run. If errors result, it will create a new layer, which can be deleted. '''Note''': this step will take a very long time for large areas.
*Now we'll use v.generalize one more to smooth the simplified layer. This time, choose the chaiken algorithm with the same tolerance. Click Run. '''Note''': this step will take a very long time for large areas.
<gallery>
Vectorized.png|Vectorized NLCD data
Simplified.png|Simplified NLCD data
Smoothed.png|Smoothed NLCD data
</gallery>
*At this point, it is a good idea to compare the smoothed vector layer to the original NLCD raster data and make sure the smoothing steps have not introduced too much error in the shape of coastlines and other features. If they have, you may need to redo some or all of the last several steps (r.neighbors, r.to.vect, and v.generalize) adjusting the number of neighbors and tolerance until you get satisfactory results. However, I've found these parameters to produce quite good results.
*We now have a high-resolution vectorized dataset that can be used to define landclasses in FlightGear. We just need to save individual shape files representing each landclass. At this point, it is a good idea to have the NLCD map with the legend displayed to match to values in the shapefiles. That map can be found here:
https://www.mrlc.gov/viewer/
*You will also want to have default_priorities.txt from your TerraGear installation open to map NLCD landclass types to available materials in FlightGear. Some artistic license is possible here, but I use this mapping:
{| class="wikitable"
|-
! NLCD Value !! Legend label !! FlightGear material
|-
| 11 || Open Water || Lake or Ocean
|-
| 12 || Perennial Ice/Snow || SnowCover or Glacier
|-
| 21 || Developed, Open Space || Greenspace
|-
| 22 || Developed, Low Intensity || Town
|-
| 23 || Developed, Medium Intensity || SubUrban
|-
| 24 || Developed, High Intensity || Urban
|-
| 31 || Barren Land (Rock/Sand/Clay) || Rock or Sand (depends on location)
|-
| 32 || Unconsolidated Shore || Sand
|-
| 41 || Deciduous Forest || DeciduousForest
|-
| 42 || Evergreen Forest || EvergreenForest
|-
| 43 || Mixed Forest || MixedForest
|-
| 51,52 || Dwarf Scrub/Shrub/Scrub || Scrub
|-
| 71 || Grasslands/Herbaceous || Grassland
|-
| 72-74 || Herbaceous/Moss/Lichens (AK only) || I haven't used these, so check default_priorities.txt if you find them in your data
|-
| 81 || Pasture/Hay || DryCrop
|-
| 82 || Cultivated Crops || IrrCrop
|-
| 90 || Woody Wetlands || Marsh
|-
| 95 || Emergent Herbaceous Wetlands || Bog
|}
*To actually select and export individual shapefiles for each type, do the following:
**Select the final smoothed vector data layer. Click the Deselect Features from All Layers button near the top middle of the QGIS toolbar. Then click the button right next to it, Select Features by Value. In the dialog, enter the number from the NLCD legend in the value field, make sure the filter says Equal to (=), and then click Select Features and Close. Alternatively, if you want to combine multiple NLCD values into a single output shapefile, rather than clicking Close, you could enter in another value and then choose Add to Current Selection in the dropdown next to Select Features.
**Once you have the desired features selected, right click on the smoothed vector layer, click Export -> Save Selected Features As, choose the ESRI shapefile format, and save it somewhere under the project directory with a name corresponding to the appropriate area type in default_priorities.txt (e.g. data/shapefiles/Lake.shp). For CRS, choose the project CRS EPSG:4326 - WGS 84.
**Repeat this process until you've gone over all the values in the NLCD data legend. When finished, you should have individual layers representing different area types covering the entire area of interest. The result for the sample area is shown in the image below.
<gallery>
Nlcd shapefiles.jpg|Shapefiles from NLCD data
</gallery>

=== Converting with ogr-decode ===

If you have saved the shapefiles with a name corresponding to a material in default_priorities.txt, then they can be batch-converted with ogr-decode as follows:

# work and shapefiles directories
WORK=./work
DATADIR=./data/shapefiles/NLCD

# Process landcover
for file in $DATADIR/*.shp; do
fname=$(basename $file)
area_type=$(echo $fname | cut -f 1 -d '.')
if [ -d $WORK/$area_type ]; then
rm -rf $WORK/$area_type
fi
ogr-decode --area-type $area_type work/$area_type $file
done

(The script above is a bash/sh script for Linux. Something similar could probably done with a .bat script on Windows. The locations of the work and data directories will need to be edited if you use a different directory structure.)

== Supplementary data ==

[[Category:Scenery enhancement]]

Howto:Using QGIS to process NLCD data

2020-03-15T22:04:51Z

Montagdude: Add NLCD shapefiles image

File:Nlcd shapefiles.jpg

2020-03-15T22:02:41Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=Shapefiles from NLCD data}}
|date=2020-03-15
|source={{own}}
|author=[[User:Montagdude|Montagdude]]
|permission=
|other versions=
}}

=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

[[Category:Scenery]]
[[Category:Scenery enchancement]]

Howto:Using QGIS to process NLCD data

2020-03-15T19:37:08Z

Montagdude: Add to /* Processing the data */

Howto:Using QGIS to process NLCD data

2020-03-07T12:00:55Z

Montagdude: Add vectorizing, simplifying, and smoothing steps

The default scenery (World Scenery 2.0) in FlightGear is of higher quality in some areas than others. For example, European scenery is generally of very high quality due to the use of [https://land.copernicus.eu/pan-european/corine-land-cover CORINE land cover data]. Compared to the European scenery, the US scenery is much less detailed and less accurate. Thankfully, there is a wealth of freely-available geographical information for the US that can be used to improve the scenery. This tutorial will describe how to use data from the [https://www.usgs.gov/centers/eros/science/national-land-cover-database?qt-science_center_objects=0#qt-science_center_objects National Land Cover Database (NLCD)] to create US scenery that is on par with the existing European scenery.

== Getting set up for scenery generation ==
=== Prerequisites ===
Please read and understand the [[Using TerraGear|Using TerraGear wiki article]]. This tutorial will follow the same process but replace the "Landuse data" section with custom shapefiles generated from NLCD data.

=== Tools ===
This tutorial will make use of the following GIS software:
# QGIS (version 3.10)
# GRASS (version 7.8, used as a QGIS plugin)
# TerraGear

=== Elevation data ===
Elevation data for the entire world is available from the Shuttle Radar Topology Mission (SRTM). It is highly recommended to use the void-filled version of these datasets, which can be obtained from the [https://earthexplorer.usgs.gov/ USGS EarthExplorer] (note: it is required to create a login to download data from that page). The void-filled datasets reduce or eliminate the small gaps that can occur in scenery generated using the original SRTM datasets. The elevation data is in GeoTIFF format. To convert it to hgt format as expected by TerraGear, use gdal_translate as follows:

gdal_translate -of SRTMHGT input_file.tif output_file.hgt

For the output file, TerraGear might expect it to follow a certain naming convention, like N38W080.hgt. The data from USGS follows a naming convention like n38_w080_3arc_v2.tif. The following (sh/bash shell) script will batch convert all the files in the working directory following the expected naming convention:

for file in *.tif; do
base=$(echo $file | cut -f 1 -d '.')
NORTH=$(echo $base | cut -f 1 -d '_' | cut -f 2 -d 'n')
WEST=$(echo $base | cut -f 2 -d '_' | cut -f 2 -d 'w')
gdal_translate -of SRTMHGT $file N${NORTH}W${WEST}.hgt
done

== NLCD data ==
You can download the NLCD data [https://www.mrlc.gov/data?f%5B0%5D=category%3Aland%20cover here]. At the time of this writing, the latest dataset is from 2016. For the continental US, you should choose the dataset labeled "NLCD 2016 Land Cover (CONUS)." There is also a separate dataset for Alaska. The download is a large (>1 GB) zip file. After downloading, unzip the files.

=== Processing the data ===
* Fire up QGIS and select the WGS 84 template. If you don't have a template to choose from, don't worry about it, as we'll be setting the coordinate reference system for the project later. Just create a new project in that case.
* In the menu bar, select Project -> Properties -> CRS. Pick WGS 84 - EPSG:4326 as the project coordinate reference system.
<gallery>
49532654886 e94a32328d o.png|Setting the CRS
</gallery>
* Set a basemap. An easy way to do this is to double click on XYZ Tiles -> OpenStreetMap in the browser on the left. You can also get them via the QuickMapServices plugin.
* Browse to the location where you have unzipped the NLCD data in the QGIS project browser and double click the .img file to load it as a layer.
* Right-click on the NLCD layer -> Set CRS -> Set Layer CRS and select NAD83 / Conus Albers - EPSG:5070 -> Transformation 1. This will place the layer in the right location on top of the basemap. Note: it may not look like it's in the right spot until you zoom in on it.
<gallery>
49532655611 64dc27a6fd o.png|NLCD data loaded in QGIS
</gallery>
* Now we need to clip the data to the region of interest. The best way to do this is to by clicking the New Shapefile Layer button (see screenshot below). In the dialog, change Geometry type to Polygon and save it as a shapefile, and then click OK.
* Click the Toggle Editing button to edit the layer.
* Click the Add Polygon Feature button to create a polygon. Now you can click on the map to create a polygon covering the area of interest. When you are done, you can right-click to stop adding vertices.
* If you want to edit the vertex locations, you can click the Vertex Tool button, right-click on your polygon, and then edit the coordinates in the table on the bottom left of the GUI window.
* When you are done editing the layer, click the Save Layer Edits button (next to the Toggle Editing button).
'''Note''': when you are learning the process, start with a small polygon, say 1x1 degrees. Some of the steps take a very long time if processing very large sections of this data.
<gallery>
New layer.png|New shapefile layer
Shapefile dialog.png|Shapefile dialog
Toggle editing.png|Toggle editing
Add polygon.png|Add polygon
Vertex tool.png|Vertex tool
</gallery>
*Once the polygon is completed, the next step is to clip the NLCD layer using this polygon. Select the NLCD layer and choose Raster -> Extraction -> Clip Raster by Mask Layer. Make sure the input layer is your NLCD layer and the mask layer is the polygon you have created. For Source CRS choose the Conus Albers CRS of the original NLCD layer, and for Target CRS choose the Project CRS, EPSG:4326. Near the bottom of the dialog box, where under Clipped (mask) it says [Save to temporary file], click the button to the right and choose Save to File. Select IMG files as the filter and save it somewhere in your working directory with a descriptive name like NLCD_clip.img. Click Run. Once it completes, if you hide the original NLCD layer and the polygon, you should see the new clipped layer. As long as it worked correctly, you can now safely remove the big original NLCD layer. The image below shows a small sample section in Idaho that has been clipped using this method.
<gallery>
49532154383 b437eb21b7 o.png|Clipped NLCD data
</gallery>
*If you zoom in on the data, you will see that it is highly detailed but also pixelated due to the raster format. It needs to be simplified and smoothed out to look good in FlightGear and also to reduce the polygon count on the resulting shapefiles. We will first do some smoothing of the raster itself, and then we'll do additional smoothing after converting to vector format. In the Processing Toolbox, find GRASS -> r.neighbors. The clipped NLCD layer should be selected for both of the top input dropdowns. For Neighborhood Operation, select mode with a Neighborhood size of 5. As before, you may want to choose an actual IMG file to save the output to instead of a temporary file. Click Run.
'''Note''': never use an averaging neighborhood operation, as the raster values are uniquely associated with a specific type of landcover. The averaging operation will introduce new values that do not correspond to a landcover type.
*Now we'll run r.neighbors once more on the resulting layer using the median Neighborhood Operation with a Neighborhood of 3. The result should look a lot smoother and a bit less detailed than the original dataset (but still very highly detailed compared to the default scenery). An image from the sample dataset is below. This area shows McCall, Idaho and the south end of Payette Lake.
<gallery>
49539367447 cde1c6666a o.png|After r.neighbors operations
</gallery>
*We're now ready to convert the raster data to vector. In the Processing Toolbox, find Grass -> r.to.vect. Choose the output layer from the last step (median neighbors filter), choose area as the feature type, and check the option "Smooth corners of area features." This will make the output vector a little less pixely than the input. As before, you may want to save to a real shapefile instead of a temporary one. Click Run. This step will take longer than any of the previous ones to process.
*We could use this vector data as is, but it's still a bit blocky and unnatural looking. Instead, we'll do a couple processing steps on the vector data. GRASS -> v.generalize. Choose the default douglas algorithm with a tolerance of 0.0003 (this is in degrees, since our CRS is in degrees). You may want to play around with different values of tolerance until you find something that works best. Click Run. If errors result, it will create a new layer, which can be deleted. '''Note''': this step will take a very long time for large areas.
*Now we'll use v.generalize one more to smooth the simplified layer. This time, choose the chaiken algorithm with the same tolerance. Click Run. '''Note''': this step will take a very long time for large areas.
<gallery>
Vectorized.png|Vectorized NLCD data
Simplified.png|Simplified NLCD data
Smoothed.png|Smoothed NLCD data
</gallery>
*At this point, it is a good idea to compare the smoothed vector layer to the original NLCD raster data and make sure the smoothing steps have not introduced too much error in the shape of coastlines and other features. If they have, you may need to redo some or all of the last several steps (r.neighbors, r.to.vect, and v.generalize) adjusting the number of neighbors and tolerance until you get satisfactory results. However, I've found these parameters to produce quite good results.

To be continued...

File:Smoothed.png

2020-03-07T11:55:44Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=Smoothed NLCD data}}
|date=2020-03-07
|source={{own}}
|author=[[User:Montagdude|Montagdude]]
|permission=
|other versions=
}}

=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

[[Category:Scenery]]
[[Category:Scenery enchancement]]

File:Simplified.png

2020-03-07T11:55:43Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=Simplified NLCD data}}
|date=2020-03-07
|source={{own}}
|author=[[User:Montagdude|Montagdude]]
|permission=
|other versions=
}}

=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

[[Category:Scenery]]
[[Category:Scenery enchancement]]

File:Vectorized.png

2020-03-07T11:55:43Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=Vectorized NLCD data}}
|date=2020-03-07
|source={{own}}
|author=[[User:Montagdude|Montagdude]]
|permission=
|other versions=
}}

=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

[[Category:Scenery]]
[[Category:Scenery enchancement]]

Howto:Using QGIS to process NLCD data

2020-03-06T01:32:13Z

Montagdude: Add more to Processing the data section

File:49539367447 cde1c6666a o.png

2020-03-06T01:30:52Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=After r.neighbors operations}}
|date=2020-03-05
|source={{own}}
|author=[[User:Montagdude|Montagdude]]
|permission=
|other versions=
}}

=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

[[Category:Scenery]]
[[Category:Scenery enchancement]]

File:49532154383 b437eb21b7 o.png

2020-03-06T01:18:56Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=Clipped NLCD data}}
|date=2020-03-05
|source={{own}}
|author=[[User:Montagdude|Montagdude]]
|permission=
|other versions=
}}

=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

[[Category:Scenery]]
[[Category:Scenery enchancement]]

Howto:Using QGIS to process NLCD data

2020-03-05T23:31:38Z

Montagdude: Fix headings

Howto:Using QGIS to process NLCD data

2020-03-05T19:06:56Z

Montagdude: Fix typo

Howto:Using QGIS to process NLCD data

2020-03-05T19:06:23Z

Montagdude: Add to Processing the data section

File:Add polygon.png

2020-03-05T19:03:32Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=Add polygon}}
|date=2020-03-05
|source={{own}}
|author=[[User:Montagdude|Montagdude]]
|permission=
|other versions=
}}

=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

[[Category:Scenery]]
[[Category:Scenery enchancement]]

File:Vertex tool.png

2020-03-05T19:03:32Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=Vertex tool}}
|date=2020-03-05
|source={{own}}
|author=[[User:Montagdude|Montagdude]]
|permission=
|other versions=
}}

=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

[[Category:Scenery]]
[[Category:Scenery enchancement]]

File:New layer.png

2020-03-05T19:03:32Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=New shapefile layer}}
|date=2020-03-05
|source={{own}}
|author=[[User:Montagdude|Montagdude]]
|permission=
|other versions=
}}

=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

[[Category:Scenery]]
[[Category:Scenery enchancement]]

File:Toggle editing.png

2020-03-05T19:03:32Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=Toggle editing}}
|date=2020-03-05
|source={{own}}
|author=[[User:Montagdude|Montagdude]]
|permission=
|other versions=
}}

=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

[[Category:Scenery]]
[[Category:Scenery enchancement]]

File:Shapefile dialog.png

2020-03-05T19:03:31Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=Shapefile dialog}}
|date=2020-03-05
|source={{own}}
|author=[[User:Montagdude|Montagdude]]
|permission=
|other versions=
}}

=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

[[Category:Scenery]]
[[Category:Scenery enchancement]]

File:49532655611 64dc27a6fd o.png

2020-03-05T18:37:16Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=NLCD data loaded in QGIS}}
|date=2020-03-05
|source={{own}}
|author=[[User:Montagdude|Montagdude]]
|permission=
|other versions=
}}

=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

[[Category:Scenery]]
[[Category:Scenery enchancement]]

File:49532654886 e94a32328d o.png

2020-03-05T18:32:45Z

Montagdude: User created page with UploadWizard

=={{int:filedesc}}==
{{Information
|description={{en|1=Setting the CRS}}
|date=2020-03-05
|source={{own}}
|author=[[User:Montagdude|Montagdude]]
|permission=
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}}

=={{int:license-header}}==
{{self|cc-by-sa-4.0}}

Howto:Using QGIS to process NLCD data

2020-03-05T18:16:35Z

Montagdude: Add information about converting elevation data

Howto:Using QGIS to process NLCD data

2020-03-05T18:07:17Z

Montagdude: Create page

Portal:Developer/Scenery

2020-03-05T13:38:24Z

Montagdude: Add link to Howto:Using QGIS to process NLCD data

{{Main page/Header
|title=Welcome to the [[scenery]] portal
|introduction=Please visit [http://forum.flightgear.org/viewforum.php?f=5 our forum] if you cannot find what you're looking for.
|rightbox=[[Portal:Developer|Back to the Developer portal]]

|rightcolumn1= </div>
Getting started 

[[Scenery FAQ|Frequently asked questions]] · [[Understanding FlightGear Scenery|Understanding scenery]]
|rightcolumn2= </div>
General 

[[Howto:Modelling clouds|Modelling clouds]] · [[Howto:Nasal in scenery object XML files|Nasal in scenery objects]] 
[[Current Scenery Projects|Current projects]] · [[Landmark Wish List|Scenery wish list]] 
[[Scenery Development Resources|Development resources]]
|rightcolumn3= </div>
Airports 

[[Howto:Make an airport|Making an airport]] · [[Howto:Make a helipad|Making a helipad]] 
[[Howto:Add details to your airport|Adding airport details]] · [[Howto:Animated jetways|Animated jetways]] · [[Signs]] 
[[Howto:Test your airport layout: quick and easy|Testing your airport layout]] 
[[TaxiDraw]] · [[WorldEditor]] 
[[AI Traffic]]
|rightcolumn4= </div>
Objects 

[[Modeling - Getting Started|Getting started]] · [[Howto:Place 3D objects with the UFO|Placing objects]] 
[[Howto:Calculate elevations|Calculating elevation offsets]] 
[[Howto:Decrease the number of faces|Decreasing number of faces]] 
[[Howto:Illuminate faces|Illuminating faces]] · [[Howto:Texture a building|Texturing]] 
[[Howto:Convert objects from X-Plane|Converting from X-Plane/MSFS]] 
[[Howto:Convert objects with ModelConverterX|Converting with ModelConverterX]] 
[[FlightGear Scenery Database|Scenery database]] 
[[Blender]] · [[SketchUp]]
|rightcolumn5= [[File:Portal scenery.png|40px]] </div>
Terrain 

[[Howto:Create custom terrain|Creating custom terrain]] · [[Procedural Texturing]] · [[Photoscenery]] 
[[Howto:Editing tile textures and materials|Editing materials]] · [[TerraGear]] · [[TerraMaster]] 
[[Howto:Using QGIS to process NLCD data|Using QGIS to process NLCD data]]

|sections=
{{Main page/Leftbox|title=Scenery, a world class on its own
|contents='''Scenery''' is the landscape in [[FlightGear]] (or any other simulator) and is therefore an essential part of the simulator. Contributing something back to the FlightGear world (literally) is both fun and rewarding.

One of the best aspects of FlightGear's open source character is the [[FlightGear Scenery Database|global scenery database]].
}}
}}

[[Category:Portals]]