Cheat sheet for GDAL/OGR command-line geodata tools
Get vector information
ogrinfo -so input.shp layer-name
Or, for all layers
ogrinfo -al -so input.shp
Print vector extent
ogrinfo input.shp layer-name | grep Extent
List vector drivers
ogr2ogr --formats
Convert between vector formats
ogr2ogr -f "GeoJSON" output.json input.shp
Print count of features with attributes matching a given pattern
ogrinfo input.shp layer-name | grep "Search Pattern" | sort | uniq -c
Clip vectors by bounding box
ogr2ogr -f "ESRI Shapefile" output.shp input.shp -clipsrc <x_min> <y_min> <x_max> <y_max>
Clip one vector by another
ogr2ogr -clipsrc clipping_polygon.shp output.shp input.shp
Reproject vector:
ogr2ogr output.shp -t_srs "EPSG:4326" input.shp
Merge features in a vector file by attribute ("dissolve")
ogr2ogr -f "ESRI Shapefile" dissolved.shp input.shp -dialect sqlite -sql "select ST_union(Geometry),common_attribute from input GROUP BY common_attribute"
Merge features ("dissolve") using a buffer to avoid slivers
ogr2ogr -f "ESRI Shapefile" dissolved.shp input.shp -dialect sqlite -sql "select ST_union(ST_buffer(Geometry,0.001)),common_attribute from input GROUP BY common_attribute"
Merge vector files:
ogr2ogr merged.shp input1.shp
ogr2ogr -update -append merged.shp input2.shp -nln merged
Extract from a vector file based on query
To extract features with STATENAME 'New York','New Hampshire', etc. from states.shp
ogr2ogr -where 'STATENAME like "New%"' states_subset.shp states.shp
To extract type 'pond' from water.shp
ogr2ogr -where "type = pond" ponds.shp water.shp
Subset & filter all shapefiles in a directory
Assumes that filename and name of layer of interest are the same...
basename -s.shp *.shp | xargs -n1 -I % ogr2ogr %-subset.shp %.shp -sql "SELECT field-one, field-two FROM '%' WHERE field-one='value-of-interest'"
Extract data from a PostGis database to a GeoJSON file
ogr2ogr -f "GeoJSON" file.geojson PG:"host=localhost dbname=database user=user password=password" \
-sql "SELECT * from table_name"
Get raster information
gdalinfo input.tif
List raster drivers
gdal_translate --formats
Force creation of world file (requires libgeotiff)
listgeo -tfw mappy.tif
Report PROJ.4 projection info, including bounding box (requires libgeotiff)
listgeo -proj4 mappy.tif
Convert between raster formats
gdal_translate -of "GTiff" input.grd output.tif
Convert 16-bit bands (Int16 or UInt16) to Byte type
(Useful for Landsat 8 imagery...)
gdal_translate -of "GTiff" -co "COMPRESS=LZW" -scale 0 65535 0 255 -ot Byte input_uint16.tif output_byte.tif
You can change '0' and '65535' to your image's actual min/max values to preserve more color variation or to apply the scaling to other band types - find that number with:
gdalinfo -mm input.tif | grep Min/Max
Convert a directory of raster files of the same format to another raster format
basename -s.img *.img | xargs -n1 -I % gdal_translate -of "GTiff" %.img %.tif
Reproject raster:
gdalwarp -t_srs "EPSG:102003" input.tif output.tif
Be sure to add -r bilinear if reprojecting elevation data to prevent funky banding artifacts.
Georeference an unprojected image with known bounding coordinates:
gdal_translate -of GTiff -a_ullr <top_left_lon> <top_left_lat> <bottom_right_lon> <bottom_right_lat> -a_srs EPSG:4269 input.png output.tif
Clip raster by bounding box
gdalwarp -te <x_min> <y_min> <x_max> <y_max> input.tif clipped_output.tif
Clip raster to SHP / NoData for pixels beyond polygon boundary
gdalwarp -dstnodata <nodata_value> -cutline input_polygon.shp input.tif clipped_output.tif
Crop raster dimensions to vector bounding box
gdalwarp -cutline cropper.shp -crop_to_cutline input.tif cropped_output.tif
Merge rasters
gdal_merge.py -o merged.tif input1.tif input2.tif
Alternatively,
gdalwarp input1.tif input2.tif merged.tif
Or, to preserve nodata values:
gdalwarp input1.tif input2.tif merged.tif -srcnodata <nodata_value> -dstnodata <merged_nodata_value>
Stack grayscale bands into a georeferenced RGB
Where LC81690372014137LGN00 is a Landsat 8 ID and B4, B3 and B2 correspond to R,G,B bands respectively:
gdal_merge.py -co "PHOTOMETRIC=RGB" -separate LC81690372014137LGN00_B{4,3,2}.tif -o LC81690372014137LGN00_rgb.tif
Fix an RGB TIF whose bands don't know they're RGB
gdal_merge.py -co "PHOTOMETRIC=RGB" input.tif -o output_rgb.tif
Export a raster for Google Earth
gdal_translate -of KMLSUPEROVERLAY input.tif output.kmz -co FORMAT=JPEG
Raster calculation (map algebra)
Average two rasters:
gdal_calc.py -A input1.tif -B input2.tif --outfile=output.tif --calc="(A+B)/2"
Add two rasters:
gdal_calc.py -A input1.tif -B input2.tif --outfile=output.tif --calc="A+B"
etc.
Create a hillshade from a DEM
gdaldem hillshade -of PNG input.tif hillshade.png
Change light direction:
gdaldem hillshade -of PNG -az 135 input.tif hillshade_az135.png
Use correct vertical scaling in meters if input is projected in degrees
gdaldem hillshade -s 111120 -of PNG input_WGS1984.tif hillshade.png
Apply color ramp to a DEM
First, create a color-ramp.txt file:
(Height, Red, Green, Blue)
0 110 220 110
900 240 250 160
1300 230 220 170
1900 220 220 220
2500 250 250 250
Then apply those colors to a DEM:
gdaldem color-relief input.tif color_ramp.txt color-relief.tif
Create slope-shading from a DEM
First, make a slope raster from DEM:
gdaldem slope input.tif slope.tif
Second, create a color-slope.txt file:
(Slope angle, Red, Green, Blue)
0 255 255 255
90 0 0 0
Finally, color the slope raster based on angles in color-slope.txt:
gdaldem color-relief slope.tif color-slope.txt slopeshade.tif
Resample (resize) raster
gdalwarp -ts <width> <height> -r cubic dem.tif resampled_dem.tif
Entering 0 for either width or height guesses based on current dimensions.
Alternatively,
gdal_translate -outsize 10% 10% -r cubic dem.tif resampled_dem.tif
For both of these, -r cubic specifies cubic interpolation: when resampling continuous data (like a DEM), the default nearest neighbor interpolation can result in "stair step" artifacts.
Burn vector into raster
gdal_rasterize -b 1 -i -burn -32678 -l layername input.shp input.tif
Extract polygons from raster
gdal_polygonize.py input.tif -f "GeoJSON" output.json
Create contours from DEM
gdal_contour -a elev -i 50 input_dem.tif output_contours.shp
Get values for a specific location in a raster
gdallocationinfo -xml -wgs84 input.tif <lon> <lat>
Convert GRIB band to .tif
Assumes data for entire globe in WGS84. Be sure to specify band, or you may end up with a nonsense RGB image composed of the first three.
gdal_translate input.grib -a_ullr -180 -90 180 90 -a_srs EPSG:4326 -b 1 output_band1.tif
Convert KML points to CSV (simple)
ogr2ogr -f CSV output.csv input.kmz -lco GEOMETRY=AS_XY
Convert KML to CSV (WKT)
First list layers in the KML file
ogrinfo -so input.kml
Convert the desired KML layer to CSV
ogr2ogr -f CSV output.csv input.kml -sql "select *,OGR_GEOM_WKT from some_kml_layer"
CSV points to SHP
This section needs retooling
Given input.csv
lon_column,lat_column,value
-81,32,13
-81,32,14
-81,32,15
Make a .dbf table for ogr2ogr to work with from input.csv
ogr2ogr -f "ESRI Shapefile" input.dbf input.csv
Use a text editor to create a .vrt file in the same directory as input.csv and input.dbf. This file holds the parameters for building a full shapefile based on values in the DBF you just made.
<OGRVRTDataSource>
<OGRVRTLayer name="output_file_name">
<SrcDataSource relativeToVRT="1">./</SrcDataSource>
<SrcLayer>input</SrcLayer>
<GeometryType>wkbPoint</GeometryType>
<LayerSRS>WGS84</LayerSRS>
<GeometryField encoding="PointFromColumns" x="lon_column" y="lat_column"/>
</OGRVRTLayer>
</OGRVRTDataSource>
Create shapefile based on parameters listed in the .vrt
mkdir shp
ogr2ogr -f "ESRI Shapefile" shp/ inputfile.vrt
The VRT file can be modified to give a new output shapefile name, reference a different coordinate system (LayerSRS), or pull coordinates from different columns.
MODIS operations
First, download relevant .hdf tiles from the MODIS ftp site: ftp://ladsftp.nascom.nasa.gov/; use the MODIS sinusoidal grid for reference.
List MODIS Subdatasets in a given HDF (conf. the MODIS products table)
gdalinfo longFileName.hdf | grep SUBDATASET
Make TIFs from each file in list; replace 'MOD12Q1:Land_Cover_Type_1' with desired Subdataset name
mkdir output
find . '*.hdf' -exec gdalwarp -of GTiff 'HDF4_EOS:EOS_GRID:"{}":MOD12Q1:Land_Cover_Type_1' output/{}.tif \;
Merge all .tifs in output directory into single file
gdal_merge.py -o output/Merged_Landcover.tif output/*.tif
BASH functions
Size Functions
This size function echos the pixel dimensions of a given file in the format expected by gdalwarp.
function gdal_size() {
SIZE=$(gdalinfo $1 |\
grep 'Size is ' |\
cut -d\ -f3-4 |\
sed 's/,//g')
echo -n "$SIZE"
}
This can be used to easily resample one raster to the dimensions of another:
gdalwarp -ts $(gdal_size bigraster.tif) -r cubicspline smallraster.tif resampled_smallraster.tif
Extent Functions
These extent functions echo the extent of the given file in the order/format expected by gdal_translate -projwin.
(Originally from Linfiniti).
function gdal_extent() {
if [ -z "$1" ]; then
echo "Missing arguments. Syntax:"
echo " gdal_extent <input_raster>"
return
fi
EXTENT=$(gdalinfo $1 |\
grep "Upper Left\|Lower Right" |\
sed "s/Upper Left //g;s/Lower Right //g;s/).*//g" |\
tr "\n" " " |\
sed 's/ *$//g' |\
tr -d "[(,]")
echo -n "$EXTENT"
}
function ogr_extent() {
if [ -z "$1" ]; then
echo "Missing arguments. Syntax:"
echo " ogr_extent <input_vector>"
return
fi
EXTENT=$(ogrinfo -al -so $1 |\
grep Extent |\
sed 's/Extent: //g' |\
sed 's/(//g' |\
sed 's/)//g' |\
sed 's/ - /, /g')
EXTENT=`echo $EXTENT | awk -F ',' '{print $1 " " $4 " " $3 " " $2}'`
echo -n "$EXTENT"
}
function ogr_layer_extent() {
if [ -z "$2" ]; then
echo "Missing arguments. Syntax:"
echo " ogr_extent <input_vector> <layer_name>"
return
fi
EXTENT=$(ogrinfo -so $1 $2 |\
grep Extent |\
sed 's/Extent: //g' |\
sed 's/(//g' |\
sed 's/)//g' |\
sed 's/ - /, /g')
EXTENT=`echo $EXTENT | awk -F ',' '{print $1 " " $4 " " $3 " " $2}'`
echo -n "$EXTENT"
}
Extents can be passed directly into a gdal_translate command like so:
gdal_translate -projwin $(ogr_extent boundingbox.shp) input.tif clipped_output.tif
or
gdal_translate -projwin $(gdal_extent target_crop.tif) input.tif clipped_output.tif
This can be a useful way to quickly crop one raster to the same extent as another. Add these to your ~/.bash_profile file for easy terminal access.
http://live.osgeo.org/en/quickstart/gdal_quickstart.html
https://github.com/nvkelso/geo-how-to/wiki/OGR-to-reproject,-modify-Shapefiles
ftp://ftp.remotesensing.org/gdal/presentations/OpenSource_Weds_Andre_CUGOS.pdf
http://linfiniti.com/2010/12/a-workflow-for-creating-beautiful-relief-shaded-dems-using-gdal/
http://linfiniti.com/2009/09/clipping-rasters-with-gdal-using-polygons/
http://nautilus.baruch.sc.edu/twiki_dmcc/bin/view/Main/OGR_example
http://www.gdal.org/frmt_hdf4.html
http://planetflux.adamwilson.us/2010/06/modis-processing-with-r-gdal-and-nco.html
http://trac.osgeo.org/gdal/wiki/FAQRaster
http://dirkraffel.com/2011/07/05/best-way-to-merge-color-relief-with-shaded-relief-map/
http://gfoss.blogspot.com/2008/06/gdal-raster-data-tips-and-tricks.html
http://osgeo-org.1560.x6.nabble.com/gdal-dev-Dissolve-shapefile-using-GDAL-OGR-td5036930.html
https://www.mapbox.com/tilemill/docs/guides/terrain-data/