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https://grasswiki.osgeo.org/wiki/Global_datasets Raster data Elevation data ASTER topography (GDEM V1) Improved ASTER GDEM 1 from 2009: GDEM global 30m elevation calculated from stereo-pair images collected by the Terra satellite. "This is the most complete, consistent global digital elevation data yet made available to the world." This is a very new dataset, at version 1 (treat as experimental). Accuracy will be improved in forthcoming versions (validation with SRTM, etc.; see assessment here and here). pre-release announcement NASA press release Warehouse Inventory Search Tool or Easy search tool (Data download) Tutorial: ASTER topography See also: ASTER GDEM 30m quality assessment ASTER topography (GDEM V2) Improved ASTER GDEM 2 from 2011: https://lpdaac.usgs.gov/products/aster_products_table/astgtm The ASTER GDEM covers land surfaces between 83°N and 83°S and is comprised of 22,702 tiles. Tiles that contain at least 0.01% land area are included. The ASTER GDEM is distributed as Geographic Tagged Image File Format (GeoTIFF) files with geographic coordinates (latitude, longitude). The data are posted on a 1 arc-second (approximately 30–m at the equator) grid and referenced to the 1984 World Geodetic System (WGS84)/ 1996 Earth Gravitational Model (EGM96) geoid. Notes: this DEM can be rather well filtered and smoothed with the Sun's denoising algorithm (using GDAL and free / open source program <mdenoise> or simply GRASS add-on r.denoise. Experiments showed that the best smoothing of ASTER GDEM 2 is reached with such parameters of <mdenoise>: threshold = 0.8 iterations = 10-20 Also filtering with r.neighbors by "average" method and window size >=5 is quite useful to remove some noise from DEM. See also: Validation of the ASTER Global Digital Elevation Model Version 2 over the Conterminous United States ACE2 The ACE2 Global Digital Elevation Model is available at 3", 30" and 5' spatial resolutions. http://tethys.eaprs.cse.dmu.ac.uk/ACE2/ Import example: r.in.bin -f input="00N105E_3S.ACE2" output="ACE2_00N105E" bytes=4 \ order="native" north=15 south=0 east=120 west=105 \ rows=18000 cols=18000 CleanTOPO2 (DEM) CleanTOPO2 download: Edited SRTM30 Plus World Elevation Data Import in GRASS: r.in.gdal CleanTOPO2.tif out=cleanTOPO2.tmp -l -o g.region rast=cleanTOPO2 -p -g # rescale from odd integer values to true world values r.rescale cleanTOPO2.tmp out=cleanTOPO2 to=-10701,8248 r.colors cleanTOPO2_final col=terrain Rescaled ClearTOPO2 map EGM2008 Geoid Data (Earth Gravitational Model) Global 2.5 Minute Geoid Undulations: download GIS Format at http://earth-info.nga.mil/GandG/wgs84/gravitymod/egm2008/egm08_gis.html Geoid undulations in Trentino, Italy Verifications of points can be done with the http://geographiclib.sourceforge.net/cgi-bin/GeoidEval ETOPO (DEM) The ETOPO datasets provide global topography and bathymetry at 1', 2', and 5' per-cell resolutions. ETOPO1 (DEM) http://www.ngdc.noaa.gov/mgg/global/ The cell registered version can be loaded directly into a lat/lon location. GRASS raster data is cell registered (see the GRASS raster semantics page) Special care must be taken with the grid registered version. It can not be loaded directly into a lat/lon location as the parameters found in the .hdr file exceed the limits of polar coordinate space: they have N,S rows which go 1/2 a cell beyond 90 latitude, when considered in the cell registered convention. So the data needs to have those 90deg N,S rows cropped away, and while we're at it we crop away a redundant overlapping column at 180 longitude. To do this we have to first tell the GIS a little fib during import to squeeze the data into lat/lon space, then crop away the spurious rows and column, then finally reset the resulting map's bounds to its true extent. # Import grid registered binary float, fibbing about its true extent r.in.bin -f in=etopo1_bed_g.flt out=etopo1_bed_g.raw \ n=90 s=-90 e=180 w=-180 rows=10801 cols=21601 anull=-9999 # reduce the working region by 1 cell g.region rast=etopo1_bed_g.raw eval `g.region -g` g.region n=n-$nsres s=s+$nsres e=e-$ewres -p # save smaller raster and remove original r.mapcalc "etopo1_bed_g.crop = etopo1_bed_g.raw" g.remove etopo1_bed_g.raw # re-establish the correct bounds, now that they'll fit r.region etopo1_bed_g.crop n=89:59:30N s=89:59:30S w=179:59:30E e=179:59:30E g.region rast=etopo1_bed_g.crop # check that N,S,E,W and Res are all nice and clean: r.info etopo1_bed_g.crop # looks good, so accept the results by resetting the map name g.rename etopo1_bed_g.crop,etopo1_bed_g # set to use appropriate color rules r.colors etopo1_bed_g color=etopo2 # set the 'units' metadata field (for elevation data contained within the map) r.support etopo1_bed_g units=meters For the problematic grid registered version, the resulting r.info report should look like: | Rows: 10799 | | Columns: 21600 | | Total Cells: 233258400 | | Projection: Latitude-Longitude | | N: 89:59:30N S: 89:59:30S Res: 0:01 | | E: 179:59:30E W: 179:59:30E Res: 0:01 | | Range of data: min = -10898 max = 8271 | (the east and west bounds of the map touch 1/2 a cell west of 180 longitude) For the problematic grid registered version, since the data's grid is 1/2 a cell shifted from nicely rounded 1 arc-minutes (0:01), you'll need to ensure that the mapset's region preserves that alignment after zooming or panning: g.region align=etopo1_bed_g -p (or oversample and set the region resolution to 1/2 arc-minutes (0:00:30), which will be four times as slow) ETOPO2 (DEM) See the ETOPO2 (2' global) article by M.H. Bowman in the GRASS Newsletter, 1:8-11, August 2004. ETOPO2v2 data download (take for example the ETOPO2v2g_f4_LSB.flt file) GTOPO30 (DEM) Data download - Import with r.in.gdal. Note: To avoid that the GTOPO30 data are read incorrectly, you can add a new line "PIXELTYPE SIGNEDINT" in the .HDR to force interpretation of the file as signed rather than unsigned integers. Then the .DEM file can be imported. Finally, e.g. the 'terrain' color table can be assigned to the imported map with r.colors. Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) Data download: Web and FTP - Import with r.in.gdal. See also related GDAL blog post Tiles: Import of GMTED2010 tiles in GRASS GIS: r.in.gdal 30N000E_20101117_gmted_mea075.tif out=gmted2010_30N000E_20101117 r.colors gmted2010_30N000E_20101117 color=elevation g.region rast=gmted2010_30N000E_20101117 r.relief input=gmted2010_30N000E_20101117 output=gmted2010_30N000E_20101117.shade r.shade shade=gmted2010_30N000E_20101117.shade color=gmted2010_30N000E_20101117 \ output=gmted2010_30N000E_20101117_shaded d.mon wx0 d.rast gmted2010_30N000E_20101117_shaded d.grid 1 color=red textcolor=red GMTED2010 example: Trento - Garda Lake - Verona area (Northern Italy) Full maps: # mean elevation global GMTED2010 map, 30 arc-sec wget http://edcintl.cr.usgs.gov/downloads/sciweb1/shared/topo/downloads/GMTED/Grid_ZipFiles/mn30_grd.zip unzip mn30_grd.zip Important: the GMTED2010 map exceeds the -180°..+180° range due to the GMTED2010 pixel geometry (PDF). Note that this cannot be handled in GRASS GIS < 7.4. Please update to GRASS GIS 7.4 or newer. GEBCO Bathymetric Chart The General Bathymetric Chart of the Oceans (original 1' release 2003, new 1' and 30" releases 2008) http://www.gebco.net/data_and_products/gridded_bathymetry_data/ http://www.bodc.ac.uk/data/online_delivery/gebco/ r.in.gdal can be used to import the GMT netCDF files directly, or if that doesn't work you can use GMT tools to convert to an old-style native GMT format and import that with r.in.bin. example: (GEBCO 2003 1' data) # convert to an old style GMT binary .grd using grdreformat $ grdreformat 3n24s47w14w.grd 3n24s47w14w_Native.grd=bs # then import into GRASS, GRASS> r.in.bin -h -s bytes=2 in=3n24s47w14w_Native.grd out=3n24s47w14w # and set some nice colors GRASS> r.colors 3n24s47w14w rules=- << EOF nv magenta 0% black -7740 0:0:168 0 84:176:248 0 40:124:0 522 68:148:24 1407 148:228:108 1929 232:228:108 2028 232:228:92 2550 228:160:32 2724 216:116:8 2730 grey 2754 grey 2760 252:252:252 2874 252:252:252 2883 192:192:192 2913 192:192:192 100% 252:252:252 EOF Global Multi-Resolution Topography (GMRT DEM) From Columbia University's Lamont-Doherty Earth Observatory (it is reported that this is what Google Maps uses for their global bathymetry) Global ~1 arc-second (~90 m) topography using multi-beam and satellite data in the oceans combined with SRTM on land. Full information at: http://www.marine-geo.org/portals/gmrt/ Accessible via GeoMapApp or Virtual Ocean software. Very convenient to download into GRASS via wget: export `g.region -g` wget "http://www.marine-geo.org/cgi-bin/getgridB?west=${w}&east=${e}&south=${s}&north=${n}&resolution=1" -O /tmp/test.grd r.in.gdal /tmp/test.grd output=GMRT -o rm /tmp/test.grd Note: Downloaded file contains no projection information, but is EPSG:4326 (WGS84 Geographic). The file size is limited, but lower resolution (resolution=2,4,8) data can be downloaded for larger areas. Smith and Sandwell DEM Merge info here from the Marine Science wiki page SRTM DEM Space Shuttle Radar Topography Mission - several SRTM Data Products are available: Original data - SRTM 3 V001 arc-seconds Non-Void Filled elevation data (US: 1 arc-second (approximately 30 meters); outside the US at 3 arc-seconds (approximately 90 meters)) SRTM V003 3 Arc-Second Global Void Filled elevation data, with voids filled using interpolation algorithms in conjunction with other sources of elevation data (US: 1 arc-second (approximately 30 meters); outside the US at 3 arc-seconds (approximately 90 meters)) SRTM V3 tiles at 3 arc seconds resolution from: http://e4ftl01.cr.usgs.gov/SRTM/SRTMGL3.003/2000.02.11/ - or simply use r.in.srtm.region SRTM V003 1 Arc-Second Global elevation data offer worldwide coverage of void filled data at a resolution of 1 arc-second (30 meters) and provide open distribution of this high-resolution global data set. EarthExplorer can be used to search, preview, and download Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global data. The collections are located under the Digital Elevation category. FTP download: http://e4ftl01.cr.usgs.gov/SRTM/SRTMGL1.003/2000.02.11/ - or simply use r.in.srtm.region Web-based 30-Meter SRTM Tile Downloader (select from map) Import: Using r.in.gdal or r.import or r.in.srtm or r.in.srtm.region see HOWTO import SRTM elevation data, focused on the SRTM 3 arc-seconds Non-Void Filled elevation data SRTM30plus data DEM SRTM30plus data consists of 33 files of global topography in the same format as the SRTM30 products distributed by the USGS EROS data center. The grid resolution is 30 seconds which is roughly one kilometer (1 km). Land data are based on the 1-km averages of topography derived from the USGS SRTM30 grided DEM data product created with data from the NASA Shuttle Radar Topography Mission. GTOPO30 data are used for high latitudes where SRTM data are not available. Ocean data are based on the Smith and Sandwell global 2-minute grid between latitudes +/- 72 degrees. Higher resolution grids have been added from the LDEO Ridge Multibeam Synthesis Project and the NGDC Coastal Relief Model. Arctic bathymetry is from the International Bathymetric Chart of the Oceans (IBCAO). All data are derived from public domain sources and these data are also in the public domain. GRASS 6 script r.in.srtm described in GRASSNews vol. 3 won't work with this dataset (as it was made for the original SRTM HGT files). But you can import SRTM30plus tiles into GRASS this way: r.in.bin -sb input=e020n40.Bathymetry.srtm output=e020n40_topex bytes=2 \ north=40 south=-10 east=60 west=20 r=6000 c=4800 r.colors e020n40_topex rules=etopo2 Source GRASS Users Mailing List http://lists.osgeo.org/pipermail/grass-user/2005-August/030063.html Getting as SRTM30plus tiles ftp://topex.ucsd.edu/pub/srtm30_plus/srtm30/data/ Getting as SRTM30plus huge file ftp://topex.ucsd.edu/pub/srtm30_plus/topo30/ SRTMPLUS WCS server http://svn.osgeo.org/gdal/trunk/autotest/gdrivers/data/srtmplus.wcs (read with r.external) SRTM Water Body Database SRTMSWBD V003 SRTM Water Body Database V003 Format documentation: https://lpdaac.usgs.gov/dataset_discovery/measures/measures_products_table/srtmswbd_v003 FTP raster data (30m res water bodies): http://e4ftl01.cr.usgs.gov/SRTM/SRTMSWBD.003/ Import into GRASS GIS 7 (lat-long location): r.in.bin -sb input=N00E108.raw output=N00E108_swbd bytes=1 north=0 south=-10 east=108 west=98 r=3601 c=3601 <<= DRAFT - TODO fix n,s,e,w - calculate from filename Soil data Harmonized World Soil Database (HWSD Database) Download: http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/ Spatial reference system: EPSG:4326 (LatLong WGS84) Import: grass70 -c EPSG:4326 ~/grassdata/hwsd # -e: expand location to dataset; -o: override (missing) projection in input dataset: r.in.gdal input=hwsd.bil output=hwSoil -e -o g.region raster=hwSoil -p r.category hwSoil The data is distributed with an MSAccess .mdb which contains additional data for each of the categories in the raster file. Opening the file in access, the data is found in the query "HWSD_Q". Save this query in .csv format (with a name like "HWSD_Q.csv") so that it may then be imported into GRASS. After that, it is necessary to replace the commas with dots (find & replace) in the .csv file. Before you can import it, you also need a file "HWSD_Q.csvt", which contains a single line listing the type for each column in the database: "Integer","String","Integer","Integer","Integer","String","Integer","Integer","Real","Integer","String","Integer","String","Integer","String","Integer","Integer","Integer","Integer","Integer","Integer","Integer","Integer","Integer","Integer","Integer","Integer","Integer","Integer","Integer","Integer","Real","Real","Real","Real","Real","Real","Real","Real","Real","Real","Real","Real","Integer","Integer","Integer","Integer","Integer","Real","Real","Real","Real","Real","Real","Real","Real","Real","Real","Real","Real" With both the .csv and the .csvt file in the same directory, you can then import them into GRASS: db.in.ogr input=~/grassdata/hwsd/HWSD_Q.csv output=hwsdData The data cannot be connected directly to the raster, it must be converted to a vector first: g.region raster=hwSoil r.to.vect -v input=hwSoil output=hwSoil feature=area v.db.droptable hwSoil db.droptable -f hwSoil # delete the table completely Note that the table includes multiple rows for each polygon, corresponding to the dominant and various numbers of subdominant soils. To select only the dominant soil layer: db.select table=hwsdData sql='select * from hwsdData where SEQ = 1' \ output=domSoil.csv separator=, This saves a copy of the table that contains only the dominant soil type for each polygon as domSoil.csv. This needs to be reloaded into the GRASS database. Since it has the same columns as HWSD_Q.csv, we can use the labels for that file: cp HWSD_Q.csvt domSoil.csvt Then we can load domSoil.csv: db.in.ogr \ input=~/grassdata/downloads/harmonized_world_soil_database/domSoil.csv \ output=domSoil Now at last we can connect the database to the vector file: v.db.connect -o map=hwsd table=domSoil driver=sqlite key=MU_GLOBAL To create a new raster map taking the values from the table: g.region raster=hwSoil ## make sure we get the whole map v.to.rast in=hwSoil out=T_SAND col=T_SAND SoilGrids.org 250m soil taxonomy map SoilGrids is a system for automated soil mapping based on global soil profile and environmental covariate data. SoilGrids represents a collection of updatable soil property and class maps of the world at 1 km and 250 m spatial resolution produced using automated soil mapping based on machine learning algorithms. It aims at becoming OpenStreetMap and/or OpenWeatherMap for soil data. SoilGrids predictions are updated on a regular basis (at least every few months). For more details about the SoilGrids system, please refer to the SoilGrids project site: https://www.soilgrids.org/#/?layer=geonode:taxnwrb_250m URL=ftp://ftp.soilgrids.org/data/recent/TAXNWRB_250m_ll.tif # the Soilgrids GeoTIFF data suffer from a resolution precision problem since they were produced with SAGA: # resolution is stored as 0.002083333000000 while it should be 0.002083333333333, hence the geometry is not fully correct # this likely originates from Soilgrids being processed in SAGA which cuts decimals after the 10th decimal place, hence comes with a precision problem export NAME=`basename $URL .tif` wget $URL OLD OLD OLD start -- # see below for the better way how to fix SoilGrids data gdal_translate --config GDAL_CACHEMAX 2000 -a_ullr $COORDS -co "COMPRESS=DEFLATE" $NAME.tif ${NAME}_fixed.tif gdalinfo ${NAME}_fixed.tif grass72 -c ${NAME}_fixed.tif ~/grassdata/latlong --exec r.import input=${NAME}_fixed.tif output=${NAME} -- OLD OLD OLD end New fix & import method: Starting with GRASS GIS 7.4.x, there is new flag in r.in.gdal to auto-adjust such small resolution precision issues: -a - Auto-adjustment for lat/lon. Attempt to fix small precision errors in resolution and extents. r.in.gdal -a input=TAXNWRB_250m_ll.tif output=TAXNWRB_250m_ll Landcover data ESA Globcover dataset Download: http://due.esrin.esa.int/page_globcover.php Or via command line: wget http://due.esrin.esa.int/files/Globcover2009_V2.3_Global_.zip unzip Globcover2009_V2.3_Global_.zip # rm -f Globcover2009_V2.3_Global_.zip Note, also a coloured version of the map in GeoTIFF format is available at: http://due.esrin.esa.int/files/GLOBCOVER_L4_200901_200912_V2.3.color.tif Unfortunately the Globcover map exceeds the -180°..+180° range etc, indicating a shift of the map (see also this assessment by DWD😞 gdalinfo GLOBCOVER_L4_200901_200912_V2.3.tif Driver: GTiff/GeoTIFF Files: GLOBCOVER_L4_200901_200912_V2.3.tif Size is 129600, 55800 Coordinate System is: GEOGCS["WGS 84", ... Origin = (-180.001388888888897,90.001388888888883) ... Corner Coordinates: Upper Left (-180.0013889, 90.0013889) (180d 0' 5.00"W, 90d 0' 5.00"N) Lower Left (-180.0013889, -64.9986111) (180d 0' 5.00"W, 64d59'55.00"S) Upper Right ( 179.9986111, 90.0013889) (179d59'55.00"E, 90d 0' 5.00"N) Lower Right ( 179.9986111, -64.9986111) (179d59'55.00"E, 64d59'55.00"S) Center ( -0.0013889, 12.5013889) ( 0d 0' 5.00"W, 12d30' 5.00"N) ... How to fix this? Option 1: You can use the -l flag of r.in.gdal to constrain the map coordinates to legal values (ref. But the resulting pixels will no longer have the original resolution. We will not do that. Option 2: Shift the Globcover map slightly into the right position using gdal_translate: # coords are shifted, fix raster map # -a_ullr Assign/override the georeferenced bounds of the output file # use larger cache and compress result gdal_translate --config GDAL_CACHEMAX 1200 -a_ullr -180 90 180 -65 \ -co "COMPRESS=LZW" GLOBCOVER_L4_200901_200912_V2.3.tif GLOBCOVER_L4_200901_200912_V2.3_fixed.tif # result: gdalinfo GLOBCOVER_L4_200901_200912_V2.3_fixed.tif ... Origin = (-180.000000000000000,90.000000000000000) Pixel Size = (0.002777777777778,-0.002777777777778) ... Corner Coordinates: Upper Left (-180.0000000, 90.0000000) (180d 0' 0.00"W, 90d 0' 0.00"N) Lower Left (-180.0000000, -65.0000000) (180d 0' 0.00"W, 65d 0' 0.00"S) Upper Right ( 180.0000000, 90.0000000) (180d 0' 0.00"E, 90d 0' 0.00"N) Lower Right ( 180.0000000, -65.0000000) (180d 0' 0.00"E, 65d 0' 0.00"S) Center ( 0.0000000, 12.5000000) ( 0d 0' 0.01"E, 12d30' 0.00"N) Voilà! Now we can import the map into GRASS GIS: r.in.gdal input=GLOBCOVER_L4_200901_200912_V2.3_fixed.tif output=esa_globcover2009 Legend conversion: The ZIP file contains a XLS table describing the classes and the RGB colors. Using ogr2ogr can directly convert XLS --> CSV: ogr2ogr -f CSV Globcover2009_Legend.csv Globcover2009_Legend.xls Applying the legend: # suppress table header and only consider category value and label, apply on the fly: cat Globcover2009_Legend.csv | grep -v '^Value' | cut -d',' -f1-2 | r.category esa_globcover2009 separator=comma rules=- # verify (0E, 0N is the Atlantic Ocean) r.what esa_globcover2009 coor=0,0 -f 0|0||210|Water bodies Global Forest Change http://earthenginepartners.appspot.com/science-2013-global-forest Download info on: http://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.3.html Imagery AVHRR see the AVHRR wiki page Blue Marble imagery NASA's Blue Marble is a 500m-8 degree per-cell world wide visual image of the Earth from space, with the clouds removed. see the Blue Marble wiki page EO-1 imagery (Earth Observing-1) "Advanced Land Imager (ALI) provides image data from ten spectral bands (band designations). The instrument operates in a pushbroom fashion, with a spatial resolution of 30 meters for the multispectral bands and 10 meters for the panchromatic band." -- http://eros.usgs.gov/products/satellite/eo1.php On-board Atmospheric Corrections Global Land Cover Characteristics USGS et al. generated dataset at 1km resolution. Provides global landcover characteristics. see the Global Land Cover Characteristics wiki page LANDSAT imagery Since October 1, 2008 all Landsat 7 ETM+ scenes held in the USGS EROS archive are available for download at no charge. Download via the Glovis online search tool (req. Java) Download via the USGS's EarthExplorer interface Import Modules r.in.gdal - Main import tool for complete multiband scenes r.in.wms - Download data covering current map region via WMS server r.in.onearth - WMS frontend for NASA's OnEarth Global Landsat Mosaic Color balancing modules i.landsat.rgb (GRASS 6.x) | i.colors.enhance (GRASS 7.x) - Color balancing/enhancement tool See also Processing tips can be found on the LANDSAT wiki page ESA Sentinel imagery All Sentinel 1 and 2 data is available for download from the Open Access Hub via the online interactive interface via the API For pre-processing, different tools are available at http://step.esa.int/main/ Scientific Toolbox Exploitation Platform https://github.com/Fernerkundung/awesome-sentinel ("Awesome Sentinel" - list of tools) Miscellaneous Data sources Some datasource links: http://www.ruf.rice.edu/~ben/gmt.html Geotorrent.org Import Modules The r.in.gdal modules may be used to import data of many formats, including GMT netCDF The r.in.bin module may be used to import raw binary files MODIS imagery see the MODIS wiki page Natural Earth imagery Natural Earth II: World environment map in natural color. GeoTIFF (use the r.in.gdal module) see also 1:10 million, 1:50 million and 1:110million scale maps from http://www.naturalearthdata.com/ Orthoimagery Sources of free orthoimagery Pathfinder AVHRR SST imagery see the Pathfinder AVHRR SST wiki page QuickBird imagery See the QuickBird wiki page SeaWiFS imagery see the SeaWiFS wiki page SPOT Vegetation imagery SPOT Vegetation (1km) global: NDVI data sets SPOT Vegetation (1km, global) NDVI data set server for import, see i.in.spotvgt True Marble imagery True Marble: 250m world wide visual image of the Earth from space, with the clouds removed. GeoTIFF (use the r.in.gdal module) Climatic data OGC WCS - Albedo example TODO: update this example e.g. to http://demo.mapserver.org/cgi-bin/wcs?SERVICE=wcs&VERSION=1.0.0&REQUEST=GetCapabilities GRASS imports OGC Web Coverage Service data. Example server (please suggest a better one!) <WCS_GDAL> <ServiceURL>http://laits.gmu.edu/cgi-bin/NWGISS/NWGISS?</ServiceURL> <CoverageName>AUTUMN.hdf</CoverageName> <Timeout>90</Timeout> <Resample>nearest</Resample> </WCS_GDAL> Save this as albedo.xml. Import into a LatLong WGS84 location: r.in.gdal albedo.xml out=albedo Unfortunately this server sends out the map shifted by 0.5 pixel. This requires a fix to the map boundary coordinates: r.region albedo n=90 s=-90 w=-180 e=180 Now apply color table and look at the map: r.colors albedo color=byr d.mon x0 d.rast albedo SNODAS maps Snow Data Assimilation System data that support hydrological modeling and analysis. First download the data, and untar them (once for each month, and once for each day), and you should get pairs of “.dat” and “.Hdr” files. The data files are stored in flat 16-bit binary format, so assuming that “snowdas_in.dat” is the name of the input file, at the GRASS prompt: r.in.bin -bs bytes=2 rows=3351 cols=6935 north=52.874583333332339 \ south=24.949583333333454 east=-66.942083333334011 west=-124.733749999998366 \ anull=-9999 input=snowdas_input.dat output=snowdas CHELSA climate maps CHELSA – Climatologies at high resolution for the earth’s land surface areas is a high resolution (30 arc sec) climate data set for the earth land surface areas currently under development, see http://chelsa-climate.org/ Version 1.1 has some coordinate issues originating from SAGA being used (coordinate precision issue), see http://chelsa-climate.org/known-issues/ # WARNING: dirty hack - Better wait for the new release V1.2 of CHELSA! for i in `ls /scratch/chelsa_climate/*.zip` ; do unzip $i NAME=`basename $i .zip` gdal_translate --config GDAL_CACHEMAX 2000 -a_ullr -180 84 180 -90 -co "COMPRESS=DEFLATE" $NAME.tif ${NAME}_fixed.tif rm -f $NAME.tif done WorldClim maps WorldClim is a set of global climate layers (climate grids) with a spatial resolution of a square kilometer. Besides long-term average climate layers (representing the period 1950 - 2000) it also includes projections for future conditions based on downscaled global climate model (GCM) data from CMIP5 (IPPC Fifth Assessment) and projections of past conditions (downscaled global climate model output). Load into a Lat/Lon WGS84 location (EPSG:4326) The data set is provided in two formats: BIL and ESRI Grd. Import with r.in.bin or r.in.gdal. Version 1.4 has some coordinate issues: a) BIL: binary format is 2 byte integer. Multiply by 10 using r.mapcalc to convert units. See http://www.worldclim.org/format.htm for more information and the MODIS help page for example of converting raw to data units. Note that the file header is missing a line. To fix: # fix WorldClim's BIL; tmean example for i in $(seq 1 12); do echo “PIXELTYPE SIGNEDINT” >>tmean$i.hdr; done b) ESRI grd files: Note that the WorldClim ESRI grd files suffer from a quality issue of coordinate precision. See here for a solution. # fix WorldClim's ESRI Grd; tmean example export GDAL_CACHEMAX=2000 mkdir -p ~/tmp/ # fix broken WorldClim files, see https://lists.osgeo.org/pipermail/grass-user/2011-January/059358.html # note: 60S, not 90S for i in $(seq 1 12); do gdal_translate -a_ullr -180 90 180 -60 tmean_$i $HOME/tmp/tmean_${i}_fixed.tif; done # # import for i in $(seq 1 12) ; do r.in.gdal input=$HOME/tmp/tmean_${i}_fixed.tif out=tmp --o ; g.region raster=tmp -p ; r.mapcalc "tmean_${i} = 0.1 * tmp" --o ; r.colors tmean_${i} color=celsius ; done # # clean up g.remove raster name=tmp -f rm -f ~/tmp/tmean_?_fixed.tif ; rm -f ~/tmp/tmean_??_fixed.tif Africlim maps Africlim provides four baseline data sets for current climate, including: CRU CL 2.0 WorldClim v1.4 TAMSAT TARCAT v2.0 (rainfall only) CHIRPS v1.8 (rainfall only). It furthermore provides data sets with projections of future climates based on combinations of ten general circulation models (GCMs), downscaled using five regional climate models (RCMs) and the four above mentioned contemporary baselines, under two representative concentration pathways of the IPCC-AR5 (RCP4.5 and RCP8.5). The data layers are available as GeoTIF files at spatial resolutions of 10', 5', 2.5', 1' and 30". Population maps WorldPop http://www.worldpop.org.uk/ Gridded Population of the World http://sedac.ciesin.columbia.edu/gpw/global.jsp Import with r.in.gdal, assign population color table with r.colors Topographic maps Soviet topographic maps Soviet topographic maps as geocoded GeoTIFFs Vector data Natural Earth http://www.naturalearthdata.com/ data scaled for 1:10 million, 1:50 million and 1:110million CDC Geographic Boundary and Public Health Maps http://www.cdc.gov/epiinfo/maps.htm Global Administrative Areas GADM is a database of the location of the world's administrative areas (boundaries) available in shapefiles. http://gadm.org (extracted by country here) World Borders Dataset including ISO 3166-1 Country codes available in shapefiles. http://thematicmapping.org/downloads/world_borders.php Free GIS data from Mapping Hacks http://mappinghacks.com/data/ GSHHS World Coastline GSHHS is a high resolution shoreline dataset. It is derived from data in the public domain and licensed as GPL. The shorelines are constructed entirely from hierarchically arranged closed polygons. It is closely linked to the GMTproject. Availability Download the original data set from http://www.soest.hawaii.edu/pwessel/gshhg/index.html. Also available at http://www.ngdc.noaa.gov/mgg/shorelines/data/gshhg/latest/. The data set, or parts from it, can be extracted from NOAA's shoreline extractor. For GRASS 6 you can download 1:250,000 shoreline data from NOAA's site in Mapgen format, which can be imported with the v.in.mapgen module. ESRI Shapefiles of the latest version are available at http://www.ngdc.noaa.gov/mgg/shorelines/data/gshhg/latest/. The old 1.6 version is available at ftp://ftp.ihg.uni-duisburg.de/GIS/GISData/GSHHS/. Import Import with the GRASS6 add-on module v.in.gshhs OpenStreetMap See the OpenStreetMap wiki page. Administrative boundaries from OpenStreetMap For a convenient download in GeoJSON and SHAPE, see https://wambachers-osm.website/boundaries/ (using the amost invisible triangle, you can pop out details of a country down to admin level 😎 SALB Second Administrative Level Boundaries: "The SALB dataset is a global digital dataset consisting of digital maps and codes that can be downloaded on a country by country basis." http://www.who.int/whosis/database/gis/salb/salb_home.htm VMap0 1:1 million vector data. Formerly known as Digital Chart of the World see the two articles in GRASS Newsletter vol. 3 (June 2005) Check the Wikipedia page on VMAP, see the links at the bottom of that article to shapefile versions of VMAP0 and VMAP1. Those look like the versions that were, several years ago, on a NIMA (predecessor to NGA, and successor to the Defense Mapping Agency that managed the Digital Chart of the World and VMAP project) Website. Many GRASS users may prefer the shapefiles to the original Vector Product Format data. VMap0 data in ESRI shape format See also Global datasets list by T. Hengl (with dataset download) http://freegisdata.rtwilson.com/ The FreeGIS.org database: http://www.freegis.org/database/ http://finder.geocommons.com/ http://wiki.openstreetmap.org/wiki/Potential_Datasources http://www.geonames.org/data-sources.html Open Knowledge Foundation link collection Open Weather Map free weather data and forecast API suitable for any cartographic services like web and smartphones applications. Ideology is inspired by OpenStreetMap and Wikipedia that make information free and available for everybody. Metadata Catalogues Catalog Service for the Web (CSW) is an OGC standard for offering access to catalogues of geospatial information over the Internet (HTTP). CSW allow for discovering, browsing, and querying metadata about data, services, and similar resources. A list of Metadata Catalogues / CSW services from member states of the European Union can be found here: http://inspire-geoportal.ec.europa.eu/INSPIRERegistry/ And here: http://inspire-geoportal.ec.europa.eu/discovery/ one can search European Metadata Catalogues online. European datasets European datasets Global Risk Data Platform European Commission Opendata Portal: 5800+ datasets E-OBS This is the download page for the ENSEMBLES daily gridded observational dataset for precipitation, temperature and sea level pressure in Europe MARS @ JRC Temperature, vapour pressure, rainfall, relative humidity, cloud cover, solar radiation, wind speed. EFAS @ JRC is a High resolution pan-European dataset for hydrologic modelling. JRC Data Portal In this catalogue, you can find an inventory of data that produced by the JRC in accordance with the JRC data policy. The content is continuously updated and shall not be seen as a complete inventory of JRC data. Currently, the inventory describes only a small subset of JRC data. National datasets Australian Spatial Data Directory Australian Ecological Knowledge and Observation System Italian Geodata collection New Zealand data from Koordinates.com United States from NOAA/USGSs data portal (FIXME: link?) Greek Public Geodata (in Greek) Various datasets worldwide GEOSPATIAL DATA REPORT: Finding and Using GIS Data Edenext data portal: Land Cover, Transport networks, Elevation, Orthoimagery, Human health and safety, Species Distribution, Atmospheric Conditions and Meteorological Geographical Features, Training Program Presentations and Data, Utility and governmental services, Hydrography, Soil, Bio geographical regions, Population distribution and Demographics Global Data Explorer USGS: ASTER, SRTM, GTOPO etc landcover GRIPWEB’s Data & Informational Portal: hazard & risk SEDAC: Agriculture, Climate, Conservation, Framework Data, Governance, Hazards, Health, Infrastructure, Land Use, Marine and Coastal, Population, Poverty, Remote Sensing, Sustainability, Urban, Water Prevention Web: hazard & risk UNdata: UN database UNDP home page global climatic data cosortium for spatial information CGIAR-CSI GeoSpatial Toolkits Links to over 300 sites providing freely available geographic datasets Free Spatial Data Global Land Cover Characteristics Data Base Version 2.0 A Portal to High-Resolution Topography Data and Tools HadGHCND is a gridded daily temperature dataset based upon near-surface maximum (TX) and minimum (TN) temperature observations. WMS servers See WMS page River discharge data Global Runoff Data Centre Global River Discharge Database CSDMS

all software using for 3d mapping use waypoint method, and required your drone to fly autonomously related to the signal, that something you need to search for, maybe incompatibility with software, your phone or something others

Thanks, @Lurker @dexgeo great introduction for a newbie like me. I have been using the trial version of dronedeploy. Not sure why the drone signal is not stable, as I never let the drone fly autonomously so I am bit nervous when the drone is out of my control.

now we can ditch sandboxie, seems this feature will come in build 1903, finger crossed 😁 complete story: https://techcommunity.microsoft.com/t5/Windows-Kernel-Internals/Windows-Sandbox/ba-p/301849

Hello everyone, there is a Terrain Model I wasn't aware of, so I would like to share with you some links. For Global project, I usually GTOPO30 as a global DTM, but recently I discovered GMTED2010 (Global Multi-resolution Terrain Elevation Data 2010 ) a free avaIlable data model of the world at 250m, 500m and 1km resolution. GMTED2010 provides a new level of detail in global topographic data. Previously, the best available global DEM was GTOPO30 with a horizontal grid spacing of 30 arc-seconds. The GMTED2010 product suite contains seven new raster elevation products for each of the 30-, 15-, and 7.5-arc-second spatial resolutions and incorporates the current best available global elevation data. The new elevation products have been produced using the following aggregation methods: minimum elevation, maximum elevation, mean elevation, median elevation, standard deviation of elevation, systematic subsample, and breakline emphasis. Metadata have also been produced to identify the source and attributes of all the input elevation data used to derive the output products. Many of these products will be suitable for various regional features for hydrologic modeling, and geometric and radiometand continental applications, such as climate modeling, continental-scale land cover mapping, extraction of drainage ric correction of medium and coarse resolution satellite image data. The global aggregated vertical accuracy of GMTED2010 can be summarized in terms of the resolution and RMSE of the products with respect to a global set of control points (estimated global accuracy of 6 m RMSE) provided by NGA. At 30 arc-seconds, the GMTED2010 RMSE range is between 25 and 42 meters; at 15 arc-seconds, the RMSE range is between 29 and 32 meters; and at 7.5 arc-seconds, the RMSE range is between 26 and 30 meters. GMTED2010 is a major improvement in consistency and vertical accuracy over GTOPO30, which has a 66 m RMSE globally compared to the same NGA control points. In areas where new sources of higher resolution data were available, the GMTED2010 products are substantially better than the aggregated global statistics; however, large areas still exist, particularly above 60 degrees North latitude, that lack good elevation data. As new data become available, especially in areas that have poor coverage in the current model, it is hoped that new versions of GMTED2010 might generated and thus gradually improve the global model. Link to Document https://pubs.er.usgs.gov/publication/ofr20111073 Link to Archive for Download as a folder https://edcintl.cr.usgs.gov/downloads/sciweb1/shared/topo/downloads/GMTED/ as a viewer https://topotools.cr.usgs.gov/gmted_viewer/viewer.htm Additionals https://topotools.cr.usgs.gov/gmted_viewer/

maybe you can apply a statistical test to your both index there is a tool in IDRISI called CORRELATE you may try it

from their blog : this extension will provide Adobe Creative Cloud users with web maps so that they can download these maps directly into their Adobe design environment from ArcGIS Online. For example, with ArcGIS Maps for Adobe Creative Cloud, a user can sign into their ArcGIS Online account directly from Illustrator, find geographic content that fits their map requirements, and download this content into their Adobe Illustrator file. It is a seamless workflow since cartographers and designers can create a map with spatial data without having to leave Adobe Illustrator. The result is a vector map containing specifications that are set by the user. Designers and cartographers can then leverage Illustrator’s graphic editing power to design their map as they desire. There are a lot of mapping and GIS organizations and companies who have a GIS analyst or team and a designer or team of designers. These two groups, GIS analysts and graphic designers, often have different priorities during their map-making process, and this application provides a collaborative tool for analysts and designers to work together in their organization’s map-production workflow. GIS analysts can add their well-prepared and precisely analyzed map data to their organization’s ArcGIS Online account, and designers can easily access this data from Adobe Creative Cloud applications. Allowing designers the opportunity to create their maps in a graphic design tool that they are familiar with greatly improves the map editing workflow efficiency and results. Of course there is the third type of user – the person who is skilled at both GIS and graphic design. The ArcGIS for Adobe Creative Cloud application is a great tool for these users as well. A lot of static mapping is completed in Adobe Illustrator. In fact, as a cartographer. sites : http://www.esri.com/software/maps-for-adobecc