Ok, sorry about the incomplete post. I reprojected from a latlong location to a cylindrical equal area projection, using the default nearest neighbor resampling method.
original location: >GRASS 7.1.svn (base_maps):~ > g.region -p raster=gdem_etopo1_ice projection: 3 (Latitude-Longitude) zone: 0 datum: wgs84 ellipsoid: wgs84 north: 90N south: 90S west: 180W east: 180E nsres: 0:01 ewres: 0:01 rows: 10800 cols: 21600 cells: 233280000 Equal area projection: >r.proj location=latlong mapset=base_maps input=gdem_etopo1_ice -g Input map <gdem_etopo1_ice@base_maps> in location <latlong>: n=6363885.33192604 s=-6363885.33192604 w=-20037508.34278924 e=20037508.34278924 rows=10800 cols=21600 >GRASS 7.1.svn (eqarea):~ > g.region -p n=6363885.33192604 s=-6363885.33192604 w=-20037508.34278924 e=20037508.34278924 rows=10800 cols=21600 projection: 99 (Equal Area Cylindrical) zone: 0 datum: wgs84 ellipsoid: wgs84 north: 6363885.33192604 south: -6363885.33192604 west: -20037508.34278924 east: 20037508.34278924 nsres: 1178.49728369 ewres: 1855.32484655 rows: 10800 cols: 21600 cells: 233280000 GRASS 7.1.svn (eqarea):~ > r.proj location=latlong mapset=base_maps input=gdem_etopo1_ice Here are the outputs of r.univar, for both locations: Latlong GRASS 7.1.svn (base_maps):~ > r.univar map=gdem_etopo1_ice -ge percentile=100 n=58320000 null_cells=0 cells=58320000 min=-10753 max=8333 range=19086 mean=-1892.08140334362 mean_of_abs=2644.85220128601 stddev=2650.12442373911 variance=7023159.46129855 coeff_var=-140.063974998956 sum=-110346187443 first_quartile=-4286 median=-2456 third_quartile=214 percentile_100=8333 Equal-area GRASS 7.1.svn (eqarea):~ > r.univar map=gdem_etopo1_ice -ge percentile=100 n=233280000 null_cells=0 cells=233280000 min=-10803 max=8333 range=19136 mean=-2382.28934158093 mean_of_abs=2845.10169015775 stddev=2508.93105538271 variance=6294735.0406638 coeff_var=-105.315966939504 sum=-555740457604 first_quartile=-4544 median=-3285 third_quartile=93 percentile_100=8333 I also tested in python, flattening the raster to an array. The results are the same as r.univar (in both cases). best Carlos On Mon, Nov 16, 2015 at 5:32 PM, Markus Neteler <[email protected]> wrote: > On Mon, Nov 16, 2015 at 5:34 PM, Carlos Grohmann > <[email protected]> wrote: > > Hi all. > > > > I'm analyzing some global-scale DEMs, like ETOPO1/2, SRTM30_PLUS, etc. > > > > I'm getting the statistics for the whole dataset with r.univar, but > today I > > noticed that the results differ if I use different projections. (GRASS > 7.1) > > > Please post also *how* you reprojected (resampling method etc) > > Markus > -- Prof. Carlos Henrique Grohmann Institute of Energy and Environment - Univ. of São Paulo, Brazil - Digital Terrain Analysis | GIS | Remote Sensing - http://carlosgrohmann.com http://orcid.org/0000-0001-5073-5572 ________________ Can’t stop the signal.
_______________________________________________ grass-user mailing list [email protected] http://lists.osgeo.org/mailman/listinfo/grass-user
