Dear Carlos, I’m not an expert for projections. However, on Lat/Long WGS84 the actual area of cells decline from the equator towards the poles. Thus, I would expect that cell values near the poles have “more weight” using Lat/Long WGS84 than using an equal area projection.
Near the poles I don’t understand how the values for extent and resolution should be correct (equal area), except there is a huge distortion (very likely for a cylindrical projection)! Are there really 21600 cols with a nsres = 1178 m at the north and south pole. I would call this a huge distortion. As a test, I would calculate the statistics for a smaller area centered at the equator. I would expect that the results are very similar comparing the lat/long and the reprojected dataset. Regards Jörg Von: grass-user [mailto:[email protected]] Im Auftrag von Carlos Grohmann Gesendet: Montag, 16. November 2015 23:32 Cc: GRASS user list Betreff: Re: [GRASS-user] r.univar: different results with different projections? Hello Cesar That was weird, so I tested it again. The number of cells is the same for both projections, but the values differ. This must be related to reprojecting. To me, they shouldn't de different, since a nearest neighbor should preserve the original values. I'm not really comfortable with this, as I'm not sure I can trust the stats after projecting. best Carlos GRASS 7.1.svn (latlong):~ > g.region raster=gdem_etopo1_ice -pa 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 GRASS 7.1.svn (latlong):~ > r.univar map=gdem_etopo1_ice -ge percentile=100 n=233280000 null_cells=0 cells=233280000 min=-10803 max=8333 range=19136 mean=-1892.40422534294 mean_of_abs=2644.91906490912 stddev=2649.98339302808 variance=7022411.98332463 coeff_var=-140.032629262802 sum=-441460057688 first_quartile=-4286 median=-2457 third_quartile=214 percentile_100=8333 GRASS 7.1.svn (eqarea):~ > g.region -p 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.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 On Mon, Nov 16, 2015 at 7:43 PM, César Augusto Ramírez Franco <[email protected]<mailto:[email protected]>> wrote: Carlos, 2015-11-16 14:47 GMT-05:00 Carlos Grohmann <[email protected]<mailto:[email protected]>>: GRASS 7.1.svn (base_maps):~ > g.region -p raster=gdem_etopo1_ice cells: 233280000 GRASS 7.1.svn (base_maps):~ > r.univar map=gdem_etopo1_ice -ge percentile=100 cells=58320000 GRASS 7.1.svn (eqarea):~ > r.univar map=gdem_etopo1_ice -ge percentile=100 cells=233280000 Notice how the number of pixels differs, that's the reason the statistics are not the same, I don't get why the region has a different number of pixels than the raster itself in the original latlong projection... I think that's the root of the issue -- César Augusto Ramírez Franco Laboratorio de Sistemas Complejos Naturales Escuela de Geociencias - Facultad de Ciencias Universidad Nacional de Colombia - Sede Medellín Teléfono: (57-4) 430 9369 - 300 459 6085 http://labscn-unalmed.github.io/ -- Prof. Carlos Henrique Grohmann Institute of Energy and Environment - Univ. of São Paulo, Brazil - Digital Terrain Analysis | GIS | Remote Sensing - http://carlosgrohmann.com<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
