Hello, another question, regarding *i.nightlights.intercalibration, *can I run this code as python package/lbrary loading it from Spyder or Jupiter Notebook instead of using GRASS interface, if so how is a convenient way to install *i.nightlights.intercalibration *in python using Spyder? Thanks a lot. Gabriel
On Sat, Aug 17, 2019 at 4:54 PM Gabriel Cotlier <gabikl...@gmail.com> wrote: > Dear Nikos. > After a long time I'm trying to reproduce a routine I have for doing > intercallibratrion of DMSP 1992-2012 but for some reason It doesn't work to > me. I think is because the problem between the region of the layers 30 arc > sec should resolution be from 0.008333333300000 to 0.008333333333333, i.e. > exactly 30 arc-seconds? and the computational region be the same ? I got > stuck on how to set it to work... from the side of the region setting. > However in addition my routing also has a for loop which does not work ok > as well. > I would appreciate a lot of you can give it a look and tell me how to make > it work... > Thanks a lot in advance > Kind regards, > Gabriel > > #####----------------------------------------------------------------------------------------- > # complete routine for intercalliration of DSMP/OLS light stable product > > import grass.script as gscript > import os > import os,glob > > # get working directory > print os.getcwd() > > # change working directory where raster files are > os.chdir('C:\\Users\\Gabriel\\Documents\\grassdata\\lights') > > # see files in directory > ls > > # import all raster files to grass --- here is a kind of problem...??? > for tif_file in glob.glob("*.tif"): > new_rast = os.path.splitext(tif_file)[0] > grass.run_command("r.in.gdal", flags="a", input=tif_file, > output=new_rast) > > # get info of one of the imported raster > r.info map=F121996 > > # run intercalliration algorithm > i.nightlights.intercalibration > image=F101992,F101993,F101994,F121994,F121995,F121996,F121997,F121998,F121999,F141997,F141998,F141999,F142000,F142001,F142002,F142003,F152000,F152001,F152002,F152003,F152004,F152005,F152006,F152007,F162004,F162005,F162006,F162007,F162008,F162009,F182010,F182011,F182012,F182013 > suffix=c model=elvidge2014 -t > > # correct general region adjust to raster file --- here the region is > exactly 30 arc for the raster as I could see.... > g.region raster=F121996 > > # cerate a list of rasters in the mapset > # rastlist=grass.read_command("g.list",type="rast").split() > rasters = grass.read_command('g.list', type='raster').splitlines() > > # change working directory > os.chdir('C:\\Users\\Gabriel\\Desktop\\out') > > # save rasters in mapset to file > for raster in rasters: > grass.run_command('r.out.gdal', input=raster, output=raster + '.tiff', > format='GTiff') > > On Wed, Aug 22, 2018 at 10:06 AM Gabriel Cotlier <gabikl...@gmail.com> > wrote: > >> Dear Nikos, >> >> Thanks a lot for your answer and the orientation. >> The information and the link are very useful. >> Kind regards, >> Gabriel >> >> >> On Wed, Aug 22, 2018 at 5:19 AM Nikos Alexandris <n...@nikosalexandris.net> >> wrote: >> >>> * Gabriel Cotlier <gabikl...@gmail.com> [2018-08-21 12:00:24 -0300]: >>> >>> >Dear Nikos and GRASS users, >>> > >>> >I would like to ask if nonetheless the effect due to "stray light" the >>> >*i.landsat8.swlst* code for split window is still applicable to Landsat >>> 8 >>> >data and whether these error is specially visible on water bodies? and >>> >whether band 10 is better than band 11 in terms of correction processing >>> >for Level -1 data products? >>> > >>> >Thanks a lot. >>> > >>> >Kind regards, >>> >Gabriel >>> >>> Dear Gabriel, >>> >>> for details and references, refer to >>> >>> >>> https://landsat.gsfc.nasa.gov/landsat-8-thermal-data-ghost-free-after-stray-light-exorcism/ >>> >>> Make sure you use the newest Level-1 Collection 1 Landsat 8 products. >>> >>> I use `i.landsat8.swlst` and plan to improve it further. >>> >>> However, whether to prefer a Split-Window based approach, or another >>> Single-Channel one, depends on what you want to do. Think of spatial >>> extent and coverage of various land (cover) types, temporal extent >>> and more. >>> >>> Thermal remote sensing is hard(er) also because it's hard to get >>> ground-truth data sets so as to validate LST estimations. >>> >>> Nikos >>> >>
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