[gdal-dev] how to aggregate an image to a lower resolution using gdal (gdalwarp?)

Tue Dec 3 12:37:14 PST 2013

I added a ticket http://trac.osgeo.org/gdal/ticket/5311

Etienne


2013/12/2 Etienne Tourigny <etourigny.dev at gmail.com>

> The "average" resampling mode of gdalwarp does "average resampling,
> computes the average of all non-NODATA contributing pixels".
>
> It was meant to compute the average of all the pixels in the "aggregation
> window". However, it may have issues in the corners.
>
> I am the author of the average and mode algorithms, and I basically copied
> the aggregation logic from the other algorithms (i.e. which pixels are
> selected for the aggregation), so there may be something wrong in the logic.
>
> Certainly, looking at this simple example shows that something is wrong.
>
> I tested average and mode resampling with a fairly large dataset and did
> not find these problems.
>
> Can you create a new bug in the tracker and upload the scripts and data
> used?  I don't have much (any) time to work on this but would be happy to
> apply a working patch.
>
> Cheers, Etienne
>
>
> On Mon, Dec 2, 2013 at 12:22 PM, Etienne B. Racine <etiennebr at gmail.com>wrote:
>
>> I've tried to dig this a bit. I couldn't understand the logic behind gdal
>> aggregation (or downsampling). I've simplified your example using a smaller
>> raster and deterministic values. Maybe someone could enlighten us by
>> looking at the aggregation values ? Note that the lower right cells values
>> were identical in all dimensions I've tried (about 4 - 10).
>>
>> The example is run on GDAL 1.11dev, released 2013/04/13
>>
>> Thanks,
>> Etienne
>>
>> # in R:
>>
>> require(raster)
>> filei <- '10by10.tif'
>> fileo <- '5by5.tif'
>>
>> dm = 4
>> r <- raster(matrix(1:(dm^2), dm, dm))
>>
>>
>> writeRaster(r, filename=filei, overwrite = TRUE)
>>
>> ## aggregate using R aggregate function using the mean
>> r1 <- aggregate(r, fact=2, fun = mean, na.rm = TRUE)
>>
>> file.remove(fileo)
>> cmd <- paste("gdalwarp -r average -tr", paste(res(r1), collapse = " "),
>> filei, fileo)
>> system(cmd)
>> r2 <- raster(fileo)
>>
>> >as.matrix(r)
>>      [,1] [,2] [,3] [,4]
>> [1,]    1    5    9   13
>> [2,]    2    6   10   14
>> [3,]    3    7   11   15
>> [4,]    4    8   12   16
>> > lapply(list(r1, r2, r2-r1), as.matrix)
>> [[1]]
>>      [,1] [,2]
>> [1,]  3.5 11.5
>> [2,]  5.5 13.5
>>
>> [[2]]
>>      [,1] [,2]
>> [1,]  6.0 12.0
>> [2,]  7.5 13.5
>>
>> [[3]]
>>      [,1] [,2]
>> [1,]  2.5  0.5
>> [2,]  2.0  0.0
>>
>>
>> 2013/8/27 Verbesselt, Jan <jan.verbesselt at wur.nl>
>>
>> Hi all,
>>>
>>> I have been testing gdalwarp to aggregate (using -r average) an image.
>>> In order to better understand what is happening I have created a
>>> reproducible example within an R environment and compared it with the
>>> aggregate function of the R raster package (see below). There are some
>>> differences between the gdalwarp raster (r2) and the aggregated raster
>>> (r1).
>>>
>>> How is the gdalwarp -r average working? Which pixels are selected for
>>> averaging (e.g.the corner pixels, center pixels, or all within the
>>> aggregation window)?
>>>
>>> If there is a gdal aggregate option to average pixels comparable to the
>>> aggregate function in the R raster package, it would be great as that
>>> would potentially faster, and you could also reproject and aggregate at
>>> once.
>>>
>>> Thanks!
>>> Jan
>>>
>>> http://bfast.r-forge.r-project.org
>>> http://goo.gl/1mBC5F
>>>
>>>
>>> ## R script
>>> require(raster)
>>> filei <- '10by10.tif'
>>> fileo <- '5by5.tif'
>>>
>>> set.seed(123)
>>> r <- raster(ncols=36, nrows=18)
>>> r <- setValues(r, round(runif(ncell(r))*10))
>>> r
>>> plot(r)
>>> writeRaster(r, filename=filei, overwrite = TRUE)
>>>
>>> ## aggregate using R aggregate function using the mean
>>> r1 <- aggregate(r, fact=2, fun = mean, na.rm = TRUE)
>>>
>>> cmd <- paste("gdalwarp -r average -tr 20 20 -te -180 -90 180 90 ",
>>>              filei, " ", fileo, sep = "")
>>> system(cmd)
>>> r2 <- raster(fileo)
>>>
>>> ## compare
>>> plot(r1)
>>> plot(r2)
>>> r1
>>> r2
>>> getValues(r1)
>>> getValues(r2)
>>>
>>> ##
>>> plot(r1-r2)
>>> sessionInfo()
>>>
>>> R> sessionInfo()
>>> R version 3.0.1 (2013-05-16)
>>> Platform: x86_64-pc-linux-gnu (64-bit)
>>>
>>> locale:
>>>  [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C         LC_TIME=C
>>> LC_COLLATE=C         LC_MONETARY=C
>>>  [6] LC_MESSAGES=C        LC_PAPER=C           LC_NAME=C
>>> LC_ADDRESS=C         LC_TELEPHONE=C
>>> [11] LC_MEASUREMENT=C     LC_IDENTIFICATION=C
>>>
>>> attached base packages:
>>> [1] stats     graphics  grDevices utils     datasets  methods   base
>>>
>>> other attached packages:
>>> [1] rgdal_0.8-10  raster_2.1-49 sp_1.0-11
>>>
>>> loaded via a namespace (and not attached):
>>> [1] grid_3.0.1      lattice_0.20-23 tools_3.0.1
>>>
>>>
>>> rgdal: version: 0.8-10, (SVN revision 478)
>>> Geospatial Data Abstraction Library extensions to R successfully loaded
>>> Loaded GDAL runtime: GDAL 1.10.0, released 2013/04/24
>>> Path to GDAL shared files: /usr/share/gdal/1.10
>>> Loaded PROJ.4 runtime: Rel. 4.8.0, 6 March 2012, [PJ_VERSION: 480]
>>> Path to PROJ.4 shared files: (autodetected)
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> _______________________________________________
>>> gdal-dev mailing list
>>> gdal-dev at lists.osgeo.org
>>> http://lists.osgeo.org/mailman/listinfo/gdal-dev
>>>
>>
>>
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