Hi Janet,
I believe you could do this using the `clump()` and `zonalGeog()` functions in
the fasterRaster package for R, which calls GRASS
(https://github.com/adamlilith/fasterRaster).
It's not clear from your description if the "patch" raster is the reclassed
NLCD raster or another raster, but I'll assume it is. So the "patch" raster
has groups of cells that had had same ID but are composed of cells that, before
reclassing, may have had a different class.
I would:
# Setup fasterRaster
devtools::install_github(' https://github.com/adamlilith/fasterRaster',
dependencies = TRUE)
library(fasterRaster)
faster('C:/Program Files/GRASS GIS 8.3') # install path for GRASS on your
system... should be the stand-alone version, not OSGeo
# Use fast() to create the rasters:
nlcd <- fast('C:/folder/nlcd_raster.tif')
nlcd_reclass <- fast('C:/folder/nlcd_reclassed_raster.tif')
names(nlcd) <- 'nlcd'
names(nlcd_reclass) <- 'nlcd_reclass'
# Use clump() to assign unique IDs to each reclassed clump cells. Clumps are
sets of adjacent cells with the same value (assuming argument minDiff = 0):
clumps <- clump(nlcd_reclass, minDiff = 0)
# Get unique clump IDs
clump_ids <- freq(clumps)
clump_ids <- clump_ids$value
# for saving results
results <- data.frame(clump_ids = clump_ids)
# Loop over each vegetation type of interest in the NLCD
# Assumes you have a vector veg_ids with integers indicating the veg types of
interest
for (veg_id in veg_ids) {
# create mask for just this vegetation type
fun <- paste0('= if(nlcd == ', veg_id, ', 1, null())`
veg_mask <- app(nlcd, fun = fun)
# raster of clumps but only with cells in clumps overlapping this veg type
veg_by_clump <- veg_mask * clumps
names(veg_by_clump) <- 'veg_by_clump'
# calculate area of cells in each masked clump
stats <- zonalGeog(veg_by_clump)
stats <- stats$veg_by_clump # output is a list of data frames, so just get
the df
# remember the value of "area" for each relevant patch
# I'm hazy on this part, as I always have to try match() to see how it
works, but you can figure it out.
areas <- stats$area[match(stats$value, results$clump_ids)]
results$NEW <- areas # add to results
names(results)[ncol(results)] <- paste0('veg_id_', veg_id) # rename column
}
To get % of each patch with each veg type, simply sum across the "area" rows
and divide each value by that sum.
I think you could do something similar with a) patches() to assign cell numbers
to patches, b) cycle over unique patch IDs and mask out anything that does not
have that ID, then c) count the number of cells in the veg-type-by-clump raster
using global(..., fun = 'sum', na.rm = TRUE). If you have a lot of patches x
veg-type combinations, it could take a while, though.
Adam
Center for Conservation & Sustainable Development
Missouri Botanical Garden
4344 Shaw Boulevard
Saint Louis, MO 63110 USA
+01 314-577-9473 ext. 76314
www.earthSkySea.org
-----Original Message-----
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Sent: Thursday, June 6, 2024 2:00 PM
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Today's Topics:
1. computing percent cover (Janet Choate)
2. Re: computing percent cover (Anna Petr??ov?)
3. Re: computing percent cover (Micha Silver)
----------------------------------------------------------------------
Message: 1
Date: Wed, 5 Jun 2024 17:09:45 -0700
From: Janet Choate <[email protected]>
To: grass list <[email protected]>
Subject: [GRASS-user] computing percent cover
Message-ID:
<CAEqw1VzTdprBBQoO=ywbjog910j2mvko75npu4yafrifn2d...@mail.gmail.com>
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Hi GRASS community,
I downloaded land cover data from NLCD and reclassed values into 6 categories
to generate a vegetation/landcover type raster (tree, shrub, grass, non-veg,
water, developed) that is composed of IDs (i.e. 11=tree, 5-shrub, etc...) I
also have a 90 meter patch raster.
Any given 90 meter patch may have more than one vegetation type ID occurring in
it.
I would like to generate percent coverage maps to find the percent that each
vegetation type occupies of each patch (i.e. patch 1 is composed of 60% tree,
30% shrub, 10%grass).
Is it possible to compute percent cover from a vegetation type ID map?
Any advice would be much appreciated, I have unsuccessfully tried to do this in
GRASS as well as R.
thank you,
Janet
--
Tague Team Lab Manager
1005 Bren Hall
UCSB, Santa Barbara, CA.
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Message: 2
Date: Wed, 5 Jun 2024 23:24:39 -0400
From: Anna Petr??ov? <[email protected]>
To: Janet Choate <[email protected]>
Cc: grass list <[email protected]>
Subject: Re: [GRASS-user] computing percent cover
Message-ID:
<cae0edeqjnbj+7trf8kwldojgqkugrrpth0fck352rn8r86o...@mail.gmail.com>
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Hi Janet,
you could run r.report:
r.report map=patches,landcover
But the output is not easily parseable. r.report internally runs r.stats:
r.stats -a input=patches,landcover separator=comma
you get output like (patch id, landcover id, area):
2,1,1205379.000000
1,5,938148.750000
3,1,904034.250000
2,3,557203.500000
1,1,512529.750000
1,4,361451.250000
so you need to postprocess it then.
On Wed, Jun 5, 2024 at 8:10?PM Janet Choate via grass-user <
[email protected]> wrote:
> Hi GRASS community,
> I downloaded land cover data from NLCD and reclassed values into 6
> categories to generate a vegetation/landcover type raster (tree,
> shrub, grass, non-veg, water, developed) that is composed of IDs (i.e.
> 11=tree, 5-shrub, etc...) I also have a 90 meter patch raster.
> Any given 90 meter patch may have more than one vegetation type ID
> occurring in it.
> I would like to generate percent coverage maps to find the percent
> that each vegetation type occupies of each patch (i.e. patch 1 is
> composed of 60% tree, 30% shrub, 10%grass).
> Is it possible to compute percent cover from a vegetation type ID map?
> Any advice would be much appreciated, I have unsuccessfully tried to
> do this in GRASS as well as R.
> thank you,
> Janet
>
> --
> Tague Team Lab Manager
> 1005 Bren Hall
> UCSB, Santa Barbara, CA.
> _______________________________________________
> grass-user mailing list
> [email protected]
> https://lists.osgeo.org/mailman/listinfo/grass-user
>
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Message: 3
Date: Thu, 6 Jun 2024 17:55:42 +0300
From: Micha Silver <[email protected]>
To: Janet Choate <[email protected]>, grass list
<[email protected]>
Subject: Re: [GRASS-user] computing percent cover
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