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# load required libraries
library(terra)
terra version 1.0.10
library(raster)
Loading required package: sp
library(rgdal)
rgdal: version: 1.5-23, (SVN revision 1121)
Geospatial Data Abstraction Library extensions to R successfully loaded
Loaded GDAL runtime: GDAL 3.0.4, released 2020/01/28
Path to GDAL shared files: /usr/share/gdal
GDAL binary built with GEOS: TRUE 
Loaded PROJ runtime: Rel. 6.3.1, February 10th, 2020, [PJ_VERSION: 631]
Path to PROJ shared files: /usr/share/proj
Linking to sp version:1.4-5
To mute warnings of possible GDAL/OSR exportToProj4() degradation,
use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.

Attaching package: 'rgdal'
The following object is masked from 'package:terra':

    project
library(tmap)
library(sf)
Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1

Introduction

Forest Carbon Emissions are GHG emissions that originate from forest cover loss and subsequent ABG and BEG biomass loss. Net forest carbon flux represents the net exchange of carbon between forests and the atmosphere. Forest act as both a Source and Sink for Carbon.

In order to calculate net carbon flux or simply carbon balance, we need to follow these steps: - Download raster r1 of the desired grid - Clip extent of raster to AOI polygon to create new raster r2 - Rasterize WDPA vector polygon v1 to the extent of r2 to get r3 - Load r2 (base raster layer) and r3 (raster zone layer) - Perform Zonal Statistics between r2 and r3

# load raster from file - here we have used raster data from Amazonas state of Brazil
zone_ras <- raster("/home/rstudio/shared/Om/test/Zone_Stats.tif")
crs(zone_ras) <- "+proj=utm +zone=48 +datum=WGS84"
zone_ras
class      : RasterLayer 
dimensions : 1832, 4900, 8976800  (nrow, ncol, ncell)
resolution : 0.00025, 0.00025  (x, y)
extent     : -63.3025, -62.0775, -9.13275, -8.67475  (xmin, xmax, ymin, ymax)
crs        : +proj=utm +zone=48 +datum=WGS84 +units=m +no_defs 
source     : /home/rstudio/shared/Om/test/Zone_Stats.tif 
names      : Zone_Stats 
# load your vector from file - this vector contains wdpa polygons with ID 33608 & 33714
amz <- vect(read_sf("/home/rstudio/shared/Om/test/Amazonas.shp"))
crs(amz) <- "+proj=utm +zone=48 +datum=WGS84"
plot(amz)

# create raster `raster_amz` to match the extent from raster files
raster_amz <- rast(ncol=4900, nrow=1832, xmin=-63.3025, xmax=-62.0775, ymin=-9.13275, ymax=-8.67475)
# set the crs to UTM Zone 48 - WGS84
crs(raster_amz) <- "+proj=utm +zone=48 +datum=WGS84"
# check data
raster_amz
class       : SpatRaster 
dimensions  : 1832, 4900, 1  (nrow, ncol, nlyr)
resolution  : 0.00025, 0.00025  (x, y)
extent      : -63.3025, -62.0775, -9.13275, -8.67475  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=utm +zone=48 +datum=WGS84 +units=m +no_defs 
# since the extent of both rasters match now we can perform rasterization
x <- rasterize(amz, raster_amz, amz$WDPAID, length, background=NA, update=FALSE,   touches=is.lines(amz), cover=FALSE, filename="/home/rstudio/shared/Om/test/amaz_raster.tif")
# check data
x
class       : SpatRaster 
dimensions  : 1832, 4900, 1  (nrow, ncol, nlyr)
resolution  : 0.00025, 0.00025  (x, y)
extent      : -63.3025, -62.0775, -9.13275, -8.67475  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=utm +zone=48 +datum=WGS84 +units=m +no_defs 
source      : amaz_raster.tif 
name        : value 
min value   : 33608 
max value   : 33714 
# plot rasterized map
plot(x)

# load rasterized data 
x1 <- raster("/home/rstudio/shared/Om/test/amaz_raster.tif")
#check data
x1
class      : RasterLayer 
dimensions : 1832, 4900, 8976800  (nrow, ncol, ncell)
resolution : 0.00025, 0.00025  (x, y)
extent     : -63.3025, -62.0775, -9.13275, -8.67475  (xmin, xmax, ymin, ymax)
crs        : +proj=utm +zone=48 +datum=WGS84 +units=m +no_defs 
source     : /home/rstudio/shared/Om/test/amaz_raster.tif 
names      : amaz_raster 
values     : 33608, 33714  (min, max)
# perform zonal statistics
statsZone <- zonal(zone_ras, x1, fun='sum', na.rm=T)
statsZone
      zone       sum
[1,] 33608 -28333590
[2,] 33714  38159593
[3,]   NaN         0

sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04 LTS

Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] sf_0.9-7     tmap_3.3     rgdal_1.5-23 raster_3.4-5 sp_1.4-5    
[6] terra_1.0-10

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.6         lattice_0.20-41    png_0.1-7          class_7.3-17      
 [5] rprojroot_2.0.2    digest_0.6.27      utf8_1.1.4         R6_2.5.0          
 [9] evaluate_0.14      e1071_1.7-4        highr_0.8          pillar_1.5.0      
[13] rlang_0.4.10       jquerylib_0.1.3    rmarkdown_2.7      stringr_1.4.0     
[17] htmlwidgets_1.5.3  compiler_4.0.3     httpuv_1.5.5       xfun_0.21         
[21] pkgconfig_2.0.3    tmaptools_3.1-1    base64enc_0.1-3    htmltools_0.5.1.1 
[25] tidyselect_1.1.0   tibble_3.1.0       workflowr_1.6.2    codetools_0.2-16  
[29] XML_3.99-0.5       fansi_0.4.2        viridisLite_0.3.0  crayon_1.4.1      
[33] dplyr_1.0.2        later_1.1.0.1      grid_4.0.3         jsonlite_1.7.2    
[37] lwgeom_0.2-5       lifecycle_1.0.0    DBI_1.1.1          git2r_0.28.0      
[41] magrittr_2.0.1     units_0.7-0        KernSmooth_2.23-17 stringi_1.5.3     
[45] fs_1.5.0           promises_1.2.0.1   leafsync_0.1.0     leaflet_2.0.4.1   
[49] bslib_0.2.4        ellipsis_0.3.1     generics_0.1.0     vctrs_0.3.6       
[53] RColorBrewer_1.1-2 tools_4.0.3        dichromat_2.0-0    leafem_0.1.3      
[57] glue_1.4.2         purrr_0.3.4        crosstalk_1.1.1    abind_1.4-5       
[61] parallel_4.0.3     yaml_2.2.1         stars_0.5-1        classInt_0.4-3    
[65] knitr_1.31         sass_0.3.1