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Rmd 7f1106c Johannes Schielein 2021-03-12 minor changes to carbon flux scheme
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html 611bd97 Ohm-Np 2021-03-05 create net carbon flux rmd 
knitr::opts_knit$set(root.dir = '../')
# load required libraries
library("terra")
library("sf")
library("wdpar")
library("dplyr")

Introduction

Forest Carbon Emissions are greenhouse gas emissions that originate from forest cover loss and subsequent Above Ground Biomass and Below Ground Biomass loss. Forest cover loss might be the result of either anthropogenic deforestation or disasters such as forest fires and same goes for biomass loss too. Forest acts as both a Source and Sink for Carbon. Sink, when the forest absorbs more carbon than it releases. Source, when forest releases more carbon than it absorbs. To determine whether forests in defined zone acts as source or sink of carbon, net forest carbon flux is calculated which represents the net exchange of carbon between forests and the atmosphere.

The purpose of this analysis is to compute net forest carbon flux for the desired wdpa polygons. This is achieved through zonal statistics operation. A zonal statistics operation is one that calculates statistics on cell values of a raster (a value raster) within the zones defined by another dataset [ArcGIS definition].

To calculate zonal statistics for net forest carbon flux that changed between 2001 to 2019, following processing routine is followed in this analysis:

  • Download raster data of the desired grid using function get_net_carbon_flux
  • Fetch country level wdpa data from library wdpar
  • Select desired wdpa polygon from wdpa data
  • Clean the data
  • Clip the carbon_flux raster by the selected polygon to its extent and mask layer
  • Rasterize the selected polygon area
  • Perform zonal statistics

Datasource and Metadata Information

  • Dataset: Net Forest Carbon Flux - Global Forest Watch (Harris et al. (2021))
  • Geographical Coverage: Global
  • Spatial resolution: 30 meter
  • Temporal Coverage: 2001-2019
  • Temporal resolution: Annual Updates
  • Unit: megagrams CO2 emissions/ha
  • Data downloaded: 5th March, 2021
  • Metadata Link
  • Download Link

Raster data preparation

At first you should link to the source functions to go through this routine. The script carbon-flux.R contains the function to download the raster file of the desired grid.

source("code/carbon-flux.R")

How to use the function?

  • call the function get_net_carbon_flux by passing (lat, lon) arguments as string for eg. (“10S”, “050W”) or (“10N”, “020E”)
  • check the coordinates of your desired area and find out the grid under which interval of latitude and longitude does it fall
  • or simply visit the GFW Dataset Portal to verify chosen grid coordinates
  • Note: If in case you choose to compute zonal statistics for larger polygon level or for many polygon levels, then one raster data might not be enough for your computation. Then you must download multiple raster files so as to cover the polygon extent and merge them later simply using merge function.
options(timeout=180) # sets timeout for downloads to 180seconds
# call the function to download raster for a part of the country Brazil where we want to compute zonal statistics
# Note: raster value is 'Mg_CO2_ha-1'
myRaster <- 
  get_net_carbon_flux("00N", "060W")

After successfully running this function, you can see that the raster file is downloaded and stored in the temporary directory of R and is loaded to the working directory as the layer name myRaster as class object ‘SpatRaster’.

# view raster metadata
myRaster
class       : SpatRaster 
dimensions  : 40000, 40000, 1  (nrow, ncol, nlyr)
resolution  : 0.00025, 0.00025  (x, y)
extent      : -60, -50, -10, 0  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs 
source      : carbon_flux_00N_060W.tif 
name        : carbon_flux_00N_060W 
# plot the raster
plot(myRaster)

Version Author Date
7f1106c Johannes Schielein 2021-03-12
0640985 Ohm-Np 2021-03-11

Polygon data preparation

Since we already prepared raster data for our analysis. Now, we will try to get the country level polygon data from wdpar package. wdpar is a library to interface to the World Database on Protected Areas (WDPA). The library is used to monitor the performance of existing PAs and determine priority areas for the establishment of new PAs. We will use Brazil - for other countries of your choice, simply provide the country name or the ISO name e.g. Gy for Guyana, COL for Colombia

# fetch the raw data from wdpar of country
br_raw_pa_data <- wdpa_fetch("Brazil")

Since there are more than 3000 enlisted protected areas in Brazil, we want to compute zonal statistics only for the polygon data of: - Reserva Biologica Do Rio Trombetas - wdpaid 43, - Reserva Extrativista Rio Cajari - wdpaid 31776, and - Estacao Ecologica Do Jari - wdpaid 4891

For this, we have to subset the country level polygon data to the pa level.

# subset three wdpa polygons by their wdpa ids
bra<-
  br_raw_pa_data%>%
  filter(WDPAID %in% c(43,4891,31776))

The next immediate step would be to clean the fetched raw data. Cleaning is done so as to: - exclude protected areas that are not yet implemented - exclude protected areas with limited conservation value - replace missing data codes (e.g. “0”) with missing data values (i.e. NA) - replace protected areas represented as points with circular protected areas that correspond to their reported extent - repair any topological issues with the geometries - erase overlapping areas

# clean the data
brac <- wdpa_clean(bra)
# SpatialPolygonsDataFrame for sf compatibility
brac_sf <- st_as_sf(brac)

Crop the Carbon Flux Raster

As we completed raster and vector data preparation, the next step would be to clip the raster layer by the selected shapefile polygon both by its extent and mask layer. If we clip by extent, it does clipping the raster by its bounding box. However, mask layer clipping returns the raster to defined vector polygon layer.

# extent preparation
myExtent <- st_transform(brac_sf, "+proj=longlat +datum=WGS84 +no_defs")
# SpatVector for `terra` compatibility
myExtent_v <- vect(myExtent)
# plot the extent that will be used to crop the raster layer
plot(myExtent_v)

Version Author Date
7f1106c Johannes Schielein 2021-03-12
0640985 Ohm-Np 2021-03-11 
# crop raster using polygon extent
myCrop <- terra::crop(myRaster, myExtent_v)
# plot the data - shows the raster after getting cropped by the extent of polygon
plot(myCrop)

Version Author Date
7f1106c Johannes Schielein 2021-03-12
0640985 Ohm-Np 2021-03-11 
# crop raster using polygon mask
myMask <- terra::mask(myCrop, myExtent_v)
# plot the data - shows the raster after getting cropped by the polygon mask
plot(myMask)

Version Author Date
7f1106c Johannes Schielein 2021-03-12
0640985 Ohm-Np 2021-03-11

Rasterize the polygon layer

To compute the zonal statistics, it is necessary to rasterize the polygon layer. Doing so, values are transferred from the spatial objects to raster cells. We need to pass the extent layer and the mask layer to the rasterize function.

# rasterize
r <- terra::rasterize(myExtent_v, myMask, myExtent_v$WDPAID, background=NA, update=FALSE, touches=is.lines(myExtent_v), cover=FALSE)

Compute zonal statistics

A zonal statistics operation is one that calculates statistics on cell values of a raster (a value raster) within the zones defined by another dataset [ArcGIS definition].

# zonal stats
zstats <- zonal(myMask, r, fun='sum', na.rm=T)
# create dataframe
df.zstats <- data.frame(WDPAID=NA,
                        Net_Forest_Carbon_Flux=NA)
# rename column to match with dataframe
colnames(zstats) <- colnames(df.zstats)
# view the data
rbind(df.zstats,zstats)[-1,]
  WDPAID Net_Forest_Carbon_Flux
2     43             -192709050
3   4891             -123947679
4  31776             -136209414

By mathematical definition, net forest carbon flux is the difference between average annual gross emissions and average annual gross removals. Hence, positive result denotes forests as net sources of carbon and negative results denotes forests as net sinks of carbon.

For all the three polygons we considered, we got the negative result. That means forests in these three Protected Areas act as the net sinks of carbon.

References

[1] Harris, N.L., D.A. Gibbs, A. Baccini, R.A. Birdsey, S. de Bruin, M. Farina, L. Fatoyinbo, M.C. Hansen, M. Herold, R.A. Houghton, P.V. Potapov, D. Requena Suarez, R.M. Roman-Cuesta, S.S. Saatchi, C.M. Slay, S.A. Turubanova, A. Tyukavina. 2021. Global maps of twenty-first century forest carbon fluxes. Nature Climate Change. https://doi.org/10.1038/s41558-020-00976-6


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] dplyr_1.0.2  wdpar_1.0.6  sf_0.9-7     terra_1.0-10

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0   xfun_0.21          bslib_0.2.4        purrr_0.3.4       
 [5] lattice_0.20-41    vctrs_0.3.6        generics_0.1.0     htmltools_0.5.1.1 
 [9] yaml_2.2.1         utf8_1.1.4         rlang_0.4.10       e1071_1.7-4       
[13] jquerylib_0.1.3    later_1.1.0.1      pillar_1.5.0       glue_1.4.2        
[17] DBI_1.1.1          rappdirs_0.3.3     sp_1.4-5           lifecycle_1.0.0   
[21] stringr_1.4.0      workflowr_1.6.2    raster_3.4-5       codetools_0.2-16  
[25] evaluate_0.14      knitr_1.31         httpuv_1.5.5       curl_4.3          
[29] class_7.3-17       fansi_0.4.2        highr_0.8          Rcpp_1.0.6        
[33] KernSmooth_2.23-17 promises_1.2.0.1   classInt_0.4-3     lwgeom_0.2-5      
[37] jsonlite_1.7.2     countrycode_1.2.0  fs_1.5.0           digest_0.6.27     
[41] stringi_1.5.3      grid_4.0.3         rprojroot_2.0.2    tools_4.0.3       
[45] magrittr_2.0.1     sass_0.3.1         tibble_3.1.0       crayon_1.4.1      
[49] whisker_0.4        pkgconfig_2.0.3    ellipsis_0.3.1     httr_1.4.2        
[53] assertthat_0.2.1   rmarkdown_2.7      R6_2.5.0           units_0.7-0       
[57] git2r_0.28.0       compiler_4.0.3