Last updated: 2021-06-30

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Knit directory: mapme.protectedareas/

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File Version Author Date Message
Rmd 6637f1d Johannes Schielein 2021-03-16 updates to the the wwf routine
html 6637f1d Johannes Schielein 2021-03-16 updates to the the wwf routine
Rmd 45f3a67 Ohm-Np 2021-03-16 add biomes name column & slight modifications
html 45f3a67 Ohm-Np 2021-03-16 add biomes name column & slight modifications
Rmd 0a7f686 Johannes Schielein 2021-03-15 updated teow workflow
html 0a7f686 Johannes Schielein 2021-03-15 updated teow workflow
Rmd c93be45 Johannes Schielein 2021-03-15 update to teow routine
Rmd b548133 Ohm-Np 2021-03-15 create wwf teow rmd
html b548133 Ohm-Np 2021-03-15 create wwf teow rmd 
# load required libraries
library("sf")
library("terra")
library("wdpar")
library("dplyr")
library("rmarkdown") # only used for rendering tables  for this website

starttime<-Sys.time() # mark the starttime of this routine to calculate processing time at the end

Introduction

Terrestrial Ecoregions of the World (TEOW) is a biogeographic regionalization of the Earth’s terrestrial biodiversity. The biogeographic units are ecoregions, which are defined as relatively large units of land or inland water containing a distinct assemblage of natural communities sharing a large majority of species, dynamics, and environmental conditions. There are 867 terrestrial ecoregions, classified into 14 different terrestrial biomes such as forests, grasslands, or deserts. Ecoregions represent the original distribution of distinct assemblages of species and communities. The biome is more concrete aggregation of the organisms classified according to their adaptation to the environment in which they exist.
Visit Link for more information on TEOW from WWF.

Datasource and Metadata Information

  • Dataset: Terrestrial Ecoregions of the World - World Wildlife Fund [1]
  • Geographical Coverage: Global
  • Temporal Coverage: 2001
  • Temporal Resolution: Cross-sectional
  • Unit: hectare
  • Data downloaded: 15th March, 2021
  • Metadata Link
  • Download Link

Here we are going to carry out an analysis that intersects WDPA polygons with the Ecoregions to calculate the area of different ecoregions and biomes within supported PAs.

Processing Workflow

To carry out this analysis, we will follow this processing routine:

Download and prepare WDPA polygons

First of all, 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 ISO3 name e.g. GY for Guyana, COL for Colombia

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

Since there are more than 3000 enlisted protected areas in Brazil, we will demonstrate this routine using only three wdpa polygons: - 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 PAs level.

# subset three wdpa polygons by their wdpa ids
br_wdpa_subset<-
  br_wdpa_raw%>%
  filter(WDPAID %in% c(43,4891,31776))
# plot the selected polygons
plot(br_wdpa_subset[1])

Prepare TEOW polygons

Since, we prepared WDPA polygon data for our analysis, we now load the TEOW global geopackage layer from archived file. Here we are using the validated geopackage from TEOW. The function st_make_valid removes the invalid geometry within the polygons, which was already applied to our teow geopackage, so no need to use this function in this analysis.

# load TEOW global polygons as spatVector
teow <- 
  vect("../../datalake/mapme.protectedareas/input/teow/Terrestrial_Ecoregions_World_validated.gpkg")
# plot the teow polygon
plot(teow)

Intersect TEOW and WDPA Polygon layer

To analyse how much of wdpa area is within which part of the ecoregion, it is necessary to carry out intersection of the polygons. For this, the function st_intersection from sf is applied. To be able to apply st_intersection, the polygon layers should be provided as an object with the sf class. To carry out intersection function, coordinate reference system of both the polygons should be harmonized. We then compare the number of polygons in both the original and the resulting WDPA layer to see whether our process split up any polygon from the intersection.

Note We first loaded the teow polygon as spatVector so that there won’t be issue with missing CRS. But, to be able to apply st_intersection it is necessary to convert spatVector object to the sf object

# convert from terra object to sf object 
teow <- 
  st_as_sf(teow)
# apply intersection
teow_wdpa_intersection <- 
  st_intersection(teow,
                  br_wdpa_subset)
# plot the biome - intersected polygon
plot(teow_wdpa_intersection[20])

Version Author Date
6637f1d Johannes Schielein 2021-03-16
45f3a67 Ohm-Np 2021-03-16
0a7f686 Johannes Schielein 2021-03-15
b548133 Ohm-Np 2021-03-15 
# plot the ecoregion - intersected polygon
plot(teow_wdpa_intersection[5])

# compare the number of polygons in both layers 
nrow(br_wdpa_subset)
[1] 3
nrow(teow_wdpa_intersection)
[1] 4

We can see that there is one intersection in the research area i.e that we now have four polygons whereas before the intersection we had only three.

Calculate Areas

Since, we already carry out the intersection, now we want to extract the actual area of intersection between wdpa polygons and teow polygons.

# extract areas (SqKm) and save it as new column
teow_wdpa_intersection$teow_intersect_sqkm <- 
  st_area(teow_wdpa_intersection)/1000000
# tibble - turns existing object to tibble dataframe from library `dplyr`
myData <- 
  as_tibble(teow_wdpa_intersection)
# select only necessary columns from the intersected polygon
myData_f <- myData %>% 
  select(WDPAID,
         BIOME_NAME,
         ECO_NAME,
         teow_intersect_sqkm)
With the results looking like this

In the end we are going to have a look how long the rendering of this file took so that we could get an idea about the processing speed of this routine.

stoptime<-Sys.time()
print(starttime-stoptime)
Time difference of -3.853733 mins

References

[1] Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D’Amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938.


sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

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

other attached packages:
[1] rmarkdown_2.6 dplyr_1.0.6   wdpar_1.0.6   terra_1.2-15  sf_1.0-0     

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.6         countrycode_1.2.0  lattice_0.20-44    binman_0.1.2      
 [5] class_7.3-19       ps_1.5.0           assertthat_0.2.1   rprojroot_2.0.2   
 [9] digest_0.6.27      utf8_1.2.1         R6_2.5.0           evaluate_0.14     
[13] e1071_1.7-7        httr_1.4.2         pillar_1.6.1       rlang_0.4.11      
[17] curl_4.3.1         whisker_0.4        raster_3.4-10      RSelenium_1.7.7   
[21] wdman_0.2.5        stringr_1.4.0      proxy_0.4-26       compiler_3.6.3    
[25] httpuv_1.6.1       xfun_0.20          pkgconfig_2.0.3    askpass_1.1       
[29] htmltools_0.5.1.1  openssl_1.4.4      tidyselect_1.1.1   tibble_3.1.2      
[33] workflowr_1.6.2    codetools_0.2-18   XML_3.99-0.3       fansi_0.5.0       
[37] crayon_1.4.1       later_1.2.0        wk_0.4.1           bitops_1.0-6      
[41] rappdirs_0.3.3     grid_3.6.3         jsonlite_1.7.2     lifecycle_1.0.0   
[45] DBI_1.1.1          git2r_0.28.0       magrittr_2.0.1     semver_0.2.0      
[49] units_0.7-2        KernSmooth_2.23-20 stringi_1.6.2      fs_1.5.0          
[53] promises_1.2.0.1   sp_1.4-5           xml2_1.3.2         ellipsis_0.3.2    
[57] generics_0.1.0     vctrs_0.3.8        s2_1.0.5           tools_3.6.3       
[61] glue_1.4.2         purrr_0.3.4        processx_3.5.2     yaml_2.2.1        
[65] caTools_1.18.1     classInt_0.4-3     knitr_1.30