Last updated: 2021-10-19

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Knit directory: mapme.protectedareas/

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Rmd 69b1b4c Ohm-Np 2021-10-19 create clay content analysis routine
html 69b1b4c Ohm-Np 2021-10-19 create clay content analysis routine 
# load required libraries
library("sf")
library("terra")
library("wdpar")
library("tidyverse")

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

Introduction

From the agricultural perspective, the information on the amount of clay content in the soil is vital as it defines the productivity of the soil. Since the clay holds water and nutrients and allows them to remain in soil, the more clay content thus results in high productive capacity [1]. The variable that we are processig here that provides information on the percentage of clay content in the soil can be used as an indicator for soil fertility and thus be part of an agricultural aptitude index. OpenLandMap is a data portal which provides open access to several geo-datasets. The global rasters on clay content can be accessed freely from this portal at six standard depths (0, 10, 30, 60, 100 and 200 cm).

Datasource and Metadata Information

  • Dataset: Clay content in percentage (OpenLandMap.org)
  • Geographical Coverage: Global
  • Spatial resolution: 250 meter
  • Temporal Coverage: 1950-2017
  • Unit: % (kg / kg)
  • Data downloaded: 16th September, 2021
  • Metadata Link
  • Download Link

Processing Workflow

The purpose of this analysis is to compute mean percentage of clay content in the soil for the desired protected area of interest. For this, following processing routine is followed:

Download and Prepare WDPA Polygons

For this analysis, we would 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 Venezuela - for other countries of your choice, simply provide the country name or the ISO name e.g. BRA for Brazil, Gy for Guyana, COL for Colombia.

# fetch the raw data from wdpar of country
vn_wdpa_raw <- 
  wdpar::wdpa_fetch("VEN")

Since there are 290 enlisted protected areas in Venezuela (as of time of writing), we want to compute zonal statistics only for the polygon data of: - Sierra Nevada - wdpaid 321, - Páramos de Tamá, Cobre y Judio - wdpaid 322, and - Henri Pittier - wdpaid 323

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

# subset three wdpa polygons by their wdpa ids
vn_wdpa_subset <-
  vn_wdpa_raw%>%
  filter(WDPAID %in% c(321,322,323))

Now, we will reproject the polygon data to the WGS84 and then apply vect function from terra package to further use it for terra functionalities.

# reproject to the WGS84
vn_wdpa_subset <- st_transform(vn_wdpa_subset,
                               "+proj=longlat +datum=WGS84 +no_defs")
# spatvector for terra compatibility
vn_wdpa_subset_v <- 
  vect(vn_wdpa_subset)
# we can plot the data to see the three selected polygons
plot(vn_wdpa_subset_v)

Version Author Date
05aa46b Ohm-Np 2021-10-19

Prepare clay content raster data

The global raster datasets are available to download for six standard depths (0, 10, 30, 60, 100 and 200 cm) and also with options on mean and median. Here, we are going to load the mean raster layer for the standard depth of 10 cm using package terra.

# load clay content raster
clay_rast <- 
  terra::rast("../../datalake/mapme.protectedareas/input/clay_content/clay_content_10_cm.tif")
# view raster details
clay_rast
class       : SpatRaster 
dimensions  : 71698, 172800, 1  (nrow, ncol, nlyr)
resolution  : 0.002083333, 0.002083333  (x, y)
extent      : -180, 179.9999, -62.00081, 87.37  (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (EPSG:4326) 
source      : clay_content_10_cm.tif 
name        : clay_content_10_cm

Crop the clay content raster

As we completed raster and vector data preparation, the next step would be to clip the raster layer by the selected protected areas polygon both by its extent and mask layer. If we apply function crop, it gives us the raster layer by extent. However, using mask function returns the raster to defined vector polygon layer, which is required for zonal statistics computation.

# crop raster by polygon
clay_rast_crop <- 
        terra::crop(clay_rast,
                    vn_wdpa_subset_v)
# plot the cropped raster layer
plot(clay_rast_crop)

Version Author Date
05aa46b Ohm-Np 2021-10-19 
# mask the raster by polygon 
clay_rast_mask <- 
        terra::mask(clay_rast_crop,
                    vn_wdpa_subset_v)
# plot the masked raster layer
plot(clay_rast_mask)

Version Author Date
05aa46b Ohm-Np 2021-10-19

Note: With these above two plots, you cans see the difference between the use of functions crop & mask. Thus, always use mask to compute zonal statistics.

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 the polygon
vn_subset_rast <-terra::rasterize(vn_wdpa_subset_v,
                                  clay_rast_mask,
                                  vn_wdpa_subset_v$WDPAID)
# plot the rasterized polygon
plot(vn_subset_rast)

Version Author Date
05aa46b Ohm-Np 2021-10-19

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]. Here, we are interested on only to compute the mean percentage of clay content in the soil, so, we would use function mean for zonal operation.

# zonal stats
zstats <- terra::zonal(clay_rast_mask,
                       vn_subset_rast,
                       fun='mean',
                       na.rm=T)
# create dataframe to receive the result
df.zstats <- data.frame(WDPAID=NA,
                        clay_content_percentage_mean=NA)
# rename column to match with dataframe
colnames(zstats) <- 
  colnames(df.zstats)
# view the data
rbind(df.zstats,zstats)[-1,]
  WDPAID clay_content_percentage_mean
2    321                     26.57843
3    322                     30.02754
4    323                     29.77513

From the zonal statistics result, we can see that the polygon with wdpaid 322 contains more percentage of clay in the soil while the polygon with id 321 contains less compared to other polygons. In this way, we can compute percentage of clay content in the soil for our desired region of interest.

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

stoptime<-Sys.time()
print(starttime-stoptime)
Time difference of -5.677369 secs

References

[1] Paul Schroeder (2016): Clays in the Critical Zone: An Introduction . Clays and Clay Minerals 65 (5): 586-587. DOI: 10.1346/CCMN.2016.064045


sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.6 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] forcats_0.5.1   stringr_1.4.0   dplyr_1.0.7     purrr_0.3.4    
 [5] readr_1.4.0     tidyr_1.1.4     tibble_3.1.5    ggplot2_3.3.4  
 [9] tidyverse_1.3.1 wdpar_1.0.6     terra_1.4-9     sf_1.0-3       

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.7         countrycode_1.2.0  lubridate_1.7.10   class_7.3-19      
 [5] assertthat_0.2.1   rprojroot_2.0.2    digest_0.6.27      utf8_1.2.2        
 [9] cellranger_1.1.0   R6_2.5.1           backports_1.2.1    reprex_2.0.0      
[13] evaluate_0.14      e1071_1.7-9        highr_0.8          httr_1.4.2        
[17] pillar_1.6.3       rlang_0.4.11       curl_4.3.2         readxl_1.3.1      
[21] rstudioapi_0.13    whisker_0.4        jquerylib_0.1.4    rmarkdown_2.11    
[25] munsell_0.5.0      proxy_0.4-26       broom_0.7.6        compiler_3.6.3    
[29] httpuv_1.6.1       modelr_0.1.8       xfun_0.24          pkgconfig_2.0.3   
[33] htmltools_0.5.1.1  tidyselect_1.1.1   workflowr_1.6.2    codetools_0.2-18  
[37] fansi_0.5.0        withr_2.4.2        crayon_1.4.1       dbplyr_2.1.1      
[41] later_1.2.0        rappdirs_0.3.3     grid_3.6.3         jsonlite_1.7.2    
[45] gtable_0.3.0       lifecycle_1.0.1    DBI_1.1.1          git2r_0.28.0      
[49] magrittr_2.0.1     units_0.7-2        scales_1.1.1       KernSmooth_2.23-20
[53] cli_3.0.1          stringi_1.6.2      fs_1.5.0           promises_1.2.0.1  
[57] xml2_1.3.2         bslib_0.2.5.1      ellipsis_0.3.2     generics_0.1.0    
[61] vctrs_0.3.8        tools_3.6.3        glue_1.4.2         hms_1.0.0         
[65] yaml_2.2.1         colorspace_2.0-1   rvest_1.0.0        classInt_0.4-3    
[69] knitr_1.34         haven_2.3.1        sass_0.4.0