Last updated: 2022-02-11
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The following graphics are intented to support a peer-discussion on how publicly available geodata can be used to characterize the threat level of protected areas (short PAs). The goal of this exercise is to define data products that could support the identification of new intervention sites and thus help KfW and its partners in the project preparation phase. In addition, information about the current portfolio and its relevance to protect the most threatened areas can be derived.
The general idea of this analysis is to match the complete network of existing PAs in countries where KfW is active to the the database of supported PAs by KfW. The same methodology can be used later in the project preparation phase to analyse the existing portfolio together with newly projected intervention sites. The analysis is based on publicly available data from the World Database of Protected Areas (WDPA/IUCN) which was downloaded from the Protected Planet Website.1 Our current portfolio database contains 373 areas from a total of 4722 in the partnering countries.
In a first analysis step we focus on habitat destruction in terms of primary forest cover loss. To quantify forest cover loss we utilize data from the Global Forest Watch (Hansen et al, 2013)2. Forest cover loss is defined in their work “as a stand-replacement disturbance, or a change from a forest to non-forest state.”. Loss can either be the result of human activities or natural factors such as droughts, forest fires or hurricanes, amongst others. More information on the interpretation and usefullness of this data as well as suggested further steps to advance the threat assessment are given below in the discussion part.
In the analysis below we will focus on two key outcome indicators:
Total forest cover loss: Measures the total sum of loss areas in hectare. This variable is able to identify PAs with the highest primary forest cover loss between 2001 and 2020. The identification of high loss areas can be usefull for targeting areas where we might achieve the largest impact in terms of reducing emissions from deforstation and forest degradation.
Relative forest cover loss: Measures the percentage of primary forest cover loss inside a PA compared to its total primary forest area in 2000. The identification of PAs with high relative losses can be relevant from a biodiversity perspective. PAs with high relative losses might be places where large parts of the functional forest habitat is lost. Targeting these areas might not only help to protect the floral biodiversity but also the fauna and humans that inhabit these areas and profit from the local forest ecosystem services.
| Absolute Forst Loss (ha) | % of all PAs | % of KfW Supported PAs |
|---|---|---|
| 0-10 | 33.42 | 18.87 |
| 10-100 | 23.15 | 15.49 |
| 100-1,000 | 25.51 | 25.92 |
| 1,000-10,000 | 14.00 | 28.73 |
| >10,000 | 3.91 | 10.99 |
| Relative Forst Loss | % of all PAs | % of KfW Supported PAs |
|---|---|---|
| 0-2 % | 52.50 | 65.35 |
| 2-5 % | 19.39 | 16.62 |
| 5-10 % | 13.17 | 9.01 |
| 10-20 % | 8.94 | 5.35 |
| >20 % | 5.99 | 3.66 |
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] knitr_1.37 plotly_4.9.3 RColorBrewer_1.1-2
[4] htmltools_0.5.1.1 scales_1.1.1 ggsci_2.9
[7] leaflet.extras2_1.1.0 leaflet.extras_1.0.0 leaflet_2.0.4.1
[10] sf_1.0-4 forcats_0.5.1 stringr_1.4.0
[13] dplyr_1.0.7 purrr_0.3.4 readr_1.4.0
[16] tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.4
[19] tidyverse_1.3.1
loaded via a namespace (and not attached):
[1] httr_1.4.2 sass_0.4.0 viridisLite_0.4.0
[4] jsonlite_1.7.2 modelr_0.1.8 bslib_0.2.5.1
[7] assertthat_0.2.1 highr_0.8 cellranger_1.1.0
[10] yaml_2.2.1 pillar_1.6.4 backports_1.2.1
[13] glue_1.5.1 digest_0.6.27 promises_1.2.0.1
[16] rvest_1.0.0 leaflet.providers_1.9.0 colorspace_2.0-1
[19] httpuv_1.6.1 pkgconfig_2.0.3 broom_0.7.6
[22] haven_2.3.1 s2_1.0.7 later_1.2.0
[25] git2r_0.28.0 proxy_0.4-26 farver_2.1.0
[28] generics_0.1.1 ellipsis_0.3.2 withr_2.4.2
[31] lazyeval_0.2.2 cli_3.1.0 magrittr_2.0.2
[34] crayon_1.4.2 readxl_1.3.1 evaluate_0.14
[37] fs_1.5.0 fansi_1.0.2 xml2_1.3.2
[40] class_7.3-19 tools_3.6.3 data.table_1.13.6
[43] hms_1.1.1 lifecycle_1.0.1 munsell_0.5.0
[46] reprex_2.0.0 compiler_3.6.3 jquerylib_0.1.4
[49] e1071_1.7-9 rlang_1.0.0 classInt_0.4-3
[52] units_0.7-2 grid_3.6.3 rstudioapi_0.13
[55] htmlwidgets_1.5.3 crosstalk_1.1.1 labeling_0.4.2
[58] rmarkdown_2.11 wk_0.6.0 gtable_0.3.0
[61] DBI_1.1.2 R6_2.5.1 lubridate_1.7.10
[64] utf8_1.2.2 workflowr_1.6.2 rprojroot_2.0.2
[67] KernSmooth_2.23-20 stringi_1.7.6 Rcpp_1.0.8
[70] vctrs_0.3.8 dbplyr_2.1.1 tidyselect_1.1.1
[73] xfun_0.29 UNEP-WCMC and IUCN (2022), Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM) [Online], February 2022, Cambridge, UK: UNEP-WCMC and IUCN. Available at: www.protectedplanet.net.↩
“Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest."↩