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Introduction

The following analysis is intented to support a peer-discussion on the threat level of protected areas (short: PAs) in the Bolivian Amazon Basin. The goal of this exercise is to assist 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 goal of this analysis is to look at a set of predefined PAs, rank it according to the their threat level and compare it to the past portfolio from KfW. The analysis is based on publicly available data from the World Database of Protected Areas (WDPA/IUCN) 1 and other freely available geodata-sources. In a first analysis step we focus on habitat destruction in terms of primary forest cover loss and in the second step we look at fires in protected areas between 2000 and 2021. Both datasets can indicate human pressures on the ecosystem as well as natural/climatic stressors that could harm the long-term stability and provision of ecosystem services.

Forest cover loss (2000-2020)

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 methdology “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. The data also include annual forest fire counts, which are taken from the NASA FIRMS data3.

In the analysis 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.

Map

The following map depicts relative and absolute forest cover loss in the selected areas. The size of circles is dependent on the total loss (the bigger the total loss, the larger the circle). The color is dependend on the relative loss (red circles indicate areas with high loss). From a threat perspective big red circles could be especially relevant areas for conservation. This map is interactive meaning that you can zoom into the map to see specific countries in more detail and click on areas to get summary statistics. Furthermore, supported PAs from the current and past portfolio of KfW (blue) are displayed with their actual polygon boundaries as well as all other PAs in Bolivia (grey) which can be activated manually in the map. Furthermore the analyzed data from Global Forest watch can be seen when activated in the layer control panel as well as the distribution of primary forests in 2001.

Absolute Trend

Lollipop Plots can be used to compare PAs in terms of their treat level. The following figure shows absolut forest cover loss in suggested PAs. It also shows whether any of these PAs has been or is currently supported by a project from KfW. You can isolate single areas with a double click on their name in the legend.

Yearly Forest Cover Loss

Stacked Barplots are used to assess the time-trend of one or several PAs. They show the variation in the total number of fires which can be amongst others attributed to El Niño and La Niña which are opposite extremes of ENSO, referring to cyclical environmental conditions that occur across the Equatorial Pacific Ocean. For more information see here. The different parts of each bar represent the different PAs. You can easily see which PAs take a larger share of the yearly total fire counts. Those are the ones where most fires occur. You can get more detailed information by hovering with the mouse over the bars.

Fire counts (2000 to 2021)

Map

The following map depicts a summary of fire counts in the suggested Protected Areas according to NASA Firms. In general larger protected areas expirience more fires due to their spatial extension. Nevertheless there are also exeptions. It appears that large protected areas in the Andean mountain range expirience less forest fires (you can activate a Topography layer in the map). In contrast, PAs in the east which are located closer to the Brazilian boarder (Rondonia) expirience more fires. There is also a notable correlation between forest cover loss and forest fires (You can activate the forest cover loss layer in the map). This seems plausible since fires are often used as a strategy for forest cleansing or large natural wildfires might cause permanent damage to the forest cover. However, the relationship is not perfect. The map and the subsequent lollipop plots also exhibit which of the analyzed areas have been already part of past or ongoing KfW projects. The data suggests that only one area “Noel Kempff Mercado” expirienced a larger amount of fires (6,724) between 2000 and 2021. You can get individual area statistics by clicking on the colored circles in the map.

Absolute Trend

Lollipop Plots can be used to compare PAs in terms of their treat level. The following figure shows forest fires in suggested PAs. It also shows whether any of these PAs has been or is currently supported by a project from KfW. <

Yearly Firecounts

Stacked Barplots are used to assess the time-trend of one or several PAs. They show the variation in the total number of fires which can be amongst others attributed to El Niño and La Niña which are opposite extremes of ENSO, referring to cyclical environmental conditions that occur across the Equatorial Pacific Ocean. For more information see here. The different parts of each bar represent the different PAs. You can easily see which PAs take a larger share of the yearly total fire counts. Those are the ones where most fires occur. You can get more detailed information by hovering with the mouse over the bars.

Interpretation & Discussion

Forest Loss Data

As outline above the GFW methodology defines forest cover loss as a “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. Thus, the data currently does not allow the differentiate permanent loss and conversion from temporary loss due to natural factors. A more in-depth analysis of pre-selected areas is therefore recommended when using the current data.

GFW data can be used to get an assessment of old-growth and primary forests as well as associated carbon emissions. It can indicate which areas had been highly threatened either due to natural causes (hurricanes/fires) or human causes (deforestation/logging/degradation and conversion to agriculture/silviculture). Especially useful in this context is to have a look at the whole trend from 2001 to 2020. Huge spikes in the individual trend data might indicate natural causes such as fires and hurricanes. A quick search with google always helps to confirm this hypothesis or you might look at Hurricane data e.g. on the NOAA website which provides historical data for hurricane tracks. More continuous growth in forest loss is probably due to the conversion of natural forests for agricultural purposes. Again you might look at the map given above and activate the satellite layer to see what might be happening below the detected loss. GFW does not (currently) allow to detect regrowth and regeneration but it is planned to provide that feature soon.

Possible Improvements

  • Analyze subsequent land-cover in loss-areas. This would be possible e.g. by using Landcover data from the ESA Copernicus mission. It would allow to seperate permanent conversion of forest area e.g. for agricultural purposes from natural losses e.g. due wildfires.
  • Aggregate existing loss data on a finer spatial grid or create heatmaps. This would allow to better visualize highly threatened spots on the map. This could be especially relevant to quickly identify high pressure zones in large protected areas.
  • Analyse bufferzones: This would allow to detect areas with high forest loss dynamics in its sourroundings.

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/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so

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] plotly_4.10.0         RColorBrewer_1.1-3    htmltools_0.5.3      
 [4] scales_1.2.1          ggsci_2.9             leaflet.extras2_1.2.0
 [7] leaflet.extras_1.0.0  leaflet_2.0.4.1       sf_1.0-9             
[10] forcats_0.5.1         stringr_1.5.0         dplyr_1.0.10         
[13] purrr_1.0.1           readr_2.1.2           tidyr_1.2.1          
[16] tibble_3.1.8          ggplot2_3.3.6         tidyverse_1.3.1      

loaded via a namespace (and not attached):
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 [7] backports_1.4.1         bslib_0.3.1             utf8_1.2.2             
[10] R6_2.5.1                KernSmooth_2.23-20      DBI_1.1.3              
[13] lazyeval_0.2.2          colorspace_2.0-3        withr_2.5.0            
[16] tidyselect_1.2.0        bit_4.0.5               compiler_3.6.3         
[19] git2r_0.30.1            cli_3.6.0               rvest_1.0.3            
[22] xml2_1.3.3              labeling_0.4.2          sass_0.4.2             
[25] classInt_0.4-8          proxy_0.4-27            digest_0.6.29          
[28] rmarkdown_2.16          pkgconfig_2.0.3         dbplyr_2.1.1           
[31] fastmap_1.1.0           htmlwidgets_1.5.4       rlang_1.0.6            
[34] readxl_1.4.1            rstudioapi_0.14         farver_2.1.1           
[37] jquerylib_0.1.4         generics_0.1.3          jsonlite_1.8.4         
[40] crosstalk_1.2.0         vroom_1.5.7             magrittr_2.0.3         
[43] s2_1.1.2                Rcpp_1.0.9              munsell_0.5.0          
[46] fansi_1.0.3             lifecycle_1.0.3         stringi_1.7.12         
[49] whisker_0.4             yaml_2.3.5              grid_3.6.3             
[52] parallel_3.6.3          promises_1.2.0.1        crayon_1.4.2           
[55] haven_2.5.1             hms_1.1.2               knitr_1.37             
[58] pillar_1.8.1            markdown_1.1            wk_0.7.1               
[61] reprex_2.0.0            glue_1.6.2              evaluate_0.16          
[64] leaflet.providers_1.9.0 data.table_1.14.2       modelr_0.1.8           
[67] vctrs_0.5.1             tzdb_0.3.0              httpuv_1.6.5           
[70] cellranger_1.1.0        gtable_0.3.1            assertthat_0.2.1       
[73] xfun_0.29               mime_0.12               broom_1.0.0            
[76] e1071_1.7-12            later_1.3.0             class_7.3-20           
[79] viridisLite_0.4.1       workflowr_1.7.0         units_0.8-1            
[82] timechange_0.2.0        ellipsis_0.3.2         

  1. 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.

  2. “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."

  3. “Justice, C.O., Giglio, L., Korontzi, S., Owens, J., Morisette, J., Roy, D., Descloitres, J., Alleaume, S., Petitcolin, F., & Kaufman, Y.J. (2002). The MODIS fire products. Remote Sensing of Environment, 83:244-262. doi:10.1016/S0034-4257(02)00076-7