Last updated: 2023-01-25
Checks: 6 1
Knit directory: mapme.protectedareas/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20210305) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version b84a4f4. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .RData
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: README.html
Ignored: data-raw/addons/docs/rest/
Ignored: data-raw/addons/etc/
Ignored: data-raw/addons/scripts/
Unstaged changes:
Modified: analysis/threat_assessment_bolivia.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/threat_assessment_bolivia.Rmd) and HTML (docs/threat_assessment_bolivia.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | b84a4f4 | Johannes Schielein | 2023-01-25 | update threat assessment |
| html | b84a4f4 | Johannes Schielein | 2023-01-25 | update threat assessment |
| Rmd | 743819b | Johannes Schielein | 2023-01-24 | update Bolivia Threat Assessment. |
| Rmd | ca3931d | Johannes Schielein | 2023-01-24 | update Bolivia Threat Assessment |
| html | ca3931d | Johannes Schielein | 2023-01-24 | update Bolivia Threat Assessment |
| html | d4cb549 | Andreas Petutschnig | 2023-01-24 | Build site. |
| Rmd | ee06b13 | Andreas Petutschnig | 2023-01-24 | workflowr::wflow_publish(“analysis/threat_assessment_bolivia.Rmd”) |
| html | a9e82c8 | Andreas Petutschnig | 2023-01-23 | Build site. |
| Rmd | e613f77 | Andreas Petutschnig | 2023-01-23 | first draft of Bolivia fire threat analysis |
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.
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.
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.
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.
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.
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.
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. <
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.
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
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):
[1] fs_1.5.2 lubridate_1.9.0 bit64_4.0.5
[4] httr_1.4.4 rprojroot_2.0.3 tools_3.6.3
[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 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."↩
“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”↩