Dataset Information

High deforestation trajectories in Cambodia slowly transformed through economic land concession restrictions and strategic execution of REDD+ protected areas.

ABSTRACT: Protected Areas (PAs) are continuously being established in tropical forests in an effort to preserve biodiversity and reduce deforestation. It was recently demonstrated that PAs are more effective at reducing forest loss than unprotected control sites across southeast Asia. The voluntary REDD+ scheme offers a new framework for the protection of high deforestation landscapes, jurisdictions, and countries backed by international carbon finance. Here we analyzed the economic drivers of deforestation in Cambodia and the effectiveness of 3 REDD+ projects vs. adjacent protected areas. We find that Economic Land Concessions were a predominant driver of deforestation in Cambodia and influenced the trajectory of illegal forest conversion in PAs. Furthermore, REDD+ projects offer significantly more protection against deforestation than adjacent PAs in two of the three analyzed cases, likely due to enhanced funding enabling implementation of targeted community activities and rigorous monitoring and enforcement.

SUBMITTER: Pauly M

PROVIDER: S-EPMC9568659 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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High deforestation trajectories in Cambodia slowly transformed through economic land concession restrictions and strategic execution of REDD+ protected areas.

Pauly Maren M Crosse Will W Tosteson Joshua J

Scientific reports 20221014 1

Protected Areas (PAs) are continuously being established in tropical forests in an effort to preserve biodiversity and reduce deforestation. It was recently demonstrated that PAs are more effective at reducing forest loss than unprotected control sites across southeast Asia. The voluntary REDD+ scheme offers a new framework for the protection of high deforestation landscapes, jurisdictions, and countries backed by international carbon finance. Here we analyzed the economic drivers of deforestati ...[more]

PMID: 36241634

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Project description:Community forests are known to play an important role in preserving forests in Cambodia, a country that has seen rapid deforestation in recent decades. The detailed evaluation of the ability of community-protected forests to retain forest cover and prevent degradation in Cambodia will help to guide future conservation management. In this study, a combination of remotely sensing data was used to compare the temporal variation in forest structure for six different community forests located in the Phnom Kulen National Park (PKNP) in Cambodia and to assess how these dynamics vary between community-protected forests and a wider study area. Medium-resolution Landsat, ALOS PALSAR data, and high-resolution LiDAR data were used to study the spatial distribution of forest degradation patterns and their impacts on above-ground biomass (AGB) changes. Analysis of the remotely sensing data acquired at different spatial resolutions revealed that between 2012 and 2015, the community forests had higher forest cover persistence and lower rates of forest cover loss compared to the entire study area. Furthermore, they faced lower encroachment from cashew plantations compared to the wider landscape. Four of the six community forests showed a recovery in canopy gap fractions and subsequently, an increase in the AGB stock. The levels of degradation decreased in forests that had an increase in AGB values. However, all community forests experienced an increase in understory damage as a result of selective tree removal, and the community forests with the sharpest increase in understory damage experienced AGB losses. This is the first time multitemporal high-resolution LiDAR data have been used to analyze the impact of human-induced forest degradation on forest structure and AGB. The findings of this work indicate that while community-protected forests can improve conservation outcomes to some extent, more interventions are needed to curb the illegal selective logging of valuable timber trees.

Project description:BACKGROUND: To implement the REDD+ mechanism (Reducing Emissions for Deforestation and Forest Degradation, countries need to prioritize areas to combat future deforestation CO2 emissions, identify the drivers of deforestation around which to develop mitigation actions, and quantify and value carbon for financial mechanisms. Each comes with its own methodological challenges, and existing approaches and tools to do so can be costly to implement or require considerable technical knowledge and skill. Here, we present an approach utilizing a machine learning technique known as Maximum Entropy Modeling (Maxent) to identify areas at high deforestation risk in the study area in Madre de Dios, Peru under a business-as-usual scenario in which historic deforestation rates continue. We link deforestation risk area to carbon density values to estimate future carbon emissions. We quantified area deforested and carbon emissions between 2000 and 2009 as the basis of the scenario. RESULTS: We observed over 80,000 ha of forest cover lost from 2000-2009 (0.21% annual loss), representing over 39 million Mg CO2. The rate increased rapidly following the enhancement of the Inter Oceanic Highway in 2005. Accessibility and distance to previous deforestation were strong predictors of deforestation risk, while land use designation was less important. The model performed consistently well (AUC?>?0.9), significantly better than random when we compared predicted deforestation risk to observed. If past deforestation rates continue, we estimate that 132,865 ha of forest could be lost by the year 2020, representing over 55 million Mg CO2. CONCLUSIONS: Maxent provided a reliable method for identifying areas at high risk of deforestation and the major explanatory variables that could draw attention for mitigation action planning under REDD+. The tool is accessible, replicable and easy to use; all necessary for producing good risk estimates and adapt models after potential landscape change. We propose this approach for developing countries planning to meet requirements under REDD+.

Project description:Protected areas (PAs) have been established to conserve tropical forests, but their effectiveness at reducing deforestation is uncertain. To explore this issue, we combined high resolution data of global forest loss over the period 2000-2012 with data on PAs. For each PA we quantified forest loss within the PA, in buffer zones 1, 5, 10 and 15 km outside the PA boundary as well as a 1 km buffer within the PA boundary. We analysed 3376 tropical and subtropical moist forest PAs in 56 countries over 4 continents. We found that 73% of PAs experienced substantial deforestation pressure, with >0.1% a(-1) forest loss in the outer 1 km buffer. Forest loss within PAs was greatest in Asia (0.25% a(-1)) compared to Africa (0.1% a(-1)), the Neotropics (0.1% a(-1)) and Australasia (Australia and Papua New Guinea; 0.03% a(-1)). We defined performance (P) of a PA as the ratio of forest loss in the inner 1 km buffer compared to the loss that would have occurred in the absence of the PA, calculated as the loss in the outer 1 km buffer corrected for any difference in deforestation pressure between the two buffers. To remove the potential bias due to terrain, we analysed a subset of PAs (n = 1804) where slope and elevation in inner and outer 1 km buffers were similar (within 1° and 100 m, respectively). We found 41% of PAs in this subset reduced forest loss in the inner buffer by at least 25% compared to the expected inner buffer forest loss (P<0.75). Median performance (P) of subset reserves was 0.87, meaning a reduction in forest loss within the PA of 13%. We found PAs were most effective in Australasia (P = 0.16), moderately successful in the Neotropics (P = 0.72) and Africa (p = 0.83), but ineffective in Asia (P = 1). We found many countries have PAs that give little or no protection to forest loss, particularly in parts of Asia, west Africa and central America. Across the tropics, the median effectiveness of PAs at the national level improved with gross domestic product per capita. Whilst tropical and subtropical moist forest PAs do reduce forest loss, widely varying performance suggests substantial opportunities for improved protection, particularly in Asia.

Dataset Information

High deforestation trajectories in Cambodia slowly transformed through economic land concession restrictions and strategic execution of REDD+ protected areas.

Publications

High deforestation trajectories in Cambodia slowly transformed through economic land concession restrictions and strategic execution of REDD+ protected areas.

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