Mapping national REDD+ initiatives in the Asia-Pacific region

Maraseni, Tek; Poudyal, Bishnu Hari; Rana, Eak; Khanal, Sudarshan Chandra; Ghimire, Puspa Lal; Subedi, Bhishma P.

doi:10.1016/j.jenvman.2020.110763

Cited by 31 publications

(7 citation statements)

References 27 publications

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“…Another future work would be to improve the detail level and accuracy of future LULC maps. Recent studies on carbon emissions from land cover changes in Vietnam, like Avitabile et al (2016) [103], and the REDD+ readiness status [104] have called for highly reliable and detailed LULC and forest maps. Carbon emission assessments would be more accurate if the input LULC maps contained sub-categories of natural forests such as bamboo, rich forests, medium forests, poor forests, and regrowth forests.…”

Section: Future Research Directionsmentioning

confidence: 99%

New JAXA High-Resolution Land Use/Land Cover Map for Vietnam Aiming for Natural Forest and Plantation Forest Monitoring

et al. 2020

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Highly detailed and accurate forest maps are important for various applications including forest monitoring, forestry policy, climate change, and biodiversity loss. This study demonstrates a comprehensive and geographically transferable approach to produce a 12 category high-resolution land use/land cover (LULC) map over mainland Vietnam in 2016 by remote sensing data. The map included several natural forest categories (evergreen broadleaf, deciduous (mostly deciduous broadleaf), and coniferous (mostly evergreen coniferous)) and one category representing all popular plantation forests in Vietnam such as acacia (Acacia mangium, Acacia auriculiformis, Acacia hybrid), eucalyptus (Eucalyptus globulus), rubber (Hevea brasiliensis), and others. The approach combined the advantages of various sensor data by integrating their posterior probabilities resulting from applying a probabilistic classifier (comprised of kernel density estimation and Bayesian inference) to each datum individually. By using different synthetic aperture radar (SAR) images (PALSAR-2/ScanSAR, PALSAR-2 mosaic, Sentinel-1), optical images (Sentinel-2, Landsat-8) and topography data (AW3D30), the resultant map achieved 85.6% for the overall accuracy. The major forest classes including evergreen broadleaf forests and plantation forests had a user’s accuracy and producer’s accuracy ranging from 86.0% to 95.3%. Our map identified 9.55 × 106 ha (±0.16 × 106 ha) of natural forests and 3.89 × 106 ha (±0.11 × 106 ha) of plantation forests over mainland Vietnam, which were close to the Vietnamese government’s statistics (with differences of less than 8%). This study’s result provides a reliable input/reference to support forestry policy and land sciences in Vietnam.

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Section: Future Research Directionsmentioning

confidence: 99%

New JAXA High-Resolution Land Use/Land Cover Map for Vietnam Aiming for Natural Forest and Plantation Forest Monitoring

et al. 2020

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“…Undertaking this exercise is demanding given global geographical diversity, and, thus, building technical capacity to this end is essential. Modeling future emissions based on historical trend rates and understanding the relationships between deforestation patterns and the drivers of deforestation are essential for RL estimation [68][69][70].…”

Section: Future Scopementioning

confidence: 99%

Framework for Accounting Reference Levels for REDD+ in Tropical Forests: Case Study from Xishuangbanna, China

et al. 2021

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The United Nations’ expanded program for Reducing Emissions from Deforestation and Forest Degradation (REDD+) aims to mobilize capital from developed countries in order to reduce emissions from these sources while enhancing the removal of greenhouse gases (GHGs) by forests. To achieve this goal, an agreement between the Parties on reference levels (RLs) is critical. RLs have profound implications for the effectiveness of the program, its cost efficiency, and the distribution of REDD+ financing among countries. In this paper, we introduce a methodological framework for setting RLs for REDD+ applications in tropical forests in Xishuangbanna, China, by coupling the Good Practice Guidance on Land Use, Land Use Change, and Forestry of the Intergovernmental Panel on Climate Change and land use scenario modeling. We used two methods to verify the accuracy for the reliability of land classification. Firstly the accuracy reached 84.43%, 85.35%, and 82.68% in 1990, 2000, and 2010, respectively, based on high spatial resolution image by building a hybrid matrix. Then especially, the 2010 Globeland30 data was used as the standard to verify the forest land accuracy and the extraction accuracy reached 86.92% and 83.66% for area and location, respectively. Based on the historical land use maps, we identified that rubber plantations are the main contributor to forest loss in the region. Furthermore, in the business-as-usual scenario for the RLs, Xishuangbanna will lose 158,535 ha (158,535 × 104 m2) of forest area in next 20 years, resulting in approximately 0.23 million t (0.23 × 109 kg) CO2e emissions per year. Our framework can potentially increase the effectiveness of the REDD+ program in Xishuangbanna by accounting for a wider range of forest-controlled GHGs.

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“…Deforestation and forest degradation are recognized as critical environmental issues that contribute significantly to global greenhouse gases (GHGs) emissions (Fujisaki et al, 2020; Mattsson et al, 2012; Sheng et al, 2016). The United Nations Framework Convention on Climate Change (UNFCCC) introduced an international management mechanism to confront climate change, namely Reducing Emissions from Deforestation and Forest Degradation (REDD+) (Maraseni et al, 2020; Sheng, 2020; Srinivasarao et al, 2022). In recent years, REDD+ projects have been considered as a worldwide policy to avoid deforestation in developing countries (Atela et al, 2014; Lin et al, 2014; Maraseni et al, 2014; Wei et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Impact of Land Cover Changes on Reducing Greenhouse Emissions: Site Selection, Baseline Modeling, and Strategic Environmental Assessment of REDD+ Projects

et al. 2023

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Reducing emissions from deforestation and forest degradation (REDD+) is way key to reduce the emission of greenhouse gases (GHGs) while also protecting vulnerable forest ecosystems. The purpose of this study was to recognize suitable areas for REDD+ Programme projects and calculate the reduction in CO2 emissions through the prevention of forest cover degradation in the Central Hyrcanian forests. For this purpose, the cover changes of the Central Hyrcanian forests were assessed using LANDSAT satellite images. Applying the voluntary carbon standard (VCS) methodology and the calibration period 1984–2014 (30 years), forest cover changes were predicted. The results showed that under the business‐as‐usual scenario, 155,698 ha of Central Hyrcanian forests will be declined by 2044. In general, the REDD+ Programme project implementation will prevent the release of 1,209,231 tCO2e. Based on the social cost of carbon (SCC) approach, the REDD+ Programme project implementation can save 12,092,310 US$. In addition, this approach can be used for the project design document (PDD) of the forest development mechanism.

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Mapping national REDD+ initiatives in the Asia-Pacific region

Cited by 31 publications

References 27 publications

New JAXA High-Resolution Land Use/Land Cover Map for Vietnam Aiming for Natural Forest and Plantation Forest Monitoring

New JAXA High-Resolution Land Use/Land Cover Map for Vietnam Aiming for Natural Forest and Plantation Forest Monitoring

Framework for Accounting Reference Levels for REDD+ in Tropical Forests: Case Study from Xishuangbanna, China

Impact of Land Cover Changes on Reducing Greenhouse Emissions: Site Selection, Baseline Modeling, and Strategic Environmental Assessment of REDD+ Projects

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