Assessment of carbon stocks of semi-evergreen forests in Cambodia

Chheng, Kimsun; Sasaki, Nophea; Mizoue, Nobuya; Khorn, Saret; Kao, Dana

doi:10.1016/j.gecco.2015.11.007

Cited by 15 publications

(16 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This decline can be attributed to forest clearing to accommodate an increase in population and new households [66], expansion of Croplands [67], and the government's Economic Land Concession program to promote plantations [68]. Other major drivers of deforestation and degradation were the expansion of agriculture to grow cassava, orchard plantations, and paddy fields [16,47]. Evergreen, semi-evergreen, and deciduous forests have been subjected to logging [59].…”

Section: Discussionmentioning

confidence: 99%

“…Other field-based measurement approaches use the real weight of all parts of all trees in the targeted sample plots to determine forest biomasses using allometric equations (e.g., diameter at breast height and tree height obtained from field-based forest inventory methods) to convert inventory measurements into biomass estimates [12,13]. Although these methods provide the most accurate assessment of biomasses and carbon stocks, they are time consuming [12,[14][15][16], costly and difficult to scale [17,18].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Applications of the Google Earth Engine and Phenology-Based Threshold Classification Method for Mapping Forest Cover and Carbon Stock Changes in Siem Reap Province, Cambodia

et al. 2020

Self Cite

View full text Add to dashboard Cite

Digital and scalable technologies are increasingly important for rapid and large-scale assessment and monitoring of land cover change. Until recently, little research has existed on how these technologies can be specifically applied to the monitoring of Reducing Emissions from Deforestation and Forest Degradation (REDD+) activities. Using the Google Earth Engine (GEE) cloud computing platform, we applied the recently developed phenology-based threshold classification method (PBTC) for detecting and mapping forest cover and carbon stock changes in Siem Reap province, Cambodia, between 1990 and 2018. The obtained PBTC maps were validated using Google Earth high resolution historical imagery and reference land cover maps by creating 3771 systematic 5 × 5 km spatial accuracy points. The overall cumulative accuracy of this study was 92.1% and its cumulative Kappa was 0.9, which are sufficiently high to apply the PBTC method to detect forest land cover change. Accordingly, we estimated the carbon stock changes over a 28-year period in accordance with the Good Practice Guidelines of the Intergovernmental Panel on Climate Change. We found that 322,694 ha of forest cover was lost in Siem Reap, representing an annual deforestation rate of 1.3% between 1990 and 2018. This loss of forest cover was responsible for carbon emissions of 143,729,440 MgCO2 over the same period. If REDD+ activities are implemented during the implementation period of the Paris Climate Agreement between 2020 and 2030, about 8,256,746 MgCO2 of carbon emissions could be reduced, equivalent to about USD 6-115 million annually depending on chosen carbon prices. Our case study demonstrates that the GEE and PBTC method can be used to detect and monitor forest cover change and carbon stock changes in the tropics with high accuracy.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applications of the Google Earth Engine and Phenology-Based Threshold Classification Method for Mapping Forest Cover and Carbon Stock Changes in Siem Reap Province, Cambodia

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The dot symbols show the locations of the Permanent Sample Plots (PSPs) in evergreen (EG), semievergreen (SEG), and deciduous forests (DD). The red dots represent semievergreen forest locations where an inventory was conducted (refer to Reference [67] for details.) The photos shown on the maps were taken during the field visits and represent flooded forests (FF), PSPs, mangroves (MG), EG, rice or paddy fields (CR), DD, SEG, and rubber plantations (RB).…”

Section: Study Areamentioning

confidence: 99%

“…For this study, we selected 4 PSPs in Siem Reap province followed by EG, SEG, and DD; 3 EG PSPs in Kokong province; and 6 PSPs (5 EG and 1 DD location) in Kampong Thom province. In addition, we geolocated 5 SEG forest inventory locations in Kratie and Ratanakiri provinces by referring to Figure 1b [67]. The forest cover types (2002 and 2006), PSPs, and SEG sampling locations were cross-validated using field reference data and high spatial resolution Google Earth images to produce the reference data for this study.…”

Section: Reference Datamentioning

confidence: 99%

Determination of Vegetation Thresholds for Assessing Land Use and Land Use Changes in Cambodia using the Google Earth Engine Cloud-Computing Platform

et al. 2019

View full text Add to dashboard Cite

As more data and technologies become available, it is important that a simple method is developed for the assessment of land use changes because of the global need to understand the potential climate mitigation that could result from a reduction in deforestation and forest degradation in the tropics. Here, we determined the threshold values of vegetation types to classify land use categories in Cambodia through the analysis of phenological behaviors and the development of a robust phenology-based threshold classification (PBTC) method for the mapping and long-term monitoring of land cover changes. We accessed 2199 Landsat collections using Google Earth Engine (GEE) and applied the Enhanced Vegetation Index (EVI) and harmonic regression methods to identify phenological behaviors of land cover categories during the leaf-shedding phenology (LSP) and leaf-flushing phenology (LFS) seasons. We then generated 722 mean phenology EVI profiles for 12 major land cover categories and determined the threshold values for selected land cover categories in the mid-LSP season. The PBTC pixel-based classified map was validated using very high-resolution (VHR) imagery. We obtained a cumulative overall accuracy of more than 88% and a cumulative overall accuracy of the referenced forest cover of almost 85%. These high accuracy values suggest that the very first PBTC map can be useful for estimating the activity data, which are critically needed to assess land use changes and related carbon emissions under the Reducing Emissions from Deforestation and forest Degradation (REDD+) scheme. We found that GEE cloud-computing is an appropriate tool to use to access remote sensing big data at scale and at no cost.

show abstract

“…Impacts by human disturbance, uncontrolled shifting cultivation, and illegal logging are higher in semi-mixed evergreen forests than in evergreen forests [ 50 – 52 ]. Despite tropical and subtropical forests attracting attention from a large number of researchers, studies on the semi-mixed evergreen forest are comparatively limited [ 53 ]. In Sri Lanka, 40% of the rural population live throughout the dry zone and intermediate zone (IZ) of the country, largely dependent on semi-mixed evergreen forests for wood, non-timber forest products (NTFPs), and fodder [ 54 , 55 ].…”

Section: Introductionmentioning

confidence: 99%

Impacts of community forestry on forest condition: Evidence from Sri Lanka’s intermediate zone

2020

View full text Add to dashboard Cite

Sri Lanka's community forestry (CF) program emerged in the early 1980s following a global trend to conserve forest resources and provide benefits to the local community. However, very little is known about the effect of CF on forest resources. We assess the impacts of CF on forest conditions of semi-mixed evergreen forest in the intermediate zone of Sri Lanka using the before-after control-impact method. The study examines tree density, regeneration, woody species diversity, and evidence of disturbance as parameters to analyze the impact of the CF program. Data are analyzed using the difference in differences approach. The results show that the CF program has increased seedling and sapling density to a significant degree and reduced human disturbances. A major contribution of the CF program is that it was found to reduce invasive species and forest fires. The program reduced the amount of invasive species up to six times less than previous. The findings revealed that the impact of CF on forests may vary depending on pre-existing forest conditions, length of period to implement, perception, and decisions by local people. Community understanding and decision-making, in tandem with government policy, will weigh heavily on its future effectiveness.

show abstract

Assessment of carbon stocks of semi-evergreen forests in Cambodia

Cited by 15 publications

References 44 publications

Applications of the Google Earth Engine and Phenology-Based Threshold Classification Method for Mapping Forest Cover and Carbon Stock Changes in Siem Reap Province, Cambodia

Applications of the Google Earth Engine and Phenology-Based Threshold Classification Method for Mapping Forest Cover and Carbon Stock Changes in Siem Reap Province, Cambodia

Determination of Vegetation Thresholds for Assessing Land Use and Land Use Changes in Cambodia using the Google Earth Engine Cloud-Computing Platform

Impacts of community forestry on forest condition: Evidence from Sri Lanka’s intermediate zone

Contact Info

Product

Resources

About