气候变化领域集成服务门户(CCPortal): Quantifying aboveground biomass dynamics from charcoal degradation in Mozambique using GEDI Lidar and Landsat

CCPortal

DOI	10.1016/j.rse.2022.113367
	Quantifying aboveground biomass dynamics from charcoal degradation in Mozambique using GEDI Lidar and Landsat
	Liang, Mengyu; Duncanson, Laura; Silva, Julie A.; Sedano, Fernando
发表日期	2023
ISSN	0034-4257
EISSN	1879-0704
卷号	284
英文摘要	Understanding changes to aboveground biomass (AGB) in forests undergoing degradation is crucial for accurately and completely quantifying carbon emissions from forest loss and for environmental monitoring in the context of climate change. Monitoring forest degradation as compared to deforestation presents technical challenges because degradation involves widespread, low-intensity AGB removal under varying temporal dynamics. Charcoal production is a key driver for forest degradation in Africa and is projected to increase in the future years. In Sub-Saharan Africa (SSA), where charcoal production drives widespread ABG removal, the utility of optical remote sensing for degradation quantification is challenged by the large inter-seasonal variation and high complexities in ecosystem structure. Limited field measurements on tree structure and aboveground biomass density (AGBD) in many parts of the SSA also impose constraints. In this study, we present a novel data fusion approach combining 3D forest structure from NASA's GEDI Lidar with optical time-series data from Landsat to quantify biomass losses associated with charcoal-related forest degradation over a 10-year time period. We used machine learning models with Landsat spectral indices from the time period of limited hydric stress (LHS) as predictor variables. By applying the best performing Random Forest (RF) model to LandTrendrstabilized annual LHS Landsat composites, we produced annual forest AGBD maps from 2007 to 2019 over the Mabalane district in southern Mozambique where the dry forest ecosystem was under active charcoal-related degradation since 2008. The RF model achieved an RMSE value of 7.05 Mg/ha (RMSE% = 42%) and R2 value of 0.64 using a 10-fold cross-validation dataset. We quantified a total AGB loss of 2.12 & PLUSMN; 0.06 Megatons (Mt) over the 10-year period, which is only 6.35 & PLUSMN; 2.56% less than the total loss estimated using field-based data as previously published for the same area and time. In addition to quantifying biomass loss, we constructed annual AGBD maps that enabled the characterization of disturbance and recovery. Our framework demonstrates that fusing GEDI and Landsat data through predictive modeling can be used to quantify past forest AGBD dynamics in low biomass forests. This approach provides a satellite-based method to support REDD+ monitoring and evaluation activities in areas where field data is limited and has the potential to be extended to investigate a variety of different disturbance events.
英文关键词	GEDI; Landsat; Data fusion; Charcoal; Forest biomass; Forest degradation; Africa; Miombo; Forest recovery
语种	英语
WOS研究方向	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:001029154700001
来源期刊	REMOTE SENSING OF ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281085
作者单位	University System of Maryland; University of Maryland College Park
推荐引用方式 GB/T 7714	Liang, Mengyu,Duncanson, Laura,Silva, Julie A.,et al. Quantifying aboveground biomass dynamics from charcoal degradation in Mozambique using GEDI Lidar and Landsat[J],2023,284.
APA	Liang, Mengyu,Duncanson, Laura,Silva, Julie A.,&Sedano, Fernando.(2023).Quantifying aboveground biomass dynamics from charcoal degradation in Mozambique using GEDI Lidar and Landsat.REMOTE SENSING OF ENVIRONMENT,284.
MLA	Liang, Mengyu,et al."Quantifying aboveground biomass dynamics from charcoal degradation in Mozambique using GEDI Lidar and Landsat".REMOTE SENSING OF ENVIRONMENT 284(2023).