Forest Emissions Reduction Assessment Using Optical Satellite Imagery and Space LiDAR Fusion for Carbon Stock Estimation

Autor: Yue Jiao, Dacheng Wang, Xiaojing Yao, Shudong Wang, Tianhe Chi, Yu Meng
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Remote Sensing, Vol 15, Iss 5, p 1410 (2023)
Druh dokumentu: article
ISSN: 2072-4292
DOI: 10.3390/rs15051410
Popis: Forests offer significant climate mitigation benefits, but existing emissions reduction assessment methodologies in forest-based mitigation activities are not scalable, which limits the development of carbon offset markets. In this study, we propose a measurement method using optical satellite imagery and space LiDAR data fusion to assess forest emissions reduction. Compared with the ALS-based carbon stock density estimation method, our approach presented a strong scalability for mapping 10 m-resolution carbon stock at a large scale. It was observed that dense canopy top height estimated by combining GEDI and Sentinel-2 could accurately predict forest carbon stock measurements estimated by the ALS-based method (R2 = 0.72). By conducting an on-site experiment of an ongoing forest carbon project in China, we found the consistency between the emissions reduction assessed by the data fusion measurement method (589,169 tCO2e) and the official ex post-monitored emissions reduction in the monitoring report (598,442 tCO2e). Our results demonstrated that forest carton stock estimation using optical satellite imagery and space LiDAR data fusion is efficient and economical for forest emissions reduction assessment. The acquisition of the data was more efficient over large areas with high frequencies using space-based technology. We further discussed the challenge of building a near-real-time monitoring system for forest-based mitigation activities by utilizing optical satellite imagery and space LiDAR data and pointed out that a quality control framework should be established to help us understand the sources of uncertainty in LiDAR-based models and improve carbon stock estimation from individual trees to forest carbon projects to meet the requirements of carbon standards better.
Databáze: Directory of Open Access Journals
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