从经验模型到智能估算:东北森林碳储量遥感监测技术革新路径OA

Forest carbon storage plays a crucial role in regulating the global carbon cycle and mitigating climate change.Accurate estimation and dynamic monitoring of forest carbon stocks constitute a fundamental basis for advancing the understanding of terrestrial ecosystem carbon cycling processes and for achieving national carbon neutrality and carbon peaking goals.In recent years,development in remote sensing technology has substantially enhanced the capability for large-scale estimation of forest carbon storage.However,accurate carbon stock estimation in northeast China still faces challenging due to the region's high spatial heterogeneities in forest structure,species composition,origin,and age.This study systematically reviews research progress in forest carbon monitoring in northeast China based on 233 Chinese and English literature,and integrates key institutional milestones in global climate governance,including the establishment of the REDD+mechanism and the implementation guidelines of the Paris Agreement,together with major technological advances such as the emergence of spaceborne LiDAR-enabled global forest structural observations and the widespread adoption of cloud-computing platforms.The development process is divided into three stages:coarse-resolution empirical estimation(pre-2010),medium-resolution mechanistic modeling(2011-2018),and high-resolution intelligent estimation(since 2019).Based on the characteristics of data sources,modeling approaches,and major achievements at each stage,a comprehensive analysis was conducted on the key challenges in current research and future development directions.Literature analysis indicated that forest carbon stock monitoring in northeast China has evolved from sole reliance on coarse resolution optical remote sensing data to the deep integration of high-resolution multimodal,multi-temporal,and multi-scale remote sensing datasets.Concurrently,estimation methods have transitioned from empirical statistical models to process-based models and,more recently,to data-driven deep learning approaches.These advances have significantly improved the accuracy and spatiotemporal representativeness of forest carbon stock estimates,providing essential scientific evidence and technical support for assessing forest carbon sink capacity,strengthening the shields for ecological security,and promoting high-quality forestry development in northeast China.