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

doi:10.11707/j.1001-7488.LYKX20250770

摘要/Abstract

摘要：

森林碳储量是陆地生态系统最大且最活跃的碳库，在调节全球碳平衡和减缓气候变化中发挥着至关重要的作用，准确估算并动态监测森林碳储量是深入理解陆地生态系统碳循环过程、助力实现国家“双碳”目标的重要基础。近年来，遥感技术的迅速发展显著提升了大尺度森林碳储量的估算能力；然而，由于我国东北地区森林在林分结构、树种组成、起源和林龄上存在极大空间异质性，碳储量的精确估算仍面临较大挑战。本研究依据233篇中英文文献总结东北地区森林碳储量遥感监测方向的研究工作，并综合REDD+机制确立与《巴黎协定》实施细则等制度节点以及激光雷达全球森林结构观测能力形成与云计算平台普及等关键技术进展，将其发展历程划分为3个阶段：粗分辨率经验估算阶段（2010年以前）、中分辨率机理建模阶段（2011—2018年）以及高分辨率智能估算阶段（2019年至今）。针对各阶段的数据源特征、模型方法和主要成果，进一步分析当前研究存在的关键问题以及未来发展方向。文献分析表明，东北地区森林碳储量遥感监测技术经历了由单一粗分辨率光学遥感数据向高分辨率多模态、多时相、多尺度遥感数据深度融合的演进，同时在估计方法上实现了由经验统计模型向过程模型、再向数据驱动的深度学习模型的持续转变。相关研究显著提升了碳储量估算的精度和时空表达能力，为支撑东北地区森林碳汇能力评估、推进生态安全屏障建设与林业高质量发展提供了重要的科学依据和技术支撑。

关键词: 森林碳储量, 多源遥感, 人工智能, 激光雷达, 随机森林

Abstract:

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.

Key words: forest carbon storage, multi-source remote sensing, artificial-intelligence, light detection and ranging (LiDAR), random forest (RF)

中图分类号:

S757

全迎,邵国凡,李明泽. 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[J]. 林业科学, 2026, 62(6): 236-248.

Ying Quan,Guofan Shao,Mingze Li. From Empirical Models to Intelligent Estimation: An Innovation Pathway of Remote Sensing Technology in Monitoring Forest Carbon Stocks in Northeast China[J]. Scientia Silvae Sinicae, 2026, 62(6): 236-248.

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排序 Rank	2010年以前 Pre-2010	2011—2018年 2011—2018	2019年至今 Since 2019
1	回归模型 Regression model	中分辨率成像光谱仪 Moderate resolution imaging spectroradiometer	随机森林 Random forest
2	中分辨率成像光谱仪 Moderate resolution imaging spectroradiometer	北方生态系统生产力模拟 Boreal ecosystem productivity simulator	激光雷达 Light detection and ranging
3	Landsat 卫星专题制图仪 Landsat thematic mapper	叶面积指数 Leaf area index	机器学习 Machine learning
4	净初级生产力 Net primary productivity	激光雷达 Light detection and ranging	集成学习 Ensemble learning
5	地形因子 Topographic factors	碳循环 Carbon cycle	地基激光雷达 Terrestrial laser scanning
6	插值法 Interpolation method	地球科学激光测高系统 Geoscience laser altimeter system	哨兵2号多光谱成像仪 Sentinel-2 multispectral instrument
7	光能利用率模型 Light use efficiency model	净初级生产力 Net primary productivity	地理加权回归 Geographically weighted regression
8	神经网络 Neural network	后向散射系数 Backscattering coefficient	ALOS-2 L波段合成孔径雷达 ALOS-2 L band SAR
9	气候变化 Climate change	CASA模型 CASA model	机载激光雷达 Airborne laser scanning
10	模拟 Simulation	生态过程模型 Ecological process model	无人机激光雷达 Unmanned aerial vehicle laser scanning

年份 Year	研究区域 Study area	数据源 Data	模型 Models	精度 Accuracy	面积 Area/ hm²	碳密度 Carbon density/ (Mg·hm^?2)	碳储量 Carbon stock/Pg	参考文献 Reference
1999	东北地区 Northeast China	NOAA/AVHRR	线性回归 Linear regression	R²=0.56 RMSE=13.98 Mg·hm^?2	4.695× 10⁷	46.31	2.17	Tan et al.，2007
2000	长白山地区 Changbai Mountains Region	MODIS	森林景观模型 Forest landscape model	R²=0.89	1.571× 10⁷	50.29^*	0.79^*	王耀等，2025
2009	凉水自然保护区 Liangshui National Nature Reserve	航空影像 Aerial image	支持向量回归 Support vector regression	R²=0.88 rRMSE= 22.31%	1.14× 10⁴	121.34±43.89^*	（1.38±0.5） × 10^?3*	Meng et al.，2016
2015	黑龙江省 Heilongjiang Province	Landsat 8	地理加权随机森林 Geographically weighted random forest	R²=0.48 RMSE=59.5 t·hm^?2	2.012× 10⁷	40.88	0.82	Zhou et al.，2025
2020	东北地区 Northeast China	MODIS、ICESat	随机森林 Random forest	R²=0.6 RMSE=18.19 Mg·hm^?2	4.124× 10⁷	61.37±15.32	2.53±0.63	Luo et al.，2020
2020	吉林省 Jilin Province	ICESat-2、Landsat 8	随机森林 Random forest	R²=0.65	8.43× 10⁶	30.58^*	0.06^*	Wang et al.，2023
2022	辽宁省 Liaoning Province	UAV-RGB、GEDI、 ICESat-2、Sentinel-2	集成学习 Ensemble learning	R²=0.83 rRMSE=8.91%	5.63× 10⁶	17.71^*	0.10±0.04^*	Fu et al.，2025
2022	大兴安岭 Greater Khingan Mountains	Sentinel-1、Sentinel-2、 PALSAR	XGBoost	R²=0.67 RMSE=22.57 Mg·hm^?2	6.47× 10⁶	39.71^*	0.25^*	Li et al.，2024
2022	帽儿山林场 Mao’ershan Forest Farm	Sentinel-1、Sentinel-2	回归模型 Regression model	R²=0.66 RMSE=24.95 t·hm^?2	2.64× 10⁴	84.9^*	（4.51±0.08）× 10^?3*	Wu et al.，2025
2025	东北地区 Northeast China	GEDI、Landsat、 ALOS-2	ForestCarbonNet	R²=0.83 rRMSE=24.02%	4.145× 10⁷	45.39±6.36^*	2.74±0.38^*	Lü et al.，2025

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