森林生物量估算研究进展
Research Progress on Estimating Forest Biomass
DOI: 10.12677/ije.2026.154070, PDF,    科研立项经费支持
作者: 何 萍, 刘延坤*, 戚福坤：黑龙江省生态研究所，黑龙江 哈尔滨；黑龙江省森林生态与林业生态工程重点实验室，黑龙江 哈尔滨；陈 瑶, 刘玉龙, 李云红：黑龙江省生态研究所，黑龙江 哈尔滨；黑龙江牡丹江森林生态系统定位观测研究站，黑龙江 牡丹江
关键词: 森林生物量；传统测量方法；遥感反演；Forest Biomass； Traditional Measurement Methods； Remote Sensing Inversion
摘要: 森林生物量是评价森林生态系统生产力的重要指标，也是森林资源清查监测评价的重要参数。传统测量法和遥感估算法是开展森林生物量估算研究中广泛应用的方法。传统测量法精度高，可用于验证遥感估算的森林生物量，但费时费力，遥感估算法可获取大尺度的森林生物量，主要包括统计模型法、过程模型法和混合模型法3种。统计模型法是基于光学遥感或主动遥感数据结合实测数据建立数学模型，这类方法被广泛应用于区域森林生物量的估算研究中，过程模型法包括生理生态过程模型和光利用率模型，这类方法从机理层面上模拟森林植被生长中的光合作用、呼吸作用和养分循环等过程，具有较强的物理意义。混合模型结合了机理模型与数据驱动模型，兼顾机理模型的物理一致性以及深度学习模型的高数据驱动性能。本文综述了这3类方法获取森林生物量的研究进展以及存在的优缺点，最后提出未来的研究方向，为获取高精度的森林生物量研究提供理论参考。
Abstract: Forest biomass is a key indicator for evaluating the productivity of forest ecosystems and an important parameter for forest resource inventory, monitoring, and assessment. Traditional measurement methods and remote sensing estimation methods are widely used in forest biomass estimation research. Traditional measurement methods offer high accuracy and can be used to validate remote sensing estimates of forest biomass, but they are costly in terms of time and labor. Remote sensing estimation methods can provide forest biomass datasets at large scales. These mainly comprise three types: statistical modelling, process modelling and hybrid modelling. Statistical modeling methods establish mathematical models based on optical or active remote sensing data combined with field measurements. These methods are widely used in regional forest biomass estimation studies. Process modeling methods include physiological-ecological process models and light-use efficiency models. These methods simulate photosynthesis, respiration, and nutrient cycling processes during forest vegetation growth at the mechanistic level and possess strong physical significance. Hybrid models combine mechanistic models with data-driven models, balancing the physical consistency of mechanistic models with the high data-driven performance of deep learning models. This paper reviews the research progress, advantages, and disadvantages of these three approaches for estimating forest biomass. Lastly, we propose future research directions to provide a theoretical framework for studies aimed at obtaining high-precision forest biomass estimates.
文章引用：何萍, 刘延坤, 陈瑶, 刘玉龙, 李云红, 戚福坤. 森林生物量估算研究进展[J]. 世界生态学, 2026, 15(4): 643-652. https://doi.org/10.12677/ije.2026.154070

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