基于遥感的森林地上生物量估算方法综述：进展、挑战与展望,Forests

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基于遥感的森林地上生物量估算方法综述：进展、挑战与展望
Forests ( IF 2.4 ) Pub Date : 2023-05-24 , DOI: 10.3390/f14061086
Lei Tian _{1,

2} , Xiaocan Wu ₁ , Yu Tao _{1,

3} , Mingyang Li ₁ , Chunhua Qian ₄ , Longtao Liao ₁ , Wenxue Fu ₂

Affiliation

量化森林地上生物量 (AGB) 对于阐明全球碳循环和森林生态系统对气候变化的响应至关重要。在过去的五年中，遥感技术在不同尺度的森林 AGB 估算中发挥了重要作用。在这里，我们概述了基于遥感的森林 AGB 估计的进展。更详细地说，我们首先描述了遥感技术在森林AGB估计中的原理：即与AGB相关的参数的构建和使用（而不是AGB值的直接测量）。其次，我们回顾了森林 AGB 遥感数据源（包括无源光学、微波和 LiDAR）和方法（例如，经验、物理、机械和综合模型）及其局限性和优势。第三，我们讨论了由此产生的森林 AGB 估计中不确定性的可能来源，包括与遥感图像、样地调查数据、林分结构和统计模型相关的不确定性来源。最后，我们对基于遥感的森林 AGB 估计的前瞻性研究方向提出了前瞻性的观点和见解。预计遥感将在未来的森林 AGB 估算和碳循环研究中发挥越来越重要的作用。总的来说，这项综合审查可能 (1) 有利于专注于碳循环、遥感和气候变化解释的研究团体，(2) 为碳循环和全球气候变化的研究提供理论基础，(3) 为森林提供信息生态系统和碳管理，以及 (4) 帮助阐明森林对气候变化的反馈。

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Review of Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation: Progress, Challenges, and Prospects

Quantifying forest aboveground biomass (AGB) is essential for elucidating the global carbon cycle and the response of forest ecosystems to climate change. Over the past five decades, remote-sensing techniques have played a vital role in forest AGB estimation at different scales. Here, we present an overview of the progress in remote sensing-based forest AGB estimation. More in detail, we first describe the principles of remote sensing techniques in forest AGB estimation: that is, the construction and use of parameters associated with AGB (rather than the direct measurement of AGB values). Second, we review forest AGB remotely sensed data sources (including passive optical, microwave, and LiDAR) and methods (e.g., empirical, physical, mechanistic, and comprehensive models) alongside their limitations and advantages. Third, we discuss possible sources of uncertainty in resultant forest AGB estimates, including those associated with remote sensing imagery, sample plot survey data, stand structure, and statistical models. Finally, we offer forward-looking perspectives and insights on prospective research directions for remote sensing-based forest AGB estimation. Remote sensing is anticipated to play an increasingly important role in future forest AGB estimation and carbon cycle studies. Overall, this comprehensive review may (1) benefit the research communities focused on carbon cycle, remote sensing, and climate change elucidation, (2) provide a theoretical basis for the study of the carbon cycle and global climate change, (3) inform forest ecosystems and carbon management, and (4) aid in the elucidation of forest feedbacks to climate change.

更新日期：2023-05-24

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