基于冠层结构指标的森林生态系统恢复评估综述

Review of Forest Ecosystem Restoration Assessment Based on Canopy Structure Indicators

摘要: 森林生态系统在气候变化与人类的干扰下亟需有效的恢复评估方法。传统依赖植被组成和生物量的指标虽能反映生态状况，但在大尺度、长期及定量化监测中存在局限。因此，如何有效监测森林生态恢复动态，特别是在冠层尺度上的恢复过程，成为了森林生态学和遥感学研究的热点之一。本文系统综述了基于森林冠层结构的生态恢复评估进展，重点探讨了叶面积指数(LAI)、冠层覆盖度(CC)和冠层高度(CHM)三类关键指标的应用。研究表明，这些冠层指标可通过多源遥感高效获取，能够揭示森林在光合作用、水分循环和碳储量积累等过程中的恢复轨迹，补充传统指标不足。结合文章分析，阐明冠层结构在监测森林生态系统恢复动态中的优势，并提出未来应加强多源数据融合、机器学习方法与地面验证，以提升跨尺度和跨区域的恢复评估能力。该综述为森林资源管理与生态保护提供了科学依据和实践参考。

Abstract: Forest ecosystems urgently require effective restoration assessment methods under the pressures of climate change and human disturbances. While traditional indicators based on vegetation composition and biomass can reflect ecological conditions, they have limitations in large-scale, long-term, and quantitative monitoring. Therefore, effectively monitoring forest ecological restoration dynamics, particularly the restoration process at the canopy scale, has become a key focus in forest ecology and remote sensing research. This paper systematically reviews advancements in ecological restoration assessment based on forest canopy structure, with a focus on the application of three key indicators: Leaf Area Index (LAI), Canopy Cover (CC), and Canopy Height Model (CHM). The study demonstrates that these canopy indicators can be efficiently obtained through multi-source remote sensing, revealing restoration trajectories in processes such as photosynthesis, water cycling, and carbon storage accumulation, thereby supplementing the shortcomings of traditional indicators. Through analysis, the advantages of canopy structure in monitoring forest ecosystem restoration dynamics are clarified, and future research should strengthen multi-source data integration, machine learning methods, and ground validation to enhance restoration assessment capabilities across scales and regions. This review provides a scientific basis and practical reference for forest resource management and ecological conservation.