森林冠层结构调控着植物与大气间的物质交换与能量流动，形成区域微气候影响生态系统过程与功能，准确刻画冠层结构对于森林生态系统碳汇研究具有重要意义。冠层结构复杂性是一种用于描述冠层内部枝干与叶片空间分布情况的综合参数，被广泛应用于森林生态系统研究中。受限于传统地面调查方法，冠层结构复杂性只能采用树高和胸径等调查因子的统计值来表征，难以全面地刻画冠层复杂程度。随着激光雷达技术的快速发展，获取完整森林的三维结构信息成为了可能，也为全面、准确地表征冠层结构复杂性提供了新机遇。本文将厘清当前冠层结构复杂性定量化方法，重点阐述如何利用激光雷达技术表征冠层结构复杂性，综述了其在森林生态系统光、降水分配、微气候、生产力和稳定性等调控方面应用进展，并展望其在森林生态系统过程与功能研究中需要重点关注的问题与方向，以期推动我国生态遥感研究对森林冠层结构复杂性的关注，拓展近地面遥感技术在我国遥感科学和生态学研究中的应用。

The forest canopy structure plays a crucial role in regulating the exchange of substances and energy between plants and the atmosphere, thereby influencing regional microclimate and ecosystem functionality. Accurate characterization of vegetation canopy structure is of significant importance for forest ecosystem research, such carbon storage estimation, carbon cycle simulation etc. Canopy structural complexity, also known as canopy structural biodiversity, which describes the spatial distribution of branches and leaves within the canopy, has emerged as a key attribute in forest ecosystems and has found wide application in related research. For example, carbon cycle, mechanisms of community composition, sustainable forest management, wildlife conservation, forest disturbance monitoring and restoration, forest microclimate research and so on. Traditional ground-based survey methods have limitations as they only provide partial information through statistical values, which primarily involve plot-based surveys using tools such as diameter tapes, clinometers, and angle gauges to obtain individual tree information such as tree position, diameter at breast height, tree height, and crown width. The heterogeneity of these measured tree attributes and their distribution, such as diameter at breast height and tree height, or combinations of tree height, diameter at breast height, and tree density, are used to quantify canopy structure complexity, including the standard deviation, coefficient of variation, and Gini coefficient of survey attributes. However, these indices may not fully represent canopy structural complexity. The rapid development of lidar technology has enabled the rapid acquisition of three-dimensional structural information for entire forests, offering new opportunities for comprehensive and accurate characterization of canopy structure complexity. In addition to the indicators used in traditional ground-based survey methods, existing quantitative indices for canopy structure complexity based on lidar data can generally be divided into three categories: horizontal distribution indices, vertical distribution indices, and integrated distribution indices. Horizontal distribution indices primarily quantify the horizontal spatial distribution of canopy elements, without considering their vertical distribution, such as canopy cover, canopy closure, and leaf area index. Vertical distribution indices mainly describe the heterogeneity of canopy element distribution in the vertical direction while neglecting their horizontal distribution including canopy effective layers and leaf height diversity and so on. Integrated distribution indices consider both the horizontal and vertical distribution heterogeneity of canopy structure, thereby overcoming the limitations of solely considering a single direction in horizontal or vertical distribution indices, for example canopy fractal dimension, canopy roughness, and canopy entropy. Finally, we summarize the current applications of canopy structure complexity in regulating forest ecosystem functions, including light resource utilization, precipitation interception, microclimate modulation, productivity, and ecosystem stability. Additionally, there are key issues and directions that require emphasis in forest ecosystem research related to canopy structure complexity. These include investigating the cross-platform generality of lidar-based indicators, addressing scale issues, and establishing long-term monitoring methods. While the concept of forest canopy structure complexity is relatively new and has limited application in China, we anticipate that advancements in characterization methods and a deeper understanding of its implications will be facilitated by the increasing availability of long-term, multi-source remote sensing data and the utilization of various deep learning methods.