森林是陆地生态系统中最大的碳库,厘清森林碳储量本底和增汇潜力对实现国家的“双碳”战略目标具有重要意义。遥感具有宏观、综合、动态、快速、可重复等特点,针对遥感技术在森林植被碳计量中的瓶颈,本文基于单木的结构和生长方程构建森林植被碳计量新体系：①融合空-地激光雷达数据,提取单木胸径、树高和冠幅结构参数,建立单木级森林样地碳储量计算方法；②以冠层高度和郁闭度为核心变量,建立具有物理解释性的像素级区域森林碳储量模型,克服机器/深度学习遥感回归反演的不确定性；③基于像素级森林碳储量模型和单木生长方程,通过预测未来森林的冠层高度和郁闭度准确估算区域森林碳汇。本文以“森林样地碳储量-区域森林碳储量-区域森林碳汇”为主线,从样地到区域是空间尺度的拓展,从碳储量到碳汇是时间尺度的延伸,以此构建基于遥感的森林植被碳计量新体系。

Objective: Forests constitute the largest carbon pools in terrestrial ecosystems, and elucidating their baseline carbon stock and carbon sink potential is crucial for attaining the nation"s "dual carbon" strategic objectives. Remote sensing, owing to its macro-scale, comprehensive, dynamic, rapid, and reproducible nature, has addressed the limitations in carbon accounting for forest vegetation. The estimation of global/regional forest vegetation carbon sinks using spectral information from satellite remote sensing images, vertical structure information collected by airborne/satellite LiDAR, and ground observation data has become a hot topic. At present, there is an urgent need to establish a forest vegetation carbon accounting method based on remote sensing to serve the national "dual carbon" goals and the demand of carbon trading market. Method: This paper introduces a novel system for forest vegetation carbon accounting, grounded in the structure and growth equations of individual trees. Specifically, it comprises: ①Integrating airborne and terrestrial LiDAR data to extract structural parameters, including diameter at breast height (DBH), tree height, and crown width, thereby establishing a carbon stock calculation method for forest plots at the individual tree level; ②Developing a pixel-level regional forest carbon stock model with physical interpretability, utilising canopy height and crown closure as key variables, to mitigate the uncertainty of machine/deep learning remote sensing regression inversion; ③Accurately estimating regional forest carbon sinks by forecasting future forest canopy height and crown closure, based on the pixel-level forest carbon stock model and individual tree growth equations. Result: ①Calculation of forest plot carbon stock. The breast height diameter and tree height parameters are extracted by ground stations and unmanned aerial vehicle (UAV) LiDAR, the biomass of individual trees is obtained by the allometric growth equation, and the carbon stock of forest plots is calculated. ②Calculation of regional forest carbon stock. The carbon stock density of forests is closely related to height. A pixel level forest carbon stock explicit model is established using forest canopy density and height, the model parameters are automatically calculated from the structure equation of individual tree (diameter at breast height-tree height-crown width). ③The prediction of regional forest carbon sinks. Derive future forest canopy closure and height using the structure and growth (tree height-tree age) equation of individual tree, combine forest carbon stock model and the latest remote sensing data to calculate and update forest carbon sinks. Conclusion: This paper adheres to the overarching theme of "forest plot carbon stock - regional forest carbon stock - regional forest carbon sink, expanding from plot to regional spatial scales and extending from carbon stock to carbon sink across temporal scales, thereby establishing a novel remote sensing-based system for forest vegetation carbon accounting.