Aboveground biomass (AGB) is a key parameter for assessing the carbon sequestration potential of urban ecosystems. However, traditional empirical models for AGB estimation often have poor transferability in urban environments, leading to overestimation or underestimation and limiting the ability to create continuous spatial maps of AGB. Recently, the relatively stable allometric relationships between forest structure and AGB have been further validated. With the increasing use of UAV remote sensing to monitor forest structural diversity (FSD) in urban areas, there is an urgent need to develop a method for quickly and accurately estimating AGB using FSD. This study focuses on an urban forestry demonstration base as the research area, aiming to establish an allometric growth model based on FSD to estimate AGB, grounded in the power-law relationship between forest structure and AGB. By systematically defining FSD, integrating UAV-LiDAR and multispectral data, and performing regression analysis, allometric modeling, model comparison, and accuracy assessment of extracted indicators, we thoroughly explored the optimal parameter combinations and estimation accuracy for estimating urban forest AGB using the FSD allometric model. The results show that combining FSD indicators through allometric relationships can improve AGB estimation accuracy to 80 % (R2b=0.80, RMSEb=2.79 kg/m2, MAEb=2.19 kg/m2), surpassing models that use only simplified FSD indicators (R2b=0.63). Additionally, the proposed method captures nonlinear relationships and complex interactions better than traditional MLR, avoiding the overfitting that can occur with RF and XGBoost. This study confirms that allometric relationships with FSD indicators can be used for AGB prediction, highlighting the biological and physiological significance of FSD. It provides an alternative solution for rapid and large-scale AGB assessment in Urban forest.

中文翻译：

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基于无人机-LiDAR和多光谱扫描的城市森林地上生物量空间连续估计：森林结构多样性的异速生长模型


地上生物量 （AGB） 是评估城市生态系统固碳潜力的关键参数。然而，传统的 AGB 估计经验模型在城市环境中往往传递性差，导致高估或低估，限制了创建 AGB 连续空间图的能力。近年来，森林结构与 AGB 之间相对稳定的异速生长关系得到了进一步验证。随着无人机遥感越来越多地用于监测城市地区的森林结构多样性 （FSD），迫切需要开发一种使用 FSD 快速准确地估计 AGB 的方法。本研究以城市林业示范基地为研究区域，旨在建立基于森林结构与 AGB 之间幂律关系的基于 FSD 的异速生长模型来估计 AGB。通过系统定义 FSD，整合 UAV-LiDAR 和多光谱数据，对提取的指标进行回归分析、异速生长建模、模型比较和精度评估，深入探索了使用 FSD 异速生长模型估计城市森林 AGB 的最优参数组合和估计精度。结果表明，通过异速生长关系结合 FSD 指标可以将 AGB 估计精度提高到 80% （R2b=0.80，RMSEb=2.79 kg/m2，MAEb=2.19 kg/m2），超过了仅使用简化 FSD 指标的模型 （R2b=0.63）。此外，所提出的方法比传统的 MLR 更好地捕获非线性关系和复杂的交互，避免了 RF 和 XGBoost 可能发生的过拟合。 本研究证实，与 FSD 指标的异速生长关系可用于 AGB 预测，突出了 FSD 的生物学和生理意义。它为城市森林中快速、大规模的 AGB 评估提供了一种替代解决方案。