Data from: Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data

Forests are a major component of the global carbon cycle, and accurate estimation of forest carbon stocks and fluxes is important in the context of anthropogenic global change. Airborne laser scanning (ALS) data sets are increasingly recognized as outstanding data sources for high-fidelity mapping of carbon stocks at regional scales. We develop a tree-centric approach to carbon mapping, based on identifying individual tree crowns (ITCs) and species from airborne remote sensing data, from which individual tree carbon stocks are calculated. We identify ITCs from the laser scanning point cloud using a region-growing algorithm and identifying species from airborne hyperspectral data by machine learning. For each detected tree, we predict stem diameter from its height and crown-width estimate. From that point on, we use well-established approaches developed for field-based inventories: above-ground biomasses of trees are estimated using published allometries and summed within plots to estimate carbon density. We show this approach is highly reliable: tests in the Italian Alps demonstrated a close relationship between field- and ALS-based estimates of carbon stocks (r2 = 0·98). Small trees are invisible from the air, and a correction factor is required to accommodate this effect. An advantage of the tree-centric approach over existing area-based methods is that it can produce maps at any scale and is fundamentally based on field-based inventory methods, making it intuitive and transparent. Airborne laser scanning, hyperspectral sensing and computational power are all advancing rapidly, making it increasingly feasible to use ITC approaches for effective mapping of forest carbon density also inside wider carbon mapping programs like REDD++.森林是全球碳循环的核心组成部分，在人为驱动的全球气候变化背景下，精准估算森林碳储量与碳通量具有重要意义。机载激光扫描（Airborne Laser Scanning, ALS）数据集正日益被视为区域尺度下高精度碳储量制图的优质数据源。 本研究提出一种以单木为核心的碳制图方法：首先从机载遥感数据中识别单木树冠（Individual Tree Crowns, ITCs）与树种类型，进而计算单木碳储量。研究通过区域生长算法从激光扫描点云中提取单木树冠，并借助机器学习方法从机载高光谱数据中判别树种；针对每一棵识别出的树木，通过其树高与树冠宽度的估算值预测胸径。后续步骤则采用成熟的野外调查通用方法：利用已发表的异速生长方程估算树木地上生物量，再通过样地内生物量求和得到碳密度。 本研究证实该方法具备极高可靠性：在意大利阿尔卑斯山区开展的测试显示，野外调查与机载激光扫描得到的碳储量估算结果呈极强相关性（决定系数r²=0.98）。但小型树木无法被机载传感器识别，因此需要引入校正因子以修正该效应带来的误差。 相较于现有基于样区的制图方法，该单木核心法的优势在于可生成任意尺度的碳分布图，且其核心逻辑源自野外调查方法，因此具备直观性与透明性。机载激光扫描、高光谱传感与计算能力均在快速迭代升级，这使得单木树冠识别方法在包括REDD++在内的全球碳制图项目中，可更高效地应用于森林碳密度精准制图。