高光谱和LiDAR联合反演森林生物量研究

估算森林地上生物量(AGB)对于全球实现碳中和目标至关重要。本文以美国缅因州Howland森林为研究区域,借助地面实测样地数据,对比分析协同不同数据源(高光谱和LiDAR)和机器学习算法(随机森林、支持向量机、梯度提升决策树和K最邻近回归)的研究,以改善Howland森林的生物量估计精度。结果表明,采用LiDAR和高光谱植被指数变量模型的最佳精度分别为0.874和0.868,协同高光谱和LiDAR变量并采用梯度提升决策树回归模型的精度为0.927,即多源遥感数据要优于单一数据源。高光谱和LiDAR数据的协同使用对于提高类似于Howland地区或更广泛区域的生物量估计的准确性,具有普遍的适用性与一定的应用前景。

Study on inversion of forest biomass by LiDAR and hyperspectral

Estimating forest aboveground biomass (AGB) is critical to achieving global carbon neutral goals. In this study, Howland forest in Maine, USA is taken as the research area. With the ground measured sample site data, different data sources (airborne LiDAR and hyperspectral remote sensing data) and machine learning algorithms (random forest, support vector machine, gradient boosting decision tree and K-nearest neighbor) are compared and analyzed. It is to improve the estimation accuracy of Howland forest AGB. The results show that the optimal accuracy of the model with airborne LiDAR and hyperspectral vegetation index variables is 0.874 and 0.868 respectively. The accuracy of the regression model with the combination of airborne LiDAR and hyperspectral vegetation index variables and gradient boosting decision tree is 0.927, that is, multi-source remote sensing data is better than a single data source. The synergistic use of LiDAR and hyperspectral data has applicability and application prospects for improving the accuracy of biomass estimates in areas such as Howland and beyond.