The present study aims to map forest canopy height by integrating ICESat-2 and Sentinel-1 data and investigate the effect of integrating forest canopy height information with Sentinel-2 data-derived spectral variables on the prediction of spatial distribution of forest aboveground biomass (AGB). Random forest (RF) algorithm was used to develop forest canopy height and AGB models. It was observed that ICESat-2 and Sentinel-1 based model was able to predict forest canopy height with R² = 0.84 and %RMSE = 4.48%. Two forest AGB models were developed, with only spectral variables and by incorporating forest height information with spectral variables. The results reflected that incorporation of forest canopy height in the forest AGB model improved the accuracy of the AGB predictions (R² = 0.83, %RMSE = 4.64%). The study presents a comprehensive methodology for mapping forest canopy height and AGB.

中文翻译：

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在印度西北部喜马拉雅山麓使用随机森林算法通过集成 ICESat-2、Sentinel-1 和 Sentinel-2 数据绘制森林高度和地上生物量图

本研究旨在通过整合 ICESat-2 和 Sentinel-1 数据绘制森林冠层高度，并研究将森林冠层高度信息与 Sentinel-2 数据衍生的光谱变量整合对森林地上生物量空间分布预测的影响（AGB ）。随机森林 (RF) 算法用于开发森林冠层高度和 AGB 模型。据观察，基于 ICESat-2 和 Sentinel-1 的模型能够以R ²  = 0.84 和 %RMSE = 4.48%预测森林冠层高度。开发了两个森林 AGB 模型，只有光谱变量，并且通过将森林高度信息与光谱变量相结合。结果表明，在森林 AGB 模型中加入森林冠层高度提高了 AGB 预测的准确性（R²  = 0.83，%RMSE = 4.64%）。该研究提出了一种绘制森林冠层高度和 AGB 的综合方法。