Forest ecosystems play a critical role in regulating the ecological climate and carbon cycling,and forest height is a fundamental parameter for assessing their functional capacity.However,the acquisition of forest canopy height using single remote sensing data is subject to various constraints.Therefore,in this paper,we use high-quality discrete forest canopy height points from the spaceborne laser altimeter ICESat-2,combined with Sentinel-1,Landsat-8,and terrain data,to establish regression models of forest canopy height with different combinations of image features by using the random forest method,analyzing the impact of each feature on forest height inversion,and applying the model to forest canopy height mapping in Guangxi.The experimental results indicate that multisource remote sensing data effectively enhance the accuracy of forest canopy height estimation.Among the utilized remote sensing data,the feature importance descends in the order:optical features,terrain features,SAR features.The combination of"L8+SRTM+Sentinel-1+neighborhood mean"features achieves the highest accuracy in canopy height estimation,with the inclusion of neighborhood mean feature yielding the best results.The random forest model demonstrates precision in mapping forest canopy height.
维普
万方数据