基于星载激光雷达ICESat–2/ATLAS数据的森林郁闭度估测研究

Estimation of Forest Canopy Closure Based on Spaceborne LiDAR ICESat–2/ATLAS Data

以云南香格里拉市为研究区,基于ICESat–2/ATLAS数据,采用随机森林回归、梯度提升树回归及最近邻回归的方法分别建立遥感森林郁闭度估测模型,选择最优模型反演研究区光斑内的森林郁闭度。结果表明:采用随机森林建模估测森林郁闭度时效果最好,其R^(2)为0.9446,RMSE为0.0560,P为90.60%。研究得到香格里拉市内74873个有效林地光斑对应的郁闭度预测值,结合光斑中心坐标得到全市内所有光斑森林郁闭度的空间分布图。研究结果可为低纬度高海拔地区森林郁闭度遥感估测提供参考。

Taking Shangri-La City,Yunnan Province as the research area,based on ICESat‒2/ATLAS data,the remote sensing forest canopy density estimation models were established by random forest regression,gradient boosting tree regression and nearest neighbor regression,respectively.The optimal model was selected to invert the forest canopy closure within the study area spots.The results showed that random forest modeling was the best method to estimate forest canopy closure,the coefficient of determination(R^(2))was 0.9446,mean square error(RMSE)was 0.0560 and the prediction accuracy(P)was 90.60%.The predicted values of canopy closure corresponding to 74873 effective forest spots in Shangri-La City were obtained,and the spatial distribution map of canopy closure of all forest spots in the city was obtained by combining the spot center coordinates.The results can provide a reference for remote sensing estimation of forest canopy closure at low-high altitude areas.