整合主被动遥感数据特征插值与机器学习的森林地上生物量反演与制图方法

打开文本图片集

中图分类号：TP79 文献标识码：A

doi：10.37188/0PE.20253322.3460

CSTR：32169.14.OPE.20253322.3460

Abstract： Aiming at the problem of spatial sparsity of spaceborne lidar data，this paper proposed a multisource data fusion method based on feature interpolation，which realized regional-scale forest aboveground biomass（AGB）.Firstly，the three-dimensional features were extracted from GEDI L2A/L2B and ICESat-2/ATLO8 data;the data set of spot-scale feature variables was constructed by combining Sentinel-2 spectral feature variables and terrain factors. Then，the correlation analysis was carried out to eliminate the high-collinearity feature variables，and the three regression algorithms of CatBoost，RFand LightGBM were compared to identify the optimal model. Subsequently，based on CatBoost feature importance and SHAP analysis，key predictor variables were further identified.Finaly，the key feature variables of LiDAR were interpolated to obtain continuous raster features，and then the forest AGB spatial mapping was realized by the optimal regression model. The validation results demonstrated that CatBoost per formed best in spot-scale modeling （ R2=0.88 ，RMSE =78.74Mg/ha ，rRMSE =20.93% ）；the spatial mapping accuracy based on feature interpolation and multi-source data fusion is R2=0.82 ，RMSE =60.90 Mg/ha，and rRMSE =36.54% . Compared to regression mapping using optical remote sensing imagery alone，the rRMSE was reduced by approximately 34.7% . The feature interpolation strategy was used to spatially continuous the key structural variables of the laser spot and fuse them with high-resolution optical and topographic information，which can mitigate sparse laser-footprint sampling and the lack of verticalstructure information in optical images.It enhanced regional forest AGB estimation accuracy. And the method provides a valuable reference for large-scale forest carbon stock assessment and ecosystem monitoring.

Key Words： Global Ecosystem Dynamics Investigation （GEDI） ； ICESat-2； LiDAR； optical remotesensing imagery； feature interpolation； aboveground biomass mapping

1引言

森林在陆地生态系统中发挥着关键的碳储存和气候调节功能，其中森林地上生物量作为全球碳循环中的重要组成部分，是衡量森林碳汇能力与碳循环过程不可或缺的核心参数，能够直接反映森林的固碳潜力与生产力水平[2]。（剩余17699字）