基于无人机多光谱和激光雷达数据的荒漠梭梭林地上生物量估算OA

[Objective]To address the challenges of remote sensing estimation caused by the sparse,short,and structurally complex characteristics of Haloxylon ammodendron in desert regions,this study explores a high accuracy method for aboveground biomass(AGB)estimation in arid desert shrublands,providing technical support for carbon stock assessment.[Method]The H.ammodendron stands along the southern margin of the Gurbantunggut Desert in Xinjiang were used as the research object.Individual shrub segmentation was performed using UAV-LiDAR point cloud data,and AGB of the coverage area was estimated for expanded plots based on allometric equations.On this basis,spectral,textural,and structural features were extracted separately from UAV-MSI and UAV-LiDAR data,and random forest(RF)importance ranking was used for feature selection.Three machine learning algorithms RF,support vector machine(SVM),and extreme gradient boosting(XGBoost)were applied to develop regional-scale AGB models.Model performance was evaluated using leave-one-out cross-validation,and modeling results based on MSI features alone,LiDAR features alone,and their combination were compared.The optimal model was then used to map the spatial distribution of AGB in the H.ammodendron sites.[Result]1)Feature selection revealed that vegetation indices such as normalized difference vegetation index(NDVI),ratio vegetation index(RVI),and maximum point cloud height(Hmax)contributed significantly to AGB estimation.The combined MSI and LiDAR features exhibited a more balanced importance distribution,demonstrating strong complementarity.2)Among all modeling methods,the AGB models based solely on UAV-MSI features outperformed those based solely on LiDAR features.RF achieved an R2 of 0.82 and RMSE of 0.66 t∙hm-2,SVM achieved an R2 of 0.79 and RMSE of 0.75 t∙hm-2,while XGBoost performed best with an R2 of 0.84 and RMSE of 0.63 t∙hm-2,indicating that spectral features had greater predictive power.3)The fusion of UAV-MSI and UAV-LiDAR features further improved model accuracy.The XGBoost model combining both feature sets achieved the highest accuracy,with an R2 of 0.89 and RMSE of 0.53 t∙hm-2,confirming the complementary value of spectral and structural information.4)Among the four sampling sites,Site 1 exhibited the highest average AGB at 2.50 t∙hm-2.Sites 2,3,and 4 showed progressively lower mean AGB values(0.90,0.84,and 0.64 t∙hm-2,respectively),with over 70%of the area having AGB values below 1 t∙hm-2.AGB spatial distribution varied significantly across sites,showing a decreasing trend with increasing distance from the oasis.[Conclusion]This study has established a site-level AGB estimation workflow tailored to desert shrubs in arid regions and demonstrated the synergistic potential of combining UAV-MSI and UAV-LiDAR data in desert shrub AGB estimation.Compared to conventional field-based methods,the proposed approach offers advantages such as non-destructiveness,high resolution,and low cost,making it suitable for biomass estimation of desert shrublands in arid ecosystems.