Monitoring above-ground biomass of deciduous trees in mining areas with integrated air-ground multisource remote sensing
LIAN Xugang
WANG Leixue
GAO Pan
YUAN Jiahui
CHEN Yucheng
CAI Yinfei
HU Haifeng
Abstract：Mine ecological restoration is an essential guarantee for sustainable mineral resource development,and vegeta-tion biomass serves as a key indicator to evaluate the effectiveness of ecological restoration.Traditional biomass estima-tion relies on field survey data,which suffers from limitations such as high time costs and labor intensiveness.Multi-spec-tral and LiDAR sensors mounted on drones,combined with hand-held LiDAR scanning,were used to collect parameters of evergreen and deciduous trees at the Pingshu Coal Mine in Shouyang County,Jinzhong City,Shanxi Province.By ap-plying Pearson correlation analysis to select model variables,above-ground biomass models for evergreen and deciduous trees were developed at the individual tree scale using multiple linear stepwise regression and random forest methods.Res-ults show that the random forest-based above-ground biomass model achieved the highest accuracy.Specifically,the ever-green tree model yielded an R2 of 0.78 and an RMSE of 11.043 kg/tree,while the deciduous tree model resulted in an R2 of 0.74 and an RMSE of 33.29 kg/tree.The study also applied the Maximum Likelihood Classification(MLC)algorithm to multispectral imagery with feature combinations for tree species identification.Results indicate that incorporating the near-infrared band,red-edge band,and the Normalized Difference Vegetation Index(NDVI)significantly improved the accur-acy of tree species identification.Additionally,the random forest-based single-tree above-ground biomass model,com-bined with the watershed segmentation algorithm for canopy area estimation,was used to calculate single-tree biomass density.Subsequently,an above-ground biomass inversion of the study area was performed on a per-pixel basis according to land categories.The study provides a reference for monitoring and analyzing above-ground biomass in mining areas and offers data support for quantitatively evaluating the effectiveness of ecological restoration and environmental protection in mining regions.
Keywords：air-ground synergistic remote sensingecological restorationrandom foresttree species classificationbiomass retrieval
Publication Date：2026-03-31
Online Publishing Date：2026-03-10(First online date of this platform, not the publication date of the document)
Pages：13( 90-102 )