Spatial distribution of vegetation type in the Genhe River Basin based on multi-source data

Vegetation critically regulates permafrost stability and carbon-water cycles in the climate-sensitive Da Xing’anling Mountains (DXL), Eurasia’s southern permafrost margin. Warming threatens ecosystem integrity and ground ice here. Detailed distribution of vegetation type is essential for accurately identifying the extent of permafrost. Moreover, it is also necessary for modelling ecosystem dynamics and carbon cycles in this vulnerable region. This study generated a high-resolution (30 m) distribution of vegetation type for a representative cold region within the DXL using multi-source remote sensing data from 2018 to 2023, combined with field surveys conducted in July – August 2023 and July – August 2024. We focused on the Genhe River Basin (GRB) on the western slope of the DXL. This area exemplifies the region’s diverse vegetation types, including forests, wetlands, and grasslands, alongside extensive permafrost. Integrating extensive field vegetation surveys with multi-source remote sensing data, we employed a Random Forest (RF) approach to systematically incorporate and evaluate three feature selection methods for performance optimization. The optimal classification result reached an overall accuracy of 0.78 and a Kappa coefficient of 0.73. The result demonstrates high user accuracy for key types such as wetland (0.94), deciduous broad-leaved forest (0.82), mixed forest (0.65), and deciduous needle-leaved forest (0.74). This provides reliable foundational data for research requiring precise wetland delineation, such as permafrost distribution mapping and ecosystem studies. This study has some limitations. Specifically, sampling ambiguities arise from inconsistencies in multi-source data integration, particularly pronounced in dynamic vegetation areas. Furthermore, discrepancies in mixed forest classification outcomes stem from conflicting international and domestic standards, which respectively set the thresholds for dominant species at 60% and 75%. This product facilitates ecosystem restoration, carbon source-sink analysis, regional permafrost mapping, and water cycle management, while furnishing foundational data for classifying discontinuous permafrost vegetation in the DXL.