UAS-LiDAR Mapping of Bog Microrelief Enhances Accuracy of Ground-Layer Phytomass Estimation

Reliable upscaling of peatland carbon stocks is fundamentally challenged by fine-scale microrelief heterogeneity, which remains unresolved by conventional field or satellite methods. We demonstrate the critical advantage of Unmanned Aerial System LiDAR (UAS-LiDAR) for mapping the hierarchical microrelief (ridges/hollows, hummocks/depressions) of a Western Siberian ombrotrophic bog to enhance ground-layer phytomass estimation. We developed and validated a straightforward, rule-based method to classify microforms from a normalized digital terrain model using optimized elevation thresholds. The resulting map was used to upscale field-measured phytomass and compared against estimates from satellite imagery (SuperView-2) and traditional field-visual extrapolation. While total landscape-level phytomass stocks were similar across methods (~93–97 t ha⁻¹), their spatial allocation among microtopographic elements differed fundamentally. Crucially, the satellite-based method exhibited a predictable, landscape-dependent systematic bias (overestimation in ryam with hollows, underestimation in ryam), which remained hidden when using only aggregate accuracy metrics. Only the LiDAR-based approach accurately resolved the biomass of critical small microforms (e.g., hummocks within hollows), which were missed or misaggregated by traditional techniques. We conclude that objective, high-resolution microrelief mapping via UAS-LiDAR is essential for spatially explicit and ecologically coherent phytomass upscaling, providing an indispensable structural template for accurate carbon accounting in heterogeneous peatlands.