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

Abstract

Highlights: What are the main findings? Unveiling hidden bias and the critical role of microforms: Traditional satellite and field methods create an “illusion of accuracy” for total peatland phytomass (~93–97 t ha−1) but introduce systematic errors (7–25%) by misallocating carbon. Our LiDAR-based classification reveals that capturing fine-scale microforms (hummocks/depressions) nested within microtopography is critical, as they drive the ground-layer phytomass gradient and account for significant carbon stocks (e.g., hummocks within hollows contribute up to 6.2 ± 1.4 tonnes per landscape unit). Resolution is critical: The UAS-LiDAR microform map achieved a 95% overall accuracy (Kappa = 0.89) for microtopography, far surpassing the satellite-based map (77%, Kappa = 0.53), proving that resolving hummocks and depressions (~0.5–3 m) is essential. What are the implications of the main findings? A new standard for carbon accounting: Spatially explicit microrelief mapping via UAS-LiDAR is not an optional improvement but a necessity to avoid hidden, landscape-dependent biases that compromise carbon stock estimates and model predictions in heterogeneous peatlands. A practical, objective workflow: We provide a formalized, rule-based hierarchical classification method that replaces subjective field extrapolation with a reproducible structural template, setting a robust baseline for restoration and carbon project verification. The accurate upscaling of peatland carbon stocks is fundamentally limited by fine-scale microrelief (hummocks/depressions), which has not yet been resolved by conventional satellite or field methods. We demonstrate the critical advantage of using Uncrewed Aerial System LiDAR (UAS-LiDAR) for mapping the hierarchical microrelief of a Western Siberian ombrotrophic bog to enhance ground-layer phytomass estimation. The rule-based classification of a normalized digital terrain model generated a high-resolution microform map (overall accuracy = 79%, Kappa = 0.72). This map was used to upscale field-measured phytomass and compared against estimates generated through 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 differed fundamentally. The satellite-based method exhibited a predictable, landscape-dependent systematic bias (overestimation by 7–25% in some units) and a substantially lower microtopography accuracy (OA = 77%, Kappa = 0.53) compared to the aggregated LiDAR map (OA = 95%, Kappa = 0.89). Crucially, only the LiDAR-based approach accurately resolved the biomasses of key microforms (e.g., hummocks within hollows contributing up to 6.2 ± 1.4 tonnes per unit), which were missed or misaggregated when using 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 credible carbon accounting in heterogeneous peatlands.