Forest structure is a crucial component in the assessment of whether a forest is likely to act as a carbon sink under changing climate. Detailed 3D structural information about the tundra–taiga ecotone of Siberia is mostly missing and still underrepresented in current research due to the remoteness and restricted accessibility. Field based, high-resolution remote sensing can provide important knowledge for the understanding of vegetation properties and dynamics. In this study, we test the applicability of consumer-grade Unmanned Aerial Vehicles (UAVs) for rapid calculation of stand metrics in treeline forests. We reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia. Subsequently, we detected individual tree tops using a variable-window size local maximum filter and applied a marker-controlled watershed segmentation for the delineation of tree crowns. With this, we successfully detected 67.1% of the validation individuals. Simple linear regressions of observed and detected metrics show a better correlation (R²) and lower relative root mean square percentage error (RMSE%) for tree heights (mean R² = 0.77, mean RMSE% = 18.46%) than for crown diameters (mean R² = 0.46, mean RMSE% = 24.9%). The comparison between detected and observed tree height distributions revealed that our tree detection method was unable to representatively identify trees 15–20 m to capture homogeneous and representative forest stands. Additionally, we identify sources of omission and commission errors and give recommendations for their mitigation. In summary, the efficiency of the used method depends on the complexity of the forest’s stand structure.

The Nacho Nyäk Tagé (Stewart River) watershed in central Yukon (Canada) is characterized by discontinuous permafrost that is locally highly sensitive to thaw. This study aims to map the spatial distribution of permafrost terrain disturbances (PTDs) in the watershed and model thaw susceptibility to support community‐led land‐use planning by the First Nation of Nacho Nyäk Dun. A total of 246 PTDs, including active and stabilized retrogressive thaw slumps (RTSs) and minor thermoerosional features, were manually delineated from satellite imagery. PTD susceptibility was modeled using random forest machine learning, based on predictor variables reflecting the underlying topography, permafrost probability, and glacial history. The resulting susceptibility maps identified areas with high permafrost sensitivity. RTS susceptibility is highest along northwest‐ to northeast‐facing slopes and riverbanks in east–west striking valleys within the limits of the McConnell glaciation. Field visits to riverbank RTSs revealed the importance of fine‐grained and ice‐rich glaciolacustrine deposits for RTS occurrence in the study area. Model validation indicated strong predictive performance (average AUROC across tenfold cross‐validation = 0.93) of the RTS susceptibility model. We detected a significant positive trend in the number of average annual RTS initiations over the past four decades that is consistent with intensification trends observed across other permafrost landscapes. These findings provide valuable insights into the sensitivity of discontinuous permafrost in the Yukon Plateau. Areas of moderate to very high RTS susceptibility are predicted to respond strongly to anthropogenic disturbances, climate warming, changes in precipitation patterns, and wildfires.