As the climate continues to warm, the thawing of ice-rich permafrost leads to changes in the polygonal patterned ground (PPG) landscape, exhibiting an array of spatial heterogeneity in trough patterns, governing permafrost stability and hydrological and ecosystem dynamics. Developing accurate methods for detecting trough areas will allow us to better understand where the degradation of PPG occurs. The Geomorphon approach is proven to be a computationally efficient method that utilizes digital elevation models (DEMs) for terrain classification across multiple scales. In this study, we firstly evaluate the appliance of the Geomorphon algorithm in trough mapping in Prudhoe Bay (PB) in Alaska and the Wudaoliang region (WDL) on the central Qinghai–Tibet Plateau. We used the optimized DEM resolution, flatness threshold (t), and search radius (L) as input parameters for Geomorphon. The accuracy of trough recognition was evaluated against that of hand-digitized troughs and field measurements, using the mean intersection over union (mIOU) and the F1 Score. By setting a classification threshold, the troughs were detected where the Geomorphon values were larger than 6. The results show that (i) the lowest t value (0°) captured the microtopograhy of the troughs, while the larger L values paired with a DEM resolution of 50 cm diminished the impact of minor noise, improving the accuracy of trough detection; (ii) the optimized Geomorphon model produced trough maps with a high accuracy, achieving mIOU and F1 Scores of 0.89 and 0.90 in PB and 0.84 and 0.87 in WDL, respectively; and (iii) compared with the polygonal boundaries, the trough maps can derive the heterogeneous features to quantify the degradation of PPG. By comparing with the traditional terrain indices for trough classification, Geomorphon provides a direct classification of troughs, thus advancing the scientific reproducibility of comparisons in PB and WDL. This work provides a valuable method that may propel future pan-Arctic studies of trough mapping.

Polygonal patterned ground (PPG) is an important remote-sensing indicator of permafrost dynamics, yet its high-resolution distribution in alpine permafrost regions remains unknown. In this study, we developed an ensemble modeling framework to map PPG in the Three-River Source Region (TRSR), northeastern Qinghai–Tibet Plateau. A total of 4200 PPG occurrence samples and multi-source environmental variables were used to train four meta models: Random Forest (RF), Support Vector Machine (SVM), Maximum Entropy (MaxEnt), and the BIOCLIM package. Model performance was evaluated using the area under the curve (AUC) and true skill statistics (TSS). An AUC-weighted ensemble model was constructed from the best-performing models. RF showed the highest accuracy, with an AUC of 0.97 and TSS of 0.85, followed by SVM and MaxEnt. The final ensemble map achieved an overall accuracy of 93.7% and a kappa coefficient of 0.91 based on validation with high-resolution satellite imagery. The mapped PPG area was 59,082 km², accounting for 25.76% of the permafrost area and 16.01% of the TRSR. PPG was mainly distributed in the Yangtze River Source Region, with limited distribution in the Yellow River Source Region and no occurrence in the Lancang River Source Region. Compared with a previous Northern Hemisphere-scale PPG distribution product, our map reduced the estimated PPG area by 21.79% and improved local spatial detail. Variable importance analysis indicated that solar radiation, elevation, freeze–thaw indices, active-layer thickness, topography, soil moisture, and ground ice jointly controlled PPG distribution. PPG mainly occurred at elevations of 4400–5000 m, on gentle slopes of 0–3°, and in areas with 30–40% ground ice content. We also found that PPG can serve as a reliable geomorphic proxy for ice-rich and thermally sensitive permafrost in alpine regions. These findings highlight the utility of PPG for permafrost dynamic assessment and associated eco-hydrological impacts in alpine permafrost regions.