부산대학교 도서관

Rock glaciers – periglacial landforms commonly found in high mountain systems – are of significant scientific value for inferring the presence of permafrost, understanding mountain hydrology, and assessing climate impacts on high mountain environments. However, inventories remain patchy in many alpine regions, and as a result they are poorly understood for some areas of High Mountain Asia such as the Tibetan Plateau. To address this gap, we propose an innovative deep learning-based mapping method that utilizes a powerful neural network, DeepLabv3+, to develop a full inventory of rock glaciers across the entire Tibetan plateau, i.e., TPRoGI [v1.0]. We use visually consistent image mosaics sourced from Planet Basemaps and existing local inventories from diverse regions as input training data to develop the deep learning model. This approach generated tens of thousands of candidate rock glaciers on the plateau which were then validated and screened by several analysts. The final inventory consists of a total of 44,273 rock glaciers, covering approximately 6,000 km2, with a mean area of 0.14 km2. They are predominantly situated at elevations ranging from 4,000 to 5,500 m.a.s.l., with a mean of 4,729 m.a.s.l.. They tend to occur on slopes with gradients between 10° and 25°, with a mean of 17.7°. Across the plateau, rock glaciers are widespread in the northwestern and southeastern areas, with dense concentrations in the Western Pamir and Nyainqêntanglha, while they are sparsely distributed in the inner part. Our inventory serves as a benchmark dataset, which will be further maintained and updated in the future. This dataset constitutes a significant contribution towards understanding, future monitoring and assessment of permafrost on the Tibetan Plateau in the context of climate change. [ABSTRACT FROM AUTHOR]