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Bare permafrost refers to permafrost with almost no vegetation on the surface, which is an essential part of the ecosystem of the Tibetan Plateau. An accurate extraction of the boundaries of bare permafrost is vital for studying how it is being impacted by climate change. The accuracy of permafrost and bare land distribution maps is inadequate, and the spatial and temporal resolution is low. This is due to the challenges associated with obtaining significant amounts of data in high-altitude and alpine regions and the limitations of current mapping techniques in effectively integrating multiple factors. This study introduces a novel approach to extracting information about the distribution of bare permafrost. The approach introduced here involves amalgamating a sample extraction method, the fusion of multi-source remote sensing information, and a hierarchical classification strategy. Initially, the available multi-source permafrost data, expert knowledge, and refinement rules for training samples are integrated to produce extensive and consistent permafrost training samples. Using the random forest method, these samples are then utilized to create features and classify permafrost. Subsequently, a methodology utilizing a hierarchical classification approach in conjunction with machine learning techniques is implemented to identify an appropriate threshold for fractional vegetation cover, thereby facilitating the extraction of bare land. The bare permafrost boundary is ultimately derived through layer overlay analysis. The permafrost classification exhibits an overall accuracy of 90.79% and a Kappa coefficient of 0.806. The overall accuracies of the two stratified extractions in bare land were 97.47% and 96.99%, with Kappa coefficients of 0.954 and 0.911. The proposed approach exhibits superiority over the extant bare land and permafrost distribution maps. It is well-suited for retrieving vast bare permafrost regions and is valuable for acquiring bare permafrost distribution data across a vast expanse. It offers technical assistance in acquiring extended-term data on the distribution of exposed permafrost on the Tibetan Plateau. Furthermore, it facilitates the elucidation of the impact of climate change on exposed permafrost. [ABSTRACT FROM AUTHOR]

The Cenozoic rise of the Tibetan Plateau has caused a thorough reorganization of the eastern Asia river network. Unravelling the still unclear evolution of the Red River, formerly a larger paleoriver system, represents one essential step towards a better understanding of the interaction between climate and mountain building in this extremely tectonically active region of the Earth. We here present original and compiled petrographic, heavy-mineral, Sr and Nd isotope, and U Pb detrital-zircon geochronological data from Red River sediments, upper reaches of modern rivers sourced in Tibet, stratigraphic successions of inland basins, and cores drilled in offshore basins in the South China Sea, to provide new light on the Cenozoic drainage evolution across southeast Asia. The south-flowing transcontinental paleoriver that flowed through the Sichuan, Chuxiong, Simao and Khorat basins in the latest Cretaceous was disrupted in the Paleogene in response to regional uplift. The Pearl paleoriver then began to develop as the joint Bei and Dong paleorivers and started to incise westward to eventually reach the margin of the southeastern Tibetan Plateau in the Neogene. The Red paleoriver also progressively evolved into a larger catchment through the Paleogene, incorporating the Jinsha paleoriver and a tributary draining the western Yangtze block. In the early-middle Miocene, the Red paleoriver eventually lost its Tibetan headwaters when the Jinsha River was captured by the Yangtze River at the First Bend. The present drainage configuration of the Red and Pearl rivers started to develop in the mid-Miocene. [ABSTRACT FROM AUTHOR]