A 10 m resolution land cover map of the Tibetan Plateau with detailed vegetation types

Abstract. The Tibetan Plateau (TP) hosts a variety of vegetation types, ranging from broadleaved and needle-leaved forests at the lower altitudes and in mesic areas to alpine grassland at the higher altitudes and in xeric areas. Accurate and detailed mapping of the vegetation distribution on the TP is essential for an improved understanding of climate change effects on terrestrial ecosystems. Yet, existing land cover datasets for the TP are either provided at a low spatial resolution or have insufficient vegetation types to characterize certain unique TP ecosystems, such as the alpine scree. Here, we produced a 10 m resolution TP land cover map with 12 vegetation classes and 3 non-vegetation classes for the year 2022 (referred to as TP_LC10-2022) by leveraging state-of-the-art remote-sensing approaches including Sentinel-1 and Sentinel-2 imagery, environmental and topographic datasets, and four machine learning models using the Google Earth Engine platform. Our TP_LC10-2022 dataset achieved an overall classification accuracy of 86.5 % with a kappa coefficient of 0.854. Upon comparing it with four existing global land cover products, TP_LC10-2022 showed significant improvements in terms of reflecting local-scale vertical variations in the southeast TP region. Moreover, we found that alpine scree, which is ignored in existing land cover datasets, occupied 13.99 % of the TP region, and shrublands, which are characterized by distinct forms (deciduous shrublands and evergreen shrublands) that are largely determined by the topography and are missed in existing land cover datasets, occupied 4.63 % of the TP region. Our dataset provides a solid foundation for further analyses which need accurate delineation of these unique vegetation types in the TP. TP_LC10-2022 and the sample dataset are freely available at https://doi.org/10.5281/zenodo.8214981 (Huang et al., 2023a) and https://doi.org/10.5281/zenodo.8227942 (Huang et al., 2023b), respectively. Additionally, the classification map can be viewed at https://cold-classifier.users.earthengine.app/view/tplc10-2022 (last access: 6 June 2024).