{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["12(1)"],"submitter":["Li S"],"pubmed_abstract":["Accurate land cover data was fundamental for formulating sound land planning and sustainable development strategies. This study focused on the Tibetan Plateau (TP), a globally sensitive ecological area, and developed a locally tailored annual 30 m resolution land cover dataset from 1990 to 2023 (TPLCD). Leveraging the Google Earth Engine (GEE) platform for Landsat data processing, LandTrendr was employed to generate robust, high-precision training samples. Subsequently, random forest classification and spatiotemporal smoothing strategies were applied to precisely map the land cover dynamics of the TP. Rigorous validation through visual interpretation, authoritative third-party datasets (Geo-Wiki and GLCVSS), and thematic dataset cross-comparisons, revealed an overall accuracy of 84.8%, and a Kappa coefficient of 0.78, fully affirming the dataset's high reliability. This dataset provided invaluable empirical evidence for understanding the vulnerability and adaptability of the TP's ecosystem."],"journal":["Scientific data"],"pagination":["510"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11950319"],"repository":["biostudies-literature"],"pubmed_title":["Annual 30 m land cover dataset on the Tibetan Plateau from 1990 to 2023."],"pmcid":["PMC11950319"],"pubmed_authors":["Tao Z","Ji Q","Li S","Xu D","Sun F","Liu R","Ge Q","Liu W"],"additional_accession":[]},"is_claimable":false,"name":"Annual 30 m land cover dataset on the Tibetan Plateau from 1990 to 2023.","description":"Accurate land cover data was fundamental for formulating sound land planning and sustainable development strategies. This study focused on the Tibetan Plateau (TP), a globally sensitive ecological area, and developed a locally tailored annual 30 m resolution land cover dataset from 1990 to 2023 (TPLCD). Leveraging the Google Earth Engine (GEE) platform for Landsat data processing, LandTrendr was employed to generate robust, high-precision training samples. Subsequently, random forest classification and spatiotemporal smoothing strategies were applied to precisely map the land cover dynamics of the TP. Rigorous validation through visual interpretation, authoritative third-party datasets (Geo-Wiki and GLCVSS), and thematic dataset cross-comparisons, revealed an overall accuracy of 84.8%, and a Kappa coefficient of 0.78, fully affirming the dataset's high reliability. This dataset provided invaluable empirical evidence for understanding the vulnerability and adaptability of the TP's ecosystem.","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2026-05-29T19:03:47.892Z","creation":"2026-04-08T05:45:38.329Z"},"accession":"S-EPMC11950319","cross_references":{"pubmed":["40148347"],"doi":["10.1038/s41597-025-04759-6"]}}