Huichan Liu, G. He, Yan Peng, Gui-zhou Wang, R. Yin
{"title":"A dataset of land surface water with a spatial resolution of 30 meters on the Qinghai-Tibet Plateau in 2022","authors":"Huichan Liu, G. He, Yan Peng, Gui-zhou Wang, R. Yin","doi":"10.11922/11-6035.csd.2023.0040.zh","DOIUrl":null,"url":null,"abstract":"The Tibetan Plateau is known as the Asian Water Tower. The distribution of surface water and its changes are closely related to global change, biodiversity and water-related ecosystems. Based on the collection of high-precision land surface water samples, we used the random forest classification algorithm in machine learning to extract land surface water information from Landsat series satellite images and produced a dataset of land surface water with a spatial resolution of 30 meters on the Qinghai-Tibet Plateau based on satellite remote sensing images in 2022. According to data quality assessment, the overall accuracy of the dataset is 92.9%, and the Kappa coefficient is 0.84. This dataset can provide foundational data support for water resource monitoring, ecosystem services, and global change research on the Qinghai-Tibet Plateau.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"China Scientific Data","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.11922/11-6035.csd.2023.0040.zh","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The Tibetan Plateau is known as the Asian Water Tower. The distribution of surface water and its changes are closely related to global change, biodiversity and water-related ecosystems. Based on the collection of high-precision land surface water samples, we used the random forest classification algorithm in machine learning to extract land surface water information from Landsat series satellite images and produced a dataset of land surface water with a spatial resolution of 30 meters on the Qinghai-Tibet Plateau based on satellite remote sensing images in 2022. According to data quality assessment, the overall accuracy of the dataset is 92.9%, and the Kappa coefficient is 0.84. This dataset can provide foundational data support for water resource monitoring, ecosystem services, and global change research on the Qinghai-Tibet Plateau.