青藏高原2022年30米空间分辨率陆地地表水数据集

Huichan Liu, G. He, Yan Peng, Gui-zhou Wang, R. Yin
{"title":"青藏高原2022年30米空间分辨率陆地地表水数据集","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":"{\"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}","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

摘要

青藏高原被称为亚洲水塔。地表水的分布及其变化与全球变化、生物多样性和与水有关的生态系统密切相关。在采集高精度陆地地表水样本的基础上,利用机器学习中的随机森林分类算法从Landsat系列卫星图像中提取陆地地表水信息,并基于2022年卫星遥感图像生成了空间分辨率为30米的青藏高原陆地地表水数据集。根据数据质量评价,数据集的总体准确率为92.9%,Kappa系数为0.84。该数据集可为青藏高原水资源监测、生态系统服务和全球变化研究提供基础数据支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A dataset of land surface water with a spatial resolution of 30 meters on the Qinghai-Tibet Plateau in 2022
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
389
期刊最新文献
A dataset of monthly light pollution indexes of rivers in China A dataset of observational key parameters in carbon and water fluxes in a semi-arid steppe, Inner Mongolia (2012 – 2020): based on a long-term manipulative experiment of precipitation pattern A dataset of daily surface water mapping products with a resolution of 0.05° on the Qinghai–Tibet Plateau during A dataset of the observations of carbon, water and heat fluxes over an alpine shrubland in Haibei (2011–2020) A dataset of carbon and water fluxes of the typical grasslands in Duolun County, Inner Mongolia during 2006-2015
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1