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A high-precision oasis dataset for China from remote sensing images.
Lin, Jingwu; Gui, Dongwei; Liu, Yunfei; Liu, Qi; Zhang, Siyuan; Liu, Chuang.
Affiliation
  • Lin J; State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China.
  • Gui D; University of Chinese Academy of Sciences, Beijing, 100049, China.
  • Liu Y; Cele National Station of Observation & Research for Desert Grassland Ecosystem in Xinjiang, Cele, 848300, China.
  • Liu Q; State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China. guidwei@ms.xjb.ac.cn.
  • Zhang S; University of Chinese Academy of Sciences, Beijing, 100049, China. guidwei@ms.xjb.ac.cn.
  • Liu C; Cele National Station of Observation & Research for Desert Grassland Ecosystem in Xinjiang, Cele, 848300, China. guidwei@ms.xjb.ac.cn.
Sci Data ; 11(1): 726, 2024 Jul 02.
Article de En | MEDLINE | ID: mdl-38956094
ABSTRACT
High-resolution oasis maps are imperative for understanding ecological and socio-economic development of arid regions. However, due to the late establishment and relatively niche nature of the oasis discipline, there are no high-precision datasets related to oases in the world to date. To fill this gap, detailed visual interpretation of remote sensing images on Google Earth Professional or Sentinel-2 was conducted in summer 2020, and for the first time, a high-precision dataset of China's oases (abbreviation HDCO) with a resolution of 1 meter was constructed. HDCO comprises 1,466 oases with a total area of 277,375.56 km2. The kappa coefficient for this dataset validated by the field survey was 0.8686 and the AUC value for the ROC curve was 0.935. In addition, information on the geographic coordinates, climatic conditions, major landforms, and hydrological features of each oasis was added to the attribute table of the dataset. This dataset enables researchers to quantitatively monitor location and area of oases, fosters exploration of the relationship between oases and human under climate change and urbanization.

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Langue: En Journal: Sci Data Année: 2024 Type de document: Article Pays d'affiliation: Chine

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Langue: En Journal: Sci Data Année: 2024 Type de document: Article Pays d'affiliation: Chine