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Land Suitability Assessment Dataset for Isatis tinctoria L. in Xinjiang Uygur Autonomous Region of China


ZHANG Ping1,2YU Xiangxiang1,2CHANG Cun1,2ZHANG Heng1,2FAN Jinglong*1,2
1 National Engineering Technology Research Center for Desert-Oasis Ecological Construction,Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences,Urumqi 830011,Xinjiang,China2 Taklimakan Desert Ecosystem Field Observation and Research Station of Xinjiang,Qiemo 841900,China

DOI:10.3974/geodb.2025.02.02.V1

Published:Feb. 2025

Visitors:2       Data Files Downloaded:0      
Data Downloaded: 无      Citations:

Key Words:

Maximum Entropy Model,envionment variables,suitable area,GIS

Abstract:

The authors used the Global Biodiversity Information Facility (GBIF) database to obtain distribution data for Isatis tinctoria L., and used ENMTools for filtering and screening. The data of 19 bioclimatic variables from WorldClim(2.1), 3 topographic data, and 9 soil characteristics data from Harmonized World Soil Database were utilized to conduct maximum entropy modeling using the MaxEnt model. Potential suitable areas for Isatis tinctoria L. in Xinjiang Uygur Autonomous Region of China under current climate conditions were simulated in GIS. The dataset includes: (1) spatial distribution data of suitable areas for Isatis tinctoria L. in Xinjiang, with spatial resolution of 30"; (2) coordinate points of Isatis tinctoria L. in Xinjiang (Cited data). The dataset is archived in .shp, .tif, .xlsx and .txt data formats, and consists of 16 data files with data size of 292 KB (Compressed into one file with 214 KB).

Foundation Item:

Xinjiang Uygur Autonomous Region, China (2022B03030)

Data Citation:

ZHANG Ping, YU Xiangxiang, CHANG Cun, ZHANG Heng, FAN Jinglong*.Land Suitability Assessment Dataset for Isatis tinctoria L. in Xinjiang Uygur Autonomous Region of China[J/DB/OL]. Digital Journal of Global Change Data Repository, 2025. https://doi.org/10.3974/geodb.2025.02.02.V1.

References:


     [1] Phillips, S. J., Anderson, R. P., Schapire, R. E. Maximum entropy modeling of species geographic distributions [J]. Ecological Modelling, 2006, 190(3-4): 231-259.
     [2] Phillips, S. J., Dudik, M. Modeling of species distributions with MaxEnt: New extensions and a comprehensive evaluation [J]. Ecography, 2008, 31(2): 161-175.
     [3] Tarnian, F., Kumar, S., Azarnivand, H., et al. Assessing the effects of climate change on the distribution of Daphne mucronata in Iran [J]. Environmental Monitoring and Assessment. 2021, 193(9): 562.
     [4] Zhao, Z. Y., Xiao, N. W., Shen, M., et al. Comparison between optimized MaxEnt and random forest modeling in predicting potential distribution: A case study with Quasipaa boulengeri in China [J]. Science of the Total Environment, 2022, 842: 156867.
     [5] Shao, M. H., Wang, L., Li, B. W., et al. MaxEnt modeling for identifying the nature reserve of Cistanche deserticola Ma under effects of the host (Haloxylon Bunge) forest and climate changes in Xinjiang, China [J]. Forests. 2022, 13(2): 189.
     [6] Fang, J. Q., Shi, J. F., Zhang, P., et al. Potential distribution projections for Senegalia senegal (L.) Britton under climate change scenarios [J]. Forests, 2024, 15(2): 379.
     [7] GBIF.org User. Occurrence Download [OL]. The Global Biodiversity Information Facility. 2024. https://doi.org/10.15468/dl.s9sy5y.
     

Data Product:

ID Data Name Data Size Operation
Co-Sponsors

Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences

The Geographical Society of China

Parteners

Committee on Data for Science and Technology (CODATA) Task Group on Preservation of and Access to Scientific and Technical Data in/for/with Developing Countries (PASTD)

Jomo Kenyatta University of Agriculture and Technology

Digital Linchao GeoMuseum