Data underlying the publication: Spatial pattern recognition of local high temperature increases in mountain areas based on MODIS land surface temperature images
doi:10.4121/ba236ea7-650c-4bde-859b-f6b08b5c22ac.v2
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future.
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doi: 10.4121/ba236ea7-650c-4bde-859b-f6b08b5c22ac
doi: 10.4121/ba236ea7-650c-4bde-859b-f6b08b5c22ac
Datacite citation style:
Wang, Yanxia (2023): Data underlying the publication: Spatial pattern recognition of local high temperature increases in mountain areas based on MODIS land surface temperature images. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/ba236ea7-650c-4bde-859b-f6b08b5c22ac.v2
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
choose version:
version 2 - 2023-06-02 (latest)
version 1 - 2023-05-31
usage stats
260
views
1270
downloads
categories
geolocation
Yunnan Province, China
licence
CC BY 4.0
Finding out local high temperature increase (HTI) is significant for exploiting and conserving mountain climatic resources. We proposes an image pattern recognition method to identify the HTI zones by GIS and RS technologies, and used the method to produce HTI fileds, HTI strips and other auxiliary data of Yunnan Province, China. The research paper described the method and these data will be submit to the our objective SCI magazine. Therefore, this dataset contains all available data of our submission manuscript.
history
- 2023-05-31 first online
- 2023-06-02 published, posted
publisher
4TU.ResearchData
format
*.shp;grid;*.xlsx
organizations
School of Geography and Ecotourism, Southwest Forestry University, Kunming, China
DATA
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download all files (zip)
1,273,168,577 bytes unzipped
