Data underlying the publication: Spatial pattern recognition of local high temperature increases in mountain areas based on MODIS land surface temperature images (dataset)

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. For a link that will always point to the latest version, please use
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

194

views

776

downloads

categories

keywords

high temperature increase, MODIS land surface temperature, mountain areas, spatial pattern recognition, threshold selection

geolocation

Yunnan Province, China

licence

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RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Yanxia Wang

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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