The normalized difference vegetation index (NDVI) can accurately reflect the surface vegetation coverage. At present, NDVI time series data based on spot / vegetation and MODIS satellite remote sensing images have been widely used in the research of vegetation dynamic change monitoring, land use / cover change detection, macro vegetation cover classification and net primary productivity estimation in various scale regions. The spatial distribution data set of 1km vegetation index (NDVI) in Southeast Tibet is in MODIS（ https://ladsweb.modaps.eosdis.nasa.gov/ ）Based on the 16 day 1km surface reflectance data (mod13), the monthly vegetation index data set since 2000 is generated by the maximum synthesis method. The data set effectively reflects the distribution and change of vegetation cover in Southeast Tibet on spatial and temporal scales. It has very important reference significance for the monitoring of vegetation change, the rational utilization of vegetation resources and other fields related to ecological environment. Monthly NDVI data is the maximum value of monthly NDVI data, and the data acquisition time is from February 2000 to December 2018. The downloaded data is in grid format with a spatial resolution of 1km.

File naming and required software

The data file name is "ndviyyyymmdd", where "yyyy" represents the year, "mm" represents the month, and "DD" represents the date. The data is in grid format with a spatial resolution of 1km and can be opened using ArcGIS.

Data Citations Data citation guideline What's data citation?

Cite as:

Wang, H. (2022). Monthly 1KM spatial distribution data set of vegetation index (NDVI) in southeast Tibet (2000-2018). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Terre.tpdc.271977. CSTR: 18406.11.Terre.tpdc.271977. (Download the reference： RIS | Bibtex )

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Support Program

Second Tibetan Plateau Scientific Expedition Program

Copyright & License

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License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)