Previously, we introduced the raster data of the normalized difference vegetation index (NDVI) from 2000 to 2022 (you can check the previous article), which comes from the MOD13A3 dataset regularly released by NASA! We specially processed the original raster data, and averaged the yearly normalized difference vegetation index grids from 2000 to 2022 according to the provincial administrative boundaries, prefecture-level city administrative boundaries, and district-level and county-level administrative boundaries in China. After processing the data introduced this time—the yearly normalized difference vegetation index data from 2000 to 2022 at the three levels of provinces, cities and counties in China in Shp and Excel formats !
Three-level visual display of NDVI provinces, cities and counties
1. The provincial-level 2000-2022 year-by-year normalized difference vegetation index (NDVI)
is firstly the data in Shp format, and the 2000-2022 year-by-year normalized difference vegetation index data summary of 34 provincial divisions In a Shp file, let's take 2022 as an example to preview:
2. City-level normalized difference vegetation index (NDVI) from 2000 to 2022
The first is the data in Shp format. The year-by-year normalized difference vegetation index data of 370 cities from 2000 to 2022 are summarized in a Shp file. Let’s take 2022 as an example to preview:
3. County-level normalized difference vegetation index (NDVI) from 2000 to 2022
The first is the data in Shp format. The year-by-year normalized difference vegetation index data of 2875 districts and counties from 2000 to 2022 are summarized in a Shp file. Let’s take 2022 as an example to preview:
NDVI Data Details
Data processing description: Based on the original year-by-year normalized difference vegetation index (NDVI) data, we averaged the grid values in each province\each prefecture-level city\each district and county, and obtained the provinces and cities Year-by-year normalized difference vegetation index at the county level! The administrative boundary data of provinces, cities and counties comes from the official account of "Dudu Chengshi"!
Original data source : https://search.earthdata.nasa.gov/search
NASA implements a policy of free global reception of MODIS data. Such a data reception and use policy is a rare, cheap and practical data resource for most scientists in my country. The NDVI data we share this time is derived from the MOD13A3 data under the MODIS dataset. For the introduction of the MODIS data set and the download method of NDVI data, please click me to jump to learn more!
Data format : Shp and Excel format
Time frame: February 2000-December 2022 (year by year)
Spatial scope: three levels of provinces, cities and counties
the data shows:
- Except for Sansha City in Hainan Province and the Nansha District and Xisha District of Sansha City, there are missing data, 34 provincial-level administrative regions, 370 cities, and 2875 districts and counties have no missing data on the yearly normalized difference vegetation index from 2000 to 2022!
- Since Jiayuguan City in Gansu Province, Dongguan City and Zhongshan City in Guangdong Province, and Danzhou City in Hainan Province do not have districts and counties, the data of the four prefecture-level cities are directly organized as district and county data in the county-level administrative division data. Inside.
At the bottom of the article is our official account business card. We will regularly introduce various types of urban data and data visualization and analysis technologies. For more details about the three-level yearly normalized difference vegetation index (NDVI) of provinces, cities and counties in my country from 2000 to 2022, Welcome everyone to pay more attention to us to understand~