NDVI , the full name is Normalized Difference Vegetation Index, and the Chinese name is Normalized Difference Vegetation Index. This index can be used to qualitatively and quantitatively evaluate vegetation coverage and its growth vitality, and we can also simply understand it as an indicator reflecting vegetation density and health.
Before we introduced the monthly normalized difference vegetation index (NDVI) raster data from 2000 to 2022 (see the previous article), the data comes from the MOD13A3 dataset regularly released by NASA! This time we specially processed the original raster data, and processed the monthly 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. The average processing obtained the NDVI data introduced this time - the monthly normalized difference vegetation index of the three levels of China's provinces, cities and counties in Shp and Excel formats from 2000 to 2022 !
The following is a detailed introduction to the data:
Visualization
1. Provincial 2000-2022 Monthly Normalized Difference Vegetation Index
The first is the data in Shp format. The monthly normalized difference vegetation index data of 34 provincial divisions from 2000 to 2022 are summarized in a Shp file. Let’s take December 2022 as an example to preview:
2. Monthly normalized difference vegetation index at the city level from 2000 to 2022
The first is the data in Shp format. The monthly normalized difference vegetation index data of 370 cities from 2000 to 2022 are summarized in a Shp file. Let’s take December 2022 as an example to preview:
3. Monthly normalized difference vegetation index data at the county level from 2000 to 2022
The first is the data in Shp format. The monthly normalized difference vegetation index data of 2875 districts and counties from 2000 to 2022 are summarized in a Shp file. Let’s take December 2022 as an example to preview:
NDVI Data Details
Data processing instructions: Based on the original monthly 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 provincial Monthly Normalized Difference Vegetation Index at the three levels of cities and counties! 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 (monthly)
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 monthly 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 monthly normalized difference vegetation index (NDVI) data of provinces, cities and counties from 2000 to 2022, welcome Pay attention to us to learn more~