Based on MODIS vegetation index mod13a3 and albedo from 2000 to 2017 Mcd43a3 data product is used as data source, the resolution of data is 1km, monthly synthesis, MRT tools are used to perform data splicing, projection conversion and other image processing, and then the regional boundary of China Pakistan Economic Corridor is used to cut Python batch, and the desertification difference index (DDI) is used to evaluate the desertification degree of the China Pakistan Economic Corridor, and the normalized vegetation index (NDVI) and surface albedo (albedo) are used to evaluate the desertification degree of the China Pakistan Economic Corridor ）In order to monitor indicators, by constructing the albedo NDVI feature space and using the negative correlation between albedo and NDVI, the DDI formula was constructed, and the thematic data set of desertification classification of China Pakistan Economic Corridor from 2000 to 2017 was completed, which directly reflected the desertification degree of China Pakistan Economic Corridor and provided reference for quantitative evaluation of desertification severity. The data can provide basic data support for regional scientific research of China Pakistan Economic Corridor.
The data involved in mod13a3 and mcd43a3 include: h23v04, h23v05, h23v06, h24v04, h24v05, h24v06, with a resolution of 1km, monthly composition; the regional boundary of China Pakistan Economic Corridor.
Based on the annual NDVI and annual albedo, the annual maximum value of NDVI and the annual minimum value of albedo should be taken as the basic data.
High resolution data are used to evaluate the quality of desertification classification data. Taking the 2010 data as an example, eight Landsat 7 images were selected to verify in a small scale, and the NDVI values of 8 images were calculated, and then converted into vegetation coverage. The desertification degree of each verification area was evaluated according to the desertification index classification standard. In each image, 30 desertification verification points were selected and compared with the results of the data set, and the kappa coefficient was calculated. The overall evaluation accuracy was 80.83%, and the kappa coefficient was 73.89%
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CSTR:11738.11.ncdc.nieer.2020.1360
10.12072/casnw.046.2018.db