Abstract
Aeolian desertification is poorly understood despite its importance for indicating environment change. Here we exploit Gaofen-1(GF-1) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to develop a quick and efficient method for large scale aeolian desertification dynamic monitoring in northern China. This method, which is based on Normalized Difference Desertification Index (NDDI) calculated by band1 & band2 of MODIS reflectance data (MODIS09A1). Then we analyze spatial-temporal change of aeolian desertification area and detect its possible influencing factors, such as precipitation, temperature, wind speed and population by Convergent Cross Mapping (CCM) model. It suggests that aeolian desertification area with population indicates feedback (bi-directional causality) between the two variables (P < 0.05), but forcing of aeolian desertification area by population is weak. Meanwhile, we find aeolian desertification area is significantly affected by temperature, as expected. However, there is no obvious forcing for the aeolian desertification area and precipitation. Aeolian desertification area with wind speed indicates feedback (bi-directional causality) between the two variables with significant signal (P < 0.01). We infer that aeolian desertification is greatly affected by natural factors compared with anthropogenic factors. For the desertification in China, we are greatly convinced that desertification prevention is better than control.
Highlights
Mean Absolute Distance (MAD)[13] was used to compare the MODIS-Normalized Difference Desertification Index (NDDI) time series image of aeolian desertification land with the MODIS-NDDI image of the study area for each pixel
Ningxia Hui Autonomous Region has a continental climate with average summer temperatures rising to 17 to 24 °C in July and average winter temperatures dropping to between −7 to −15 °C in January
Entropy and correlation coefficient values of fusion image using Principal Component Analysis (PCA) algorithm are greater than Brovey Transform (Fig. 3c,d)
Summary
Mean Absolute Distance (MAD)[13] was used to compare the MODIS-NDDI time series image of aeolian desertification land with the MODIS-NDDI image of the study area for each pixel. The difference between the estimated aeolian desertification area and the official data was smallest when the threshold value is 0.051.
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