Assessment of Spatio-Temporal Dynamic Vegetation Evolution and Its Driving Mechanism on the Kubuqi Desert Using Multi-Source Satellite Remote Sensing

Desert vegetation is undergoing complex and diverse changes due to global climate change and human activities. To effectively utilize water resources and promote ecological recovery in desert areas, it is necessary to clarify the main driving mechanisms of vegetation growth in these regions. In this study, based on MODIS and Landsat 8 remote sensing image data, the vegetation changes and driving mechanisms before and after water diversion in the Kubuqi Desert from 2001 to 2020 were quantitatively analyzed using multiple linear regression, random forest, support vector machine, and deep neural network. The results show that the average NDVI in the study area has increased from 0.08 to 0.13 over the past 20 years, and the year of NDVI mutation corresponded with the lowest precipitation, which occurred in 2010. After the water diversion, under the combined influence of human and natural factors, NDVI increased steadily without any abrupt changes, indicating that water is the main limiting factor for vegetation growth. The change of NDVI also showed obvious spatial heterogeneity, among which the improvement of the southwest irrigation area was the most significant, and the area with NDVI above 0.1 showed an expanding trend, and the maximum value exceeded 0.4. This demonstrates that moderate water diversion can reduce desert areas, expand lake areas, and promote vegetation growth, yielding positive ecological effects. The integration of multiple linear regression, support vector machines, random forests, and deep neural network methods effectively reveals the driving mechanisms of NDVI and indirectly informs future water diversion intervals. Overall, these research results can provide a reliable reference for the efficient development of water diversion projects and have high application value.