| Citation: | Huayu Lu, Marzieh Mokarram. Deep Learning and Electromagnetic Wave Reflection-Based Automatic Detection of Sand Dune Movement and Assessment of Its Environmental Impact on Renewable Energy Plants. Journal of Earth Science, 2026, 37(4): 1777-1788. doi: 10.1007/s12583-025-0296-0
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Sand dune movement threatens surrounding landscapes and infrastructures, especially like renewable energy plants. This study aims to predict sand dune movement and assess its risks to solar power plants in the southern Gobi Desert, Qinghai Province, China. This study integrates advanced methodologies, including the very deep super-resolution (VDSR) neural network for enhancing satellite image resolution, the multi-resolution segmentation (MRS) method for optimal dune type segmentation, the U-Net neural network for classifying and delineating desert landforms, and the long short-term memory (LSTM) method for forecasting climate parameters to assess dune movement and risks to solar power plants. The results demonstrate that the neural network significantly improves image resolution and enables clearer visualization of landscape features. When combined with MRS and U-Net, this approach accurately identifies and delineates sand dunes and solar panels, which achieve precise separation of these features in the imagery. Furthermore, the LSTM method predicts a shift in wind direction toward the south and southeast—specifically between 134 and 136 degrees—in the coming years, with wind speeds ranging from 1 to 3 m/s, which poses the greatest risk to solar panels in the northern parts of the region compared to other observation points.
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