Deforestation Detection Based on U-Net and LSTM in Optical Satellite Remote Sensing Images

The protection and monitoring of forest resources has drawn considerable national attention. Traditional deforestation monitoring requires a lot of manpower and material resources through manual visual interpretation and manual change patterns labelling, which has problems of low efficiency and high missed alarm rate. Therefore, this paper explores the detection for deforestation changes from remote sensing images based on deep learning framework, and aims to help forestry department manage and monitor forest resources. In this paper, an U-Net+LSTM framework is used to detect the changes of deforestation from remote sensing images. The evaluation data is Sentinel-2 dataset and the study area is Guangxi Sanjiang Dong Autonomous County in China. The results show that the F1 score of the framework is as high as 0.715, which proves the proposed model can effectively detect the change from forest to bare soil in remote sensing images.