Desertification Detection Based on Landsat Time-Series Images and Variational Auto-Encoder: Application in Jeffera, Tunisia

Abstract

Desertification detection is a challenging problem because of the dynamic climate change and human activity. This paper proposes a methodology to detect regions with desertification risk based on Landsat imagery (RGB bands and NDVI) and variational auto-Encoder (VAE). The considered features (RGB and NDVI) are extracted from multitemporal Landsat optical images taken from the freely available Landsat program from 2001 to 2020. The arid region around Jeffara in Medenine (Tunisia) is selected as a study area. The VAE model was evaluated and compared with two deep learning models: convolutional neural network (CNN), convolutional recurrent neural network (CNN_RNN), and VAE without NDVI. The comparative results showed that the VAE outperformed the other models for desertification detection, with an accuracy of over 98 %.