Desertification Detection using an Improved Variational AutoEncoder-Based Approach through ETM-Landsat Satellite Data Item Type Article Authors Zerrouki, Yacine; Harrou, Fouzi; Zerrouki, Nabil; Dairi, Abdelkader; Sun, Ying Citation Zerrouki, Y., Harrou, F., Zerrouki, N., Dairi, A., & Sun, Y. (2020). Desertification Detection using an Improved Variational AutoEncoder-Based Approach through ETM-Landsat Satellite Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1–1. doi:10.1109/ jstars.2020.3042760 Eprint version Publisher's Version/PDF DOI 10.1109/JSTARS.2020.3042760 Publisher Institute of Electrical and Electronics Engineers (IEEE) Journal IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Rights This work is licensed under a Creative Commons Attribution 4.0 License. Download date 02/10/2021 14:37:08 Item License https://creativecommons.org/licenses/by/4.0/ Link to Item http://hdl.handle.net/10754/666315 This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSTARS.2020.3042760, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 1 Desertification Detection using an Improved Variational AutoEncoder-Based Approach through ETM-Landsat Satellite Data Yacine Zerrouki, Fouzi Harrou, Nabil Zerrouki, Abdelkader Dairi, Ying Sun, Abstract— The accurate land cover change detection is I. INTRODUCTION critical to improve landscape dynamics analysis and He need for environmental protection, monitoring, and mitigate desertification problems efficiently. Desertification Tsecurity is increasing over the last decades [1]. Accurate detection is a challenging problem because of the high land cover change detection (LCCD) is intertwined with several degree of similarity between some desertification cases and fields. It has a vital role in improving the valuation of burned like-desertification phenomena, such as deforestation. This areas, shifting cultivation, monitoring pollution, assessing paper provides an effective approach to detect deserted deforestation, urban growth, and desertification. All over the regions based on Landsat imagery and Variational years, with the imminent need and the availability of data AutoEncoder (VAE). The VAE model, as a deep learning- repositories, various methods for change detection have been based model, has gained special attention in features devised in the remote sensing field [2-4]. This work focuses on extraction and modeling due to its distribution-free desertification detection, which is one of the most challenging assumptions and superior nonlinear approximation. Here, applications in the LCCD. a VAE approach is applied to spectral signatures for Detecting desertification within time is undoubtedly a critical detecting pixels affected by the land cover change. The factor in mitigating desertification propagation. Two significant considered features are extracted from multi-temporal factors are increasing the propagation of desertification images and include multi-spectral information, and no phenomena, namely human and natural factors. Human factors prior image segmentation is required. The proposed include unordered urban growth, deforestation, and incorrect method was evaluated on the publicly available remote sensing data using multi-temporal Landsat optical images exploitation of semi-vegetal and vegetal regions. On the other taken from the freely available Landsat program. The arid hand, natural factors are mainly intertwined with climatic region around Biskra in Algeria is selected as a study area change, humidity, and wind. These factors accelerate the sand since it is well-known that desertification phenomena movement from the desert to surrounding places, like cities, strongly influence this region. The VAE model was roads, and low-density vegetation areas. Such propagation of evaluated and compared with restricted Boltzmann desertification negatively affects the environment and the daily machines, deep learning model, and binary clustering life of the local population. Thus, the desertification problem algorithms, including Agglomerative, Birch, expected has received essential consideration by authorities and maximization, KMean clustering algorithms, and one-class governmental agencies in many affected countries, such as support vector machine. The comparative results showed Algeria. It is worth noting that the desertification problem is not that the VAE consistently outperformed the other models new in Algeria. Several projects (e.g., several tree plantation for detecting changes to land cover, mainly deserted programs and vegetation barrier by massive deforestation) have regions. This study also showed that VAE outperformed the been launched by Algerian authorities through ministries of state of the art algorithms. environment and Agriculture and Rural Development to stop or reduce mobile dunes' progression in the Sahara and Index Terms—desertification detection, feature extraction, Landsat sensors, VAE classification. preservation of vegetation regions. As a result of these projects, more than 3.3 million hectares have been preserved by these Y. Zerrouki is with Conservatoire National des Formations à l'Environnement, programs, with a potential area of 30 million hectares [5]. Algiers, Algeria. e-mail: [email protected] F. Harrou and Y. Sun are with King Abdullah University of Science and Accordingly, the evaluation and real estimation of regions Technology (KAUST) Computer, Electrical and Mathematical Sciences and affected by desertification or preserved from desertification are Engineering (CEMSE) Division, Thuwal, 23955-6900, Saudi Arabia. e-mail: necessary. [email protected]. N. Zerrouki is with Center for Development of Advanced Technologies, DI2M laboratory, Algiers, Algeria. e-mail: Over the past decades, various remote sensing-based [email protected]. A. Dairi is with University of Science and Technology of classification techniques have been developed for Oran-Mohamed Boudiaf (USTOMB), Computer Science department, SIMPA desertification detection. For instance, in [1], a method based laboratory, Oran, Algeria. on spectral mixture analysis (SMA) and a decision-tree classification is introduced and applied to time-series images This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSTARS.2020.3042760, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2 from Landsat sensors over a time interval of 18 years (1993– Specifically, this method dynamically analyzes remote sensing 2011). Yanchi County in Ningxia, China, was selected as a data at different time points and correlation linking study area to analyze the grassland desertification situation. In desertification and driving factors. It has been shown that this [8], an innovative approach is proposed for change detection method shows a satisfying performance when applied for when dealing with the 3D shape and size of dunes obtained by predicting spatial-temporal desertification expansion in Hebei Digital Terrain Models (DTM) derived from stereo province. In [30], using MODIS data, an approach has been observations made by on-board sensors of Unmanned Aerial proposed to monitor desertification n inner Mongolia. The Vehicles (UAV). Multi-features based on topographic NDVI and vegetation overlay obtained from MODIS images parameters are also explored from synthetic aperture radar are employed to monitor the grassland desertification. Over the past two decades, some studies have been (SAR) to analyze the spatial and temporal movement of conducted to map and identify Algeria's desertification using desertification at Kubuqi Desert, China. In [11], Zanchetta et Remote Sensing data. In [9], a pixel-based approach based on al. exploited Landsat images satellite time series over a time the Maximum Likelihood algorithm is focused on the mobile interval of 30 years (1984–2013) based on the Tasselled Cap dune phenomenon in the region of In-Salah Adrar situated in transform. A new set of parameters for the Tasselled Cap southern Algerian Sahara. In [13], a detailed analysis of land transform are introduced to better fit dryland conditions and degradation and desertification dynamics in North Africa using have been applied to the city of Azraq Oasis, Jordan. In [12], a remote sensing imagery is presented. In this study, a coupled deep learning framework has been applied to oasis desert approach is proposed by combining the decision tree (DT) as recognition using UAV low altitude and remote sensing supervised classification with the Isodata algorithm as imagery. This study intends to propose a system to estimate unsupervised classification. This coupled approach is then plant communities’ degradation in oasis-desert to prevent applied to the principal components extracted from Knepper desertification and protect ecological security. The used ratios. Two regions were selected as study regions, namely recognition algorithm is based on a convolution neural network Oum Zessar (Tunisia) and Biskra (Algeria), and the achieved (CNN) using VGG16 and VGG19 models, where correct correct detection