Citation: Lemenkova, P. A GRASS GIS Scripting Framework for Monitoring Changes in the Ephemeral Salt Lakes of Chotts Melrhir and Merouane, Algeria. Appl. Syst. Innov. 2023, 6, 61. https:// doi.org/10.3390/asi6040061 Academic Editor: Subhas Mukhopadhyay Received: 10 May 2023 Revised: 28 May 2023 Accepted: 20 June 2023 Published: 25 June 2023 Copyright: © 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Article A GRASS GIS Scripting Framework for Monitoring Changes in the Ephemeral Salt Lakes of Chotts Melrhir and Merouane, Algeria Polina Lemenkova Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles (Brussels Faculty of Engineering), Université Libre de Bruxelles (ULB), Building L, Campus du Solbosch, ULB—LISA CP165/57, Avenue Franklin D. Roosevelt 50, 1050 Brussels, Belgium; polina.lemenkova@ulb.be; Tel.: +32-471-86-04-59 Abstract: Automated classification of satellite images is a challenging task that enables the use of remote sensing data for environmental modeling of Earth’s landscapes. In this document, we implement a GRASS GIS-based framework for discriminating land cover types to identify changes in the endorheic basins of the ephemeral salt lakes Chott Melrhir and Chott Merouane, Algeria; we employ embedded algorithms for image processing. This study presents a dataset of the nine Landsat 8–9 OLI/TIRS satellite images obtained from the USGS for a 9-year period, from 2014 to 2022. The images were analyzed to detect changes in water levels in ephemeral lakes that experience temporal fluctuations; these lakes are dry most of the time and are fed with water during rainy periods. The unsupervised classification of images was performed using GRASS GIS algorithms through several modules: ‘i.cluster’ was used to generate image classes; ‘i.maxlik’ was used for classification using the maximal likelihood discriminant analysis, and auxiliary modules, such as ‘i.group’, ‘r.support’, ‘r.import’, etc., were used. This document includes technical descriptions of the scripts used for image processing with detailed comments on the functionalities of the GRASS GIS modules. The results include the identified variations in the ephemeral salt lakes within the Algerian part of the Sahara over a 9-year period (2014–2022), using a time series of Landsat OLI/TIRS multispectral images that were classified using GRASS GIS. The main strengths of the GRASS GIS framework are the high speed, accuracy, and effectiveness of the programming codes for image processing in environmental monitoring. The presented GitHub repository, which contains scripts used for the satellite image analysis, serves as a reference for the interpretation of remote sensing data for the environmental monitoring of arid and semi-arid areas of Africa. Keywords: Sahara Desert; Africa; salt lake; cartography; image analysis; satellite image; remote sensing; geography; GRASS GIS; mapping 1. Introduction 1.1. Background Detecting changes in the water levels of ephemeral salt lakes using remote sensing data is an important task in the hydrological analysis of arid regions. It forms the foundation for higher-level applications, such as environmental monitoring, water resource management, climate change studies, and the analysis of groundwater distribution. Located in Northern Africa’s Maghreb region in the Sahara Desert, salt lakes (or “chotts”) are subject to temporal water level fluctuations throughout the calendar year. Chotts remain dry for most of the year and are fed by water during the winter, occasional rains, and wadi. Monitoring changes in the water supplies of chotts is possible by using satellite image classification. This is based on evaluating spectral reflectances of objects that are visible on the surface [1]. Changes in lake contours are used as indicators for monitoring land cover changes and hydrological modeling through the variability of contours in ephemeral stream beds. Satellite images are key data sources in computational ecology and environmental monitoring. The various colors and brightness of the land cover classes visible on the Appl. Syst. Innov. 2023, 6, 61. https://doi.org/10.3390/asi6040061 https://www.mdpi.com/journal/asi