Monitoring and predicting land use/land cover dynamics in Djelfa city, Algeria, using Google Earth Engine and a Multi Layer Perceptron Markov Chain model

Abstract

Understanding the historical and projected changes in land use and land cover (LULC) in Djelfa city is crucial for sustainable land management, considering both natural and human influences. This study employs Landsat images from the Google Earth Engine and the support vector machine (SVM) technique for LULC classification in 1990, 2005, and 2020, achieving over 90% accuracy and kappa coefficients above 88%. The Land Change Modeler (LCM) was used for detecting changes and predicting future LULC patterns, with Markov Chain (MC) and Multi Layer Perceptron (MLP) techniques applied for 2035 projections, showing an average accuracy of 83.96%. Key findings indicate a substantial urban expansion in Djelfa city, from 924.09 hectares in 1990 to 2742.30 hectares in 2020, with a projected increase leading to 1.6% of nonurban areas transitioning to urban by 2035. There has been significant growth in steppe areas, while forested, agricultural, and barren lands have seen annual declines. Projections suggest continued degradation of bare land and a slight reduction in steppe areas by 2035. These insights underscore the need for reinforced policies and measures to enhance land management practices within the region to cater to its evolving landscape and promote sustainable development.

Keywords

• land use/land cover
• google earth engine
• support vector machine
• multi layer perceptron
• markov chain
• djelfa city