Geospatial Monitoring and Prediction of land Use/Land Cover (LULC) Dynamics Based on the CA-Markov Simulation Model in Ajdabiya, Libya

Abstract

The process of modelling land use and cover (LULC) is an essential tool for predicting changes in land area in the future. This study aims to define the LULC changing patterns of the Ajdabiya region in Libya for 2016, 2020, and 2022 and predict future LULC changes for 2030, 2040, and 2050 by combining Geographical Information Systems (GIS) and remote sensing with Land Change Modelling (LCM) included in the TerrSet. Sentinel satellite images were used to identify the LULC. In this study, Ajdabiya was classified into seven classes: water, urban, agricultural land, salt marsh, flat sand, sand dunes, and sand bars. The combined algorithm was used to classify the LULC classes. All the classified LULC maps demonstrate excellent accuracy, showing more than 92% overall accuracy. Implementing Cellular Automata–Markov Chain (CA-Markov) prediction model, future scenarios for LULC were developed. According to the statistics derived from the kappa indices and agreement/disagreement marks, the outcomes of predicting the LULC changes proved satisfactory. Kappa for no information (Kno) equals 0.832, Kappa for location (Klocation) equals 0.777, and Kappa for standard (Kstandard) equals 0.772. During the study period prediction from 2022 to 2050, the values of increase in the LULC classes of urban, agricultural land, salt marsh, flat sand, and sand bar are 63.69%, 43.26%, 71.03%, 35.08%, and 0.81%, respectively. By studying the LULC changing pattern, this study will assist urban planners and policymakers in choosing appropriate sustainable development options in the study area.