Monitoring and forecasting of land use/land cover (LULC) in Al-Hassa Oasis, Saudi Arabia based on the integration of the Cellular Automata (CA) and the Cellular Automata-Markov Model (CA-Markov)

Abstract

ABSTRACT This study sought to integrate the Cellular Automata-Markov Model (CA-Markov) and the Cellular Automata (CA) using sensing data for land cover maps for the years: 1988, 2000, 2013 and 2020 to monitor, detect, and predict the spatial and temporal of Land Use/Land Cover (LULC) change in Al-Hassa Oasis, Saudi Arabia. The maximum likelihood classifier (MLC) method was used as a supervised classification algorithm to super control classification coefficients. Training areas were extracted to classify green and water from Landsat images using The Natural Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) indicators, respectively. The Markov form has been used Cellular Automata (CA)To create a Transition probability Matrix (TPM)), Transition Area Matrix (TAM) and Transition Suitability Maps (TSM), which is the main factor for obtaining to simulate the prediction of the future of land use/land cover in the years 2030 and 2050, while was used Cellular Automata-Markov Model (CA-Markov) To validate and Forecasting LULC. The results showed an expansion in urban areas by 63.5% equivalent to about 105.7 km2, and an increase in agricultural land by 6.6% equivalent to 10.7 km2 despite the loss of about 3.2% of the old agricultural land during the period 2000–2013 AD. Also, the area of marshes and water increased by about 41.7% equivalent to about 8.7 km, which led to the loss of 5.9% or the equivalent of about 139 km2 of arid lands during the past three decades. The projected LULC maps for 2030 and 2050 indicate that these patterns of arid lands that change into built-up areas and agricultural lands will continue over the next thirty years due to urban growth and the development of agricultural activities. The main strength of this study is the use of a relatively long period that allowed us to conduct a very good assessment of LULC in the study area, as well as to predict the different patterns for 2030 and 2050. Moreover, the results obtained in this study provide information to help decision makers to carry out effective practices for future planning and management of urban land growth and land use planning, especially in the Saudi Vision 2030.