A Historical–Projected Analysis in Land Use/Land Cover in Developing Arid Region Using Spatial Differences and Its Relation to the Climate

Nivin Abdelrahim Hasan (), Dongkai Yang and Fayha Al-Shibli
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Nivin Abdelrahim Hasan: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Dongkai Yang: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Fayha Al-Shibli: Department of Land, Water and Environment, School of Agriculture, University of Jordan, Amman 11942, Jordan

Sustainability, 2023, vol. 15, issue 3, 1-24

Abstract: Land resources are under relentless pressure from metropolitan regions, pollution, and climate shifts. The urge to monitor Land Use/Land Cover (LULC) and climate changes based on technology and sustainable management are addressed. This study analyzes the historical land cover maps to calculate growth patterns for the years 1985–2022 and uses Logistic Regression (LR) and Artificial Neural Networks (ANN) to project future dynamics forecasts for the years 2030–2040 in the Amman-Zarqa Basin (AZB). The state of the climate and the extreme indices projections of CMIP5 under RCP8.5 are linked to the corrected historical LULC maps and assessed. Given greater dry covering of large surface runoff, little rainfall, and high evapotranspiration rates, the state of the climate across the AZB notably showed instability in key climatic indices and a major exacerbation of warmth and drier soil in the basin. Both climate change and land use are contributing dynamics, but land-use alterations are much more dramatic changes than climate changes. Since the effects of climate alterations are mostly identifiable through land cover forms, land use practices put the phase that may be influenced by climate change. The results revealed that the daily extremes in 1992 are aligned with the corresponding increase of barren lands and diminished the half area of forest, cultivated, rainfed, and pasture lands in 1995. Rainfed regions were converted to agriculture or shrubland with an accuracy of 0.87, and urban encroachment caused the acreage of woodland, cultivated, rainfed, and grazing fields to decrease by almost half. Predicted land cover maps were created using LR in 2030 (Kappa = 0.99) and 2040 (Kappa = 0.90), in contrast to the ANN approach (Kappa = 0.99 for 2030 and 0.90 for 2040). By combining ANN and LR, decreasing bare soil was anticipated between 325 km 2 and 344 km 2 . As a result, 20% of the total area of the major AZB cities’ urban areas will be doubled. More subjective analysis is required to study and predict drought in the future to improve the resilience of various LULC types.

Keywords: Land Use/Land Cover; CMIP5; ERA5; drought; Logistic Regression; Artificial Neural Networks; climate variables and extremes (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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