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Environmental Research and Planetary Health (ERPH)
Published by Tecno Scientifica
ISSN : ""     EISSN : 30901219     DOI : https://doi.org/10.53623/erph
Core Subject : Health, Social,
Environmental Science Health Professions Public Health
Environmental Research and Planetary Health is a multi-disciplinary journal publishing high quality and novel information about anthropogenic issues of global relevance and applicability in a wide range of environmental and human health disciplines, demonstrating environmental and health application in the real-world context. Coverage includes, but is not limited to, the following research topics and areas: Air, soil, water and biota chemical pollutants and health Analytical and bioanalytical chemistry Bioconcentration, bioaccumulation and biomagnification Biotransformation and environmental fate Contaminant behaviour and environmental processes Biomarkers Biomonitoring and adverse/toxic health effects Chemical stressors Ecological chemistry Ecotoxicology Endocrine disruption Environmental and occupational medicine Environmental biotechnology Environmental chemistry Environmental epidemiology Environmental functional materials for pollution control Environmental risks assessment and management Environmental toxicology Environment-related "omics" Food web interactions Global warming/Climate change Health, safety and environment Indoor and outdoor air pollution control Marine, freshwater and terrestrial ecosystems Occupational health Pollution detection and monitoring Public health Resource-Energy recovery during pollution control Risks and public health Solid waste management Soil and site pollution remediation Waste treatment and disposal Wastewater and sewage contaminants Water pollution control and Water security Wildlife and biota
Arjuna Subject : Ilmu Lingkungan (semua ketegori) - Ilmu Lingkungan (Lain-Lain)
Articles 11 Documents
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Drivers of Urban Growth: Cellular Automata–Markov–Analytic Hierarchy Process Modeling of Land Use Change in Amman City, Jordan Abdeljawad, Nour; Awajan, Ahmad; Adedokun, Victor
Environmental Research and Planetary Health Volume 2 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/erph.v2i1.988

Abstract

Over the past two decades, rapid urban growth significantly altered land-use patterns in Amman, raising critical concerns regarding sustainability and food security. This study utilized an integrated Cellular Automata–Markov (CA–Markov) model, in combination with the Analytical Hierarchy Process (AHP), to simulate land-use and land-cover (LULC) changes and project future scenarios for 2031 and 2040. The CA–Markov model quantified temporal land-use transitions and simulated spatial growth patterns, while AHP served as a multi-criteria decision-making tool to determine the relative influence of key driving factors on urban growth. Landsat imagery from 2004, 2013, and 2022 was classified into three main categories: built-up areas, agricultural land, and barren land. The simulation framework incorporated key driving factors, including GDP per capita, population density, road accessibility, elevation, and slope. Model validation against actual 2022 LULC data yielded a high accuracy of 91.4% and a Kappa index of 0.89, demonstrating the reliability of the predictive framework. The results projected that built-up areas would increase from 257.35 km² (32.3%) in 2022 to 309.18 km² (38.9%) in 2031 and 349.17 km² (43.9%) by 2040, accompanied by a consistent decline in both agricultural and barren lands. Spatial analysis revealed that districts with higher population density, intense economic activity, and superior road accessibility were particularly susceptible to rapid urbanization. These findings highlighted the urgent need for proactive urban planning policies to protect agricultural land and manage growing infrastructure demands. While the CA–Markov model effectively replicated historical patterns, its reliance on past trends limited its capacity to anticipate sudden policy shifts or environmental shocks. Future research should prioritize integrating higher-resolution datasets, such as QuickBird imagery and detailed cadastral or infrastructure data, to improve the spatial accuracy of LULC simulations. In addition, the development of policy-driven and scenario-based models should incorporate urban growth boundaries, agricultural land protection policies, and transportation expansion plans. This would enable more realistic forecasting of land-use dynamics and provide stronger decision-support tools for resilient and sustainable urban development.

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