Characterizing the Patterns and Trends of Urban Growth in Saudi Arabia’s 13 Capital Cities Using a Landsat Time Series

Harnessing urban analytics and machine learning for sustainable urban development: A multidimensional framework for modeling environmental impacts of urbanization in Saudi Arabia

Sustainable urban development is crucial for managing natural resources and mitigating environmental impacts induced by rapid urbanization. This study demonstrates an integrated framework using machine learning-based urban analytics techniques to evaluate spatiotemporal urban expansion in Saudi Arabia (1987-2022) and quantify impacts on leading land, water, and air-related environmental parameters (EPs). Remote sensing and statistical techniques were applied to estimate vegetation health, built-up area, impervious surface, water bodies, soil characteristics, thermal comfort, air pollutants (PM2.5, CH4, CO, NO2, SO2), and nighttime light EPs. Regression assessment and Principal Component Analysis (PCA) were applied to assess the relationships between urban expansion and EPs. The findings highlight the substantial growth of urban areas (0.067%-0.14%), a decline in soil moisture (16%-14%), water bodies (60%-22%), a nationwide increase of PM2.5 (44 μg/m3 to 73 μg/m3) and night light intensity (0.166-9.670) concentrations resulting in significant impacts on land, water, and air quality parameters. PCA showed vegetation cover, soil moisture, thermal comfort, PM2.5, and NO2 are highly impacted by urban expansion compared to other EPs. The results highlight the need for effective and sustainable interventions to mitigate environmental impacts using green innovations and urban development by applying mixed-use development, green space preservation, green building technologies, and implementing renewable energy approaches. The framework recommended for environmental management in this study provides a robust foundation for evidence-based policies and adaptive management practices that balance economic progress and environmental sustainability. It will also help policymakers and urban planners in making informed decisions and promoting resilient urban growth.

Mapping and evaluating sustainable and unsustainable urban areas for ecological management towards achieving low-carbon city: an empirical study of Asir Region, Saudi Arabia

Urbanisation can cause a variety of environmental and health issues, which has prompted experts to evaluate degraded areas and develop management strategies aimed at promoting urban sustainability and reducing carbon emissions. In low-carbon cities, sustainable urban areas have low carbon emission and prioritised carbon reduction by implementing sustainable transportation, green infrastructure, and energy-efficient buildings. On the other hand, unsustainable urban areas tend to lack these priorities and rely heavily on non-renewable energy sources and have high carbon emission. Therefore, this study aims to identify the most sustainable and unsustainable regions in the Abha-Khamis Mushayet Twin City region of Saudi Arabia in respect to urbanisation and carbon emission during the period between 1990 and 2020. To do so, we used Landsat datasets to create land use land cover (LULC) maps and then calculated carbon storage, emission, and absorption using InVest software. Additionally, the study examined micro-climatic conditions by calculating the urban heat island (UHI) effect, which allowed determining sustainable and unsustainable regions by comparing the UHI model and carbon similarity and mismatch model using coupling coordination degree model (CCDM). The study found that during the last three decades, the LULC pattern of the region underwent significant alterations, resulting in a substantial decline in carbon storage from 710,425 Mg C/hm² in 1990 to approximately 527,012.9 Mg C/hm² in 2020. Conversely, carbon emissions were observed to be very high in areas with high built-up density, with emission levels exceeding 20 tons per annum. Whilst the areas of excess carbon have decreased significantly, the areas of excess carbon emission have increased over time, resulting in the UHI effect due to high greenhouse gases. By comparing the UHI and carbon similarity and mismatch model, the researchers found that over 280 km² of the study area is unsustainable and has increased since 1990. In contrast, only about 410 km² of the study area is currently sustainable. To promote sustainability, the study recommends several strategies such as carbon capture, utilisation, and storage; green infrastructure; and the use of renewable energy to manage carbon emissions.

Box dimension of the border of Kingdom of Saudi Arabia

Fractal dimension unlike topological dimension is (usually) a non-integer number which measures complexity, roughness, or irregularity of an object with respect to the space in which the set lies. It is used to characterize highly irregular objects in nature containing statistical self-similarity such as mountains, snowflakes, clouds, coastlines, borders etc. In this article, box dimension (a version of fractal dimension) of the border of Kingdom of Saudi Arabia (KSA) is computed using a multicore parallel processing algorithm based on the classical box-counting method. A power law relation is obtained from numerical simulations which relates the length of the border with the scale size and provides a very close estimate of the actual length of the KSA border within the scaling regions and scaling effects on the length of KSA border are considered. The algorithm presented in the article is shown to be highly scalable and efficient and the speedup of the algorithm is computed using Amdahl's and Gustafson's laws. For simulations, a high performance parallel computer is employed using Python codes and QGIS software.