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The aim of the study is, therefore, to analyze the formation of the UHIs in eight different cities in arid and semi-arid regions. The analysis is based on land cover (LC) classification (urban, green, and bare areas). The study found that bare areas had the highest mean LST values compared to the urban and green areas. The results show that the difference in temperatures between the bare areas and the urban areas ranges between 1 and 2 °C, between the bare areas and green areas ranges between 1 and 7 °C, and between the urban areas and green areas ranges between 1 and 5 °C. Furthermore, the LST values varied for each of the LULC categories, and hence some areas in the three categories had lower or higher LST values than in other categories. Hence, one category may not always have the highest LST value compared to other categories. The outcomes of this study may, therefore, have critical implications for urban planners who seek to mitigate UHI effects in arid and semi-arid urban areas.

Increasing aridity across the Middle East Region has intensified concerns about the impacts of drought in conflict-affected Northeast Syria (NES). In this study, drought dynamics and their drivers from 2000 to 2023 were analyzed by integrating ERA5-Land meteorological data, MODIS land-surface indicators, FLDAS soil moisture, and ISRIC soil properties at 250 m resolution. The integration of these multisource datasets contributes to a more comprehensive understanding of drought dynamics by combining information on weather conditions, vegetation status, and soil characteristics. The proposed drought analysis framework clarifies independent controls on meteorological, agricultural, and hydrological drought, underscoring the role of land-atmosphere feedback through soil temperature. This workflow provides a transferable approach for drought monitoring and hypothesis generation in arid regions. For this purpose, different XGBoost models were trained for the vegetation health index (VHI), the standardized precipitation-evapotranspiration index (SPEI), and surface soil-moisture anomalies, excluding target-related variables to prevent data leakage. Model interpretability was achieved using SHAP, complemented by time-series, trend, clustering, and spatial autocorrelation analyses. The models performed well (R[sup.2] = 0.86–0.90), identifying soil temperature, SPEI, relative humidity, precipitation, and soil-moisture anomalies as key predictors. Regionally, soil temperature rose (+0.069 °C yr[sup.−1]), while rainfall (−1.203 mm yr[sup.−1]) and relative humidity (−0.075% yr[sup.−1]) declined. Spatial analyses demonstrated expanding heat hotspots and persistent soil moisture deficits. Although 2018–2019 were anomalously wet, recent years (2021–2023) exhibited severe drought.

Geographical exclaves face distinctive development challenges as spatial separation creates cross-border dependencies and institutional vulnerabilities. Musandam Governorate, Oman’s exclave separated from the mainland by United Arab Emirates (UAE) territory, exemplifies how exclave status shapes development trajectories, cross-border interactions, and population resilience. This study examines Musandam’s socio-economic dynamics, development patterns, and cross-border relationships, addressing gaps in understanding how exclave residents navigate spatial discontinuity while maintaining mainland and cross-border connections. Mixed methods combined quantitative assessment using the adapted Vera Carstairs Index (VCI) across seven domains (education, skills, employment, housing, living environment, household facilities, health) with qualitative fieldwork spanning four campaigns (2019–2023). Semi-structured interviews with 47 residents across all four wilayaat (provinces), complemented by citizen science approaches engaging twelve community participants, documented mobility patterns and cross-border transactions. Secondary data from the 2010 Population Census and national statistics provided contextual depth. Findings reveal two of four Musandam wilayaat (Daba and Khasab) ranking in the lower half nationally, with low health scores (ranks 1 and 9) and education institution deficits reflecting structural integration into transnational economic and services systems. COVID-19 border closures amplified pre-existing dependencies, converting eight-month isolation into a humanitarian crisis with food shortages, medicine unavailability, and social fragmentation. Residents maintain stronger functional connections with UAE cities than with mainland Oman despite preserving national identity. Policy implications emphasize six strategic priorities: higher education institutions, transportation infrastructure, marine fisheries development, tourism enhancement, small-medium enterprise facilitation, and residential land provision.

This research aims to examine changes in the eastern part of Qatar’s shoreline from 1982 to 2018 by means of satellite imagery. Five different time periods, namely 1982, 1992, 2002, 2013, and 2018, were analysed to determine shoreline movements and shoreline variations. Techniques such as maximum likelihood classification, the normalised difference vegetation index, and tasselled cap transformation were utilised to extract the shoreline data. Linear regression rate statistics were used to quantify the rate of shoreline variations. The results indicate that the majority of shoreline accretion is a result of human activities such as coastal construction, land reclamation, and building artificial islands, which are associated with the high economic activity over the past two decades. Significant changes were observed in Lusail City, The Pearl, and Hamad International Airport (HIA). Natural sediment accumulation was also observed in Al Wakra and on the southern side of HIA. In general, there were more land gains than losses throughout the study period, and the shoreline increased by twice its previous length. The field survey confirmed the presence of sandy and rocky beaches, as well as a shoreline with protective structures such as natural limestone rocks and concrete reinforcement.

A novel coronavirus, COVID-19, appeared at the beginning of 2020 and within a few months spread worldwide. The COVID-19 pandemic had some of its greatest impacts on social, economic and religious activities. This study focused on the application of daily nighttime light (NTL) data (VNP46A2) to measure the spatiotemporal impact of the COVID-19 pandemic on the human lifestyle in Saudi Arabia at the national, province and governorate levels as well as on selected cities and sites. The results show that NTL brightness was reduced in all the pandemic periods in 2020 compared with a pre-pandemic period in 2019, and this was consistent with the socioeconomic results. An early pandemic period showed the greatest effects on the human lifestyle due to the closure of mosques and the implementation of a curfew. A slight improvement in the NTL intensity was observed in later pandemic periods, which represented Ramadan and Eid Alfiter days when Muslims usually increase the light of their houses. Closures of the two holy mosques in Makkah and Madinah affected the human lifestyle in these holy cities as well as that of Umrah pilgrims inside Saudi Arabia and abroad. The findings of this study confirm that the social and cultural context of each country must be taken into account when interpreting COVID-19 impacts, and that analysis of difference in nighttime lights is sensitive to these factors. In Saudi Arabia, the origin of Islam and one of the main sources of global energy, the preventive measures taken not only affected Saudi society; impacts spread further and reached the entire Islamic society and other societies, too.

This study offers a comprehensive analysis of the distribution, evolution, and driving factors of CO2 emissions from 1990 to 2016 at multiple spatial scales. Utilizing 26 indicators encompassing various facets of CO2 emissions, it is employed principal component analysis (PCA) and empirical orthogonal functions (EOFs) to identify the dominant characteristics of global CO2 emissions. This model retained three core components, accounting for 93% of the global CO2 variation, reflecting emission trajectories and associated economic metrics, such as Gross domestic product (GDP). The analysis differentiated the effects of these components based on countries' economic standings. Using a novel aggregated index, significant national contributors to global CO2 emissions are pinpointed. Notably, the leading contributors are found among developed nations (e.g., the United States, Canada, Japan), Gulf states (e.g., Saudi Arabia, Qatar), and emerging economies (e.g., China, Brazil, Mexico). Furthermore, these results highlight that shifts in global CO2 emissions over the past 30 years are predominantly influenced by factors like industrial emissions and GDP. Results also demonstrate a distinct relationship between a country's CO2 emissions and its physical and socioeconomic factors. Specifically, the nation's coastline length, population density in coastal regions, and the diversity of its climatic conditions significantly influence its carbon footprint. This study offers a comprehensive analysis of the distribution, evolution, and driving factors of CO2 emissions from 1990 to 2016 at multiple spatial scales. Utilizing 26 indicators encompassing various facets of CO2 emissions, the study employed principal component analysis (PCA) and empirical orthogonal functions (EOFs) to identify the dominant characteristics of global CO2 emissions. The model retained three core components, accounting for 93% of the global CO2 variation, reflecting emission trajectories and associated economic metrics, such as income level and Gross domestic product (GDP).

In the last few decades, revegetation strategies for ecosystem restoration have received great attention in dryland studies, especially those related to the restoration and revegetation of native desert plants to combat land degradation. Long-term monitoring and assessment are critical for the restoration programs to track the progress of the restoration program goals. The effectiveness and success of monitoring depend on the selected methods with respect to spatial and temporal scales. Traditional methods for vegetation monitoring are time-consuming, expensive, and require considerable labor efforts (manpower) in terms of field measurements, collecting samples, lab analysis, and the difficulty of accessing some study areas. Thus, satellite remote sensing images have been widely used to monitor land degradation and restoration programs using multispectral and hyperspectral sensors and indices such as NDVI, which is the most popular index for vegetation monitoring. However, such techniques showed many limitations when used in arid ecosystems, especially for seasonal vegetation assessments, which could mislead the monitoring and assessment of the restoration projects. This paper discusses lessons learned from previous research work, including the limitations of using satellite remote sensing in arid ecosystems and the use of UAV methods to overcome these issues and challenges to provide more accurate outcomes for seasonal assessment of vegetation in arid landscapes.

Using microdata from Statistics Canada's Labour Force Survey (LFS) and Population Census, this paper explores how spatial characteristics are correlated with temporary employment outcomes for Canada's immigrant population. Results from ordinary least square regression models suggest that census metropolitan areas and census agglomerations (CMAs/CAs) characterized by a high share of racialized immigrants, immigrants in low-income, young, aged immigrants, unemployed immigrants, and immigrants employed in health and service occupations were positively associated with an increase in temporary employment for immigrants. Furthermore, findings from principal component regression models revealed that a combination of spatial characteristics, namely CMAs/CAs characterized by both a high share of unemployed immigrants and immigrants in poverty, had a greater likelihood of immigrants being employed temporarily. The significance of this study lies in the spatial conceptualization of temporary employment for immigrants that could better inform spatially targeted employment policies, especially in the wake of the structural shift in the nature of work brought about by the COVID-19 pandemic.

This article analyses water, food, and energy security in the Gulf Cooperation Council (GCC) countries using the water-energy-food (WEF) nexus approach. The innovative focus is on identifying past and future development-based drivers of water-energy-food integration in the region. The study presents a critical review of WEF nexus in the Gulf region and identifies links to sustainable development in this area. It concludes that integrating water, energy, and food resources within the nexus is crucial for GCC nations to accomplish resource security and sustainable development.

This article analyses contemporary urbanisation patterns in Muscat, Dubai, and Doha cities, focusing on urban land cover change and the roles of governance, globalisation, oil revenues, internal migration, social factors, and urban planning forces in developing these cities. Remotely sensed and demographic data for the past 30 years were used to identify concurrent changes in urbanisation patterns, in order to gain a comprehensive understanding of the dynamics of urbanisation. The results show that the three cities have all experienced unprecedented urban transformation, with high urbanisation and population growth, but with differences in the patterns of development.