Search Results (210)

The spatial distribution of ephemeral and perennial dryland plant species is increasingly modified and restricted by ever-changing climates and development expansion. At the interface of biodiversity conservation and developmental planning in desert landscapes is the growing need for adaptable tools in identifying and monitoring these ecologically fragile plant assemblages, habitats, and, often, heritage sites. This study evaluates usage of Sentinel-2 time series composite imagery to discriminate vegetation assemblages in a hyper-arid landscape. Spatial predictor spaces were compared to classify different vegetation communities: spectral components (PCs), vegetation indices (VIs), and their combination. Further, the uncertainty in discriminating field-verified vegetation assemblages is assessed using Shannon entropy and intensity analysis. Lastly, the intensity analysis helped to decipher and quantify class transitions between maps from different spatial predictors. We mapped plant assemblages in 2022 from combined PCs and VIs at an overall accuracy of 82.71% (95% CI: 81.08, 84.28). A high overall accuracy did not directly translate to high class prediction probabilities. Prediction by spectral components, with comparably lower accuracy (80.32, 95% CI: 78.60, 81.96), showed lower class uncertainty. Class disagreement or transition between classification models was mainly contributed by class exchange (a component of spatial allocation) and less so from quantity disagreement. Different artefacts of vegetation classes are associated with the predictor space—spectral components versus vegetation indices. This study contributes insights into using feature extraction (VIs) versus feature selection (PCs) for pixel-based classification of plant assemblages. Emphasising the ecologically sensitive vegetation in desert landscapes, the study contributes uncertainty considerations in translating optical satellite imagery to vegetation maps of arid landscapes. These are perceived to inform and support vegetation map creation and interpretation for operational management and conservation of plant biodiversity and habitats in such landscapes. Full article

Image analysis is a valuable approach in a wide array of environmental applications. Mapping land cover categories depicted from satellite images enables the monitoring of landscape dynamics. Such a technique plays a key role for land management and predictive ecosystem modelling. Satellite-based mapping of environmental dynamics enables us to define factors that trigger these processes and are crucial for our understanding of Earth system processes. In this study, a reclassification scheme of image analysis was developed for mapping the adjusted categorisation of land cover types using multispectral remote sensing datasets and Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The data included four Landsat 8–9 satellite images on 2015, 2019, 2021 and 2023. The sequence of time series was used to determine land cover dynamics. The classification scheme consisting of 17 initial land cover classes was employed by logical workflow to extract 10 key land cover types of the coastal areas of Bab-el-Mandeb Strait, southern Red Sea. Special attention is placed to identify changes in the land categories regarding the thermal saline lake, Lake Assal, with fluctuating salinity and water levels. The methodology included the use of machine learning (ML) image analysis GRASS GIS modules ‘r.reclass’ for the reclassification of a raster map based on category values. Other modules included ‘r.random’, ‘r.learn.train’ and ‘r.learn.predict’ for gradient boosting ML classifier and ‘i.cluster’ and ‘i.maxlik’ for clustering and maximum-likelihood discriminant analysis. To reveal changes in the land cover categories around the Lake of Assal, this study uses ML and reclassification methods for image analysis. Auxiliary modules included ‘i.group’, ‘r.import’ and other GRASS GIS scripting techniques applied to Landsat image processing and for the identification of land cover variables. The results of image processing demonstrated annual fluctuations in the landscapes around the saline lake and changes in semi-arid and desert land cover types over Djibouti. The increase in the extent of semi-desert areas and the decrease in natural vegetation proved the processes of desertification of the arid environment in Djibouti caused by climate effects. The developed land cover maps provided information for assessing spatial–temporal changes in Djibouti. The proposed ML-based methodology using GRASS GIS can be employed for integrating techniques of image analysis for land management in other arid regions of Africa. Full article

Clean and safe air quality is essential for public health, yet particulate matter (PM) significantly degrades air quality and poses serious health risks. The Gulf Cooperation Council (GCC) countries are particularly vulnerable to frequent and intense dust storms due to their vast desert landscapes. This study presents the first health risk assessment of carcinogenic and non-carcinogenic risks associated with exposure to PM2.5 and PM10 bound heavy metals and polycyclic aromatic hydrocarbons (PAHs) based on air quality data collected during the years of 2016–2018 near Dubai International Airport and Abu Dhabi International Airport. The results reveal no significant carcinogenic risks for lead (Pb), cobalt (Co), nickel (Ni), and chromium (Cr). Additionally, AI-based regression analysis was applied to time-series dust monitoring data to enhance predictive capabilities in environmental monitoring systems. The estimated incremental lifetime cancer risk (ILCR) from PAH exposure exceeded the acceptable threshold (10⁻⁶) in several samples at both locations. The relationship between visibility and key environmental variables—PM1, PM2.5, PM10, total suspended particles (TSPs), wind speed, air pressure, and air temperature—was modeled using three machine learning algorithms: linear regression, support vector machine (SVM) with a radial basis function (RBF) kernel, and artificial neural networks (ANNs). Among these, SVM with an RBF kernel showed the highest accuracy in predicting visibility, effectively integrating meteorological data and particulate matter variables. These findings highlight the potential of machine learning models for environmental monitoring and the need for continued assessments of air quality and its health implications in the region. Full article

Understanding the interplay between climate change, landscape patterns, and carbon sequestration is critical for sustainable ecosystem management. This study investigates the spatiotemporal evolution of vegetation Net Primary Productivity (NPP) and landscape patterns in Inner Mongolia, China, from 2000 to 2020, and evaluates their implications for carbon sink capacity under climate change. Using remote sensing data, meteorological records, and landscape metrics (CONTAG, SPLIT, IJI), we quantified the relationships between vegetation productivity, landscape connectivity, and fragmentation. Results reveal a northeast-to-southwest gradient in NPP, with high values concentrated in forested regions of the Greater Khingan Range and low values in arid western deserts. Over two decades, NPP increased by 73% in high-productivity zones, driven by rising temperatures and ecological restoration policies. Landscape aggregation (CONTAG) and patch connectivity showed strong positive correlations with NPP, while higher fragmentation values (SPLIT, IJI) negatively impacted carbon sequestration. Climate factors, particularly precipitation variability, emerged as critical drivers of NPP fluctuations, with human activities amplifying regional disparities. We propose targeted strategies—enhancing landscape connectivity, regional differentiation management, and optimizing patch structure—to bolster climate-resilient carbon sinks. These findings underscore the necessity of integrating climate-adaptive landscape planning into regional carbon neutrality frameworks, offering feasible alternatives for mitigating climate impacts in ecologically vulnerable regions. Full article

Groundwater serves as a vital resource for sustainable water supply, particularly in semi-arid regions where surface water availability is limited. This study explores groundwater potential zones in the East Desert, Qift–Qena, Egypt, using a multidisciplinary approach that integrates remote sensing (RS), geographic information systems (GIS), geostatistics, and field validation with water wells to develop a comprehensive groundwater potential mapping framework. Sentinel-2 imagery, ALOS PALSAR DEM, and SMAP datasets were utilized to derive critical thematic layers, including land use/land cover, vegetation indices, soil moisture, drainage density, slope, and elevation. The results of the groundwater potentiality map of the study area from RS reveal four distinct zones: low, moderate, high, and very high. The analysis indicates a notable spatial variability in groundwater potential, with “high” (34.1%) and “low” (33.8%) potential zones dominating the landscape, while “very high” potential areas (4.8%) are relatively scarce. The limited extent of “very high” potential zones, predominantly concentrated along the Nile River valley, underscores the river’s critical role as the primary source of groundwater recharge. Moderate potential zones include places where infiltration is possible but limited, such as gently sloping terrain or regions with slightly broken rock structures, and they account for 27.3%. These layers were combined with geostatistical analysis of data from 310 groundwater wells, which provided information on static water level (SWL) and total dissolved solids (TDS). GIS was employed to assign weights to the thematic layers based on their influence on groundwater recharge and facilitated the spatial integration and visualization of the results. Geostatistical interpolation methods ensured the reliable mapping of subsurface parameters. The assessment utilizing pre-existing well data revealed a significant concordance between the delineated potential zones and the actual availability of groundwater resources. The findings of this study could significantly improve groundwater management in semi-arid/arid zones, offering a strategic response to water scarcity challenges. Full article

Surface urban heat island (SUHI) effects are intensifying in arid desert cities due to rapid urban expansion, limited vegetation, and increasing impervious and barren land surfaces. This leads to serious ecological and socio-environmental challenges in cities. This study investigates the relationship between landscape composition and land surface temperature (LST) in Phoenix and Tucson, two rapidly growing cities located in the Sonoran Desert of the southwestern United States. Landsat-9 OLI-2/TIRS-2 satellite imagery was used to derive the LST value and calculate spectral indices. A multi-resolution grid-based approach was applied to assess spatial correlations between land cover and mean LST across varying spatial scales. The strongest positive correlations were observed with barren land, followed by impervious surfaces, while green space showed a negative correlation. Furthermore, the Urban Thermal Field Variation Index (UTFVI) and the Ecological Evaluation Index (EEI) assessments indicated that over one-third of both cities are exposed to strong SUHI effects and poor ecological quality. The findings highlight the critical need for ecologically sensitive urban planning, emphasizing the importance of the morphological structure of cities, the necessity of planning holistic blue–green infrastructure systems, and the importance of reducing impervious surfaces to decrease LST, mitigate SUHI and SUHI impacts, and increase urban resilience in desert environments. These results provide evidence-based guidance for landscape planning and climate adaptation in hyper-arid urban environments. Full article

Despite Qatar’s depressions being ecologically significant for biodiversity in arid desert regions, they remain poorly studied. This study aimed at assessing the floristic diversity of Qatar’s depression habitat and examining the key environmental drivers shaping vegetation patterns. We applied multivariate analyses, including Canonical Correspondence Analysis (CCA) and Two-Way Indicator Species Analysis (TWINSPAN), to understand the environmental factors that shape vegetation communities and classify the depression sites. A total of 139 plant species from 35 families were recorded from 26 depression sites across Qatar. Both therophytes and chamaephytes were the dominant life forms. Biregional chorotypes were the most prevalent among phytogeographical groups. CCA indicated that grazing pressure, latitude, nitrogen concentration, clay content, and soil pH were among the variables that influenced the vegetation patterns of depressions, while longitude and soil carbon content showed marginal significance in explaining the observed floristic variation. TWINSPAN classified the sites into four distinct clusters, each associated with specific indicator species and habitat conditions. Northern depressions supported higher species richness compared to central and southern depressions, which are dominated by sandy soils and experience intensive grazing patterns that reduce the floristic diversity and limited regeneration of key shrubs such as Vachellia tortilis (Forssk.) Galasso & Banfi. This study helps fill a critical knowledge gap about Qatar’s depression habitat, enhancing efforts to conserve these vulnerable ecosystems, identify ecological threats, and better understand patterns of species distribution across arid landscapes. Full article

Owl pellets offer a distinctive, noninvasive perspective on the feeding ecology and morphological traits of owl species. This study presents the first comprehensive comparison of pellet dimensions—specifically length, breadth, and mass—across all 11 resident owl species in Israel. A total of 816 pellets were collected from diverse habitats, including Mediterranean woodlands, agricultural landscapes, and arid deserts. Pellet measurements were analyzed in relation to the average body length of each species, revealing significant interspecific variation in all three dimensions. Statistical analyses confirmed strong positive correlations between body size and pellet length (r = 0.95), breadth (r = 0.91), and mass (r = 0.96), highlighting the influence of morphological constraints on pellet structure. Larger owls, such as Bubo bubo and B. ascalaphus, produced the largest pellets, whereas smaller species, such as Otus brucei and O. scops, generated notably smaller and lighter pellets, consistent with their known dietary preferences. Habitat differences and ecological specialization likely contribute to further variability in pellet morphology, even among closely related taxa. By focusing on pellet morphometrics rather than prey composition, this study offers a standardized and replicable method for interspecific comparisons. The findings support the use of pellet size as a proxy for predator body size and ecological strategies and provide a valuable baseline for future research on owl diets, habitat use, and species identification in the Middle East and elsewhere. This study enhances the utility of pellet analysis in both ecological monitoring and conservation biology. Full article

Ecological risk refers to the potential threat that landscape changes pose to ecosystem structure, function, and service sustainability, while ecological security emphasizes the ability of regional ecosystems to maintain stability and support human well-being. Developing an Ecological Security Pattern (ESP) provides a strategic approach to balance ecological protection and sustainable development. This study investigates the spatial and temporal dynamics of landscape ecological risk in the Tarim Basin and surrounding urban areas in the Xinjiang Uygur Autonomous Region, China, from 2000 to 2020. Using a combination of the InVEST model, landscape connectivity index, and circuit theory-based modeling, we identify ecological source areas and simulate ecological corridors. Ecological source areas are categorized by their ecological value and connectivity: primary sources represent high ecological value and strong connectivity, secondary sources have moderate ecological significance, and tertiary sources are of relatively lower priority but still vital for regional integrity. The results show a temporal trend of ecological risk declining between 2000 and 2010, followed by a moderate increase from 2010 to 2020. High-risk zones are concentrated in the central Tarim Basin, reflecting intensified land-use pressures and weak ecological resilience. The delineated ecological protection zones include 61,702.9 km² of primary, 146,802.5 km² of secondary, and 36,141.2 km² of tertiary ecological source areas. In total, 95 ecological corridors (23 primary, 37 secondaries, and 35 tertiary) were identified, along with 48 pinch points and 56 barrier points that require priority attention for ecological restoration. Valuable areas refer to those with high ecological connectivity and service provision potential, while vulnerable areas are characterized by high ecological risk and landscape fragmentation. This study provides a comprehensive framework for constructing ESPs in arid inland basins and offers practical insights for ecological planning in desert–oasis environments. Full article

As a typical desert oasis ecosystem in the arid region of Northwest China, the Ejina Delta plays a crucial role in regional ecological security through its vegetation dynamics and landscape pattern changes. Based on Landsat remote sensing images (1990–2020), runoff data, and vegetation landscape surveys, this study investigated the evolutionary patterns and driving mechanisms of vegetation degradation and restoration processes using Normalized Difference Vegetation Index (NDVI), landscape metrics, and Land Use Transition Matrix (LUTM) methods. The following key findings were obtained: (1) Since the implementation of the Ecological Water Diversion Project (EWDP) in the Heihe River Basin (HRB) in 2000, a significant recovery in vegetation coverage has been observed, with an NDVI growth rate of 0.0187/10 yr, which is five times faster than that in the pre-diversion period. The areas of arbor vegetation, shrubland, and grassland increased to 356.8, 689.5, and 2192.6 km², respectively. However, there is a lag of about five years for the recovery of arbor and shrub compared to grass. (2) The implementation of EWDP has effectively reversed the trend of vegetation degradation, transforming the previously herb-dominated fragmented landscape into a more integrated pattern comprising multiple vegetation types. During the degradation period (1990–2005), the landscape exhibited a high degree of fragmentation, with an average number of patches (NP) reaching 45,875. In the subsequent recovery phase (2005–2010), fragmentation was significantly reduced, with the average NP dropping to 30,628. (3) Stronger vegetation growth and higher NDVI values were observed along the riparian zone, with the West River demonstrating greater restoration effectiveness compared to the East River. This study revealed that EWDP serves as the key factor driving vegetation recovery. To enhance oasis stability, future ecological management strategies should optimize spatiotemporal water allocation while considering differential vegetation responses. Full article

The Gobi Wall is a 321 km-long structure made of earth, stone, and wood, located in the Gobi highland desert of Mongolia. It is the least understood section of the medieval wall system that extends from China into Mongolia. This study aims to determine its builders, purpose, and chronology. Additionally, we seek to better understand the ecological implications of constructing such an extensive system of walls, trenches, garrisons, and fortresses in the remote and harsh environment of the Gobi Desert. Our field expedition combined remote sensing, pedestrian surveys, and targeted excavations at key sites. The results indicate that the garrison walls and main long wall were primarily constructed using rammed earth, with wood and stone reinforcements. Excavations of garrisons uncovered evidence of long-term occupation, including artifacts spanning from 2nd c. BCE to 19th c. CE. According to our findings, the main construction and usage phase of the wall and its associated structures occurred throughout the Xi Xia dynasty (1038–1227 CE), a period characterized by advanced frontier defense systems and significant geopolitical shifts. This study challenges the perception of such structures as being purely defensive, revealing the Gobi Wall’s multifunctional role as an imperial tool for demarcating boundaries, managing populations and resources, and consolidating territorial control. Furthermore, our spatial and ecological analysis demonstrates that the distribution of local resources, such as water and wood, was critical in determining the route of the wall and the placement of associated garrisons and forts. Other geographic factors, including the location of mountain passes and the spread of sand dunes, were strategically utilized to enhance the effectiveness of the wall system. The results of this study reshape our understanding of medieval Inner Asian imperial infrastructure and its lasting impact on geopolitical landscapes. By integrating historical and archeological evidence with geographical analysis of the locations of garrisons and fortifications, we underscore the Xi Xia kingdom’s strategic emphasis on regulating trade, securing transportation routes, and monitoring frontier movement. Full article

Agroforestry systems (AFSs) have been recognized for their ecological potential, yet quantitative assessments of their hydrological functions in semi-arid regions remain limited. This study evaluates soil moisture retention and potential aquifer recharge in two agroforestry systems compared to a traditional rainfed maize system in the semi-desert region of Celaya, Mexico, where aquifer depletion is a growing concern. Field measurements during the 2022 rainy season included precipitation, soil moisture at multiple depths, and soil physical properties across seven vegetation covers. The results show significantly higher moisture content, improved uniformity, and enhanced recharge potential under tree species such as Bursera graveolens and Lysiloma divaricatum. These effects are attributed to vegetation cover, organic matter input, and reduced evaporation. This study provides empirical evidence supporting the integration of AFSs into regional water management strategies, offering a nature-based solution for aquifer recovery and climate adaptation in arid landscapes. Full article

Iraq faces significant challenges in sustainable water resource management, due to intensive agriculture and climate change. Modern irrigation leads to depleted natural springs and abandoned traditional canal systems, creating a nexus between climate, water availability, agriculture, and cultural heritage. This work unveils this nexus holistically, from the regional to the local scale, and by considering all the components of the nexus. This is achieved by combining five decades (1974–2024) of satellite data—including declassified HEXAGON KH-9, Copernicus Sentinel-1/2/3, COSMO-SkyMed radar, and PlanetScope’s Dove optical imagery—and on-the-ground observations (photographic and drone surveying). The observed landscape changes are categorised as “proxies” to infer the presence of the given land processes that they correlate to. The whole of southern Iraq is afflicted by dust storms and intense evapotranspiration; new areas are desertifying and thus becoming local sources of dust in the southwest of the Euphrates floodplain and close to the boundary with the western desert. The most severe transformations happened around springs between Najaf Sea and Hammar Lake, where centre-pivot and herringbone irrigation systems fed by pumped groundwater have densified. While several instances of run-off and discharge highlight the loss of water in the western side of the study area, ~5 km² wide clusters of crops in the eastern side suffer from water scarcity and are abandoned. Here, new industrial activities and modern infrastructure have already damaged tens of archaeological sites. Future monitoring based on the identified proxies could help to assess improvements or deterioration, in light of mitigation measures. Full article

Exploring the diversity and community structure of darkling beetles (Tenebrionidae) and the associated environmental factors on an alluvial fan provides useful insights into the ecology of these landscape features. This study investigated Chaqikou in the Helan Mountains, which features unique alluvial fan landforms. Sample plots (200 × 200 m) were established at three positions: the fan top, fan middle, and fan edge. From May to October 2023, pitfall traps were used to survey beetle community composition and its relationship with environmental factors. Significant variations were observed in species composition and diversity indices across different months and sample plots. Strongly xerophilous species exhibited broader ecological niche breadth, while moderately xerophilous species tended to distribute in the mid-to-upper segments of alluvial fans. Non-metric multidimensional scaling analysis revealed temporal shifts in community composition, with beta diversity analysis showing that species nestedness dominated from June to August, while species replacement was prominent in May, September, and October. Redundancy analysis indicated that environmental factors affecting species distribution varied by plot. On the landscape scale, altitude was the primary factor affecting beetle distribution. Variance partitioning analysis showed that topographic, soil, and vegetation factors explained 51.7%, 20.2%, and 9.4% of the variation in the beetle community, respectively. It is evident that altitudinal gradients shape ecological filtering pressures by creating multidimensional heterogeneity in topography, soil properties, and vegetation coverage. The adaptive matching between Tenebrionid species’ biological traits and environmental factors ultimately governs the spatial distribution patterns of darkling beetle diversity in alluvial fan desert grasslands. Full article

Urban heat islands (UHIs) constitute one of the most conspicuous anthropogenic impacts on local climates, characterized by elevated land surface temperatures in urban areas compared to surrounding rural regions. This study represents a novel and comprehensive effort to characterize the spectral signature of SUHI through the lens of the two-dimensional (2D) turbulence theory, with a particular focus on identifying energy cascade regimes and their climatic modulation. The theory of two-dimensional (2D) turbulence, first described by Kraichnan and Batchelor, predicts two distinct energy cascade regimes: an inverse energy cascade at larger scales (low wavenumbers) and a direct enstrophy cascade at smaller scales (high wavenumbers). These cascades can be detected and characterized through spatial power spectra analysis, offering a scale-dependent understanding of the SUHI phenomenon. Despite the theoretical appeal, empirical validation of the 2D turbulence hypothesis in urban thermal landscapes remains scarce. This study aims to fill this gap by analyzing the spatial power spectra of land surface temperatures across 14 cities representing diverse climatic zones, capturing varied urban morphologies, structures, and materials. We analyzed multi-decadal LST datasets to compute spatial power spectra across summer and winter seasons, identifying spectral breakpoints that separate large-scale energy retention from small-scale dissipative processes. The findings reveal systematic deviations from classical turbulence scaling laws, with spectral slopes before the breakpoint ranging from ~K^−1.6 to ~K^−2.7 in winter and ~K^−1.5 to ~K^−2.4 in summer, while post-breakpoint slopes steepened significantly to ~K^−3.5 to ~K^−4.6 in winter and ~K^−3.3 to ~K^−4.3 in summer. These deviations suggest that urban heat turbulence is modulated by anisotropic surface heterogeneities, mesoscale instabilities, and seasonally dependent energy dissipation mechanisms. Notably, desert and Mediterranean climates exhibited the most pronounced small-scale dissipation, whereas oceanic and humid subtropical cities showed more gradual spectral transitions, likely due to differences in moisture availability and convective mixing. These results underscore the necessity of incorporating turbulence theory into urban climate models to better capture the scale-dependent nature of urban heat exchange. The observed spectral breakpoints offer a diagnostic tool for identifying critical scales at which urban heat mitigation strategies—such as green infrastructure, optimized urban ventilation, and reflective materials—can be most effective. Furthermore, our findings highlight the importance of regional climatic context in shaping urban spectral energy distributions, necessitating climate-specific urban design interventions. By advancing our understanding of urban thermal turbulence, this research contributes to the broader discourse on sustainable urban development and resilience in a warming world. Full article