Search Results (38,852)

Afghanistan is highly vulnerable to climate-driven extremes because of its combination of rugged geography and socio-political instability. Frequent events of extreme precipitation, floods, and droughts pose severe socio-economic and environmental challenges. Floods are particularly destructive, yet national-scale potential flood risk in Afghanistan has not been systematically assessed, largely due to limited data and field access. This study addresses this gap by mapping flood susceptibility, vulnerability, and risk using remote sensing (RS) and geographic information systems (GIS) at both subbasin and provincial scales. We apply a hybrid approach that combines Principal Component Analysis (PCA) to identify key environmental, climatic, and socio-economic indicators with the Analytic Hierarchy Process (AHP) to derive consistent weights and reduce subjectivity in decision-making. The results show that the eastern and northeastern ssubbasins especially within the Panj-Amu and Kabul River basins, have the highest flood susceptibility due to intense precipitation, steep terrain, and efficient drainage. Vulnerability increases in the densely populated northern and northeastern provinces, where land-use change and socio-economic constraints elevate flood-related impacts. Overall, 31% and 20% of study areas are classified as Very High and High vulnerability zones, respectively. The composite potential flood-risk index identifies that approximately 24% and 22% of Afghanistan fall within Very High and High flood risk zones, concentrated in the northern and eastern provinces. Model performance, evaluated using Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC), indicates strong agreement between mapped Very High/High risk zones and frequently flooded provinces, with the upper-threshold scenario yielding an AUC of 0.913. These findings support targeted resource allocation, mitigation planning, and disaster-risk reduction in data-scarce and conflict-affected mountain regions. Full article

Jordan is witnessing a growing market trend of retrofitting existing buildings. The annual construction work on existing buildings in Amman, based on building consents, increased by approximately 46% between 2007 and 2017, while the annual newly built areas decreased by around 33%. This paper aims to establish a solid understanding of the current shift towards existing building adaptation in Jordan by exploring the drivers for this trend and the Government’s role in regulating and, possibly, encouraging it. Ten local experts with extensive experience in retrofitting projects in Jordan and around the region were interviewed. The qualitative and quantitative analysis of experts’ answers was performed using the software NVivo. Findings highlight nine main drivers for retrofitting existing buildings in Jordan, namely: (1) land value and location; (2) reducing capital costs compared to new builds; (3) architectural heritage conservation; (4) social and cultural considerations; (5) adapting to population increase; (6) reusing, adapting, and retrofitting to extend the life of buildings; (7) increasing tourism capacity; (8) improving building performance and resource efficiency; and (9) municipal incentives. Not all these drivers have the same value as they depend on the client and the project context. The experts’ ranking of drivers in terms of priority showed higher consideration for land value and location benefits, social–cultural aspects, and population increase, while municipal incentives emerged as low priority. Further research is needed to design context-specific effective retrofit policies, contributing to the literature in this emerging field in Jordan and beyond. Full article

Gayatri Spivak suggests that we turn our attention to the planet rather than to the globe. While she recognizes the planet in the species of alterity, she considers the globe to be an abstract quantity linked with the desire for control through digital quantification methods. This article discusses Claudia Bosse’s choreographic approach of re-imagining the human being as a planetary subject by investigating her dance performance WASTEland (2025), which took place on a piece of fallow land near Vienna Central Station. The choreographer turned this wasteland into her artistic laboratory and workplace for seven months. Using a mixed-method approach—combining performance analysis and discourse analysis—and drawing from planetary thinking and new materialism, I analyze Bosse’s artistic research, which raises the question of the relationship of precarious landscapes and the precarity of the bodies that perform (on) them, exposed to their climatic and ecological conditions as well as to their uncontrollable inhabitants, both human and other-than-human. How can wasteland and building sites be artistically activated? Does working and dancing on/with wasteland signify a withdrawal from urgent political issues or does this physical exposure enable a shift of perspective in regard to political miseries? Full article

Cross-domain semantic classification of 3D point clouds remains challenging due to strong domain shifts between heterogeneous sensing modalities. Most existing classification frameworks are domain-specific, limiting their use in integrated land–water mapping applications. This study evaluates the transferability of classical geometric machine learning classifiers between terrestrial and underwater point cloud domains without target-domain retraining. Experiments were conducted using terrestrial data acquired with a Leica BLK360 terrestrial laser scanner (TLS) and underwater point clouds collected with a Blueview BV5000 mechanical scanning sonar (MSS). Two dimensionality-based frameworks, CANUPO–Support Vector Machine (SVM) and 3DMASC–Random Forest (RF), were implemented in CloudCompare and assessed under intra-domain and cross-domain configurations. Strong intra-domain performance was achieved, with terrestrial–terrestrial accuracies of 0.99 for CANUPO–SVM and 0.97 for 3DMASC. In underwater evaluation, CANUPO maintained high accuracy (0.97), whereas 3DMASC decreased to 0.86 due to increased variability in the submerged data. Under cross-domain transfer, CANUPO achieved 0.93 accuracy for terrestrial-to-underwater and 0.89 for underwater-to-terrestrial classification, while 3DMASC demonstrated stable generalisation with 0.95 accuracy in both directions. Overall, dimensionality-based geometric descriptors capture stable structural cues across sensing environments, providing an interpretable and efficient pathway for applications such as hydrographic surveying, coastal monitoring, and underwater search-and-rescue detection. Future work will extend validation to larger datasets and explore domain adaptation strategies to further reduce cross-modality domain shift. Full article

The Yellow River Basin in Inner Mongolia (YRBIM) is a typical arid—semiarid ecological transition zone highly sensitive to climate change. Using long-term Normalized Difference Vegetation Index (NDVI) data together with meteorological and land cover data, this study applied the Sen+Mann–Kendall method and path coefficient analysis to quantify the direct and indirect effects of climatic factors on vegetation coverage. The YRBIM experienced a non-significant warm–wet trend from 1998 to 2019, characterized by slight increases in precipitation and temperature with asynchronous spatial patterns. Vegetation coverage generally improved, with high coverage areas expanding by 12.66% and low coverage areas decreasing by 10.04%. Improvement occurred mainly in eastern croplands and grasslands, while degradation in the northwest coincided with urban expansion and mining. Precipitation showed a highly significant positive correlation with the NDVI at 0.7510. The direct effect of precipitation was dominant at 0.7515, while the indirect effect was negligible at 0.0005. Temperature showed a weak inhibitory effect with a comprehensive effect of 0.0302, where the indirect inhibitory effect at 0.0400 slightly exceeded the direct promotional effect at 0.0098. These response patterns were consistent across most land cover types, except in rural settlements and unused land where temperature showed a weak positive influence. This study provides a scientific basis for ecological conservation and sustainable management in arid—semiarid transition zones. Full article

Although species identification is crucial for land-snail eggs, limited effort has been made to identify the species responsible for producing the eggs. In this study, we used laser ablation inductively coupled plasma mass spectrometry (LA–ICP–MS) to measure 54 elements in both the eggshells and adult shells of Bradybaena ravida and Cathaica fasciola and we used scanning electron microscope (SEM) to analyze the microstructure of the eggshells of the two species. Our results reveal that while the concentrations of Sr, Na, Mg, P, and Ba in the adult shells of the two species are not distinct, they are distinct or partially distinct between their eggshells, indicating that these elements have the potential to differentiate the eggs of the two species. Moreover, the eggshells of C. fasciola exhibit a blocky morphology without cavities, whereas those of B. ravida, while also blocky, contain irregular cavities. These distinct elemental and microstructural characteristics enable the effective differentiation of the eggs of B. ravida and C. fasciola. Our study demonstrates the feasibility of a critical microscopic methodology for identifying land-snail eggs at the genus/species level, thereby facilitating deeper exploration of their value in understanding biological, climatic, and ecological changes. Full article

Cognition in older adults may be influenced by environmental factors; however, the pathways linking environmental exposures and cognition remain unclear. The aim of this narrative review is to synthesize evidence on the association between the environment and cognition in older adults, integrating biological, environmental, and behavioral elements. Systematic reviews and original studies addressing this topic were identified in Web of Science, PubMed, and Scopus. The primary neural processes associated with maintaining cognition during aging are neuronal plasticity and compensatory scaffolding. Participation in intellectually stimulating activities, physical exercise, and a healthy diet; mitigation of chronic stress; reduction in the severity of depressive symptoms; and buffering against the adverse effects of air pollution are proposed as plausible pathways that may mediate the relationship between neural processes and the environment. In this context, environmental factors that affect cognition can be classified at three levels: (i) micro-level (family and home): social interaction with family members and indoor pollution; (ii) meso-level (community and services): social interaction, land-use diversity, transportation systems, environmental design, and urban green spaces; and (iii) macro-level (society in general and public policies): social representations of old age and aging (positive aging vs. ageism) and public policies aimed at improving pathways related to cognitive maintenance. Overall, the environment may influence cognition in older adults; however, the available studies show methodological and conceptual heterogeneity, inconsistent findings, and important gaps in knowledge. Full article

Star-based schemes, such as the 5-star Linked Open Data model and its geospatial extensions, are widely used to characterize openness and interoperability. However, in practice, higher star ratings are often assigned on the basis of technical properties such as RDF exposure or schema publication without requiring the satisfaction of foundational metadata quality conditions. This weakens the monotonic interpretation of star levels and can produce ambiguous signals for data users. To address this issue, we propose the LETTER framework, a multi-dimensional evaluation model in which seven independent binary dimensions describe metadata readiness for reuse: Provenance (P), Access (A), Structure (S), Connections (C), License (L), Identifiers (I), and Quality (Q). The framework is aligned with FAIR principles and mapped to ISO 19115 and ISO 19157 concepts. We evaluate it through an exploratory comparative case study of four purposively selected datasets from the German Spatial Data Infrastructure (GDI-DE): Municipal Points of Interest (Trier), Thuringia Digital Elevation Model (DEM), Administrative Units (VG250), and INSPIRE Digital Land Model (DLM). The results show that datasets receiving comparatively high star ratings may still lack machine-actionable provenance, quality evidence, stable identifiers, or robust access conditions. In particular, the analysis highlights a recurring ‘PDF Trap’, where relevant trust information exists only in narrative documentation and therefore remains inaccessible to automated reuse workflows. We conclude that LETTER provides clearer diagnostic power than scalar star ratings by exposing which metadata functions are actually satisfied and which remain missing. Full article

This paper offers a critical review of cutting-edge research on multi-energy microgrids (MEMs), with a novel exploration of their potential role in supporting urban air mobility (UAM), specifically electric vertical takeoff and landing (eVTOL) aircraft. While extensive research has focused on improving the economic performance and emission reductions of MEMs, particularly in the context of electric vehicle (EV) charging, there remains a significant gap in understanding how microgrids can support the decarbonization of UAM. The paper examines the opportunities and challenges of integrating microgrids with UAM operations, highlighting the need for more research to optimize energy management systems that balance renewable energy use with the growing demand for aerial transport. Thermal energy storage systems are emphasized as a critical component for addressing transportation energy needs, offering a promising solution to reduce carbon emissions while enhancing system efficiency. This review aims to provide new insights into how the coupling of microgrids and UAM can contribute to the development of economically and environmentally sustainable smart cities. Full article

Ecological vulnerability assessment serves as a prerequisite for ecological governance, yet evaluating large-scale ecological vulnerability remains challenging. To address this challenge, this study integrates geological elements into ecological vulnerability assessment, taking Ruyuan Area in the Northern Guangdong Mountains, China, as a case study. The area faces ecological hazards such as land desertification and soil erosion, indicating severe governance challenges. This study selected 14 ecological vulnerability factors and constructed assessment models based on Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs). A total of 800 ecological vulnerability sampling points were obtained by combining field survey data with remote sensing imagery. The models were trained using binary vulnerability labels. The resulting continuous probability outputs were then classified into five vulnerability levels using the natural breaks method to generate the final ecological vulnerability map. It should be noted that the multi-level vulnerability map represents graded probability-based differentiation rather than supervised multi-class prediction. Model performance was validated using three metrics: Area Under Receiver Operating Characteristic Curve (AUC–ROC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The CNN (AUC = 0.916) model outperformed the DNN model (AUC = 0.895). According to the CNN-based classification results, non-vulnerable, slightly vulnerable, mildly vulnerable, moderately vulnerable, and highly vulnerable areas accounted for 36.19%, 22.85%, 14.24%, 12.31%, and 14.41% of the total area, respectively. High ecological vulnerability zones were concentrated in Daqiao, Luoyang, Dabu, and parts of Rucheng towns, with soil parent material and vegetation coverage identified as the main contributing factors, among which parent material was the most important. This finding underscores the notable impact of geological factors on local ecological vulnerability. Based on these results, nine ecological–geological subareas were delineated, and targeted ecological protection and restoration recommendations were proposed. This study, employing machine learning techniques, constructed an ecological vulnerability assessment model incorporating geological elements, thereby providing scientific support for targeted ecological governance in the study area. Full article

Eastern Thailand serves as a critical case study for the escalating tension between agricultural preservation and urban expansion, a dynamic recently intensified by the COVID-19 pandemic. This study addresses a pivotal research question: To what extent do emerging socio-economic realities, such as policy shifts, labor fluctuations, and climatic extremes, alter the spatiotemporal continuity of urban expansion? Employing a mixed-methods approach, we integrated multi-stakeholder insights with quantitative spatial modeling to simulate context-specific land use futures through 2030. Qualitative findings indicate that while COVID-19 accelerated agricultural modernization, evidenced by increased mechanization and e-commerce integration, these shifts have limited long-term impact on land use patterns. Instead, regional policy, climate change, and technological innovation emerged as the primary drivers of landscape transformation. Quantitative simulations reveal that urban growth will concentrate in the western provinces bordering Bangkok and the southern coastal corridors of Chon Buri and Rayong. Crucially, across all scenarios, approximately 60% of new urban land is projected to be converted from existing croplands, followed by significant losses in natural forest cover. These results demonstrate that current growth-oriented policies may undermine regional food security and ecosystem services. This study provides a framework for balancing agricultural modernization with ecological preservation, offering essential evidence for developing the integrated, sustainability-focused land use frameworks required to meet 2030 development goals. Full article

Reanalysis datasets provide gridded, high-frequency estimates of atmospheric variables that are essential for studying weather and climate, particularly in regions with sparse observational networks. Despite their widespread use, the quality of reanalysis products remains insufficiently validated in tropical regions, particularly in areas with complex terrain. In this study, we evaluate the performance of surface-level temperature and atmospheric pressure fields from ERA5 and ERA5-Land in the state of Alagoas, northeastern Brazil. The analysis is based on a 12-year comparison (2008–2019) with observational data from the National Institute of Meteorology (INMET). Prior to validation, altitude corrections were applied to minimize elevation-induced biases in the reanalysis fields. Performance was assessed using statistical metrics. Both reanalyses showed strong agreement with observations, with average correlations exceeding 0.91 for temperature and pressure. ERA5 temperature biases ranged from −0.2 °C to 0.3 °C, and those for ERA5-Land from −0.6 °C to −0.3 °C, with RMSE around 1.6 °C. Pressure biases were initially larger (−20 hPa to +6 hPa in ERA5), but were reduced to below 0.5 hPa at key reference stations after correction. Diurnal and seasonal cycle analyses confirmed the datasets’ ability to reproduce temporal variability, though both reanalyses tended to overestimate minimum temperatures and underestimate maximum temperatures. Further investigation is needed to identify the origin of anomalous temperature jumps in ERA5’s diurnal cycle, which seem unrelated to the assimilation cycles. Overall, the results highlight the robust performance of ERA5 and ERA5-Land in representing surface atmospheric conditions in tropical coastal regions, while also emphasizing the continued need for regional validation and preprocessing before application in high-resolution or short-term studies. Full article

Land use transition and its effects on farmers’ well-being are central to the transformation and sustainable development of resource-exhausted areas (REAs). While extensive research has emerged in recent years, there remains a critical lack of systematic synthesis and clarity regarding key scientific issues in this domain. To bridge this research gap, an R-based bibliometric analysis was conducted on an extensive corpus encompassing 8245 papers on land use transition and 931 papers on farmers’ well-being published between 2001 and 2024, systematically investigating the mechanisms of transition, regional transformation dynamics, and the multi-dimensional determinants of well-being. The findings indicate that: (1) land use transition research has evolved from spatial patterns to management strategies, yet it lacks comprehensive regional and multi-scale characterization; (2) although land use is recognized as central to REA studies, the underlying theoretical frameworks require significant refinement; and (3) research on farmers’ well-being has shifted from broad ecosystem services to multidimensional micro-analyses, though the explicit correlation mechanisms with land use remain unclear. Based on these insights, four pivotal directions are identified for future research in REAs: establishing theoretical and analytical frameworks that link land use transitions to well-being under regional development logic; uncovering the spatiotemporal processes and multi-scale driving mechanisms of these transitions; quantitatively measuring their impacts on multidimensional well-being; and developing regulatory policies that balance regional coordination with well-being enhancement. This review provides a robust scientific foundation for optimizing land resources and promoting sustainable human–environment interactions in REAs. Full article

To explore the regulatory mechanisms of different vegetation types on soil crust grain-size characteristics in sandy lands, this study focused on five typical plant species (Haloxylon ammodendron, Artemisia ordosica, Nitraria tangutorum, Agriophyllum squarrosum, and Phragmites australis) in an artificial vegetation restoration area on the northeastern edge of the Ulan Buh Desert. Using laser granulometry and graphical methods, we systematically determined the soil particle size composition and parameters of the crust (Layer A) and sub-crust (Layer B) layers, and analyzed their correlations with plant morphological parameters (crown width, plant height, basal diameter). The results showed that (1) different vegetation types significantly increased the content of soil fine particulate matter (silt and clay), with fine sand accounting for 42.85% and silt accounting for 23.64%; (2) there are significant differences in the impact of different vegetation types on particle size parameters. The average particle size of soil crust under Phragmites australis is the smallest (1.91), and the sorting is the worst (standard deviation 2.01). Under the vegetation type of Nitraria tangutorum, the average particle size of the soil crust layer is the largest (5.25), and the fractal dimension is the highest (2.46). (3) The crown width, plant height, and basal diameter of vegetation are negatively correlated with mean particle size, kurtosis, and fractal dimension (r= −0.62 to −0.45), and positively correlated with standard deviation and skewness (r = 0.51 to 0.68). (4) The frequency curve indicates that vegetation types broaden the distribution range of soil particles, and Phragmites australis and Artemisia ordosica exhibit bimodal characteristics. This study reveals the impact of vegetation restoration on soil grain size parameters in arid regions. These findings provide actionable strategies for optimizing vegetation configuration in actual desert restoration projects, notably proposing a “herbs first, shrubs follow” approach that can be directly applied to enhance restoration efficiency. Full article

The water–land classification is fundamental for shoreline extraction and coastal habitat mapping, which is the basis of a comprehensive assessment and ecosystem-based coastal zone management. This study aims to separate water and land for coastal zones by taking advantage of both high-resolution satellite imagery and airborne lidar point clouds. Considering physical principles of optical remote sensing and lidar, we developed a prior knowledge-based localization classification approach that eliminates the need for collecting training sets and handling temporal differences across multiple data sources. Our approach first created the initial classification using the WorldView-2 (WV2) Normalized Difference Water Index. Then, the Connected Components Labeling algorithm was used to create a non-overlapping partition of the working area. The third step involved processing the water blocks using prior land cover knowledge. Finally, we used lidar point clouds to refine the initial water blocks and their neighboring areas. This classification approach showed promising results along Matagorda Bay, Texas, an approximately 2449 km² area that is covered by 26 WV2 images and 1568 lidar tiles. Full article