Search Results (161)

Urban growth often surpasses the actual needs of the population, leading to inefficient land use and long-term environmental challenges. This study provides an integrated perspective on urban landscape transformation by linking socio-demographic dynamics with ecological consequences, notably vegetation loss and increased impervious surfaces. The study area is Năvodari Administrative-Territorial Unit (ATU), a coastal tourist city located along the Black Sea in Romania. By integrating geospatial datasets such as Urban Atlas and Corine Land Cover with population- and construction-related statistics, the analysis reveals a disproportionate increase in urbanized land compared to population growth. Time-series analyses based on the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) from 1990 to 2022 highlight significant ecological degradation, including vegetation loss and increased built-up density. The findings suggest that real estate investment and tourism-driven development play a more substantial role than demographic dynamics in shaping land use change. Understanding urban expansion as a coupled social–ecological process is essential for promoting sustainable planning and enhancing environmental resilience. While this study is focused on the coastal city of Năvodari, its insights are relevant to a broader international context, particularly for rapidly developing tourist destinations facing similar urban and ecological pressures. The findings support efforts toward more inclusive, balanced, and environmentally responsible urban development, aligning with the core principles of Sustainable Development Goal 11, particularly Target 11.3, which emphasizes sustainable urbanization and efficient land use. Full article

Oases in arid regions are critical ecosystems, providing essential ecological, agricultural, and socio-economic functions. However, urbanization and climate change increasingly threaten their sustainability. This study examines land cover (LULC) and land surface temperature (LST) dynamics in four representative North African oases: Tolga (Algeria), Nefta (Tunisia), Ghadames (Libya), and Siwa (Egypt) over the period 2000–2023, using Landsat satellite imagery. A three-step analysis was employed: calculation of NDVI (Normalized Difference Vegetation Index), NDBI (Normalized Difference Built-up Index), and LST, followed by supervised land cover classification and statistical tests to examine the relationships between the studied variables. The results reveal substantial reductions in bare soil (e.g., 48.10% in Siwa) and notable urban expansion (e.g., 136.01% in Siwa and 48.46% in Ghadames). Vegetation exhibited varied trends, with a slight decline in Tolga (0.26%) and a significant increase in Siwa (+27.17%). LST trends strongly correlated with land cover changes, demonstrating increased temperatures in urbanized areas and moderated temperatures in vegetated zones. Notably, this study highlights that traditional urban designs integrated with dense palm groves significantly mitigate thermal stress, achieving lower LST compared to modern urban expansions characterized by sparse, heat-absorbing surfaces. In contrast, areas dominated by fragmented vegetation or seasonal crops exhibited reduced cooling capacity, underscoring the critical role of vegetation type, spatial arrangement, and urban morphology in regulating oasis microclimates. Preserving palm groves, which are increasingly vulnerable to heat-driven pests, diseases and the introduction of exotic species grown for profit, together with a revival of the traditional compact urban fabric that provides shade and has been empirically confirmed by other oasis studies to moderate the microclimate more effectively than recent low-density extensions, will maintain the crucial synergy between buildings and vegetation, enhance the cooling capacity of these settlements, and safeguard their tangible and intangible cultural heritage. Full article

Despite high rates of emotional dysregulation among autistic children, few studies have explored interventions addressing dysregulation. Naturalistic developmental behavioral interventions (NDBIs) are a class of interventions focused on supporting social communication. As social communication and emotion regulation skills emerge from similar developmental processes, NDBIs may be one approach for addressing dysregulation among autistic children. The present study sought to characterize change in dysregulation among one-hundred and eleven caregiver–child dyads completing Project ImPACT, a caregiver-mediated NDBI. Caregivers reported on child communication and social engagement using the Social Communication Checklist and emotion regulation using the Emotional Dysregulation Inventory-Young Child at the beginning and end of services. Clinicians reported on caregiver fidelity at each intervention session. Children showed reductions in emotional dysregulation throughout Project ImPACT, though reductions were specific to children who began the program with elevated dysregulation. Child social engagement at baseline and caregivers’ fidelity to specific strategies within Project ImPACT were associated with reductions in emotional dysregulation. Very few studies have tested interventions aimed at supporting emotion regulation among young autistic children. These findings demonstrate that NDBIs may support emotion regulation as well as social communication skills. Further incorporating support for emotion regulation in NDBI may address this critical gap without increasing service coordination for families. Full article

Cultural ecosystem services (CES) play a vital role in rural well-being, yet their spatial patterns and local perceptions remain underexplored in many regions, including Türkiye. This study aims to assess the social values of CES in rural landscapes by focusing on the Şarkikaraağaç and Yenişarbademli districts of Isparta Province. Using Participatory Geographic Information Systems (PGIS) and the Social Values for Ecosystem Services (SolVES) models, we collected and analyzed spatial data from 836 community surveys, mapping 3771 CES value points. Sentinel-2A imagery and derived indices (NDVI, NDWI, SAVI, NDBI) were used to classify landscape infrastructures into green, blue, yellow, and grey categories. The results show that aesthetic and recreational services were most highly valued, followed by biodiversity, spiritual, and therapeutic values. Chi-square and Kruskal–Wallis tests revealed significant demographic and spatial variation in CES preferences, while Principal Component Analysis highlighted two key dimensions of value perception. MaxEnt-based modeling within SolVES confirmed the spatial distribution of CES with high predictive accuracy (AUC > 0.93). Our findings underscore the importance of integrating CES into sustainable land-use planning and suggest that infrastructure type and proximity to natural features significantly influence CES valuation in rural settings. Full article

Urban environments are increasingly affected by extreme weather conditions, posing significant risks to vulnerable populations, such as the homeless. This research applies geospatial analysis to identify areas of extreme heat and moisture within the Athens metropolitan area in Greece. The analysis utilizes satellite-derived land surface temperature (LST), vegetation density index (NDVI), build-up density index (NDBI), Topographic Wetness Index (TWI), and other terrain-based factors to develop high-fidelity risk zones. These zones are critical for informing targeted interventions and policy measures aimed at protecting vulnerable groups from heat waves and extreme moisture. This research integrates a geospatial analysis approach for mapping and evaluating heat and moisture vulnerability zones. This approach integrates remote sensing data, GIS-based modeling, and terrain analysis. The findings can provide local authorities and social services with the necessary information to design adaptive strategies for climate change resilience. Full article

In a world experiencing rapid urbanization, the phenomenon of land surface temperature (LST) variation has invited substantial attention due to its profound impact on the environment and human well-being. Changes in land use and land cover (LULC) within urban areas significantly influence the dynamics of LST and are a major driver of urban eco-environmental change. The complex connections between LULC dynamics, LST, and climate change are investigated in this systematic review, with a focus on the combined effects of these variables and the use of Machine Learning (ML) techniques. The data in this study, based on peer-reviewed publications from the past 25 years, were obtained from Science Direct and Web of Science databases. Based on our findings, Landsat is the most widely used dataset for analyzing the impacts of LULC on LST. Additionally, built-up areas, vegetation, and population density had the biggest effects on LST values based on focused studies. This systematic review reveals that Artificial Neural Networks (ANNs), Cellular Automata-Markov (CA-Markov), and Random Forest (RF) are the most used ML techniques in predicting LULC and LST. The study finds that NDBI and NDVI are recognized as the key LULC indices that have strong correlations with LST. We also highlight key LULC classes that have the most impact on LST variation. To validate the results, these studies employ Pearson correlation, the NDVI and NDBI index, and other linear regression methods. This review concludes by highlighting future research directions and the current need for interdisciplinary efforts to address the intricate dynamics of LULC and the Earth’s surface temperature. Full article

Studies show that Nature-Based Solutions can mitigate Urban Heat Island (UHI) effects by implementing green spaces. Green roofs (GRs) may minimize land surface temperature (LST) by modifying albedo. This research predicts, assesses, and measures the impact of reducing the LST by applying green roofs in buildings by using a Random Forest algorithm and different remote sensing methods. To this aim, the city of Granada, Spain, was used as a case study. The city is classified into different Local Climate Zones (LCZs) to determine the area available for retrofitting GRs in built-up areas. A total of 14 Surface Temperature Collection 2 Level-2 images were acquired through Landsat 8–9, while 14 images for spectral indices such as the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Building Index (NDBI), and Proportion Vegetation (PV) were calculated from Sentinel-2 in dates coinciding or close to LST images. Additional factors were considered including the sky view factor (SVF) and water distance (WD). The results suggest that Granada has limited suitable areas for retrofitting GRs, and available areas can reduce LST with a moderate impact, at an average of 1.45 °C; however, vegetation plays an important role in decreasing LST. This study provides a methodological example to identify the benefits of implementing GRs in reducing LST in semi-arid cities and recommends a combination of strategies for LST mitigation. Full article

Urban expansion threatens sustainable development in densely populated countries like Bangladesh. This study aims to quantitatively identify and evaluate the key drivers influencing the spatial distribution of urban surfaces (SDUS) in Chattogram City, providing insights into urban growth patterns over 30 years. Using Landsat 5 and 9 imageries, the Normalized Difference Built-up Index (NDBI) was computed for 1993 and 2023 to map urban surface changes. A total of 16 geospatial variables representing potential drivers were analyzed. Four statistical and machine learning methods, including GeoDetector, Distributed Random Forest (DRF), global Geographically Weighted Random Forest (GWRF), and local GWRF, were employed to quantify individual and interactive influences on SDUS. The Geodetector analysis identified the central business district (CBD) as the most influential driver of urban surface distribution, with a q statistic of 0.22, followed by river proximity (q = 0.14) and administrative boundaries (q = 0.13). Across all models, CBD consistently ranked as a dominant factor. In the Distributed Random Forest (DRF) model, CBD showed the highest importance score (0.57), followed by coastlines (0.35) and rivers (0.35). The DRF model achieved the highest performance (R² = 0.612), outperforming the global GWRF (R² = 0.59) and local GWRF (R² = 0.529). Although variables like the proximity of administrative location and forests have low individual impacts, they show a stronger coupled influence. This industrial port-based economy expanded, facing challenges of uncontrolled urbanization, poor governance, and environmental issues. Promoting mixed land use planning, decentralizing urban governance, and improving coordination among implementing agencies may better resolve these issues. This work may help planners and policymakers in planning future cities and developing policies to promote sustainable urban growth. Full article

Urbanization has emerged as a significant driver of environmental change, particularly impacting local climates through the creation of urban heat islands (SUHIs). SUHIs, characterized by higher temperatures in urban or metropolitan areas than in their rural surroundings, have become a critical focus of urban climate studies. This study aims to examine the spatial and temporal dynamics of both thermal and vegetative indices (BT, LST, NDVI, NDBI, BUI, ECI, SUHI, UTFVI) across different land cover types in Samsun, Türkiye, in order to assess their contribution to the urban heat island effect. Specifically, brightness temperature (BT), land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), built-up index (BUI), environmental condition index (ECI), urban heat island (SUHI) intensity, and urban thermal field variance index (UTFVI) were calculated and assessed. The analysis utilized cloud-free Landsat 8 imagery sourced from the US Geological Survey via the Google Earth Engine platform, employing a one-year median for each pixel using a cloud masking algorithm. Land use and land cover (LULC) classification was conducted using the random forest (RF) algorithm with satellite composite imagery, achieving an overall accuracy of 85% for 2014 and 86% for 2023. This study provides a detailed analysis of the effects of various land use and cover types on temperature, vegetation, and structural characteristics, revealing the role of changes in different land types on the urban heat island effect. In the LULC classification, water bodies consistently maintained low LST values below 23 °C for both years, while built-up land exhibited the greatest temperature increase, from approximately 25 °C in 2014 to more than 31 °C in 2023. The analysis also revealed that LST varies with the size and type of vegetation, with a mean LST differential between all green spaces and urban areas averaging 7–8 °C, and differences reaching 12 °C in industrial zones. Full article

This study investigates urban warming in Rajshahi City, Bangladesh, by examining changes in land surface temperature (LST) from 1990 to 2023 and exploring its relationship with key biophysical factors. LST was derived from Landsat thermal imagery, and both spatial and temporal variations were analyzed using Geographic Information Systems (GIS). Key biophysical indices, including Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Bareness Soil Index (NDBSI), were calculated using corresponding Landsat satellite sensors, and they evaluated the impact of LULC types (vegetation, water, soil, and built-up areas) on thermal variations. LULC was derived following the Support Vector Machine classification technique. The Urban Thermal Field Variance Index (UTFVI) was employed to assess surface urban heat island (SUHI) effects, warming conditions, ecological stress, and thermal comfort zones. Spatial trend and hotspot analyses of LST change were performed using spatial trend analysis and the Getis-Ord Gi* statistic, respectively. Linear regression analysis examined the relationship between LST and biophysical indices. Results show that winter mean LST increased by 2.66 °C during the 33-year period, with maximum LST rising by 4.29 °C. The most significant warming occurred in central-northern, central-western, and south-eastern zones. The rise in LST and the growing intensity of SUHI effects are largely due to urban growth, especially where green spaces and water bodies have been replaced by impervious surfaces. Hotspot analysis identified clusters of high-temperature zones, while UTFVI analysis confirmed a marked expansion of strong heat island conditions, especially in central urban areas. Linear regression results showed notable links between LST and key biophysical variables, where higher LST values were commonly linked to greater built-up density and declines in vegetation cover and surface water. Overall, the results highlight the need for better urban planning approaches such as increasing green cover, using permeable materials, and adopting strategies that can adapt to climate impacts. This study presents a framework for analyzing urban climate dynamics that can be adapted to other rapidly growing cities, aiding efforts to promote sustainable development and build urban resilience. Full article

Intensifying global warming has disrupted natural ecosystems and altered energy consumption patterns. Understanding the impact of land use and cover change on surface urban heat islands (SUHIs) and energy use is critical for sustainable development. In this study, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), normalized difference built-up index (NDBI), and SUHI data were derived using GIS and remote sensing (RS) technology, and quantitative analysis was performed in combination with energy consumption data. The results revealed the following key findings. In summer, the NDVI exhibited a significant negative correlation with total urban building energy consumption (r = −0.52), whereas the NDBI and SUHI showed significant positive correlations (r = 0.72 and r = 0.67, respectively). Moreover, the SUHI served as a mediating role between land use/cover change and electricity consumption, with the direct effect accounting for 36% and the indirect effect accounting for 64% of the total effect. In contrast, the NDBI was significantly positively correlated with energy consumption in winter (r = 0.53). Spline regression analysis further revealed that every one-unit increase in this index corresponded to an increase of approximately 22 million kWh in summer EC and an increase of approximately 1.16 billion kWh in winter EC. Full article

Groundwater constitutes the primary source of liquid freshwater on Earth and is essential for ecosystems, agriculture, and human consumption. However, rising demand, urbanization, and climate change have intensified groundwater depletion, particularly in semi-arid regions. Therefore, assessing groundwater recharge zones is essential for sustainable water resource management in vulnerable areas such as Dera Ismail Khan, Pakistan. This study aims to delineate groundwater potential zones (GWPZs), using an integrated approach combining the Geographic Information System (GIS), remote sensing (RS), and the analytical hierarchy process (AHP). Twelve factors were identified in a study conducted using GIS-based AHP to determine the groundwater recharge zones in the region. These include land use/land cover (LULC), rainfall, drainage density, soil type, slope, road density, water table depth, and remote sensing indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Moisture Stress Index (MSI), Worldview Water Index (WVWI), and Land Surface Temperature (LST). The results show that 17.52% and 2.03% of the area have “good” and “very good” potential for groundwater recharge, respectively, while 48.63% of the area has “moderate” potential. Furthermore, gentle slopes (0–2.471°), high drainage density, shallow water depths (20–94 m), and densely vegetated areas (with a high NDVI) are considered important influencing factors for groundwater recharge. Conversely, areas with steep slopes, high temperatures, and dense built-up areas showed “poor” potential for recharge. This approach demonstrates the effectiveness of integrating advanced remote sensing indices with the AHP model in a semi-arid context, validated through high-accuracy field data (Kappa = 0.93). This methodology offers a cost-effective decision support tool for sustainable groundwater planning in similar environments. Full article

Forested wetlands in temperate mountain ecosystems play a critical role in carbon sequestration and biodiversity maintenance, yet their accurate delineation remains challenging due to spectral similarity with forests and anthropogenic interference. Here, we present an optimized two-stage Random Forest framework integrating 2019–2022 growing season datasets from Sentinel-1 C-SAR, ALOS-2 L-PALSAR, Sentinel-2 MSI, and Landsat-8 TIRS with environmental covariates. The methodology first applied NDBI thresholding (NDBI > 0.12) to exclude 94% of urban/agricultural areas through spectral masking, then implemented an optimized Random Forest classifier (ntree = 1200, mtry = 28) with 10-fold cross-validation, leveraging 42 features including L-band HV backscatter (feature importance = 47), Sentinel-2 SWIR (Band12; importance = 57), and land surface temperature gradients. This study pioneers a 10 m resolution forest swamp map in the Changbai Mountain wetlands, achieving 87.18% overall accuracy (Kappa = 0.84) with strong predictive performance (AUC = 0.89). Forest swamps showed robust classification metrics (PA = 80.37%, UA = 86.87%), driven by L-band SAR’s superior discriminative power (p < 0.05). Quantitative assessment demonstrated that L-band SAR increased classification accuracy in canopy penetration scenarios by 4.2% compared to optical-only approaches, while thermal-IR features reduced confusion with forests. Forested swamps occupied 229.95 km² (9% of protected areas), predominantly in transitional ecotones (720–850 m elevation) between herbaceous wetlands and forest. This study establishes that multi-sensor fusion enables operational wetland monitoring in topographically complex regions, providing a transferable framework for temperate mountain ecosystems. The dataset advances precision conservation strategies for these climate-sensitive habitats, supporting sustainable development goals targets for wetland protection through enhanced machine learning interpretability and anthropogenic interference mitigation. Full article

Modern cities are rapidly evolving in terms of urban morphology, driven by exponential population growth that accelerates the urbanisation process. The changes in land use have increased urban area and density, intensifying the urban heat island (UHI) effect, which poses one of the biggest threats to human health and well-being, especially in metropolitan regions. One of the most effective strategies to counter urban heat is the implementation of green infrastructure and the use of suitable building materials that help reduce heat stress. However, access to green spaces and the affordability of efficient building materials are not the same among citizens. This paper aims to identify the socio-economic characteristics of communities in Melbourne, Australia, that contribute to their vulnerability to urban heat under local conditions. This study employs remote sensing and geographical information systems (GIS) to conduct a macro-scale analysis, to investigate the correlation between urban heat patterns and socio-economic characteristics, taking into account factors such as vegetation cover, built-up areas, and land use types. The results from the satellite images and the geospatial data reveal that Deer Park, located in the western suburbs of Melbourne, has the highest land surface temperature (LST) at 32.54 °C, a UHI intensity of 1.84 °C, a normalised difference vegetation index (NDVI) of 0.11, and a normalised difference moisture index (NDMI) of −0.081. The LST and UHI intensity indicate a strong negative correlation with the NDVI (r = −0.42) and NDMI (r = −0.6). In contrast, the NDVI and NDMI have a positive correlation with the index of economic resources (IER) with r values of 0.29 and 0.24, indicating that the areas with better finance resources tend to have better vegetation coverage or plant health with less water stress, leading to lower LST and UHI intensity. This study helps to identify the most critical areas in the Greater Melbourne region that are vulnerable to the risk of urban heat and extreme heat events, providing insights for the local city councils to develop effective mitigation strategies and urban development policies that promote a more sustainable and liveable community. Full article

Land surface temperature (LST) is an important environmental parameter in many fields. However, many studies require high-spatial- and high-temporal-resolution LST products to improve the coarse spatial resolution of moderate-resolution imaging spectroradiometer (MODIS) LSTs. Numerous approaches have downscaled MODIS LST images to a finer spatial resolution using pixel-based image analysis (PBA). Meanwhile, object-based image analysis (OBIA) methods, which have developed rapidly in the analysis of high-spatial-resolution visible and near-infrared (VNIR) band data, have received little attention in the LST downscaling field. In this paper, we propose an object-based downscaling (OBD) method for MODIS LST using high-spatial-resolution multispectral images (e.g., Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)) as auxiliary data. The fundamental principle of this method is to preserve the thermal radiance of the “object”, which is composed of several MODIS LST pixels (partly or entirely) and is unchanged after disaggregation into subpixels in the resulting LST image. The decomposition process consists of two key parts: the thermal radiance (TR) estimation of the object from MODIS LST products and the weight calculation of sub-objects or subpixels. Objects were generated from VNIR data and remote sensing indices (e.g., the normalized difference vegetation index (NDVI), the normalized difference built-up index (NDBI), and fractions of different endmembers) using a multiscale segmentation method. The radiance of subpixels or sub-objects was calculated based on the weights of their parent objects, which were estimated by the relationships between the remote sensing indices and the LST. The accuracy and the efficiency of the OBD method were validated using a pair of ASTER and MODIS datapoints that were acquired at the same time. The decomposed LST results showed that the spatial distribution of the downscaled LST image closely resembled the true LST of the ASTER, with an RMSE of 2.5 K for the entire image. A comparison with PBA methods for pixel downscaling also indicated that the OBD method achieves the lowest root mean square error (RMSE) across different landcovers, including urban areas, water bodies, and natural terrain. Therefore, the proposed OBD method significantly enhances the capability of increasing the spatial resolution of coarse MODIS LST, providing an alternative for improving the spatial resolution of MODIS LST images and expanding their applicability to studies that require high-temporal- and high-spatial-resolution LST products. Full article