Search Results (257)

Rapid urbanization and climate change have exacerbated urban heat stress, underscoring the importance of research on human thermal comfort for sustainable urban development. This study analyzes the spatiotemporal variation and driving factors of apparent temperature in the Chang–Zhu–Tan urban agglomeration, China. The Humidex index, representing apparent temperature, was derived from multi-source remote sensing data (Landsat 8, MODIS) and meteorological variables (ERA5-Land reanalysis), employing atmospheric correction, random forest modeling, and path analysis. The results indicate pronounced spatiotemporal heterogeneity: apparent temperature reached its maximum in urban centers during summer (mean 52.9 °C) and its minimum in winter (mean 5.99 °C), following a decreasing gradient from urban core to periphery. Land cover emerged as a key driver, with vegetation (NDVI, r = −0.938) showing a strong negative correlation and built-up areas (NDBI, r = +0.8) a positive correlation with apparent temperature. Uniquely, in the Chang–Zhu–Tan region’s persistently high humidity, water bodies (MNDWI, r = +0.616) exhibited a positive correlation with apparent temperature, likely due to humidity-enhanced thermal perception in summer and relatively warmer water temperature in winter. Path analysis revealed that air temperature exerts the strongest direct positive influence on apparent temperature, while relative humidity and NDVI primarily act through indirect pathways. These findings provide scientific evidence to guide climate-adaptive urban planning and enhance human living conditions in humid environments. Full article

This study investigates long-term land surface temperature (LST) trends along the shoreline of Lake Drūkšiai, a transboundary lake in eastern Lithuania that formerly served as a cooling reservoir for the Ignalina Nuclear Power Plant (INPP). Although the INPP was decommissioned in 2009, its legacy continues to influence the lake’s thermal regime. Using Landsat 8 thermal infrared imagery and NDVI-based methods, we analysed spatial and temporal LST variations from 2013 to 2024. The results indicate persistent temperature anomalies and elevated LST values, particularly in zones previously affected by thermal discharges. The years 2020 and 2024 exhibited the highest average LST values; some years (e.g., 2018) showed lower readings due to localised environmental factors such as river inflow and seasonal variability. Despite a slight stabilisation observed in 2024, temperatures remain higher than those recorded in 2013, suggesting that pre-industrial thermal conditions have not yet been restored. These findings underscore the long-term environmental impacts of industrial activity and highlight the importance of satellite-based monitoring for the sustainable management of land, water resources, and coastal zones. Full article

Drought, a natural phenomenon intricately intertwined with the broader canvas of climate change, exacts a heavy toll by ushering in acute terrestrial water scarcity. Its ramifications reverberate most acutely within the agricultural heartlands, particularly those nestled in arid regions. To address this pressing issue, this study harnesses the temperature vegetation dryness index (TVDI) as a robust drought indicator, enabling a granular estimation of land water content trends. This endeavor unfolds through the sophisticated integration of geographic information systems (GISs) and remote sensing technologies (RSTs). The methodology bedrock lies in the judicious utilization of 72 high-resolution satellite images captured by the Landsat 7 and 8 platforms. These images serve as the foundational building blocks for computing TVDI values, a key metric that encapsulates the dynamic interplay between the normalized difference vegetation index (NDVI) and the land surface temperature (LST). The findings resonate with significance, unveiling a conspicuous and statistically significant uptick in the TVDI time series. This shift, observed at a confidence level of 0.05 (Z_S = 1.648), raises a crucial alarm. Remarkably, this notable surge in the TVDI exists in tandem with relatively insignificant upticks in short-term precipitation rates and LST, at statistically comparable significance levels. The implications are both pivotal and starkly clear: this profound upswing in the TVDI within agricultural domains harbors tangible environmental threats, particularly to groundwater resources, which form the lifeblood of these regions. The call to action resounds strongly, imploring judicious water management practices and a conscientious reduction in water withdrawal from reservoirs. These measures, embraced in unison, represent the imperative steps needed to defuse the looming crisis. 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

Assessing the ecological environmental quality (EEQ) is crucial for protecting the environment. Dhaka’s rapid, unplanned urbanization, driven by economic and social growth, poses significant eco-environmental challenges. Spatiotemporal ecological and environmental quality changes were assessed using remote sensing based ecological index (RSEI) maps derived from Landsat images (1993, 2003, 2013, and 2023). RSEI was based on four indicators—greenness (NDVI), heat index (LST), dryness (NDBSI), and wetness (LSM). Landsat 5 TM and 8 OLI/TIRS images were processed on Google Earth Engine (GEE), with principal component analysis (PCA) applied to determine RSEI. The findings showed a decline in the overall RSEI (1993–2023), with low- and very low-quality areas increasing by about 39% and high- and very high-quality areas decreasing by 24% of the total area. NDBSI and LST were negatively correlated with RSEI, except in 1993, while NDVI and LSM were generally positive but negative in 1993. The global Moran’s I (0.88–0.93) indicated strong spatial correlation in the distribution of EEQ across Dhaka. LISA cluster maps showed high-high clusters in the northeast and east, while low-low clusters were concentrated in the northwest. This research examines the degradation of ecological conditions over time in Dhaka and provides valuable insights for policymakers to address environmental issues and improve future ecological management. Full article

Wildfires cause substantial ecological disturbances, altering vegetation dynamics and soil properties over extended periods. This study investigated the influence of vegetation burn severity on post-fire vegetation recovery rates using multi-temporal Landsat 8 surface reflectance imagery from 2014 to 2023. Two major fire events in Nevada, the Snowstorm Fire (2017) and the South Sugar Loaf Fire (2018), were examined through four spectral indices: the Normalized Difference Vegetation Index (NDVI), Moisture Stress Index (MSI), Modified Chlorophyll Absorption Ratio Index 2 (MCARI2), and Land Surface Temperature (LST). Statistical techniques, including the Mann–Kendall trend test and Linear Mixed Effects models, were applied to assess pre- and post-fire trends across burn severity classes. Results showed that vegetation recovery was primarily driven by temporal factors rather than burn severity, especially in the Snowstorm Fire. In the South Sugar Loaf Fire, significant changes were observed in LST and NDVI scores in low-severity areas, while MSI and MCARI2 scores exhibited significant recovery differences in high-severity zones. These findings suggest that post-fire vegetation dynamics vary spatially and temporally, with severity effects more pronounced in certain conditions. The study underscores the effectiveness of spectral indices in capturing post-disturbance recovery and supports their application in guiding site-specific restoration and long-term ecosystem management. Full article

Forest fires are a critical ecological disturbance that significantly impact vegetation dynamics, biodiversity, and ecosystem services. This study investigates the impacts of forest fires in the Ghalahmah Mountains, Saudi Arabia, using remote sensing data and fire impact models to assess fire severity, environmental drivers, and post-fire vegetation recovery. The research integrates Landsat 8, Sentinel-2, and DEM data to analyze the spatial extent and severity of a 2020 fire event using the Relativized Burn Ratio (RBR). Results reveal that high-severity burns covered 49.9% of the affected area, with pre-fire vegetation density (NDVI) and moisture (NDWI) identified as key drivers of fire severity through correlation analysis and Random Forest regression. Post-fire vegetation recovery, assessed using NDVI trends from 2021 to 2024, demonstrated varying recovery rates across vegetation types. Medium NDVI areas (0.2–0.3) recovered fastest, with 134.46 hectares exceeding pre-fire conditions by 2024, while high NDVI areas (>0.3) exhibited slower recovery, with 26.55 hectares still recovering. These findings underscore the resilience of grasslands and shrubs compared to dense woody vegetation, which remains vulnerable to high-severity fires. The study advances fire ecology research by combining multi-source remote sensing data and machine learning techniques to provide a comprehensive understanding of fire impacts and recovery processes in semi-arid mountainous regions. The results suggest valuable insights for sustainable land management and conservation, emphasizing the need for targeted fuel management and protection of ecologically sensitive areas. This research contributes to the broader understanding of fire ecology and supports efforts to post-fire management. 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

High-spatiotemporal-resolution surface temperature data play a crucial role in monitoring urban heat island effects. Compared with Landsat 8, MODIS surface temperature products offer high temporal resolution but suffer from low spatial resolution. To address this limitation, a two-step downscaling model (TSDM) was developed in this study for MODIS surface temperature by leveraging random forest (RF) algorithms. The model integrates remote sensing data, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI), alongside the land cover type, digital elevation model (DEM), slope, and aspect. Additionally, a water surface temperature fitting model (RF-WST) was established to mitigate the issue of missing data over water bodies. Validation using Landsat 8 data reveals that the average out-of-bag (OOB) error for the RF-250 m model is 0.81, that for the RF-WST model is 0.73, and that for the RF-30 m model is 0.76. The root mean square error (RMSE) for all three models is below 1.3 K. The construction of the RF-WST model successfully supplements missing water body data in MODIS outputs, enhancing spatial detail. The downscaling model demonstrates strong performance in grassland areas and shows robust applicability during winter, spring, and autumn. However, due to a half-hour temporal discrepancy in the validation data during the summer, the model exhibits reduced accuracy in that season. Full article

Rapid urbanization and climate impacts have raised concerns about the emergence and aggravation of urban heat island effects. In Africa, studies have focused more on big cities due to their growing populations and high climate impact, while mid-sized cities remain under-studied, with limited comparative insights into their distinct characteristics. This study therefore provided a spatiotemporal analysis of land use land cover change (LULCC) and surface urban heat islands (SUHI) effects in the Nigerian mid-sized cities of Akure and Osogbo from 2014 to 2023. This study used Landsat 8 and 9 imagery (2014 and 2023) and analyzed data via Google Earth Engine and ArcGIS Pro 3.4. Results showed that Akure’s built areas increased significantly from 164.026 km² to 224.191 km² while Osogbo witnessed a smaller expansion from 41.808 km² to 58.315 km² in built areas. This study identified Normalized Difference Vegetation Index (NDVI) and emissivity patterns associated with vegetation and thermal emissions and a positive association between LST and urbanization. The findings across Akure and Osogbo cities established that LULCC has different impacts on SUHI effects. As a result, evidence from a mid-sized city might not be extended to other cities of similar size and socioeconomic characteristics without caution. Full article

Coal accounts for over half of India’s energy needs currently. However, it has resulted in significant environmental impacts such as altering land cover and land surface temperatures. This study quantifies the land surface temperature (LST) of Dhanbad City (India)—home to India’s largest coal reserves. It uses the Landsat 8 image data to evaluate urban and rural temperature variations across different land use–land cover (LULC) classes. Using a Geographically Weighted Regression Model (GWR), we examined the spatial heterogeneity of the LST using key environmental indices, such as the Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Barren Index (NDBI). The seasonal LST variations revealed significant urban–rural area temperature disparities, with rural regions exhibiting stronger correlations with the key indices above. The GWR model accounted for 78.31% of the spatial variability in LST, with unexplained heterogeneity in urban areas linked to anomalies identified in the coal mining area fire map. These findings underscore the necessity of targeted mitigation strategies to reduce high LST values in coal fire-affected regions, with localized spatial measures in mining areas. Full article

Over the past quarter-century, the enhanced availability of satellite imagery, characterized by improved temporal, spectral, radiometric, and spatial resolutions, has enabled valuable insights into the spatial soil variability of annual croplands and orchards. This study investigates the impact of spatial resolution on classifying three-year, multi-temporal vegetation indices derived from satellites with coarse (30 m, Landsat 8), medium (10 m, Sentinel-2), and fine spatial resolutions (3.7 m, PlanetScope). The classification was performed using the fuzzy c-means algorithm, with the fuzziness performance index (FPI) and normalized classification entropy (NCE), which were used to determine the optimal number of management zones (MZs). Our results revealed that the Landsat 8-based NDVI images produced the highest number of clusters (nine for annual cropland and six for orchards), while the finer resolutions from PlanetScope reduced this to three clusters for both cultivation types, more accurately capturing the intra-parcel variability. Except for Landsat 8, the NDVI means of MZs generated based on Sentinel-2 and PlanetScope using the fuzzy c-means algorithm showed statistically significant differences from each other, as determined by a one-way and Welch’s ANOVA (p < 0.05). The use of PlanetScope imagery demonstrated its superiority in generating zones that reflect inherent variability, offering farmers actionable insights at a reconnaissance scale. Multi-temporal satellite imagery has proved effective in monitoring plant growth responses to edaphological soil properties. In our study, the PlanetScope satellites, which offer the highest spatial resolution, consistently produced effective zones for orchard areas. These zones have the potential to enhance farmers’ discovery of knowledge at a reconnaissance scale. With the increasing spatial resolution and enhanced spectral resolution of newer satellite sensors, using cluster analysis with insights from soil scientists promise to help farmers better understand and manage the fertility of their fields in a cost-effective manner. Full article

Understanding the impacts of land use and land cover changes on ecosystem service values (ESVs) is crucial for effective ecosystem management; however, the intricate relationship between these factors in coal mining regions remains underexplored. In particular, the influence of coal mining activities on these dynamics is insufficiently understood, leaving a gap in the literature that hinders the development of robust management strategies. To address this gap, we investigated the interplay between land use change and the ESV at the interface of Yang Coal Mine No. 2 and the Shanxi Yalinji Guanshan Provincial Nature Reserve in Yangquan City, Shanxi Province. Using Landsat 8 remote sensing data from 2013 to 2021, our approach incorporated analyses using the Google Earth Engine (GEE) platform. We employed a random forest algorithm to classify land use patterns and calculated key indices—including the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), enhanced vegetation index (EVI), and bare soil index (BSI)—which were combined with topographic features. Land use change dynamics were quantified via a transfer matrix, while changes in the ESV were evaluated using the ecosystem sensitivity index and ecological contribution rate. Our results revealed notable fluctuations: forestland increased from 2013 to 2018 before declining sharply from 2019 to 2021; grassland displayed similar variability; and constructed land experienced a continual expansion. Correspondingly, the overall ESV increased by 28.6% from 2013 to 2019, followed by a 19.5% decline in 2020 and 2021, with forest and grassland’s ESVs exhibiting similar trends. These findings demonstrate that land use changes, particularly those that are driven by human activities such as coal mining, have a significant impact on ecosystem service values in mining regions. By unraveling the nuanced relationship between land use dynamics and ESVs, our study not only fills the gap in the literature but also provides valuable insights for developing more effective ecosystem management strategies, ultimately advancing our understanding of ecosystem dynamics in human-impacted landscapes. Full article

(1) Background: Mangroves are critical ecosystems that provide essential services, including coastal protection, biodiversity support, and carbon storage. However, urbanization and infrastructure development increasingly threaten their sustainability. This study investigates the spatio-temporal trends of mangrove canopy cover in Benoa Bay, Bali, Indonesia, which is an urban area and a center of tourism activities with various supporting facilities. The analysis was conducted from 2013 to 2023, using Landsat 8 satellite imagery and Normalized Difference Vegetation Index (NDVI) analysis. In addition, the analysis was also linked to mangrove area management policies. (2) Methods: The annual NDVI time series based on Landsat 8 imagery, obtained through the Google Earth Engine (GEE), was used to characterize the vegetation canopy cover in the study area. Statistical analysis of the annual linear trend of the NDVI was conducted to examine the spatio-temporal variation in canopy cover. Additionally, policies related to regional spatial planning and area protection were analyzed to assess their role in preserving mangrove forests in urban areas. (3) Results: There was a net decrease in mangrove area in Benoa Bay of 3.97 hectares, mainly due to infrastructure development and tourism facilities. The NDVI trend shows an overall increase in canopy cover due to reforestation and natural regeneration efforts, although there was a local decrease in some areas. Conservation policies, such as the establishment of the Ngurah Rai Forest Park, have supported mangrove protection. (4) Conclusions: The analysis demonstrated that mangroves surrounded by urban areas and tourism activity centers can still be maintained quite well with the right policies. Full article

The rapid expansion of refugee settlements has caused significant environmental changes, particularly in regions experiencing forced displacement. The Rohingya refugee crisis in Cox’s Bazar, Bangladesh, has led to extensive deforestation and land transformation, affecting local climate conditions. While urbanization’s impact on land surface temperature (LST) is well-documented, the environmental consequences of unplanned refugee settlements remain understudied. This study investigates land cover changes and LST dynamics from 2013 to 2024, offering a novel perspective on refugee-induced environmental changes. Using Landsat 8 imagery, four key land cover categories (built-up, mixed forest, water bodies, and barren land) were classified through a Support Vector Machine (SVM) approach. The temporal change in these key land cover categories was examined. The surface temperature product (Band 10) from Landsat 8 Collection 2 Level 2 (C2 L2) was applied to derive LST, while Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI) were used to assess vegetation and urbanization trends. Findings reveal a 97% decline in forest cover and a 161.78% increase in built-up areas between 2013 and 2018, leading to substantial LST increases. Statistical analyses confirm strong correlations between LST and multispectral indices, with vegetation and water bodies acting as cooling agents, while urban areas amplify heat stress. This study underscores the urgent need for sustainable land management and reforestation efforts to mitigate environmental degradation. It also highlights the importance of global cooperation in balancing humanitarian needs with environmental sustainability, providing insights for policymakers and urban planners to enhance climate resilience in vulnerable regions. Full article