Search Results (978)

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

Amidst the intensification of global climate change and the increasing impacts of human activities, ecosystem patterns and processes have undergone substantial transformations. The distribution and evolutionary dynamics of mountain ecosystems have become a focal point in ecological research. The Surkhan River Basin is located in the transitional zone between the arid inland regions of Central Asia and the mountain systems, where its unique physical and geographical conditions have shaped distinct patterns of vertical zonation. Utilizing Landsat imagery, this study applies a hierarchical classification approach to derive land cover classifications within the Surkhan River Basin. By integrating the NDVI (normalized difference vegetation index) and DEM (digital elevation model (30 m SRTM)), an “NDVI-DEM-Land Cover” scatterplot is constructed to analyze zonation characteristics from 1980 to 2020. The 2020 results indicate that the elevation boundary between the temperate desert and mountain grassland zones is 1100 m, while the boundary between the alpine cushion vegetation zone and the ice/snow zone is 3770 m. Furthermore, leveraging DEM and LST (land surface temperature) data, a potential energy analysis model is employed to quantify potential energy differentials between adjacent zones, enabling the identification of ecological transition areas. The potential energy analysis further refines the transition zone characteristics, indicating that the transition zone between the temperate desert and mountain grassland zones spans 1078–1139 m with a boundary at 1110 m, while the transition between the alpine cushion vegetation and ice/snow zones spans 3729–3824 m with a boundary at 3768 m. Cross-validation with scatterplot results confirms that the scatterplot analysis effectively delineates stable zonation boundaries with strong spatiotemporal consistency. Moreover, the potential energy analysis offers deeper insights into ecological transition zones, providing refined boundary identification. The integration of these two approaches addresses the dimensional limitations of traditional vertical zonation studies, offering a transferable methodological framework for mountain ecosystem research. Full article

Coastal blue carbon ecosystems (traditional types such as mangroves, salt marshes, and seagrass meadows; emerging types such as tidal flats and mariculture) play pivotal roles in capturing and storing atmospheric carbon dioxide. Reliable assessment of the spatial and temporal variation and the carbon storage potential holds immense promise for mitigating climate change. Although previous field surveys and regional assessments have improved the understanding of individual habitats, most studies remain site-specific and short-term; comprehensive, multi-decadal assessments that integrate all major coastal blue carbon systems at the national scale are still scarce for China. In this study, we integrated 30 m Landsat imagery (1992–2022), processed on Google Earth Engine with a random forest classifier; province-specific, literature-derived carbon density data with quantified uncertainty (mean ± standard deviation); and the InVEST model to track coastal China’s mangroves, salt marshes, tidal flats, and mariculture to quantify their associated carbon stocks. Then the GeoDetector was applied to distinguish the natural and anthropogenic drivers of carbon stock change. Results showed rapid and divergent land use change over the past three decades, with mariculture expanded by 44%, becoming the dominant blue carbon land use; whereas tidal flats declined by 39%, mangroves and salt marshes exhibited fluctuating upward trends. National blue carbon stock rose markedly from 74 Mt C in 1992 to 194 Mt C in 2022, with Liaoning, Shandong, and Fujian holding the largest provincial stock; Jiangsu and Guangdong showed higher increasing trends. The Normalized Difference Vegetation Index (NDVI) was the primary driver of spatial variability in carbon stock change (q = 0.63), followed by precipitation and temperature. Synergistic interactions were also detected, e.g., NDVI and precipitation, enhancing the effects beyond those of single factors, which indicates that a wetter climate may boost NDVI’s carbon sequestration. These findings highlight the urgency of strengthening ecological red lines, scaling climate-smart restoration of mangroves and salt marshes, and promoting low-impact mariculture. Our workflow and driver diagnostics provide a transferable template for blue carbon monitoring and evidence-based coastal management frameworks. 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

The Alpine Periglacial Weathering Zone (APWZ) is a critical transitional belt between alpine vegetation and glaciers, and a highly sensitive region to climate change. Its dynamic variations profoundly reflect the surface environment’s response to climatic shifts. Taking Gongga Mountain as the study area, this study utilizes summer Landsat imagery from 1986 to 2024 and constructs a remote sensing method based on NDVI and NDSI indices using the Otsu thresholding algorithm on the Google Earth Engine platform to automatically extract the positions of the upper limit of vegetation and the snowline. Results show that over the past four decades, the APWZ in Gongga Mountain has exhibited a continuous upward shift, with the mean elevation rising from 4101 m to 4575 m. The upper limit of vegetation advanced at an average rate of 17.43 m/a, significantly faster than the snowline shift (3.9 m/a). The APWZ also experienced substantial areal shrinkage, with an average annual reduction of approximately 13.84 km², highlighting the differential responses of various surface cover types to warming. Spatially, the most pronounced changes occurred in high-elevation zones (4200–4700 m), moderate slopes (25–33°), and sun-facing aspects (east, southeast, and south slopes), reflecting a typical climate–topography coupled driving mechanism. In the upper APWZ, glacier retreat has intensified weathering and increased debris accumulation, while the newly formed vegetation zone in the lower APWZ remains structurally fragile and unstable. Under extreme climatic disturbances, this setting is prone to triggering chain-type hazards such as landslides and debris flows. These findings enhance our capacity to monitor alpine ecological boundary changes and identify associated disaster risks, providing scientific support for managing climate-sensitive mountainous regions. Full article

Urban heat islands (UHIs) present a growing environmental issue in swiftly urbanizing regions, where impermeable surfaces and a lack of vegetation increase local temperatures. This research analyzes the spatial distribution of urban heat islands in Zagreb, Croatia, utilizing remote sensing data, urban planning metrics, and spatial-statistical analysis. Composite rasters of land surface temperature (LST) and the Normalized Difference Vegetation Index (NDVI) were generated from four cloud-free Landsat 9 images obtained in the summer of 2024. The data were consolidated into regulatory planning units through zonal statistics, facilitating the evaluation of the impact of built-up density and designated green space on surface temperatures. A composite UHI index was developed by combining normalized land surface temperature (LST) and normalized difference vegetation index (NDVI) measurements, while spatial clustering was examined with Local Moran’s I and Getis-Ord Gi*. The results validate spatial patterns of heat intensity, with high temperatures centered in densely built residential areas. This research addresses the gap in past UHI studies by providing a reproducible approach for detecting thermal stress zones, linking satellite data with spatial planning variables. The results support the development of localized climate adaptation methods and highlight the importance of integrating green infrastructure into urban planning methodologies. Full article

The rapid expansion of urban areas necessitates effective monitoring systems for sustainable development planning. Time-series change detection algorithms applied to satellite imagery offer promising solutions, but their comparative effectiveness specifically for urban land cover monitoring remains poorly understood. This study aims to systematically evaluate and optimize three widely used algorithms—LandTrendr, CCDC, and BFAST—selected for their proven capabilities in different land cover change contexts and distinct algorithmic approaches. Using Landsat 5/7/8 (TM/ETM+/OLI) time-series data from 2000 to 2020 and a globally distributed dataset of 200 sample locations spanning six continents, we assess these algorithms across multiple spectral bands and parameter settings for land cover change detection in urban areas. Our analysis reveals that CCDC achieves the highest accuracy (78.14% F1 score) when utilizing complete spectral information (bands B1–B7), outperforming both BFAST (74.32% F1 score with NDVI) and LandTrendr (71.29% F1 score with B1). We demonstrated that, contrary to conventional approaches that prioritize vegetation indices, visible light bands—particularly B1 and B2—achieve higher performance across multiple algorithms. For instance, in LandTrendr, B1 yielded an F1 score of 71.29%, whereas NDVI and EVI produced 56.19% and 53.16%, respectively. Similarly, in CCDC, B2 achieved an F1 score of 72.19%, while NDVI and EVI resulted in 68.57% and 65.33%, respectively. Our findings underscore that parameter optimization and band selection significantly impact detection accuracy, with variations up to 30% observed across different configurations. This comprehensive evaluation provides critical methodological guidance for satellite-based urban expansion monitoring and identifies specific optimization strategies to enhance the application of existing algorithms for urban land cover change detection. Full article

Mangrove forests serve as critical ecological barriers in coastal zones and play a vital role in global blue carbon sequestration strategies. In recent decades, China’s mangrove ecosystems have experienced complex interactions between degradation and restoration under intense coastal urbanization and systematic conservation efforts. However, the long-term spatiotemporal patterns and driving mechanisms of mangrove ecosystem health changes remain insufficiently quantified. This study developed a multi-temporal analytical framework using Landsat imagery (1986–2021) to derive kernel normalized difference vegetation index (kNDVI) time series—an advanced phenological indicator with enhanced sensitivity to vegetation dynamics. We systematically characterized mangrove growth patterns along China’s southeastern coast through integrated Theil–Sen slope estimation, Mann–Kendall trend analysis, and Hurst exponent forecasting. A Deep Forest regression model was subsequently applied to quantify the relative contributions of environmental drivers (mean annual sea surface temperature, precipitation, air temperature, tropical cyclone frequency, and relative sea-level rise rate) and anthropogenic pressures (nighttime light index). The results showed the following: (1) a nationally significant improvement in mangrove vitality (p < 0.05), with mean annual kNDVI increasing by 0.0072/yr during 1986–2021; (2) spatially divergent trajectories, with 58.68% of mangroves exhibiting significant improvement (p < 0.05), which was 2.89 times higher than the proportion of degraded areas (15.10%); (3) Hurst persistence analysis (H = 0.896) indicating that 74.97% of the mangrove regions were likely to maintain their growth trends, while 15.07% of the coastal zones faced potential degradation risks; and (4) Deep Forest regression id the relative rate of sea-level rise (importance = 0.91) and anthropogenic (nighttime light index, importance = 0.81) as dominant drivers, surpassing climatic factors. This study provides the first national-scale, 30 m resolution assessment of mangrove growth dynamics using kNDVI, offering a scientific basis for adaptive management and blue carbon strategies in subtropical coastal ecosystems. Full article

High-spatiotemporal-resolution remote sensing data are of great significance for surface monitoring. However, existing remote sensing data cannot simultaneously meet the demands for high temporal and spatial resolution. Spatiotemporal fusion algorithms are effective solutions to this problem. Among these, the ESTARFM (Enhanced Spatiotemporal Adaptive Reflection Fusion Model) algorithm has been widely used for the fusion of multi-source remote sensing data to generate high spatiotemporal resolution remote sensing data, owing to its robustness. However, most existing studies have been limited to applying ESTARFM for the fusion of single-surface-element data and have paid less attention to the effects of multi-band remote sensing data fusion and its accuracy analysis. For this reason, this study selects Chagan Lake as the study area and conducts a detailed evaluation of the performance of the ESTARFM in fusing six bands—visible, near-infrared, infrared, and far-infrared—using metrics such as the correlation coefficient and Root Mean Square Error (RMSE). The results show that (1) the ESTARFM fusion image is highly consistent with the clear-sky Landsat image, with the coefficients of determination (R²) for all six bands exceeding 0.8; (2) the Normalized Difference Vegetation Index (NDVI) (R² = 0.87, RMSE = 0.023) and the Normalized Difference Water Index (NDWI) (R² = 0.93, RMSE = 0.022), derived from the ESTARFM fusion data, are closely aligned with the real values; (3) the evaluation and analysis of different bands for various land-use types reveal that R² generally exhibits a favorable trend. This study extends the application of the ESTARFM to inland water monitoring and can be applied to scenarios similar to Chagan Lake, facilitating the acquisition of high-frequency water-quality information. Full article

Banana is an important cash and food crop worldwide. Recent outbreaks of banana diseases are threatening the global banana industry and smallholder livelihoods. Remote sensing data offer the potential to detect the presence of disease, but formal analysis is needed to compare inferred disease data with observed disease data. In this study, we present a novel remote-sensing-based framework that combines Landsat-8 imagery with meteorology-informed phenological models and machine learning to identify anomalies in banana crop health. Unlike prior studies, our approach integrates domain-specific crop phenology to enhance the specificity of anomaly detection. We used a pixel-level random forest (RF) model to predict 11 key vegetation indices (VIs) as a function of historical meteorological conditions, specifically daytime and nighttime temperature from MODIS and precipitation from NASA GES DISC. By training on periods of healthy crop growth, the RF model establishes expected VI values under disease-free conditions. Disease presence is then detected by quantifying the deviations between observed VIs from Landsat-8 imagery and these predicted healthy VI values. The model demonstrated robust predictive reliability in accounting for seasonal variations, with forecasting errors for all VIs remaining within 10% when applied to a disease-free control plantation. Applied to two documented outbreak cases, the results show strong spatial alignment between flagged anomalies and historical reports of banana bunchy top disease (BBTD) and Fusarium wilt Tropical Race 4 (TR4). Specifically, for BBTD in Australia, a strong correlation of 0.73 was observed between infection counts and the discrepancy between predicted and observed NDVI values at the pixel with the highest number of infections. Notably, VI declines preceded reported infection rises by approximately two months. For TR4 in Mozambique, the approach successfully tracked disease progression, revealing clear spatial spread patterns and correlations as high as 0.98 between VI anomalies and disease cases in some pixels. These findings support the potential of our method as a scalable early warning system for banana disease detection. Full article

Estimating aboveground biomass (AGB) is crucial for understanding the carbon cycle in terrestrial ecosystems, particularly within the context of climate change. Therefore, it is essential to research and compare different methods of AGB estimation to achieve acceptable accuracy. This study modelled AGB in temperate forests of central Mexico using active and passive remote sensing data combined with machine learning techniques (Random Forest and XGBoost) and compared the estimations against a traditional method, such as linear regression. The main goal was to evaluate the performance of machine learning techniques against linear regression in AGB estimation and then validate against an independent forest inventory database. The models obtained acceptable performance in all cases, but the machine learning algorithm Random Forest outperformed (R²_cv = 0.54; RMSE_cv = 19.17) the regression method (R²_cv = 0.41; RMSE_cv = 25.76). The variables that made significant contributions, in both Random Forest and XGBoost modelling, were NDVI, kNDVI (Landsat OLI sensor), and the HV polarisation from ALOS-Palsar. For validation, the Machine learning ensemble had a higher Spearman correlation (r = 0.68) than the linear regression (r = 0.50). These findings highlight the potential of integrating machine learning techniques with remote sensing data to improve the reliability of AGB estimation in temperate forests. Full article

Wildfires play a pivotal role in shaping and regulating the structural characteristics of forest ecosystems. This study examined post-fire vegetation dynamics following the 2020 Bighorn Fire in the Santa Catalina Mountains, Arizona, USA, by integrating pre- and post-fire airborne LiDAR data with Landsat-derived burn severity indices from 2019 to 2024. We analyzed structural and functional vegetation traits across 12,500 hectares to assess the changes pre- to post-fire, and to evaluate how these changes were influenced by the burn severity. We applied a correlation analysis to explore the relationships among the structural variables across different vegetation cover types. Non-parametric LOESS regression revealed that the dNBR was more strongly associated with changes in the tree density than with vertical structural attributes. The functional recovery, indicated by the NDVI, generally outpaced the structural recovery captured by the NBR. Densely forested areas experienced greater declines in vegetation volumes and slower regeneration, whereas herbaceous and sparsely vegetated areas showed a more rapid, but compositionally distinct, recovery. The divergence between the NDVI and NBR trajectories underscores the importance of integrating structural and functional indicators to comprehensively assess the post-fire ecosystem resilience and inform targeted restoration efforts. Full article

Our study aims to analyze the spatiotemporal dynamics of six lakes in Ifrane National Park (Morocco) using remote sensing and satellite imagery over the period 2000–2024. Spectral indices such as NDWI, MNDWI, EWI, AWEI, and ANDWI were employed to extract water bodies from Landsat images, while the NDVI index was used to identify irrigated agricultural lands. Additionally, the SPEI and RDI indices were applied to assess the impact of climate fluctuations on the hydrological evolution of the lakes. The results reveal an alarming reduction in lake surface areas, with some lakes having completely dried up. This decline is correlated with decreased precipitation and the expansion of irrigated agricultural lands, highlighting the impact of human activities. The analysis of hydrological correlations between lakes demonstrates significant interactions, although some indices show disparities. The rapid expansion of agricultural land, particularly arboriculture, increases pressure on water resources. These changes threaten local biodiversity and heighten the socio-economic vulnerability of surrounding populations. 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

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