Search Results (647)

This study presents a computational framework combining Dynamic Time Warping (DTW) and Persistent Homology to quantify the long-term morphological evolution of the Sendai coastline following the 2011 Tōhoku tsunami. Using multispectral satellite imagery from Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 MSI (2010–2024), instantaneous shorelines were extracted via the Modified Normalized Difference Water Index (MNDWI) and reconstructed with parametric B-spline curves. DTW analysis indicated severe initial deformation, with a 90,927 m difference between pre- and post-tsunami instantaneous shorelines, followed by gradual stabilization as distances declined to 59,584 m by 2024. Persistent Homology revealed a more complex topological trajectory, with the number of 1-dimensional features (H1) rising sharply after the tsunami, consolidating by 2015, and expanding again to over 8000 by 2020–2024. The Stable Distance of Persistent Homology (SDPH) identified 2015–2020 as the key phase of transformation (38,088 m), marking a shift toward higher morphological complexity. A weak negative correlation (r = −0.362) between DTW and SDPH confirmed their complementarity in describing geometric and topological change. Overall, the results suggest that post-tsunami recovery followed a non-linear path toward a new dynamic equilibrium characterized by increased structural complexity and resilience. Full article

Woody encroachment (WE) refers to the expansion of woody vegetation, particularly scrubs, into grasslands, altering ecosystem structure, function, and vegetation phenology. WE is especially pronounced in arid and semi-arid regions, where climate variability, land use, and ecological resilience interact strongly. Even though long-term monitoring of these dynamics in protected areas is essential to understanding landscape change and guiding conservation strategies, a few studies address this. The Flora and Fauna Protection Area (FFPA) Bavispe, a sky island in northwestern Mexico, provides an ideal setting to examine WE. Using remote sensing, we analyzed 30 years of land cover change (Landsat 5 TM and Landsat 8 OLI) in two reserve zones, Los Ajos and La Madera, and their 5 km buffer areas. Additionally, NDVI-based regressions (MODIS MOD13Q1) were applied to assess phenological responses across vegetation types. Classifications showed high accuracy (Kappa > 0.75) and revealed notable woody expansion: 960 ha of oak forest and 1322 ha of scrubland gained in Los Ajos, and 1420 ha of scrubland in La Madera. Grasslands declined by 2234 ha in Los Ajos and 1486 ha in La Madera, with stronger trends in surrounding buffers. Phenologically, the onset of the growing season was delayed by ~2 days per year in Los Ajos and ~3 days in La Madera. A generalized increment of woody vegetation in the region and the observed change in phenophases in selected land cover types indicated a shift in regional drivers (human or other ecological state factor) related to land cover distribution. Full article

This study evaluates groundwater quality dynamics in the Harran Plain (∼1500 km²), a key agricultural zone within Türkiye’s Southeastern Anatolia Project (GAP). Satellite images from Landsat 5 TM and Landsat 8 OLI/TIRS were used to assess land-use changes over the years 1990, 2000, 2010, and 2020, with the GIS employed for classification and analysis. In this study, groundwater samples collected from twenty different locations in 2005, 2015 and 2025 were analyzed. For each sample, pH, EC, and various ion concentrations (Na, K, Cl, SO₄, NO₃, Ca, Mg, HCO₃) were measured. All analyses were performed using standard hydrogeochemical methods. Data from 20 wells (2005–2015) revealed significant reductions in EC (8235 to 2510 µS/cm) and NO₃⁻ (720 to 327 mg/L), due to drainage systems, improved irrigation, and fertilizer management. Nonetheless, localized pollution persisted. Land-use shifts toward high-value crops improved water efficiency, while urban and industrial expansion introduced new pressures. Results emphasize integrated water–land policies for sustainable groundwater management in arid agroecosystems. Full article

Pakistan’s coastline encompasses the Indus Delta, a critical ecosystem that sustains biodiversity, fisheries, and local livelihoods, yet it is increasingly threatened by both natural and anthropogenic pressures. This study quantifies multi-decadal shoreline changes in the Indus Delta and examines how changes in climatic factors (precipitation and wind) affect these changes, using the Digital Shoreline Analysis System (DSAS v5.1) and multi-temporal Landsat imagery (TM, ETM+, OLI) to quantify long-term shoreline dynamics from 1990 to 2020 (30-year period). Key metrics, including End Point Rate (EPR), Net Shoreline Movement (NSM), and Linear Regression Rate (LRR), indicated an overall retreat, with a mean NSM of −1810 m and a mean LRR of −173 m·year across the 30-year period. Shoreline change rates exhibited a significant relationship with climatic variables, particularly wind speed and precipitation, with dynamics shifting from erosion-dominated to localized accretion in areas where mangrove rehabilitation programs were implemented after 2005. Seasonal variability further influenced shoreline behavior: low-rainfall years intensified erosion due to reduced sediment availability, while high-rainfall years enhanced accretion through increased sediment input. These findings underscore the urgent need for integrated coastal management strategies, including mangrove conservation, sustainable sediment management, and climate-adaptive planning, to strengthen the resilience of the Indus Delta. Full article

Palm trees, referred to here as vegetation cover (VC), provide essential ecosystem services in an arid Oasis. However, because of socioeconomic transformation, the rapid urban expansion of major cities and villages at the expense of agricultural lands of the Al-Ahsa Oasis, Saudi Arabia, has placed enormous pressure on the palm-growing area and led to the loss of productive land. These challenges highlight the need for robust, integrative methods to assess their impact on the agroecosystem. Here, we analyze spatiotemporal fluctuations in vegetation cover and its effect on the agroecosystem to determine the potential influencing factors. Data from Landsat satellites, including TM (Thematic mapper of Landsat 5), ETM+ (Enhanced Thematic mapper plus of Landsat 7), and OIL (Landsat 8) and Sentinel-2A imageries were used for analysis, while GeoEye-1 satellite images as well as socioeconomic data were applied for result validation. Principal Component Analysis (PCA) was applied to extract pure endmembers, facilitating Spectral Mixture Analysis (SMA) for mapping vegetation and urban fractions. The spatiotemporal change patterns were analyzed using time- and space-oriented detection algorithms. Results indicated that vegetation fraction patterns differed significantly; pixels with high fraction values declined significantly from 1990 to 2020. The mean vegetation fraction value varied from 0.79 to 0.37. This indicates that a reduction in palm trees was quickly occurring at a decreasing rate of −14.24%. Results also suggest that vegetation fractions decreased significantly between 1990 and 2020, and this decrease had the greatest effect on the agroecosystem situation of the Oasis. We assessed urban sprawl, and our results indicated substantial variability in average urban fractions: 0.208%, 0.247%, 0.699%, and 0.807% in 1990, 2000, 2010, and 2020, respectively. Overall, the data revealed an association between changes in palm tree fractions and urban ones, supporting strategic vegetation and/or agricultural management to enhance the agroecosystem in an arid Oasis. Full article

The Liaohe Estuary, characterized by Asia’s largest reed marshes and diverse wetland types, provides critical habitats for endangered bird species and performs vital ecological functions, making it a representative international wetland. Tidal flats, as essential components of estuarine wetlands, dissipate wave energy and stabilize shorelines. However, due to their peripheral location within estuarine systems, quantitative monitoring and risk assessment of the Liaohe Estuary tidal flat remain constrained. In this study, 187 cloud-filtered Landsat TM/ETM+/OLI scenes acquired between 2001 and 2021 were integrated with a waterline-derived DEM framework to quantify sedimentation dynamics in the Liaohe Estuary wetland. During the study period, the tidal-flat area exhibited a declining trend, while interannual surface elevations generally ranged from +2.18 to −1.61 m. The mean surface elevation increased by 25.33 cm, accompanied by a mean slope increase of 0.11‰; the average sedimentation rate was 1.27 cm yr⁻¹, with a net depositional volume of 0.51 km³, indicating an overall depositional regime. Moreover, mean elevation displayed a statistically significant upward trend (Kendall’s tau = 0.636, p = 0.0057), corroborating the significant rise in tidal-flat elevation from 2001 to 2021. The coexistence of elevation gain and spatial contraction suggests limited geomorphic resilience and a shrinking spatial extent of the tidal flat. The proposed approach provides a robust framework for long-term monitoring and supports the formulation of quantifiable sustainability targets for coastal management in the Liaohe Estuary. Full article

As fluvial deposition features, river islands originate from persistently exposed sandbars. Their morphological evolution responds to hydrological dynamics, sediment budgets, and human modifications of river systems. This study conducts a quantitative analysis of the spatiotemporal evolution of four river islands in China’s Yangtze River Estuary (YRE), utilizing multitemporal Landsat imagery (MSS, TM, ETM+, and OLI) at five-year intervals from 1974 to 2024. This analysis employed thresholding, binarization, image registration, cropping, and cluster analysis. Hydrological data (runoff and sediment flux) from Datong Station were concurrently evaluated to explore the driving factors of evolution. The findings suggested the following: (1) MSS/TM/ETM+/OLI images were effective for accurately extracting river island information, and the results were consistent with the accuracy verification. (2) The cumulative area and growth rate of the river islands have exhibited an upward trend over time, with Jiuduansha growing the fastest. (3) Runoff and sediment discharge are the primary natural controls on morphological evolution, with a weak positive correlation (R = 0.293) and a strong negative correlation (R = −0.915) with the area of river islands, respectively. Anthropogenic drivers such as land reclamation, sediment enhancement projects, and the Three Gorges Dam are equally critical. Full article

Water surface temperature (WST) is an important environmental variable, and its monitoring is essential for understanding and mitigating the impacts of climate change and human activities. For this, satellite remote sensing is particularly useful in providing WST data, especially in cases when in situ monitoring is limited or absent, as is often the case in small inland water bodies. In this study, the approach of retrieving the historical WST data from Landsat-5 Thematic Mapper (TM) was tested by analysing different cases across various water bodies in Lithuania, including two small inland lakes, an artificial reservoir, the Curonian Lagoon, and the coastal waters of the southeastern Baltic Sea. Our results demonstrate that WST can be accurately estimated from single-band Landsat-5 TM images, achieving an R² of around 0.9 in comparison with both in situ (with RMSE of 1.35–1.73 °C) and with MODIS satellite (RMSE of 1.11–1.23 °C) water temperature data, thus enabling analysis of water temperature variations in small-sized lakes and other water bodies, and contributing to the reliable monitoring of WST trends. Full article

Urban thermal environments critically impact human settlements and sustainable urban development. In this study, a multi-index framework integrating Landsat TM/ETM+/OLI observations (2004–2019) is developed to quantify the contributions of “source–sink” landscapes to urban heat island (UHI) dynamics in Guangzhou, China, with direct implications for advancing sustainable development. Urban–rural gradient analysis was combined with emerging spatiotemporal hotspot modeling, revealing the following results: (1) there were thermal spatial heterogeneity with pronounced heat accumulation in core urban zones and improved thermal profiles in northern sectors, reflecting a transition from “more sources, fewer sinks” in the southwest to “fewer sources, more sinks” in the northeast; (2) UHIs were effectively mitigated within 25–35 km of the city center, with the landscape effect index (LI > 1) indicating successful sink-dominated cooling; (3) spatiotemporal hotspots were observed, including persistent UHIs in old urban areas contrasting with environmentally vulnerable coldspots in suburban mountainous regions, highlighting uneven thermal risks. This framework provides actionable strategies for sustainable urban planning, including optimizing green–blue infrastructure in UHI cores, enforcing cool material standards, and zoning expansion based on source–sink dynamics. This study bridges landscape ecology and sustainable development, offering a replicable model for cities worldwide to mitigate UHI effects through evidence-based landscape management. Full article

Algal blooms pose a serious threat not only to the lake ecosystem of Lake Bosten but also by negatively impacting its rapidly developing fisheries and tourism industries. This study focuses on Lake Bosten as the research area and utilizes multi-source remote sensing imagery from Landsat TM/ETM+/OLI and Sentinel-2 MSI. The Adjusted Floating Algae Index (AFAI) was employed to extract algal blooms in Lake Bosten from 2004 to 2023, analyze their spatiotemporal evolution characteristics and driving factors, and construct a Long Short Term Memory (LSTM) network model to predict the spatial distribution of algal-bloom frequency. The stability of the model was assessed through temporal segmentation of historical data combined with temporal cross-validation. The results indicate that (1) during the study period, algal blooms in Lake Bosten were predominantly of low-risk level, with low-risk bloom coverage accounting for over 8% in both 2004 and 2005. The intensity of algal blooms in summer and autumn was significantly higher than in spring. The coverage of medium- and high-risk blooms reached 2.74% in the summer of 2004 and 3.03% in the autumn of 2005, while remaining below 1% in spring. (2) High-frequency algal bloom areas were mainly located in the western and northwestern parts of the lake, and the central region experienced significantly more frequent blooms during 2004–2013 compared to 2014–2023, particularly in spring and summer. (3) The LSTM model achieved an R² of 0.86, indicating relatively stable performance. The prediction results suggest a continued low frequency of algal blooms in the future, reflecting certain achievements in sustainable water-resource management. (4) The interactions among meteorological factors exhibited significant influence on bloom formation, with the q values of temperature and precipitation interactions both exceeding 0.5, making them the most prominent meteorological driving factors. Monitoring of sewage discharge and analysis of agricultural and industrial expansion revealed that human activities have a more direct impact on the water quality of Lake Bosten. In addition, changes in lake area and water environment were mainly influenced by anthropogenic factors, ultimately making human activities the primary driving force behind the spatiotemporal variations of algal blooms. This study improved the timeliness of algal-bloom monitoring through the integration of multi-source remote sensing and successfully predicted the future spatial distribution of bloom frequency, providing a scientific basis and decision-making support for the sustainable management of water resources in Lake Bosten. 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

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

Red-edge bands are the most essential spectral data for multispectral remote sensing images, with them playing a critical role in monitoring vegetation growth status at regional and global scales. However, the absence of red-edge bands limits the applicability of Landsat images, the most widely used remote sensing data, to vegetation monitoring. This study proposes an innovative method to reconstruct Landsat’s red-edge bands. The consistency in corresponding bands of Landsat OLI and Sentinel-2 MSI was first investigated using different resampling approaches and atmospheric correction algorithms. Three machine learning algorithms (ridge regression, gradient boosted regression tree (GBRT), and random forest regression) were then employed to build the red-edge reconstruction model for different vegetation types. With the optimal model, three red-edge bands of Landsat OLI were subsequently obtained in alignment with their derived vegetation indices. Our results showed that bilinear interpolation resampling, in combination with the LaSRC atmospheric correction algorithm, achieved high consistency between the matching bands of OLI and MSI (R² > 0.88). With the GBRT algorithm, three simulated OLI red-edge bands were highly consistent with those of MSI, with an R² > 0.96 and an RMSE < 0.0122. The derived Landsat red-edge indices coincide with those of Sentinel-2, with an R² of 0.78 to 0.95 and an rRMSE of 3.37% to 21.64%. This study illustrates that the proposed red-edge reconstruction method can extend the spectral domain of Landsat OLI and enhance its applicability in global vegetation remote sensing. Meanwhile, it provides potential insight into historical Landsat TM/ETM+ data enhancement for improving time-series vegetation monitoring. Full article

A thorough understanding of the consequences of urbanization can be significantly advanced by examining urban environmental dynamics at high spatial and temporal resolutions. This study evaluates eco-environmental quality and investigates the underlying drivers of urban greening within the functional units of Haikou, a tropical coastal city located on Hainan Island, China, using advanced techniques from Landsat and Google Earth imagery. Ecological index and land use change analyses were conducted using Landsat 5 (TM) imagery for 2010 and Landsat 8 (OLI) imagery for 2020. In addition, Google Earth imagery was used to interpret the driving factors influencing urban functional units (UFUs) in 2010 and 2020. Spatial and temporal environmental changes were quantitatively assessed. Multi-spectral Landsat 8 data at a 30 m resolution were used to construct a remote sensing ecological index (RSEI) to assess Haikou’s ecological condition. Land use impacts on eco-environmental quality were evaluated through RSEI values from 2010 to 2020, showing that eco-environmental quality improved over time, revealing a gradual improvement over time. Land use across 190 UFUs from 2010 to 2020 was categorized into five types: trees and shrubs, herbs, built-up areas, sandy lands, and water bodies. The primary drivers of greening percentage in each UFU were identified as housing prices, maintenance duration, and construction age. The most significant changes in land cover type were observed in the herb areas. Similarly, maintenance duration emerged as the most influential factor driving changes in urban green space (UGS). In conclusion, this study offers valuable insights for future urban planning and improvements in eco-environmental quality in Haikou, Hainan Island, China. Full article

Changes in climate and land use significantly impact downstream water availability. Quantifying these effects in the Ethiopian Highlands is crucial, as 85% of the transboundary water in Egypt and Sudan originates from these highlands. While the impact of climate change on water availability has been widely studied, few experimental studies have examined how it is affected by eucalyptus reforestation. Therefore, the objective was to investigate how eucalyptus expansion impairs water availability in the Ethiopian Highlands. The study was conducted in the 39 km² Amen watershed, located in the upper reaches of the Blue Nile. Rainfall data were collected from local agencies from 1990 to 2024, while streamflow data were available only for 2002–2009 and 2015–2018. Actual evapotranspiration was obtained using the WaPOR portal, and land use was derived from Landsat 5 TM and Landsat 8 OLI. The satellite images showed that the eucalyptus acreage increased from 238 ha in 2001 to 799 ha in 2024, or 24 ha y⁻¹. The actual evapotranspiration of eucalyptus was up to 30% greater than that of other land uses during the dry monsoon phase (January to March), resulting in decreased water storage in the watershed over a 23-year period. Since runoff is generated by saturation excess runoff, it takes longer for the valley bottoms to become saturated. In the 2002–2009 period, it took an average of around 160 mm of cumulative effective rain for significant runoff to start, and from 2015 to 2018, 274 mm was needed. Additionally, base flow decreased significantly. The annual runoff trended upward when the annual rainfall was more than the additional amount of water evaporated by eucalyptus, but decreased otherwise. Full article