Search Results (163)

Climate change threatens ecosystem stability in arid Central Asia, yet regional vegetation responses remain poorly resolved at the operational scale of land-use policy. We integrated long-term meteorological records (2000–2024) from Kazhydromet with Landsat surface-reflectance imagery for four epochs (2010, 2015, 2020, 2025) across the five administrative regions of Southern Kazakhstan (≈710,000 km²). After cross-sensor harmonization of Landsat 5 TM and Landsat 8 OLI, dense vegetation cover (NDVI > 0.4) increased modestly across all regions, with the cumulative area growing from 9.09 to 9.60 million hectares (+5.6%) and a transient 2020 minimum linked to the 2018–2020 drought. Per-region OLS trend slopes were not statistically significant at p < 0.05, given the four-epoch sampling (n = 4). A composite Biodiversity–Climate Sensitivity Index (BCSI), constructed from four normalized components (temperature trend, precipitation deficit, NDVI trend, and the coefficient of variation of dense-vegetation cover as a biodiversity–vulnerability proxy), identifies the lower Syr Darya floodplain and former Aral Sea margins as the most sensitive territories and the Northern Tien Shan as the most resilient. The framework provides an operational evidence base for climate-adaptive conservation aligned with SDG 13 and SDG 15. Full article

Timely and accurate mapping of tree species is essential for forest resource inventory, biodiversity conservation, and sustainable ecosystem management, particularly in dryland environments where structural heterogeneity, spectral similarity, and data scarcity complicate classification. This study develops a machine learning-based framework implemented in Google Earth Engine to map dominant tree species in the Elnour Natural Forest Reserve (ENFR), Blue Nile, Sudan, using multi-temporal and multi-sensor remote sensing data. Multi-temporal Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 MSI imagery were integrated with vegetation index (NDVI), topographic variables derived from a digital elevation model (DEM), and field observations. The performance of Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and an unweighted ensemble approach was evaluated across four reference years (2008, 2013, 2018, and 2021). Results show that RF and SVM consistently achieved high classification performance, with overall accuracy (OA) ranging from 85.0% to 92.0% and Kappa coefficients (κ) from 0.81 to 0.89, while maintaining stable and ecologically realistic species-area estimates. CART showed greater sensitivity to class imbalance and overestimated minor species (OA = 72.0–80.0%, κ = 0.65–0.74), whereas the ensemble approach amplified misclassification of rare classes (OA = 78.0–84.0%, κ = 0.70–0.78). The integration of Sentinel-2 data improved species discrimination due to enhanced spatial and spectral resolution, particularly in the red-edge region; however, algorithm selection remained the dominant factor controlling performance. Feature importance analysis identified near-infrared (NIR), shortwave infrared (SWIR), and NDVI variables as the most influential predictors. Multi-temporal analysis revealed declining class separability, reflected by decreasing MCC values, and a shift in species composition, including a decline in Acacia seyal (Delile) and an increase in Sterculia setigera Delile. These patterns indicate increasing ecological complexity driven primarily by anthropogenic pressures, with climatic variability acting as an additional stressor. Full article

The thermal consequences of industrial land transformation remain underexplored in rapidly urbanizing regions of Bangladesh. This study presents a novel approach of how extensive industrial expansion in Narayanganj, a major manufacturing hub dominated by textile, knitwear and dyeing industries, has altered land surface temperature (LST) dynamics over the past three decades, including its variation across classes, relationships with biophysical indices and future patterns. Landsat 5 TM and Landsat 8 OLI imagery from 1991, 2007, and 2023 were utilized to map LULC using winter-season images through supervised classification, while multi-seasonal thermal bands were used to derive LST. LST variations were further evaluated using cross-sectional profiles across different land cover types, and correlations were examined with indices including the greenness index (NDVI), moisture index (NDMI), built-up index (NDBI), and barrenness index (NDBAI). Additionally, a future LST map for 2039 was generated using the cellular automata–artificial neural network (CA-ANN) model. Results show that between 1991 and 2023, built-up area and bare land expanded by 16.72% and 14.15%, while vegetation area and water bodies decreased by 26.62% and 4.25%. Average LST increased from 25.94 °C in 1991 to 28.68 °C in 2023, with projections indicating an additional 2 °C rise by 2039. Cross-sectional analysis found that built-up areas consistently showed the maximum surface temperatures, followed by bare land, vegetation and water bodies. In addition, correlation analysis revealed that LST showed an inverse relation with NDVI and NDMI, while showing a positive relationship with NDBI and NDBAI. These findings show the necessity of sustainable urban planning and green infrastructure to reduce surface heating in rapidly urbanizing areas. Full article

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

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

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

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

Land Use and Land Cover Change (LULCCs) shapes catchment dynamics and is a key driver of hydrological risks, affecting hydrological responses as vegetated land is replaced with urban developments and cultivated land. The resultant hydrological risks are likely to become more critical in the future as the climate changes and becomes increasingly variable. Understanding the effects of LULCC is vital for developing land management strategies and reducing adverse effects on the hydrological cycle and the environment. This study examines LULCC dynamics in the Niger Delta Region (NDR) of Nigeria from 1986 to 2024. A supervised maximum likelihood classification was applied to Landsat 5 TM and 8 OLI images from 1986, 2015, and 2024. Five land use classes were classified: Water bodies, Rainforest, Built-up, Agriculture, and Mangrove. The overall accuracy of the land use classification and Kappa coefficients were 93% and 0.90, 91% and 0.87, 84% and 0.79 for 1986, 2015, and 2024, respectively. Between 1986 and 2024, built-up and agriculture areas substantially increased by about 8229 and 6727 km² (561% and 79%), respectively, with a concomitant decrease in mangrove and vegetation areas of about 14,350 and 10,844 km² (−54% and −42%), respectively. The spatial distribution of changes across the NDR states varied, with Delta, Bayelsa, Cross River, and Rivers States experiencing the highest decrease in rainforest, with losses of 64%, 55, 44%, and 44% (5711 km², 3554 km², 2250 km², and 1297 km²), respectively. The NDR’s mangroves are evidently under serious threat. This has important implications, particularly given the important role played by mangrove forests in regulating hydrological hazards. The dramatic decrease in the NDR mangrove and rainforest could exacerbate climate-related impacts. The study provides quantitative information on LULCC dynamics that could be used to support planning on land management practices in the NDR as well as sustainable development. Full article

Understanding post-fire vegetation recovery dynamics is crucial for damage assessment and recovery planning, yet spatiotemporal patterns in complex plateau environments remain poorly understood. This study addresses this gap by focusing on Yunnan Province, a mountainous plateau region with high fire incidence. We developed an innovative approach combining differenced Normalized Burn Ratio (dNBR) and visual interpretation on Google Earth Engine (GEE) to generate high-quality training samples from Landsat 5 TM/7 ETM+/8 OLI imagery. Four supervised machine learning algorithms were evaluated, with Random Forest (RF) demonstrating superior accuracy (OA = 0.90) for fire severity classification compared to Support Vector Machine (SVM) OA of 0.88, Classification and Regression Tree(CART) OA o f0.85, and Naive Bayes(NB) OA of 0.78. Using RF, we generated annual fire severity maps alongside the Land Surface Water Index (LSWI), Normalized Difference Vegetation Index (NDVI), and Normalized Burn Ratio (NBR) from 2005 to 2020. Key findings include the following: (1) fire severity classification outperformed traditional remote sensing indices in characterizing vegetation recovery; (2) distinct recovery trajectories emerged across severity levels, with moderate areas recovering in 7 years, severe areas transitioning within 2 years, and low severity areas peaking at 2 years post-fire; (3) southern mountainous regions exhibited 1–2 years faster recovery than northern areas. These insights advance understanding of post-fire ecosystem dynamics in complex terrains and support more effective recovery strategies. Full article

Mangrove ecosystems are increasingly threatened by climate change and coastal development, making precise ecological stress modeling essential for informing conservation strategies. This study employs AI-based classification techniques to classify mangroves using Landsat 8-SR OLI/TIRS sensors (2023) along the Jazan Coast, identifying a total mangrove area of 19.4 km². The ensemble classifier achieved an F1 score of 95%, an overall accuracy of 93%, and a kappa coefficient of 0.86. Ecological stress was modeled via a generalized additive model (GAM) with key predictors, including trends in the NDVI, NDWIveg (vegetation water content), NDWIow (open water), and LST from 1991 to 2023, which were derived using surface reflectance (SR) products from Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI/TIRS sensors. The model exhibited strong performance, with an R² of 0.89. Model diagnostics using linear regression (R² = 0.86), a high F-statistic, minimal intercept, and 10-fold cross-validation confirmed the model’s robustness, with a consistent MSE (0.12) and cross-validated R² of 0.86. Moran’s I analysis also indicated significant spatial clustering. Findings indicate that mangroves in non-ravine, mainland coastal areas experience more ecological stress from disruptions in freshwater and sediment supply due to recent developments. In contrast, island coastal areas exhibit low stress levels due to minimal human activity, except in dense canopy regions where significant stress, likely linked to climate change, was observed. These results underscore the need for further investigation into the drivers of this ecological pressure. Full article

Highland ecologies are the most susceptible to climate change, often experiencing intensified impacts. Due to climate change and human activities, there were dramatic changes in the alpine domain of the China–Pakistan Economic Corridor (CPEC), which is a vital project of the Belt and Road Initiative (BRI). The CPEC is subjected to rapid infrastructure expansion, which may lead to potential land surface susceptibility. Hence, focusing on sustainable development goals, mainly SDG 9 (industry, innovation, and infrastructure) and SDG 13 (climate action), to evaluate the conservation and management practices for the sustainable and regenerative development of the mountainous region, this study aims to assess change detection and find climatic conditions using multispectral indices along the mountainous area of Gilgit and Hunza-Nagar, Pakistan. It has yielded practical and highly relevant implications. For sustainable and regenerative ecologies, this study utilized 30 × 30 m Landsat 5 (TM), Landsat 7 (ETM+), and Landsat-8/9 (OLI and TIRS), and meteorological data were employed to calculate the aridity index (AI). The results of the AI showed a non-significant decreasing trend (−0.0021/year, p > 0.05) in Gilgit and a significant decreasing trend (−0.0262/year, p < 0.05) in Hunza-Nagar. NDVI distribution shows a decreasing trend (−0.00469/year, p > 0.05), while NDWI has depicted a dynamic trend in water bodies. Similarly, NDBI demonstrated an increasing trend, with rates of 79.89%, 87.69%, and 83.85% from 2008 to 2023. The decreasing values of AI mean a drying trend and increasing drought risk, as the study area already has an arid and semi-arid climate. The combination of multispectral indices and the AI provides a comprehensive insight into how various factors affect the mountainous landscape and climatic conditions in the study area. This study has practical and highly relevant implications for policymakers and researchers interested in research related to land use and land cover change, environmental and infrastructure development in alpine regions. Full article

Hurricane incidents have become increasingly frequent along the coastal United States and have had a negative impact on the mangrove forests and their ecosystem services across the southeastern region. Mangroves play a key role in providing coastal protection during hurricanes by attenuating storm surges and reducing erosion. However, their resilience is being increasingly compromised due to climate change through sea level rises and the greater intensity of storms. This article examines the role of remote sensing tools in studying the impacts of hurricanes on mangrove forests in the coastal United States. Our results show that various remote sensing tools including satellite imagery, Light detection and ranging (LiDAR) and unmanned aerial vehicles (UAVs) have been used to detect mangrove damage, monitor their recovery and analyze their 3D structural changes. Landsat 8 OLI (14%) has been particularly useful in long-term assessments, followed by Landsat 5 TM (9%) and NASA G-LiHT LiDAR (8%). Random forest (24%) and linear regression (24%) models were the most common modeling techniques, with the former being the most frequently used method for classifying satellite images. Some studies have shown significant mangrove canopy loss after major hurricanes, and damage was seen to vary spatially based on factors such as proximity to oceans, elevation and canopy structure, with taller mangroves typically experiencing greater damage. Recovery rates after hurricane-induced damage also vary, as some areas were seen to show rapid regrowth within months while others remained impacted after many years. The current challenges include capturing fine-scale changes owing to the dearth of remote sensing data with high temporal and spatial resolution. This review provides insights into the current remote sensing applications used in hurricane-prone mangrove habitats and is intended to guide future research directions, inform coastal management strategies and support conservation efforts. Full article