Search Results (2,868)

Urbanization profoundly influences social, economic, and environmental systems, imposing a comprehensive understanding of spatial and temporal land use and land cover (LULC) transformations. This study aims to quantify the LULC changes from 2014 to 2023 in Penang Island, Malaysia, using SPOT 6 and SPOT 7 satellite imagery with a 1.5 m spatial resolution. After preprocessing and transforming data, five LULC classes—namely built-up, forest, water bodies, agriculture and horticulture, and barren land—were classified. The Support Vector Machine (SVM) classifier achieved accuracies of 90.8% in 2014, 91% in 2019, and 94.2% in 2023, with kappa coefficients of 0.85, 0.84, and 0.9, respectively. Analysis at the district level revealed that built-up area decreased by 4.53 km², forest expanded by 10.32 km², water bodies grew by 0.26 km², agriculture and horticulture increased by 8.47 km², and barren land declined by 11.84 km². Interestingly, the decline in built-up areas presents a paradox to the conventional narrative of urban growth, which typically anticipates an increase in developed land over time. This counterintuitive trend invites further inquiry into factors that may have driven such a reversal in urbanization patterns. Nevertheless, the findings align with SDG 11 and the NPP, which advocate for sustainable and resilient urban development. Full article

The growing adoption of cycling as part of the transition toward sustainable urban mobility, driven by climate change concerns and increasing congestion, has heightened the need to ensure cyclist safety in metropolitan areas. This study proposes an integrated spatio-temporal analytical framework to examine the evolution of reported bicycle–vehicle injury accidents in the Lisbon Metropolitan Area (LMA). The framework combines Geographic Information Systems (GIS)-based spatial statistics with Emerging Hotspot Analysis (EHA) to identify and track changes in accident clustering over time, across pre-, during-, and post-COVID-19 containment periods. This study contributes by applying Emerging Hotspot Analysis to bicycle accident data, an approach still largely unexplored, and by proposing a sequential and integrated framework that links traditional spatial analysis methods with dynamic hotspot detection and machine learning techniques, enabling a shift from static pattern identification to enhanced interpretation of evolving accident occurrence patterns and hotspot dynamics. Results reveal evidence of spatial consolidation and changing hotspot distributions over time, with emerging hotspots increasingly located in suburban transition zones and at the edges of existing cycling infrastructure. These patterns may reflect changes in mobility demand and infrastructure provision, although the absence of exposure data prevents a direct assessment of this relationship. Complementary analysis using forest-based machine learning models identifies key factors associated with hotspot formation and accident severity, including crash type, temporal patterns (e.g., day of the week), and environmental conditions such as slope and lighting. These findings highlight the value of combining spatio-temporal analysis with predictive modelling to support data-driven urban planning and targeted safety interventions. Lisbon provides a relevant case study for cities undergoing similar transitions toward sustainable transport systems. Full article

The overexploitation of groundwater resources is a significant concern due to the potential risks associated with a decline in freshwater availability. Future planning and policymaking should consider long-term groundwater availability and urban expansion patterns to understand urban growth. This study aims to investigate the impact of land cover change on groundwater depletion. Further, the land surface temperature (LST) and vegetation change using Normalized Difference Vegetation Index NDVI analysis have been performed to find the spatial spread of urbanization and its impact on surface temperature in the area. For groundwater assessment, the Gravity Recovery and Climate Experiment (GRACE) data have been used, while for land cover, NDVI, and LST assessment, Landsat data have been used. The GRACE-based groundwater storage (GWS) anomaly has been correlated with Global Precipitation Measurement (GPM) data. An annual groundwater storage decline of ~7.01 mm/year was identified. Groundwater and land-cover changes were evaluated at five-year intervals from 1990 to 2025. The urban expansion from 838 to 1470 km² coverage shows the rapid expansion and its impact on vegetation and groundwater recharge in the area. The results demonstrate a rapid increase in the urban area, which affected the vegetation and increased the surface temperature in the area. Urban expansion reduced vegetation cover and infiltration, contributing to elevated land surface temperature and groundwater depletion. This study focused on integrating the groundwater impacts due to other environmental variables, i.e., temperature increase and vegetation decrease. The temporal increase in urban expansion decreases the infiltration rate, which impacts the groundwater storage and depletion, as shown by the linear trend. These findings underscore the urgent need for effective groundwater management and vegetation management policies and integrated urban planning strategies to ensure the long-term sustainability of freshwater resources. Full article

Understanding the interactions between vegetation dynamics and surface water availability is essential for assessing the resilience of tropical floodplain ecosystems under increasing hydroclimatic variability. This study analyzes the spatio-temporal dynamics of vegetation cover, surface water, and land use in Zambrano, a floodplain-dominated sector of the lower Magdalena River basin (Colombian Caribbean), over the period 1990–2025. Multi-temporal Landsat imagery was used to derive the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), enabling the evaluation of seasonal and interannual ecohydrological variability under contrasting dry and rainy conditions. In addition, land-use classification was performed using a CORINE Land Cover methodology adapted for Colombia (CLC-C) to characterize the spatial organization of the landscape and its influence on vegetation–water interactions. Results show that vegetation dynamics are strongly controlled by hydroclimatic seasonality. Dense vegetation consistently expands during rainy periods, while dry seasons promote the expansion of open and sparse vegetation, reflecting seasonal vegetation stress rather than long-term degradation. NDWI patterns indicate that surface water and soil moisture are highly seasonal and spatially constrained, with open water largely confined to the Magdalena River channel and localized floodplain depressions. Extreme hydroclimatic events associated with the El Niño–Southern Oscillation (ENSO) produce abrupt but temporary changes in vegetation structure and surface moisture distribution. A strong inverse correlation between NDVI and NDWI reflects the contrasting spectral responses of vegetation and water surfaces resulting from the shared near-infrared (NIR) band in both indices. This spectral relationship is consistent with the observed seasonal variations in vegetation greenness and surface moisture across the floodplain. Land-use analysis reveals the progressive consolidation of the landscape, where the agropastoral matrix expanded from ~18,000 ha in 1990 to over 22,000 ha by 2025, driving a systematic reduction in natural and semi-natural forest structures. Forest conservation areas serve as critical ecological buffers, exhibiting lower seasonal variability in vegetation greenness. Overall, the results indicate that the Zambrano floodplain functions as a structurally stable yet highly responsive ecohydrological system, where vegetation dynamics and surface water availability are predominantly governed by interannual hydroclimatic pulses rather than long-term directional degradation. These findings demonstrate that while the structural matrix of the floodplain exhibits strong baseline resilience, its ecological functioning remains critically coupled with, and vulnerable to, the extreme phase shifts in ENSO cycles. Full article

Highway–rail grade crossing (HRGC) incidents in the United States declined substantially for several decades before stabilizing in recent years. Understanding this persistence is important because future safety improvements may depend on identifying locations where incident occurrence remains resistant to further reduction. This study developed an integrated framework to characterize persistent HRGC incident environments using 50 years (1976–2025) of Federal Railroad Administration incident records. Trend, structural-break, variance, and stationarity tests were first applied to determine whether the historical decline transitioned into a distinct persistence regime. A county-level persistence index (PI) was then developed to quantify the combined effects of incident burden and resistance to decline during the plateau period. Distributional analysis characterized the statistical behavior of the PI, while global and local Moran’s I statistics evaluated its spatial organization. Explainable machine learning methods were subsequently used to identify incident characteristics associated with elevated persistence. The results identified a statistically significant regime change around 2010. Prior to 2010, incidents exhibited a strong declining trend, whereas the subsequent period displayed a statistically significant but substantially weaker decline, lower variance, and behavior consistent with a persistence regime characterized by a markedly attenuated rate of improvement. The PI followed a strongly right-skewed distribution that was best represented by a bounded heavy-tailed unit log-logistic model, indicating that persistence is concentrated within a relatively small subset of counties. Spatial analysis revealed significant positive spatial autocorrelation (Moran’s I = 0.180, p = 0.001) and geographically coherent clusters concentrated primarily in the southeastern United States and several major freight-oriented regions. Explainable machine learning models identified train-operating characteristics, warning device contexts, movement patterns, and temporal conditions as key attributes associated with high-persistence counties. The findings demonstrate that the post-2010 incident plateau is sustained disproportionately by a limited number of geographically concentrated environments and provide a framework for supporting more targeted safety interventions. Full article

Hainan Tropical Rainforest National Park is a prime example of a “continental island” tropical rainforest and holds significant value for biodiversity conservation. However, human activities have led to frequent changes in land use and increased habitat fragmentation within the park; a precise analysis of the underlying mechanisms is necessary for ecological restoration. Consequently, drawing upon land-use data from 2000 to 2020, this study coupled multi-dimensional fragmentation metrics (CFI, AFI, and SFI) with the XGBoost-SHAP framework to systematically unravel the spatiotemporal dynamics and underlying driving mechanisms of landscape fragmentation in Hainan Tropical Rainforest National Park. Our findings revealed that the spatial configuration of fragmentation predominantly propagated along river networks and transport corridors, accompanied by a fluctuating ‘decline–rise–decline’ temporal trajectory. Notably, the XGBoost-SHAP attribution highlighted a distinct temporal shift in the dominant drivers: fragmentation was primarily mitigated (negatively driven) by NDVI between 2000 and 2010 but was subsequently exacerbated (positively driven) by GDP growth from 2010 to 2020. The findings of this study provide data support and a scientific basis for ecosystem restoration and land use planning in Hainan Tropical Rainforest National Park. Full article

Morocco, located at the southern margin of the Mediterranean climate-change hotspot, is exposed to a rapidly evolving precipitation regime whose national-scale characterization remains incomplete. This study delivers an integrated assessment of the spatio-temporal variability and trends of precipitation and its extremes over the country during the most recent World Meteorological Organization (WMO) climate-normal period (1991–2020), based on daily observations from 31 synoptic stations operated by the Direction Générale de la Météorologie (DGM). Trends in annual, seasonal and monthly precipitation were quantified using the non-parametric Mann–Kendall test combined with Sen’s slope estimator, while the structural transformation of the rainfall regime was characterized through three indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI): the Consecutive Dry Days (CDDs), the Simple Daily Intensity Index (SDII) and the amount of precipitation from very wet days (R95pTOT). The results reveal an apparent tendency toward a negative trend, with a predominance of negative precipitation trends in winter and early spring, most pronounced in February, that reach statistical significance at only a limited number of stations, partly offset by a spatially coherent wetting in November over central and eastern Morocco. The joint analysis of the three ETCCDI indices indicates a north–south contrasted reorganization: northern stations exhibit longer dry spells coexisting with intensified extreme rainfall, whereas southern stations show a generalized weakening of both intensity and extremes. These findings point to a structural shift toward more episodic and contrasted precipitation regimes, with the wet season starting later, ending earlier and concentrating rainfall into fewer but more intense events. The analysis provides an updated observational baseline for the validation of CMIP6 based regional projections and for the design of climate-resilient water and agricultural strategies in Morocco. Full article

Understanding how desert riparian vegetation responds to managed flow releases is essential for ecological restoration in arid inland river basins. This study examines vegetation dynamics and hydrological responses in the desert reach of the Hotan River, a seasonal river crossing the Taklimakan Desert. To avoid temporal inconsistency, two data windows were explicitly separated: Landsat-derived vegetation information was used to describe long-term vegetation changes from 1985 to 2020, while environmental flow release, river-section water consumption, and groundwater-depth analyses were limited to the period with available hydrological observations, 2006–2020. NDVI and vegetation-cover classes were derived from cloud-screened and atmospherically corrected Landsat imagery, and the response of vegetation indicators to cumulative environmental flow release and groundwater depth was evaluated using transparent regression models with diagnostic statistics. Results indicate that vegetation cover improved overall during the study period, although the response was spatially heterogeneous. Vegetation conditions were generally better near the upper and terminal parts of the desert reach, whereas a relatively vulnerable zone occurred approximately 15–115 km downstream of the river confluence. During 2006–2020, NDVI and grassland area generally increased with cumulative environmental flow release, whereas annual grassland-area change showed large interannual fluctuations and was not significantly explained by cumulative release alone. The revised analysis clarifies that the study contributes a reach-scale synthesis linking long-term vegetation mapping with monitored environmental flow releases and groundwater response in the Hotan River desert reach, rather than a full 40-year ecohydrological attribution. These findings provide a basis for improving environmental flow scheduling and monitoring design in arid desert rivers. Full article

Climate change is intensifying hydrological extremes, yet most frameworks assess drought and flood hazards independently, limiting integrated risk management. This study proposes a two-dimensional analytical framework to characterize the drought-flood continuum, moving beyond single-index approaches. We introduce the Hydro-Hazard Index (HHI) as a directionality metric (HHI = Flood Severity − Drought Severity) to classify the dominant hazard type, and the Total Severity Index (TSI = Flood Severity + Drought Severity) as a complementary metric to quantify overall hazard magnitude. Analyzing multi-temporal data from 115 hexagonal units (2018–2024), we employed dynamic features (trends, changes, volatility) and four machine learning models to classify areas as “flood-prone” based on validated flood records. Our results show HHI values ranging from −2.44 to 8.81, with 20.9% of areas classified as Flood-Dominated (mean HHI = 4.58) and 79.1% as Normal (mean HHI = 0.76). Crucially, the two-dimensional analysis revealed that areas with identical HHI values can have vastly different TSI values, under scoring the importance of our dual-index approach. Random Forest achieved the highest performance in predicting flood-prone status (Accuracy = 0.913, AUC = 0.967, Recall = 1.00), with flood_volatility as the most important predictor (24.2%). Spatial autocorrelation confirmed strong clustering of high-risk areas (Moran’s I = 0.716, p < 0.001). By analyzing flood and drought as distinct but interacting dimensions, this framework provides a more robust and nuanced tool for integrated risk assessment. While acknowledging limitations related to data availability and the need for further independent validation, the proposed framework supports sustainable water resource management and climate adaptation planning under increasing hydrological uncertainty. Full article

Distributed optical vibration sensing (DOVS) provides dense spatiotemporal measurements for pavement and traffic monitoring, but nonstationary background noise, spatially confined responses, and data-quality anomalies complicate vehicle-event detection. This study presents a deterministic, training-free, and interpretable detector for single-lane highway DOVS matrices. The algorithm forms a detrended absolute-amplitude representation and combines percentile-based temporal candidate detection, robust background estimates based on the median and median absolute deviation (MAD), a candidate spatial-width fraction derived from channel-specific thresholds, track-direction evidence, and explicit decision rules. Evaluation on 3085 manually labeled matrices acquired from 2023 to 2026 yielded 87.23% accuracy, 87.42% precision, 87.31% recall, and an F1-score of 87.36%. In a secondary analysis, excluding 98 quality-flagged matrices increased precision to 91.28% and F1-score to 89.20%; the exclusion removed 67 of 196 false positives and no false negatives. Relative to the diagnostic-refinement configuration, the final rule set increased F1-score by 2.173 percentage points, with a matrix-level bootstrap 95% confidence interval of 1.437–2.963 percentage points. The exact McNemar test for paired correctness differences gave p = 6.60 × 10⁻⁹. A sensitivity configuration changed only four classifications and produced no meaningful gain. These results quantify performance at the tested site; narrow responses, upward-like tracks, data-quality anomalies, single-annotator labels, and post hoc rule selection limit broader inference. Full article

The aim of this study was to estimate trends and multiscale variability in sea level along the southern coast of the Baltic Sea based on tide gauge and altimetry data, using Harmonic Analysis (HA) and Continuous Wavelet Transform (CWT). Particular consideration was given to the influence of time series length, data type, seafloor depth and distance from the coastline on the stability and consistency of the estimated trends and amplitudes. The results indicated that, for coastal stations, trends derived from tide gauge data averaged 2.2 mm/yr for the 1993–2024 period and 2.0 mm/yr for the 1951–2025 series, with lower estimation errors for series with an extended time range of data. Satellite altimetry data indicated a higher rate of sea level rise, averaging 4.3 mm/yr, and higher spatial consistency, particularly at virtual stations away from the coast. As the distance from the coastline increased, a more stable trend and a decrease in the influence of local hydrodynamic processes were observed. A comparison of methods demonstrated that Harmonic Analysis significantly improves the consistency of trends derived from altimetry and tide gauge data compared to classical linear regression—the correlation coefficient increased from 0.72–0.80 to 0.92–0.95. CWT confirmed the reliability of these results, while also allowing the identification of temporal modulation in cycle amplitudes and periods of increased signal nonstationarity. The results confirm that a correct interpretation of sea level changes requires that we take both long-term trends and natural variability across various timescales into account. The proposed approach provides a universal tool for analyzing nonstationary environmental signals. It can support risk assessment in coastal zones and the development of adaptation strategies in the context of climate change. Full article

Global restoration frameworks, such as the Bonn Challenge and Forest Landscape Restoration, have endorsed multistakeholder engagement in agroforestry as key to reversing land degradation at scale. This paper follows shifts in how stakeholders have been organised, coordinated and steered since 2010 when Rwanda’s National Forestry Policy came into force, a year ahead of Rwanda’s pledge to the Bonn Challenge. The specific focus is on Bugesera District, representing a national policy shift from focusing on restoration in highland areas only. Bugesera is a lowland area facing complex socio-ecological and livelihood challenges including high rates of deforestation, recurrent drought and rapid population fluctuations. This paper analyses these changes by investigating which stakeholders were involved, how they were engaged (modes of participation) and why they participated (drivers of participation). Conceptually, this follows stakeholder mapping, Reed’s theory of participation and multi-level governance theory. Through thematic analysis, we triangulated data from 15 policy-related documents with semi-structured interviews with representatives from 24 organisations. Our findings show that both before and after 2010, stakeholder engagement has remained top-down. However, since 2010 this has been qualified by an asymmetrical form of collaboration that increasingly takes the form of top-down deliberation. Changes were observed in participation patterns, engagement approaches, and governance arrangements, driven by contextual conditions, power relations, process design, and spatial–temporal dynamics. Within a centrally coordinated government system, shaped by the post-genocide political context and culturally embedded structures such as Umuganda and Ubudehe, multistakeholder restoration initiatives have largely remained state-led, with structured approaches to coordination and implementation that have varied in the extent of local stakeholder engagement. Trust emerged as an important factor influencing stakeholder interactions. Despite more diverse and expanded institutional arrangements over time, variations in levels of participation and influence among stakeholders persist, with differences in how engagement and decision-making power are distributed. We conclude that effective stakeholder engagement is contingent on existing governance structures, the political will to engage with a diversity of actors at different levels, contextual conditions including spatial-temporal dynamics, and opportunities provided by global and regional restoration frameworks. Full article

Mediterranean and semi-arid mountain forests are increasingly affected by recurrent drought, wildfire, overgrazing, and anthropogenic pressure, with direct implications for forest sustainability. This study assesses the spatio-temporal dynamics of vegetation cover in the Ouled Yagoub Forest, Khenchela Province, northeastern Algeria, from 1994 to 2025, using GIS and remote sensing. Multi-temporal satellite images, including Landsat data for historical periods and Sentinel-2 data for recent years, were processed to calculate NDVI, classify NDVI-derived vegetation-cover classes, and detect vegetation changes before and after the 2021 wildfire. Vegetation-cover classes were quantified in hectares and percentages, and NDVI change maps were produced for the periods 1994–2000, 2000–2010, 2010–2020, 2020–2021, 2021–2022, 2021–2025, and 1994–2025. Results showed that dense vegetation increased from 14.15% in 1994 to 20.71% in 2020, indicating improved pre-fire vegetation conditions. After the 2021 wildfire, dense vegetation decreased to 17.44% in 2021 and 13.44% in 2022, while very low vegetation increased sharply to 29.79% in 2022. The 2021–2022 period showed the strongest negative vegetation response, with 32.65% of the mapped area classified as vegetation decrease. By 2025, partial recovery was observed, with vegetation increase covering 20.14% of the mapped area between 2021 and 2025. However, low vegetation remained dominant, indicating incomplete and spatially heterogeneous recovery. These findings highlight the usefulness of NDVI-based multi-temporal analysis for monitoring forest degradation, post-fire recovery, and priority areas for restoration planning in semi-arid Mediterranean mountain forests, while also supporting sustainability-oriented forest management in other fire-prone regions with comparable ecological constraints. Full article

Phylogenetic diversity–habitat size relationship (PDSR), as an extension of the species–area relationship, incorporates evolutionary history to further elucidate the mechanisms shaping biogeographic patterns. However, whether PDSR is temporally unstable during community succession, and the mechanisms underlying such instability, remain poorly understood. In this study, we used a fermentation microbial community as a model and established a closed microcosm system spanning a 10–1000 mL volume gradient. By continuous sampling over 0–60 days and 16S rRNA high-throughput sequencing, we characterized the dynamic changes in PDSR throughout succession. The results showed that PDSR underwent stage-specific shifts during community succession: it was significantly positive in the early stage, weakened or became non-significant in the middle stage, and turned negative or remained non-significant in the late stage. The early positive PDSR was mainly attributable to the greater retention of rare lineages in larger-volume habitats, whereas in the late stage, phylogenetic diversity increased in smaller-volume habitats, suggesting a positive role in the resuscitation of potentially dormant lineages. Phylogenetic beta-diversity analysis further showed that lineage turnover rate increased over time, with significant divergence among samples in the later stage. In addition, pH decreased significantly over succession and, through interactions with time and volume, exerted a marked influence on community phylogenetic structure. This study reveals the temporal dynamism of biodiversity spatial patterns and confirms that PDSR shifts non-steadily across successional stages, providing new insights into the organization of microbial diversity across multiple scales. Full article

To evaluate the application potential of convolutional neural network (CNN)-based Local Climate Zone (LCZ) mapping in urban thermal environment studies, this study employed a lightweight convolutional neural network model (Light-model) to classify LCZs within the area enclosed by the Fourth Ring Road of Xuzhou City. ECOSTRESS data obtained from summer (June to September) at different times were integrated to analyze the temporal and spatial changes of surface temperature (LST) and surface urban heat island intensity (SUHII). The classification results demonstrate that the Light-model achieved an overall accuracy of 84.46%, which is markedly higher than that of the random forest model (72.07%). It also outperformed random forest in built-up area identification (built-up overall accuracy: 69.41% vs. 46.91%) and non-built-up area identification (natural overall accuracy: 91.85% vs. 84.43%), as well as in Kappa coefficient and mean F1-score. Time-series analysis based on ECOSTRESS observations revealed a typical diurnal LST pattern characterized by the lowest temperatures before dawn, a peak in the afternoon, and a decline at night. High-density built-up zones (LCZ1–LCZ3) and large impervious areas (LCZ8) exhibited the highest daytime temperatures and the slowest nocturnal cooling, whereas bare soil areas (LCZF) showed the largest diurnal temperature range and the greatest fluctuations. Vegetation-covered and bare land zones (LCZA and LCZD) generally maintained lower temperatures, while water bodies (LCZG) functioned as persistent cooling sources throughout the day due to their high specific heat capacity. Overall, the findings suggest that CNN-based LCZ classification, when integrated with high-temporal-resolution LST observations, provides a reliable technical framework for urban thermal environment monitoring and regulation at the regional scale. Full article