Search Results (4,807)

This study examines historical wildfire frequency and the burned area in the Palisades region from 1928 to 2021 and the spatial distribution of burn severity within the 2025 Palisades Fire perimeter in Los Angeles County, California. Sentinel-2 imagery was used to calculate the Normalized Difference Vegetation Index (NDVI), differenced NDVI (dNDVI), and differenced Normalized Burn Ratio (dNBR). Burn-severity clustering was evaluated with Global and Local Moran’s I and Getis-Ord Gi*, and prefire land cover was summarized from Dynamic World V1. The historical record showed an overall increase in annual fire frequency, while annual burned area remained highly variable. Within the 2025 fire perimeter, the highest dNBR severity class represented 61.92% of the classified area, and Global Moran’s I indicated strong positive spatial autocorrelation (I = 0.874, z = 2656.89, p < 0.001). NDVI and dNDVI showed widespread reductions in vegetation greenness between the prefire and early postfire observation periods. An external field-informed cross-check against the final USGS Burned Area Emergency Response Soil Burn Severity product produced a moderately strong positive rank correlation (Spearman’s ρ = 0.70), although study-specific field validation was unavailable. Area-weighted American Community Survey data provide a descriptive demographic profile of intersecting census tracts and are not interpreted as a population-weighted social vulnerability assessment. The results characterize spatial fire effects and demographic context while avoiding causal attribution to environmental or social drivers that were not directly tested. Full article

Ecosystem carbon use efficiency (CUE = NEP/GPP) is an indicator of an ecosystem’s capacity to convert photosynthetically fixed carbon into net carbon storage. Under global warming and CO₂ fertilization, vegetation greening may be accompanied by asynchronous changes in carbon use efficiency. As the world’s largest non-zonal arid region, the arid region of Central Asia still lacks systematic understanding of the spatiotemporal evolution of CUE and its primary associated factors. This study analyzed the spatiotemporal dynamics and influencing factors of CUE in the arid region of Central Asia from 1999 to 2019. The results showed that the region as a whole acted as a carbon sink, with multi-year mean GPP and NEP of 414.8 g C m⁻² and 83.3 g C m⁻², respectively, and interannual growth rates of 6.5 g C m⁻² yr⁻¹ and 1.3 g C m⁻² yr⁻¹, respectively. The mean CUE was 0.17, and its trend was essentially stable. Shrubland (SL) had the highest CUE (0.25) but showed a slight declining trend, suggesting that its carbon use efficiency may face a risk of decline. Partial correlation analysis identified the primary factors associated with CUE in Central Asia from 1999 to 2019. Across the entire region, LAI had the highest proportion as the primary associated factor. Forest (FR) had a relatively high proportion of pixels with TEM as the primary associated factor. SL showed relatively balanced associations with multiple moisture factors, including PRE, SM, and VPD. Grassland (GL) and sparse vegetation (SV) had VPD and LAI as their primary associated factors, while in cropland (CL), LAI was prominent as the primary associated factor. Geographical detector results indicated that the statistical explanatory power of multi-factor interaction combinations for the spatial differentiation of CUE was higher than that of single factors, and two-factor interactions significantly enhanced explanatory power. Across the entire region, TEM∩LAI had the highest explanatory power (q = 0.252); some combinations exhibited bi-factor enhancement, while the rest mainly showed nonlinear enhancement. Among different ecosystems, TEM∩LAI and VPD∩LAI in SL had the highest explanatory power (q = 0.501 and 0.510, respectively), TEM∩PRE in GL had prominent explanatory power (q = 0.324), and SV and CL exhibited strong multi-factor interaction characteristics. These results reveal the spatial heterogeneity of carbon use efficiency in the arid region of Central Asia and the explanatory power of its associated factors, and further highlight that high productivity does not necessarily correspond to high carbon use efficiency, providing new insights into understanding ecosystem carbon use strategies in arid regions. Full article

Alpine wetland ecosystems play a vital role in water conservation, runoff regulation, biodiversity preservation, and carbon sequestration in watersheds. Climate warming has led to pronounced spatiotemporal distribution heterogeneity in alpine swamp wetlands. This is particularly critical for arid inland river basins, whose runoff is largely replenished by alpine ecosystems. Because alpine wetlands are a core component of these systems, it is both scientifically and practically imperative to systematically analyze their change characteristics and underlying driving mechanisms. This study used long-term wetland distribution data from 1987 to 2021 in the alpine mountainous areas of the Shule River basin (China), integrated meteorological, topographic, and permafrost factors, and employed Geodetector and Pearson correlation analysis to separately identify the dominant drivers of alpine swamp wetlands (swamp meadows and marsh wetlands) along elevation gradients, and to quantify the relative contributions of climatic, topographic, cryospheric, and anthropogenic factors to the evolution of swamp wetlands in the study area. The results showed that swamp meadows were mainly distributed in permafrost regions at elevations of 3700~4300 m in the source area of the Shule River‘s main stream. Their distribution was primarily influenced by temperature, as well as other factors such as permafrost, with explanatory power q-values ranging from 0.47 to 0.6. Meanwhile, the drivers of dynamic changes in swamp meadows varied distinctly with elevation. Below 3700 m, the expansion of swamp meadows was more influenced by temperature, with a correlation coefficient of about 0.4. In the 3700~4000 m elevation zone, precipitation became the dominant factor, with a correlation coefficient of up to 0.6. Above 4000 m, swamp meadows exhibited a relatively high negative correlation with low permafrost temperatures, with a correlation coefficient of up to 0.48, and an obvious lag period was also evident. Driven by topography and geomorphology, marsh wetlands were mainly concentrated on both banks of the main channel in the source area of the Dang River at elevations of 2800~3200 m. Their rapid expansion was mainly driven by increased growing-season precipitation in the study area, with a correlation coefficient close to 0.5, and showed a significant negative correlation with glacier area. These results could inform decisions on protecting wetlands in alpine mountainous areas of arid inland river basins. Full article

Due to the combined effects of climate change and intensified human activities, large lake basins are often at risk of severe degradation of ecosystem services (ESs). To develop scientifically rigorous and effective strategies for managing ESs, this study focuses on the Poyang Lake Basin (PYLB) as the research area. By integrating Spearman correlation analysis, geographically weighted regression (GWR), optimal parameters-based geographical detector, and self-organizing map, we established a comprehensive framework integrating multi-period ES assessment, relationship identification, driving factors analysis, and spatial management zoning. The main findings are as follows: (1) Across the three study years, food production (FP) was 33.99% higher in 2020 than in 2000, whereas carbon storage (CS) and habitat quality (HQ) were 1.34% and 4.39% lower, respectively. In contrast, soil conservation (SC), water yield (WY), and water conservation (WC) exhibited the fluctuating changes. (2) Synergistic relationships predominated among CS, HQ, SC, WY, and WC, whereas FP exhibited predominantly trade-off relationships with the other ESs. Although most pairwise ES relationships weakened during the study period, GWR revealed pronounced spatial nonstationarity and directional reversals in the FP-WC, FP-WY, and SC-HQ relationships. (3) Land use and cover change, elevation, slope, and precipitation showed relatively high explanatory power for the spatial differentiation of ESs, while interactions among socio-ecological driving factors generally exhibited greater explanatory power than individual factors. (4) Based on the characteristics and spatiotemporal variation patterns of the five ecosystem service bundles identified, targeted management measures were formulated. These findings offer a foundation for scientific ecosystem management and sustainable development in the PYLB. Full article

The Kelani River is one of the most important freshwater resources in Sri Lanka, supplying drinking water, supporting aquatic ecosystems, and sustaining industrial and agricultural activities. However, increasing anthropogenic pressures have intensified the need for comprehensive long-term assessments of river water quality. This study evaluated the spatio-temporal variability of six physicochemical water-quality parameters (pH, temperature, turbidity, chemical oxygen demand, dissolved oxygen, and chloride) using monthly observations from twelve monitoring locations covering January 2007 to May 2022, representing the most up-to-date long-term CEA monitoring record available to the authors at the time of data acquisition. An integrated statistical framework comprising correlation analysis, Granger causality testing, vector autoregressive (VAR) modelling, ARIMA and seasonal ARIMA (SARIMA) forecasting, K-means time-series clustering, Pettitt temporal homogeneity testing, changepoint detection, and regression kriging was applied to investigate temporal dynamics, predictive relationships, temporal stability, and spatial variability. VAR, ARIMA, and SARIMA were selected as interpretable baseline models for assessing lagged dependence and seasonal temporal structure in the monthly water-quality series; comparison with machine-learning and hybrid models was beyond the scope of this study. The best-performing imputation method varied among parameter–location series; for pH, the minimum RMSE values of the selected methods ranged from 0.07 to 0.27. Temporal forecasting performance was also strongly parameter- and location-dependent. For DO, test-set RMSE ranged from 0.69 to 1.33 mgL⁻¹, whereas chloride forecast RMSE ranged from 5.60 to 838.56 mgL⁻¹, with the largest error observed at Victoria Bridge, reflecting the pronounced variability of the downstream chloride series. After Bonferroni correction across the 360 directional parameter–site comparisons, only two Granger-predictive relationships remained statistically significant: COD → chloride at Maha Oya and temperature → pH at Kaduwela Bridge. The corresponding VAR models had R² values of 0.20 and 0.25, respectively, indicating modest explanatory power, while ARIMA and SARIMA models provided satisfactory forecasting performance for several parameters. The Pettitt homogeneity assessment identified nine candidate shifts at the unadjusted 5% significance level, but none remained statistically significant after Bonferroni correction across the 72 parameter–location tests. Regression kriging provided an exploratory representation of spatial variability in water-quality characteristics along the river. Overall, the proposed framework provides a robust statistical approach for long-term river water-quality assessment and supports evidence-based monitoring, pollution management, and sustainable river basin management, contributing to Sustainable Development Goal 6. Full article

Large-scale smart grids have introduced significant challenges for false data injection attack (FDIA) detection due to the distributed nature of power system operation and the limited observability of individual regions. Existing horizontal federated learning approaches enable collaborative model training without sharing raw data; however, they rely on local information and often fail to capture the global operational characteristics needed to detect sophisticated attacks. To address this limitation, the present paper proposes VFedA, a federated vertical relational alignment framework for FDIA detection via cross-regional feature fusion. Specifically, each regional client employs a self-supervised spatiotemporal contrastive learning encoder to extract local representations from measurements. The extracted representations are uploaded to a control center and concatenated to construct global feature representations for attack detection. In order to mitigate inconsistencies across heterogeneous regional feature spaces, a relational alignment mechanism is introduced to enforce structural consistency by aligning similarity matrices derived from local representations. The proposed approach enables effective cross-regional information fusion while preserving data privacy. Extensive experiments are conducted on IEEE 30-bus and IEEE 118-bus benchmark systems. The results demonstrate that VFedA consistently outperforms conventional horizontal federated learning methods and achieves performance comparable to centralized detection models. Full article

Atmospheric precipitable water vapor (PWV) is an essential geophysical parameter for weather forecasting, climate change research and hydrological studies. Spaceborne passive microwave sensors support all-weather PWV observation, yet accurate retrieval of land PWV still faces great difficulties owing to the spatiotemporal complexity and variability of land surface emissivity (LSE). This study proposes a novel all-weather land PWV retrieval approach using Fengyun-3D microwave radiation imager (MWRI) observations, which combines physical radiative transfer principles with machine learning techniques. By reorganizing the radiative transfer equation, this study constructs two characteristic factors that can effectively characterize LSE and PWV, and further incorporates them into the machine learning retrieval framework. Three typical machine learning algorithms, namely Random Forest (RF), Adaptive Kernel Extreme Learning Machine (AKELM), and Extreme Gradient Boosting (XGBoost), achieve the optimal retrieval accuracy, with an RMSE of 3.80 mm and an R² of 0.91. Multiple validation results indicate that (1) compared with conventional physically constrained machine learning retrieval methods, the proposed method reduces the root mean square error (RMSE) by 16.5%; (2) against ground-based SuomiNet site observations, the minimum retrieval RMSE is as low as 2.95 mm; (3) the proposed method exhibits stable superiority over the Moderate Resolution Imaging Spectroradiometer (MODIS) MYD05 water vapor products with lower RMSE in all experimental cases. This study offers an effective and reliable technical scheme for all-weather land PWV retrieval, and can facilitate high-precision atmospheric monitoring and related practical operational services. Full article

Natural capital markets in Scotland are evolving to respond to climate and biodiversity crises. The Woodland Carbon Code (WCC) and Peatland Code (PC) are established voluntary carbon markets that have attracted investors and promoted environmental restoration. Yet, a comprehensive analysis has not been carried out to assess the potential impacts and benefits of these carbon credit projects. Here, we present an environmental geography of carbon credits in Scotland that examines their spatiotemporal distributions, landholding dynamics, protection status, restoration area, land cover change, climate characteristics, emissions reduction potential, and ecosystem service areas. We analyzed WCC and PC projects from 2000 to 2025 and compared them to a suite of environmental and socioeconomic datasets. The 906 WCC projects plan to plant 74,909 ha of woodlands, with the smallest being 0.4 ha and the largest being 5601 ha (median = 35 ha). Most of the woodlands planted under the WCC were relatively small with marginal land cover change. The 253 PC projects plan to restore 35,448 ha of peatlands, with a median project area of 115 ha. Half of the PC projects (interquartile range) were between 70 and 185 ha, and almost a fifth exceeded 200 ha. Significant warming (+1.5 °C) and increased precipitation (+150 mm) over the past 60 years have implications for present and future carbon storage. Our most interesting results were landownership dynamics, where less than 3% of the landed estates (median) were set aside for restoration. While relatively few large landowners benefit financially from carbon credit projects, most of Scotland’s population benefits from their local and regional ecosystem services. Our findings are relevant for climate change mitigation and ecosystem restoration planning in Scotland and land systems beyond. Full article

Within the framework of green sustainable development, the relationship between the digital economy (DE) and the green efficiency of water resources (GEWR) has received increasing attention. This study uses panel data from 30 provincial-level regions in China covering 2014–2023, comprising 300 province-year observations. The objective of this study is to examine the relationship between the digital economy spatial correlation network and GEWR and to explore the potential pathway and moderating role of environmental regulation. Accordingly, it is hypothesized that the DESCN is positively associated with GEWR, that industrial structure upgrading (ISU) and water conservancy industry development (WCI) constitute potential pathways related to this association, and that environmental regulation positively moderates the relationship between DC and GEWR. The super-efficiency slacks-based measure (SE-SBM) model serves as the tool for measuring the level of the GEWR. The digital economy spatial correlation network (DESCN) is constructed through a modified gravity model, which helps analyze interregional DE connections and the spatiotemporal characteristics of GEWR. Furthermore, empirical analysis is conducted to thoroughly examine the relationship between the DESCN and GEWR. The results showed that: (1) the degree centrality (DC) of the DESCN was positively and significantly associated with GEWR; (2) DC was positively and significantly associated with ISU and WCI, supporting H2a and H2b at the level of variable relationships but providing only preliminary evidence concerning the proposed pathways because the corresponding indirect effects were not estimated; and (3) the positive and statistically significant interaction between DC and environmental regulation (ER) indicates that ER strengthens the positive relationship between DC and GEWR. Full article

The construction industry is one of the largest sources of anthropogenic CO₂ emissions, and most of its carbon is embodied in upstream materials such as cement and steel. Estimates rest on three accounting frameworks, the emission-factor (EF), input-output (IO) and material-flow methods, which count different components, so their totals are not interchangeable. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we screened 2746 Web of Science records published between 2016 and 2026. We synthesized 136 research articles, 116 of them analyzing China, with 15 reviews used for positioning across four strands, namely accounting, spatiotemporal characteristics, driving factors and projection, comparing their dominant methods. The accounting frameworks are complementary. Their qualitative findings agree, while the numerical differences between them have not been measured. The accounting choice and the analytical method applied afterwards nevertheless condition reported levels, driver magnitudes and the projected peak year, which ranges from 2025 to 2045. The statistics they rest on can contain errors and discontinuities, which recent data-fusion methods can identify, assess and, where justified, reconstruct. We conclude with an integrative framework of reconciliation, diagnosis, attribution and projection, and a research agenda for heterogeneity-aware, supply-chain-resolved analysis. Full article

Global warming has intensified extreme heat events, posing growing climate risks. As a climate-sensitive region, China has experienced compound daytime and nighttime extreme heat, threatening health, ecosystems, and socioeconomic development. Accurate projections of future extreme heat in China under different emission scenarios are therefore essential for climate risk assessment, adaptation planning, and regional sustainable development. This study integrates the SimmEX extreme temperature index dataset and NEX–GDDP–CMIP6 climate model data to investigate six daytime and nighttime extreme temperature indices (TXx, TNx, TX90p, TN90p, SU, and TR). A spatiotemporal evaluation framework is developed to optimize the Multi-Model Ensemble (MME), and the Combined Nonstationary Cumulative Distribution Function matching (CNCDFm) method is applied for bias correction. The main findings are as follows: (1) CMIP6 models can generally reproduce the spatiotemporal characteristics of extreme heat over China. Intensity-based indices (TXx, TNx) showed the highest skill (spatial correlations 0.85–0.95), followed by absolute-threshold indices (SU, TR), while percentile-based indices (TX90p, TN90p) exhibited larger uncertainties. (2) MME performance did not improve monotonically with increasing ensemble size. The spatiotemporal evaluation framework identified optimal models for different indices, and the corresponding optimal MME sizes balancing accuracy and robustness were identified as 7 for TXx, 4 for TNx, 5 for TX90p, 4 for TN90p, 4 for SU, and 4 for TR. (3) CNCDFm improved MME–observation agreement, with greater improvements for TXx, TNx, SU, and TR than for the percentile-based TX90p and TN90p. Extreme heat is projected to increase across China, with stronger increases under higher-emission scenarios and distinct regional patterns among different indices. By the late 21^st century, TXx and TNx are projected to rise by 6.7 °C and 7.3 °C under SSP5-8.5, compared with 2.4 °C and 3.1 °C under SSP1-2.6. Nighttime indices exhibit stronger increases than daytime indices, highlighting greater extreme heat risks under higher emissions. Full article

Ungulates serve as a critical link between primary producers and higher-level consumers, and play an irreplaceable role in maintaining the structural and functional integrity of forest ecosystems. Understanding the spatiotemporal niche differentiation of sympatric ungulates is of great guiding significance for forest biodiversity conservation and refined habitat management. Based on camera-trapping data collected across the entire Tangjiahe National Nature Reserve from 2022 to 2024, this study systematically analyzed the relative detection intensity, multi-scale activity rhythms, spatiotemporal niche overlap characteristics, and environmental response patterns of five sympatric ungulate species: the Chinese takin (Budorcas tibetana), Chinese serow (Capricornis milneedwardsii), Chinese goral (Naemorhedus griseus), tufted deer (Elaphodus cephalophus), and forest musk deer (Moschus berezovskii). The results showed that the relative detection intensity of the five ungulates exhibited a three-tiered hierarchical pattern, with the Chinese takin showing the highest relative detection intensity and the Chinese serow and forest musk deer showing the lowest detection rates. Fitted diel activity curves were characterized mainly by crepuscular bimodal patterns, whereas forest musk deer showed a predominantly nocturnal unimodal pattern; the DI of Chinese serow showed no statistically supported overall bias between the predefined daytime and nighttime windows. Seasonal overlap coefficients and activity curves indicated species-specific seasonal adjustment, with Chinese serow showing the greatest cold–warm-season divergence and Chinese goral the highest seasonal similarity. The three widely distributed species displayed moderate to high spatiotemporal overlap, whereas the two narrowly distributed species showed pronounced differentiation in spatial-use patterns. Generalized additive models further revealed species-specific nonlinear associations with environmental variables, with elevation and distance to roads emerging as the most consistent predictors across all five species. These findings characterize the spatiotemporal niche differentiation pattern of sympatric ungulates in the Minshan Mountains, and offer a scientific basis for targeted monitoring and conservation of species with low detection rates, habitat zoning optimization, and seasonally adaptive regulation of human disturbance within the nature reserve. Full article

Accurate and timely recognition of flame and smoke is critical for fire warning systems to mitigate casualties and property damage. Most existing fire datasets are image-based and thus fail to capture the dynamic spatiotemporal information of fire. Furthermore, the lack of large-scale fire video datasets poses a significant challenge to the training of neural networks. To address these limitations, we developed the Flame-Smoke Video Recognition (FSVR) dataset, a large-scale collection of 30,000 video clips that significantly exceeds the scale of prior datasets in the same domain. Since flame and smoke exhibit multiscale characteristics, small-scale targets are often obscured by complex backgrounds. Existing methods lack robust multiscale feature learning capabilities, which limits their ability to achieve precise fire recognition under complex conditions. To address this issue, we proposed the Multi-scale Fire Feature Learning Network (MFFLNet), which integrates multiple Multi-scale Fire Feature Learning (MFFL) blocks into a Transformer backbone. Each MFFL block comprises two key components, i.e., the multiscale fire Conv3D layer and the fire spatiotemporal feature learning layer. The experimental results obtained from the FSVR and LFVR datasets demonstrate that MFFLNet surpasses the baseline model and other comparative methods. When the backbone network is initialized with pre-trained weights from the Kinetics-710 dataset, MFFLNet attained an accuracy of 79.54% and a macro-F1 score of 79.22% on the FSVR dataset, while achieving an accuracy of 95.64% and an F1 score of 95.46% on the LFVR dataset. Full article

Coastlines possess significant ecological and resource values, which are intricately associated with marine ecological civilization, the marine green economy, and coastal human well-being. Comprehending the spatiotemporal variations and driving mechanisms of coastlines is of great significance for their effective protection, rational utilization, and sustainable development. Traditional coastline quantitative analysis methods mostly interpret coastal characteristics from a single dimension and struggle to hard to effectively couple the intrinsic relationships among coastline spatial variation, attribute succession, and driving mechanisms. Therefore, this study constructs a coastline location and type (CLT) model. The model classifies coastline evolution into four typical patterns, namely stable location with stable type, stable location with transformed type, changed location with stable type, and changed location with transformed type. Furthermore, the coastline disturbance index (CDI) is proposed to identify the dominant influencing factors triggering coastline spatial displacement and type transformation at the regional scale. Taking Huizhou as the study area, we employed ArcGIS to extract the coastline vectors of Huizhou in 1973, 1988, 2004, and 2019 based on multi-source remote sensing and unmanned aerial vehicle (UAV) images. Based on the analytical results obtained from the CLT model, the primary conclusions of this study are summarized as follows: During the study period, the total coastline length increased continuously from 248.68 km to 260.81 km, while artificial coastline length increased and natural coastline length declined. Meanwhile, the CDI showed a sustained upward trend, rising from 16.90% to 41.95%. This phenomenon was primarily driven by land reclamation and aquaculture enclosures, accompanied by distinct regional disparities. Specifically, land reclamation gradually became the dominant influencing factor after 1988. This study provides an integrated analytical framework for the multi-dimensional (including location, type and driving factor) quantitative investigation of coastal spatiotemporal evolution. Full article

The rapid integration of rural distributed photovoltaics has improved rural energy self-sufficiency. However, because of low rural load density, insufficient midday electricity demand, and limited distribution-network export capability, surplus photovoltaic electricity is difficult to accommodate efficiently on site. To improve rural renewable energy utilisation without relying on large-scale stationary storage or distribution-network expansion, this study proposes an urban–rural coordinated scheduling optimisation method for electric buses with spatiotemporal transfer characteristics. First, the schedulable characteristics of agricultural flexible loads are characterised, and urban–rural electric buses are modelled as mobile energy storage resources linking rural photovoltaic-surplus areas and urban load centres by exploiting their fixed routes, predictable dwell windows, and cross-regional operation. On this basis, a coordinated optimisation model is established with the minimisation of rural photovoltaic curtailment as the primary objective, while also considering disturbances to agricultural production schedules and fluctuations in urban net load. Finally, comparative analyses are carried out under multiple progressive configuration schemes. The results show that the proposed method increases the rural renewable energy accommodation rate from 74.29% to 97.26% while effectively suppressing urban grid load fluctuations. The average daily net cost of the bus fleet is reduced by 9.61% compared with the conventional operation strategy. The results indicate that the coordinated scheduling of agricultural flexible loads and urban–rural electric buses can simultaneously improve rural renewable energy accommodation and smooth urban loads, thereby providing a feasible pathway for coordinated urban–rural energy and transport operation under high penetration of distributed photovoltaics. Full article