Search Results (23,601)

Extreme weather events increasingly affect the safety, durability, and operational performance of road infrastructure, creating the need for sustainable thermal management solutions. Among the available technologies, low-enthalpy geothermal systems offer significant advantages by providing continuous heating and cooling capabilities with reduced environmental impact compared to conventional maintenance practices. This study presents the methodology developed within the GEO-ROAD project to assess shallow geothermal resources across Spain and support the future deployment of geothermal road systems. The proposed framework integrates geological, thermal, and satellite-derived geophysical information through a unified GIS-based workflow, combining multivariate statistical analysis, map algebra, and automated geospatial processing to generate a regional geothermal potential model. In addition to conventional geological characterization, the methodology incorporates magnetic and gravity data from satellite missions, airborne surveys, and ground-based observations to improve the spatial representation of subsurface conditions. The resulting geothermal potential assessment constitutes a key component of the GEO-ROAD digital platform, where it will be combined with climatic risk maps and road infrastructure information to identify the most suitable locations for geothermal applications. By linking geothermal resource assessment with infrastructure-oriented decision-making, the proposed methodology provides a scalable and transferable framework for supporting the planning of sustainable and climate-resilient road thermal management systems. Full article

Páramo ecosystems are among the most biodiverse and hydrologically critical landscapes on Earth, yet their long-term sustainability is increasingly threatened by climate-driven wildfires. Vegetation Cover Fires (VCFs) in these high-altitude environments degrade carbon stocks, disrupt freshwater regulation, and undermine biodiversity conservation goals central to the UN Sustainable Development Goals (SDGs 13, 15, and 6). Between 2001 and 2023, 128 fire events consumed approximately 815 ha in the Sumapaz locality (the world’s largest continuous páramo), representing 64.9% of all fires recorded across Bogotá’s 20 localities. Despite this disproportionate ecological and social impact, no spatially explicit, operational risk management framework has been available for the region, representing a critical sustainability governance gap. This study addresses that gap by proposing an integrated climate-adaptive risk assessment and management strategy based on (i) the Canadian Forest Fire Danger Rating System Fire Weather Index (FWI), derived from ERA5 reanalysis climate data; (ii) spatial and temporal hotspot analysis of MODIS FIRMS active fire detections; and (iii) IDEAM’s multi-component vulnerability and threat scoring protocol. Spatial data were processed using ArcGIS, and FWI sub-indices were computed for each month of the 2001–2023 period. The FWI averaged 0.78 (low danger) across the study period yet peaked at 13.7 in February 2010 (moderate-to-high danger), consistent with the year of highest recorded fire activity (19 events). High- and very high-risk areas (3.70% combined) coincide with slopes >25%, the presence of the invasive and pyrogenic Ulex europaeus, and proximity to populated and agricultural lands. This study concludes with a three-pillar risk management framework—risk knowledge, risk reduction, and disaster management—providing spatially targeted, operationally viable strategies for local and institutional actors that directly support the sustainable conservation of páramo ecosystem services (water supply, carbon sequestration, biodiversity). The framework is designed to be updatable on a monthly basis using freely available ERA5 data, enabling continuous adaptive governance of wildfire risk as a contribution to long-term territorial sustainability. Limitations regarding MODIS detection uncertainty, ERA5 spatial resolution in complex terrain, and the need for probabilistic modeling are explicitly acknowledged. Full article

Landscape character change in mountain traditional villages is difficult to assess from a single perspective because roof-material replacement, material–color deviation, and visual exposure vary under complex terrain and settlement configurations. This study develops a multi-source UAV framework for reproducible, spatially explicit assessment of courtyard-scale visual–material integrity (CI), a spatially observable component of landscape character integrity. Using 749 courtyards in nine nationally designated traditional villages in Shangluo, China, the framework integrates UAV orthophotos, 3D mesh models, and point-cloud data to derive material penetration rate (PR), material–color conflict (CC), and standardized visual exposure (VC). PR and CC represent baseline material–color loss, whereas VC is incorporated as an exposure-amplification condition. Formula-structure sensitivity analysis against blinded ratings of 50 sampled courtyards showed that the proposed formulation had the highest rank consistency with expert judgments (Spearman’s ρ = 0.949). The frozen framework also showed a strong association with blinded professional ratings in geographically independent Longnan villages (ρ = 0.912, p < 0.001). XGBoost–SHAP identified courtyard impervious-surface ratio, primary material, and roof form as the leading model-associated predictors of CI variation. The framework supports courtyard-scale CI assessment, priority screening, and repeat-survey reassessment under comparable acquisition conditions. Full article

Under increasing cropland constraints and pressure to secure oilseed supplies, using winter–fallow cropland for winter rapeseed production can improve annual cropland-use efficiency. Taking the Wanjiang Plain, China, as the study area, this study integrated 10 m winter–fallow cropland maps (2019–2024), 30 m winter rapeseed maps (2000–2022), annual Satellite Embedding features, cropland data, and administrative boundaries. Multi-year occurrence frequency and Getis–Ord Gi* statistics characterized temporal persistence and spatial clustering. A MaxEnt model calibrated with 394 occurrence records from long-term high-frequency rapeseed areas and 26 screened embedding dimensions delineated cropland with present-day land-surface characteristics similar to historically persistent rapeseed locations. This layer was progressively intersected with the historical winter–fallow union and stable winter–fallow cropland. Stable winter–fallow cropland covered approximately 4437 km², whereas long-term high-frequency winter rapeseed covered only 568 km², revealing a marked spatial mismatch. Under five-fold spatial cross-validation, the selected linear-feature model with a regularization multiplier of 4 achieved a mean test AUC of 0.904 ± 0.015 and a 10% training-omission rate of 0.108 ± 0.022. Using the model-specific threshold of 0.3096, the final estimates were 6017 km² of potentially suitable cropland, 2943 km² of general expansion potential, and 1137 km² of candidate spatial priority areas for field verification. The last tier was concentrated mainly in Xuanzhou District, Lujiang County, urban Wuhu, Wuwei City, Nanling County, and He County. The framework provides a cautious spatial-screening basis for optimizing winter–fallow cropland use and guiding subsequent field and feasibility assessments. Full article

Food safety and quality analysis is shifting from laboratory-based end-point testing toward faster, lower-volume and matrix-adapted on-site decision-making. Near-infrared (NIR), visible-near-infrared (Vis-NIR), hyperspectral, Raman, surface-enhanced Raman scattering (SERS), fluorescence, colorimetric and terahertz approaches, together with impedance time-series readout, provide complementary information on composition, molecular vibrations, spatial distribution, reaction outputs, or electrical responses. In real foods, however, lipids, proteins, sugars, salts, pigments, particles and native fluorescence can alter spectral baselines, mass transfer and model stability. The value of microfluidics is therefore not limited to miniaturization but lies in organizing filtration, homogenization, splitting, mixing, extraction, enrichment, reaction, and readout positions into a controllable sample-to-signal workflow. This review first distinguishes chemical hazards, biological hazards, authenticity issues, and quality changes according to target and matrix characteristics, and then compares the functional boundaries of continuous-flow, paper-based, droplet, digital-hybrid and enrichment-oriented chips. It further analyses how microfluidics affects detection time, sample and reagent consumption, sensitivity, selectivity, repeatability, portability and cross-matrix applicability through spectral interfaces, signal enhancement, labelled and label-free detection, chemometrics, and machine learning. Representative applications involving pesticides, mycotoxins, pathogens, antibiotics, heavy metals, adulterants, oxidation products, and freshness indicators in real foods are discussed within a unified chain linking chip architecture, spectral signal generation and decision models. Finally, requirements for translation are proposed in terms of standard and real samples, chip-to-chip variation, external model validation, data traceability and scalable manufacturing, providing an operational framework for the joint design of broad-spectrum spectroscopic technologies and microfluidic systems. Full article

Rivers and riparian zones support a variety of woody plant species and play an important role in riverine ecosystems and fluvial processes. Rapid establishment of herbaceous and woody vegetation occurs in unvegetated river channels, mainly because of dam construction and hydrological alterations. However, repeated monitoring of the three-dimensional structure over broad river corridors remains challenging. We aimed to assess the applicability of RGB-derived canopy-height models (CHMs) inferred using Depth Anything V2 for monitoring riparian woody vegetation. A monocular depth-estimation framework was trained using the National Agriculture Imagery Program-CHM dataset and applied to three Korean riverine environments. The inferred CHMs were evaluated against the LiDAR-derived CHMs and further tested using two application-oriented assessments: individual tree detection (ITD) and woody vegetation area classification. The RGB-derived CHMs reproduced the overall spatial patterns of the riparian canopy height, although the accuracy varied with vegetation structure and image acquisition conditions. The mean absolute error between RGB- and LiDAR-derived CHMs was approximately 0.96 m across the study sites. In the ITD assessment, the RGB-derived CHM achieved an F1 score of 0.73, compared with 0.80 for the LiDAR-derived CHM. For woody vegetation area classification, the RGB-derived CHM showed high pixel-level accuracy, while Dice coefficient and IoU varied depending on the canopy-height threshold and study site. These results suggest that the RGB-derived CHMs can serve as supplementary data for monitoring riparian woody vegetation when LiDAR acquisition is limited. Full article

High-precision, multi-tier categorical soil classification faces critical bottlenecks, including the neglect of spatial context by conventional pixel-based models, the error cascade propagation phenomenon in multi-level classification networks, and the disruption of geophysical directional anisotropy by traditional geometric data augmentation. To address these challenges, in this study, we propose a multi-level soil classification model based on MTSC-ResNet-Trans, which organically couples residual convolutional blocks with a 3-layer Transformer encoder to model long-range spatial dependencies. The framework integrates a geospatial-safe data augmentation pipeline to preserve the topological fidelity of absolute geographic coordinates alongside four decoupled parallel multi-task classification heads to substantially suppress inter-level error propagation. Evaluated in the Southern Foothills of Qilian Mountains using 18 environmental covariates, the framework achieves an Overall Accuracy of 0.8931 at the Great Group level under conventional random splitting. Under a distance-stratified spatial evaluation—which isolates the contribution of spatial autocorrelation to accuracy estimates—the framework maintains robust performance, with MTSC-ResNet-Trans consistently outperforming pixel-based baselines (Random Forest) by approximately 3.7 percentage points even at spatial separation distances exceeding 400 m. This protocol transparently decomposes predictive accuracy into a component attributable to spatial proximity and a component reflecting reduced spatial proximity performance. Across the four taxonomic levels, accuracy decay is suppressed to 5.11%. Although spatial-block cross-validation indicates a lower regional extrapolation accuracy (OA = 0.7389 ± 0.0671), the decoupled parallel framework provides an effective and robust baseline for high-resolution regional digital soil mapping. Full article

Arid-zone agricultural heritage systems (AHSs) face severe challenges from ecological fragility, water scarcity, and socioeconomic pressures; scientifically understanding their system resilience is a critical prerequisite for achieving sustainable development. This study constructs a three-dimensional resilience evaluation indicator system following the Pressure–State–Response (PSR) framework tailored to arid oasis conditions. Drawing on the entropy weight method, GIS spatial analysis, the ARIMA model, and the obstacle degree model, it adopts statistical and remote sensing data of Xinjiang from 2015 to 2024 to systematically analyze the resilience levels, spatiotemporal evolution characteristics, future development trends, and core obstacle factors of six AHSs. The results reveal that the overall resilience of AHSs in Xinjiang exhibited a fluctuating upward trend over the decade, showing an obvious spatial differentiation pattern of “high in Northern Xinjiang, low in Eastern and Southern Xinjiang.” ARIMA forecasting indicates that resilience will maintain a positive growth trajectory from 2025 to 2028, yet inter-site hierarchical gaps persist. The primary constraints hindering resilience improvement include industrial structure upgrading index, per capita regional GDP, annual NDVI, vegetation coverage, and the number of intangible cultural heritage items, with a clear differentiation between long-term structural bottlenecks and temporary short-term constraints. The study concludes that, although the resilience of AHSs in Xinjiang possesses long-term improvement potential, persistent challenges such as fragile ecological foundations, low-end industrial structures, and insufficient cultural inheritance remain prominent. Targeted differentiated strategies—short-term emergency regulation, medium-term industrial–cultural integration, and long-term adaptive governance—are therefore required to enhance systemic resilience in the future. These findings enrich empirical research on AHS resilience in arid regions and provide case references for the scientific conservation, revitalized utilization, and sustainable development of AHSs in Xinjiang and other global dryland areas. Full article

Against the backdrop of China’s Dual Carbon Initiative and national transportation empowerment strategy, accelerating the low-carbon green transition of the transport sector has emerged as an imperative developmental priority. Incorporating carbon emissions as undesirable outputs into the efficiency evaluation framework, this study adopts a multi-method analytical paradigm encompassing the super-efficiency SBM model, Malmquist–Luenberger index, kernel density estimation, Dagum Gini coefficient, geographical detector model, and Geographically and Temporally Weighted Regression (GTWR). Based on panel data covering 30 provincial administrative regions in China from 2004 to 2022, this paper systematically investigates the spatio-temporal evolutionary patterns and intrinsic driving mechanisms of carbon total factor productivity (CTFP) within the transportation industry. The main findings are as follows: (1) The static efficiency results reveal that the national mean CTFP is below unity, indicating overall inefficiency. Nevertheless, it exhibits a fluctuating upward trend after 2009. Regionally, CTFP follows this pattern: Eastern China > Central China > national mean > Northeastern China ≈ Western China. (2) Dynamic productivity analysis shows that the annual average ML index is close to 1, demonstrating an overall upward trend in CTFP, and productivity growth is primarily driven by technological progress. (3) In terms of spatio-temporal patterns, inter-regional disparities constitute the principal source of overall spatial gaps in CTFP, with considerable contribution from transvariation density. (4) Geographical detector analysis suggests that energy intensity (EI) and economic development level are the two factors most strongly correlated with the spatial differentiation of CTFP, and interaction effects exist between them. The results from geographically weighted regression (GWR) further confirm that the strength of the correlation between each driving factor and CTFP presents pronounced regional heterogeneity. Based on these findings, differentiated regional policies for emission reduction and efficiency improvement should be formulated in accordance with the law of diminishing marginal returns. Full article

Accurate historical estimates of forest carbon stocks are essential for greenhouse gas inventories and REDD+ Measurement, Reporting and Verification (MRV) systems, particularly in remote and persistently cloudy regions where field inventories and optical remote sensing are limited. This study presents a retrospective mapping framework with potential relevance for Tier-3 forest carbon estimation of forest volume and carbon stocks in the temperate forests of southern Chile using historical ALOS PALSAR L-band SAR data integrated with Chile’s Continuous National Forest Inventory (CNFI). Three pilot zones in Los Lagos, Aysén, and Magallanes were analysed, covering approximately 42,000 km² of native forests dominated by Lenga, Coihue de Magallanes, Siempreverde, Roble–Raulí–Coihue, Coihue–Raulí–Tepa, and Alerce forest types. Annual 25 m ALOS PALSAR mosaics were processed to derive HH and HV backscatter, HH/HV ratio, and Radar Forest Degradation Index (RFDI) layers, which were used as predictors in k-nearest neighbours (k-NN) models calibrated with inventory plots projected to the 2010 reference year. Model performance varied substantially among forest types and pilot zones, with test r² values ranging from 0.12 to 0.90 and RMSE values between approximately 100 and 300 m³·ha⁻¹; the highest r² values were associated with forest types represented by relatively small samples and should therefore be interpreted cautiously. m³·ha⁻¹ Stratification by altitude and restriction to moderate volume ranges improved predictive performance in several cases, highlighting the influence of ecological gradients and SAR signal saturation at high levels of biomass. Despite substantial pixel-level uncertainty, the methodology reproduced broad regional patterns of forest structure and carbon distribution. Results demonstrate the potential of combining historical ALOS PALSAR archives with national forest inventories to support spatially explicit historical carbon estimation in data-limited forest regions. Full article

Climate change and rapid urbanisation are intensifying the urban heat island effect, increasing thermal stress, degrading air quality, and leading to rising energy demand. Monitoring neighbourhood-scale heat requires Land Surface Temperature (LST) observations at fine spatial and temporal resolutions, yet satellite thermal products are limited by revisit frequency, acquisition time, and cloud cover. This study developed a novel approach integrating satellite-derived land cover characteristics with continuous contact-based temperature measurements from low-cost LoRaWAN sensors and geostatistical modelling to generate hourly LST maps at 10 m resolution. The technique provides communities with simpler, affordable methods for measuring heat islands and supporting mitigation strategies. Observations from 52 locations across Curtin University’s Bentley campus in Perth, Western Australia, were combined with land cover indices. Empirical Bayesian Kriging captured spatial and temporal urban heat patterns with a root mean square error of approximately 3 °C, representing a bias of near 1 °C. Predictions were consistent with Landsat-derived LST, revealing persistent heat retention over asphalt and cooler conditions associated with vegetation. Integrating satellite-derived predictors with ground measurements provides continuous fine-scale information to identify local heat hotspots and inform targeted mitigation. Unlike satellite data, these low-cost ground measurements could be collected with the help of urban practitioners, developers, and academic institutions. Full article

Following the formal implementation of the Environmental Protection Tax Law of the People’s Republic of China in 2018, debate has persisted regarding its policy effectiveness, particularly concerning its impact on carbon emissions. In the context of the ‘dual carbon’ targets, accurately evaluating the emission-reduction effects of the environmental protection tax is of considerable policy significance. This study treats the 2018 transition from pollution discharge fees to the environmental protection tax (hereinafter referred to as the ‘fee-to-tax reform’) as a quasi-natural experiment. Utilising provincial-level panel data from China spanning 2007 to 2022, and employing TWFE, DID, and SDM-DID models, the analysis comprehensively assesses the impact of the fee-to-tax reform on per capita carbon emissions and carbon emissions intensity. The findings indicate that the transition from pollution discharge fees to the environmental protection tax significantly curbed the growth of both carbon emissions and carbon emission intensity; this result remains robust across a series of sensitivity tests. Heterogeneity analysis demonstrates that regions with a higher degree of ‘greening’ in their tax systems experience more pronounced reductions in carbon emissions. Further spatial econometric analysis reveals that while the environmental protection tax effectively curbs local carbon emissions, it may also increase the risk of ‘carbon leakage’ to neighbouring regions. These results provide empirical evidence and policy guidance to refine the design of environmental tax systems and promote coordinated regional carbon emissions reductions. Full article

Spatiotemporal prediction of groundwater quality is of great significance for regional water environmental safety assessment, pollution risk identification, and urban groundwater resource management. To address the difficulty of existing methods in simultaneously characterizing multi-indicator coupling relationships, temporal evolution processes, and spatial heterogeneity, this study proposes a spatiotemporal groundwater quality prediction model under multi-indicator coupling constraints. First, indicators including dissolved oxygen, total nitrogen, electrical conductivity, dissolved organic carbon, pH, permanganate index, and total phosphorus are uniformly mapped into a risk space to construct an integrated groundwater quality risk index. Then, based on monthly groundwater monitoring data from Yiyang City during 2000–2023, continuous regional grid sequences are generated. In terms of model design, the Temporal Difference Interaction Module (TDIM) is introduced to enhance multi-scale temporal variation modeling, Region-Guided Feature Modulation (RGFM) is used to strengthen regional heterogeneity representation, and Spatiotemporal Boundary-Aware Loss (STB Loss) is adopted to maintain spatiotemporal boundary consistency. The experimental results show that the proposed method achieves a Structural Similarity Index Measure (SSIM) of $0.9814\pm 0.0085$, a Peak Signal-to-Noise Ratio (PSNR) of $40.47\pm 2.19$, a Mean Absolute Error (MAE) of $2.80\times {10}^{-3}\pm 1.50\times {10}^{-3}$, and a Root Mean Square Error (RMSE) of $9.70\times {10}^{-3}\pm 2.69\times {10}^{-3}$, outperforming comparison models overall and providing effective support for dynamic groundwater quality prediction and water environmental safety assessment. Full article

The construction industry stands at the forefront of China’s endeavors in carbon reduction, making it imperative to examine the spatiotemporal variations in carbon emission efficiency (CEE) to drive the industry’s low-carbon transition. This research leverages statistical data from 2007 to 2022 and applies the super-efficiency SBM model to assess CEE across 30 Chinese provinces, shedding light on its spatial distribution patterns. Moran’s I index is employed to scrutinize spatial clustering of CEE, revealing clustering tendencies. Furthermore, the geographic detector method identifies the factors that account for these spatial variations. The research reveals notable spatial diversity in CEE among provinces, with the eastern coastal areas demonstrating higher efficiency and the western interior areas demonstrating lower efficiency. Spatial autocorrelation analysis further confirms the presence of spatial dependence, characterized by pronounced “High–High” or “Low–Low” agglomeration trends. Enterprise scale and energy structure emerge as primary explanatory factors of spatial differences, and interaction effects are more influential than any single factor. These findings provide empirical evidence that can inform a more balanced low-carbon transition of China’s construction industry across regions. Tailored policy suggestions are proposed to address the distinct characteristics of each region, aiming to support steady progress toward China’s dual carbon goals. Full article

Accurate and continuous air quality monitoring is essential for sustainable urban management, environmental protection, and public health. However, dense networks of low-cost sensors frequently suffer from missing observations caused by communication failures, sensor malfunctions, or maintenance operations. This paper proposes a hybrid graph convolutional network–temporal convolutional network (GCN-TCN) framework for imputing missing ${\mathrm{PM}}_{2.5}$ measurements in dense sensor networks. The model utilizes spatial relationships between neighboring monitoring stations and temporal dependencies within sensor time series. The proposed approach was evaluated using data from a network of 20 sensors deployed across the academic campus area. Three representative missing-data scenarios were considered, including isolated missing observations, continuous missing sequences, and a hybrid combination of both patterns. The proposed model achieved the lowest reconstruction errors and the highest coefficient of determination among the evaluated methods (${\mathrm{R}}^2\approx$ 0.91–0.93). Standard GCN and TCN networks achieved lower ${\mathrm{R}}^2$ values (∼0.81–0.82 and ∼0.71–0.80, respectively), while recurrent models and classical statistical methods performed substantially worse (${\mathrm{R}}^2\approx 0.6$). These findings indicate that integrating graph-based spatial learning with temporal convolution robustly reconstructs incomplete environmental observations, improving the reliability of low-cost sensor systems for sustainable air quality monitoring and management. Full article