Search Results (6,747)

Eastern Thailand serves as a critical case study for the escalating tension between agricultural preservation and urban expansion, a dynamic recently intensified by the COVID-19 pandemic. This study addresses a pivotal research question: To what extent do emerging socio-economic realities, such as policy shifts, labor fluctuations, and climatic extremes, alter the spatiotemporal continuity of urban expansion? Employing a mixed-methods approach, we integrated multi-stakeholder insights with quantitative spatial modeling to simulate context-specific land use futures through 2030. Qualitative findings indicate that while COVID-19 accelerated agricultural modernization, evidenced by increased mechanization and e-commerce integration, these shifts have limited long-term impact on land use patterns. Instead, regional policy, climate change, and technological innovation emerged as the primary drivers of landscape transformation. Quantitative simulations reveal that urban growth will concentrate in the western provinces bordering Bangkok and the southern coastal corridors of Chon Buri and Rayong. Crucially, across all scenarios, approximately 60% of new urban land is projected to be converted from existing croplands, followed by significant losses in natural forest cover. These results demonstrate that current growth-oriented policies may undermine regional food security and ecosystem services. This study provides a framework for balancing agricultural modernization with ecological preservation, offering essential evidence for developing the integrated, sustainability-focused land use frameworks required to meet 2030 development goals. Full article

Sensor data are increasingly used in monitoring spatiotemporal phenomena for diverse applications such as flood management, urban traffic, air quality control, forest fire management, etc. Real-time modelling and representation of such evolving phenomena is fundamental for efficient and near-real-time decision-making processes. In addition to simple and local alerts about occurring changes over time at a given location, as is the case in Sensor Event Service (SES), the decision-making process may require more global spatial information, such as knowing if the monitored phenomenon is expanding or contracting around a given spot or if it is moving from one spot to another, especially for non-punctual spatial features. For such cases, spatiotemporal information should be computed over the whole set of distributed data from which the geometry of monitored phenomena can be assessed. This paper proposes an event-driven fuzzy rule-based decentralized spatial reasoning approach to compute spatiotemporal changes occurring in vague shape phenomena from distributed sensor data streams. Inferring local and partial spatial changes from individual nodes over the sensor network is prior to the computation of developing changes that the monitored phenomenon undergoes over the whole area covered by the sensor network. In this approach, we suggest a Fuzzy-Extended Spatiotemporal Change Pattern (FESTCP) to compute spatiotemporal changes about fuzzy regions. To evaluate our method, simulated case studies of ambient air pollution in Quebec City are carried out. The results reveal that the proposed method could provide satisfactory information about spatiotemporal changes in real-world phenomena monitored by a sensor network for a real-time decision-making process. Full article

Rapid urbanization in South Asian coastal cities is systematically dismantling natural cooling infrastructure, driving unprecedented urban heat island (UHI) intensification with severe consequences for human health, energy systems, and urban livability. Despite growing research attention, comprehensive frameworks that simultaneously capture temporal UHI dynamics, machine learning-based thermal projections, and community-grounded validation remain scarce, particularly for secondary coastal cities in tropical developing regions. This study addresses these gaps by investigating UHI dynamics in Chattogram City Corporation (CCC), Bangladesh, through three integrated methodological pillars: (1) multi-temporal remote sensing analysis using Landsat 5 and 8 imagery spanning 2005–2025; (2) comparative evaluation of five machine learning algorithms (LightGBM, Random Forest, XGBoost, SVM, and MLP) for land use/land cover (LULC) classification and land surface temperature (LST) regression, with iterative scenario projections for 2029, 2033, and 2037; and (3) a structured public perception survey of 384 residents validated through participatory mapping and focus group discussions. Landsat analysis revealed dramatic LULC transformations: built-up areas expanded 88% (12,649 to 23,719 acres), while waterbodies declined 53.1% and vegetation decreased 21.9%. Mean LST increased by 9.09 °C (from 30.94 °C to 40.03 °C), with mean UHI intensity rising from 19.59 to 33.88 standardized units over two decades. LightGBM achieved optimal LULC classification (F1-weighted: 0.765) while Random Forest best predicted LST (RMSE: 1.51, R²: 0.809). Projections indicate continued thermal escalation, with mean LST reaching 43.64 °C and UHI intensity exceeding 37.41 standardized units by 2037. Persistent thermal hotspots were identified in the southwestern coastal corridor, western industrial belt, and central business district. Community survey data corroborated satellite-derived patterns, with 73.44% of respondents observing environmental degradation, yet only 22% aware of formal heat mitigation policies, and 87% supporting vegetation-based cooling interventions. This integrated framework advances urban thermal monitoring in tropical coastal cities and provides spatially targeted, community-endorsed evidence for climate-responsive urban planning. Full article

Extreme rainfall poses an increasing threat to mountainous transportation systems by frequently triggering landslides along road corridors. Most existing studies focus on long-term landslide susceptibility, whereas event-scale assessments remain limited, particularly in road environments. This study develops an event-scale framework for assessing landslide risk along mountain roads under extreme rainfall conditions, using the July 2023 “23·7” rainfall event in Mentougou District, Beijing, as a case study. A Random Forest model was constructed by integrating multi-source geospatial data with an event-specific inventory of 8930 landslides. The model achieved high predictive performance, with ROC–AUC values of 0.9187 and 0.9166 for the validation and test datasets, respectively. Feature importance analysis further indicates that landslide occurrence is controlled by the combined effects of rainfall, terrain conditions, vegetation cover, and anthropogenic disturbance, with rainfall acting as the primary trigger. High-risk road segments are mainly concentrated in the southeastern part of the study area, showing clear spatial clustering. These results highlight the value of event-scale analysis and demonstrate the effectiveness of the road-oriented framework for identifying hazardous segments under extreme rainfall conditions. The proposed approach provides practical support for landslide monitoring, risk mitigation, and resilient management of mountainous transportation infrastructure. Full article

Quantifying vegetation dynamics has become a critical scientific imperative in the context of global ecosystem restoration initiatives targeting degraded forests. Previous studies have explored vegetation-cover change trends at different spatial scales worldwide using the Theil–Sen (TS) estimator and Mann–Kendall (MK) test, yet few have accounted for the uncertainty in resulting trends across time-series datasets of varying lengths. Taking the coastal zone of Fujian Province in Southeast China as a case study, we investigated the uncertainty of vegetation-cover change trends using normalized difference vegetation index (NDVI) datasets of different lengths (e.g., 20-year, 15-year, and 10-year) via the TS estimator and MK test. Additionally, piece-wise regression was employed to detect turning points and shifts in vegetation trends between 2001 and 2020. The results indicate significant discrepancies in trend estimation across datasets of different lengths, with consistency ratios ranging from 46.1% to 64.7% among the 20-year, 15-year, and 10-year series. The MK test is more sensitive to time-series length than the TS estimator, with areas of significant change decreasing by over 50% when transitioning from a 20-year to a 10-year dataset. The spatial distribution of trend shifts exhibits a distinct “coastal–inland” polarization pattern, with 2010 as the turning point. Eight modes of vegetation trend shifts were identified based on pre- and post-turning point dynamics. Furthermore, piece-wise regression improved trend accuracy by approximately 15%. This research advances the mechanistic understanding of spatiotemporal vegetation dynamics and supports adaptive ecosystem management strategies. Full article

Carbon is an essential component in the regulation of climate systems through the global biogeochemical cycle. However, changes in land use/land cover (LULC) have reduced the capacity of terrestrial ecosystems like watershed to store carbon. This shows the need for a policy framework that balances conservative objectives with agricultural demands, as watersheds are required to support carbon storage and food production. Previous studies have generally assessed carbon dynamics or LULC change separately, with limited integration of policy-driven scenarios. Therefore, this study aimed to conduct multi-scenario carbon storage modeling to evaluate LULC protection strategies in the Cimanuk Watershed, Indonesia, an area experiencing significant LULC pressures. The method used consisted of Support Vector Machine (SVM)–Markov, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), Geodetector, and Getis-Ord Gi*. A total of four scenarios were used to project LULC and carbon storage in 2042, which included Business as Usual (BAU), Paddy Field Protection (PFP), Forest Protection (FOP), and Paddy Field and Forest Protection (PFFOP). The results showed that forest area declined by 39,400 ha between 2015 and 2025, thereby reducing carbon storage. The PFFOP scenario was identified as the most viable, combining the protection of paddy fields and forests to balance agricultural production and carbon sequestration. Among the factors analyzed, slope exerted the greatest influence on carbon storage. Spatial cluster analysis showed that carbon hotspots were predominantly located in the upper Cimanuk sub-watershed. These results offered valuable insights into scenario-based sustainable watershed management to optimize carbon storage and maintain agricultural function. Furthermore, the proposed framework showed promising potential for application in other tropical watersheds, serving as a reference for decision-makers in sustainable watershed management. Full article

Draining peatlands for peat extraction converts them into significant sources of greenhouse gas (GHG) emissions. Quantifying GHG emissions at the regional scale remains challenging because direct field measurements are spatially limited, while GHG accounting for land-use planning requires spatially explicit information. Building on the advances in remote sensing (RS) as a scalable low-cost emission accounting tool for large areas, this study presents a proof-of-concept workflow that integrates satellite-based land-cover classification with an emission-factor (EF) approach to support spatial upscaling of peatland GHG estimates. Using Sentinel-2 imagery and a supervised Random Forest classifier, peatland-related land-cover classes were mapped for selected sites in Latvia. The classification results show higher accuracy for spectrally distinct classes such as raised bogs and active peat-extraction areas, while more heterogeneous classes exhibited lower performance. The study provides an overview of how to utilize the RS approach to generate accurate land-cover maps, which can be used to upscale GHG estimation in Latvia when field data is limited. The study does not include calibration against site-level flux measurements, uncertainty propagation, or temporal variability analysis; therefore, the emission results are illustrative and consistent with current EF-based inventory practice rather than validated site-specific fluxes. Full article

Rapid urbanization combined with global climate change is intensifying the Surface Urban Heat Island (SUHI) effect worldwide, posing significant risks to human health, thermal comfort, and quality of life in cities. Characterized by notably higher temperatures in urban areas compared to their rural surroundings, the SUHI phenomenon is driven by factors such as increased built-up density and reduced vegetation cover. In this context, open-source remote sensing data, particularly from the Landsat satellite series, play a crucial role in studying surface urban heat islands. Available freely, Landsat’s multispectral and thermal imagery provides extensive spatial coverage and consistent temporal frequency, enabling long-term diachronic analyses. This study leverages a 40-year time series (1984–2024) of Landsat thermal data to map surface temperature variations in urban environments between Kenitra and Rabat cities, facilitating the identification of heat-excess zones linked to anthropogenic factors. Based on the results obtained, the LU/LC maps show that the study area is characterized by the notable growth of urbanization over the period 1984–2024, particularly in the dynamic poles of the region such as the city centers of Kénitra, Rabat, and Sale. This dynamic is highlighted by an increase from 1.8% to 3% in the total area of the region, accompanied by a remarkable decrease in agricultural land and bare soils. The evaluation of the Random Forest (RF) model’s performance also indicates that it successfully classified the data and predicted the LU/LC classes effectively, as confirmed by metric indices such as the Receiver Operating Characteristic curve and the Kappa index, which present very high average values exceeding 90%. Furthermore, the exploitation of the thermal bands of Landsat images provided relevant information on surface temperature variation. The SUHI maps show that the Rabat-Sale-Kenitra (RSK) region experienced a progressive increase in temperature over the study period, rising from 27 °C in 1984 to 44 °C in 2024. This value could increase further due to the continuous dynamics of urbanization. Together, these tools provide a robust framework for understanding the spatiotemporal dynamics of surface urban heat islands and support sustainable urban planning. Full article

The urban heat island effect, a typical rapid urbanization issue, arises from natural surfaces covered by impermeable layers via urban sprawl. To clarify its unclear response to urban expansion under human–land synergy, this paper proposes a multidimensional urban expansion model and a random forest–intelligence integrated method for high-precision large-region population mapping. Taking the Pearl River Delta urban agglomeration as a sample, its urban expansion is divided into five modes to explore thermal environment impacts. The results show: (1) The proposed random forest–intelligence method achieves 84% overall accuracy in 30 m resolution population mapping. (2) The Pearl River Delta urban agglomeration is dominated by vertical expansion, but all cities have population-shrinking regions, especially around Guangzhou and Shenzhen. (3) From 2010 to 2020, Pearl River Delta urban agglomeration impervious surface expansion and population growth were mismatched: impervious surface extended to fringes, while population grew in core areas. (4) The expansion of impervious surface does not always exacerbate the urban heat island effect; when the per-capita land area is less than 1.8 m², it can actually mitigate the effect. (5) Guangzhou–Foshan–Zhaoqing and Shenzhen–Dongguan–Huizhou integration reduces heat island intensity. Core cities driving surrounding areas via clustered, interconnected development alleviates this effect. Full article

Pinus cembroides is among the pine species best adapted to arid and semi-arid ecosystems in the Americas, and its potential distribution is projected to expand under climate change. However, the success of this expansion will depend on belowground processes, particularly the role of soil fungal communities, which in subtropical forests are key for nutrient cycling and plant resilience to environmental stress. Yet their vertical stratification and responses to forest management remain poorly understood, especially in semi-arid systems. Here, we characterized fungal communities from mineral soil and litter associated with P. cembroides across a forest management gradient (mature forests, disturbed stands, and pine plantations) at the southern limit of the species’ distribution. We evaluated the influence of climatic, edaphic, vegetation structure, and microbial activity variables (soil moisture, precipitation, pH, tree density, vegetation cover, temperature and extracellular enzyme activity) on fungal community composition. We found strong vertical stratification between litter and mineral soil. When both substrates were analyzed together as an integrated soil profile, forest condition had no significant effect on alpha diversity; however, substrate-specific analyses revealed higher richness in mineral soil of mature forests compared to disturbed and plantation sites, while litter communities showed no significant differences among conditions. Litter communities were characterized by saprotrophic and endophytic fungi, whereas mineral soil communities were enriched in ectomycorrhizal and other root-associated taxa. Distance-based redundancy analysis indicated that fungal community composition was primarily associated with moisture content, precipitation, pH, tree density, and carbon-degrading enzyme activity. These results highlight the importance of substrate differentiation and environmental gradients in shaping fungal communities in semi-arid pine forests, and provide a baseline for understanding how management and climate change influence soil fungal diversity and ecosystem functioning. Full article

Under the context of global change, forest cover, as a critical component of terrestrial ecosystems, exerts a profound influence on regional ecological security and sustainable development through its spatiotemporal evolution. Current research on forest cover change primarily focuses on pattern description and single-factor driver analysis, with insufficient in-depth exploration of the interactions among multiple factors and their associated nonlinear mechanisms. To address this gap, this study focuses on the Wumeng Mountain area, a typical ecologically fragile karst region in Southwest China. By comprehensively employing methods such as Theil–Sen Median trend analysis, land use transfer matrix, standard deviation ellipse, and spatial autocorrelation analysis, this study systematically reveals the spatiotemporal evolution characteristics of forest cover from 1985 to 2024. On this basis, an integrated eXtreme Gradient Boosting–SHapley Additive exPlanations (XGBoost-SHAP) model is introduced to construct an indicator system comprising 16 driving variables, including elevation, slope, aspect, temperature, precipitation, soil type, soil pH, soil thickness, soil organic matter, soil moisture content, GDP, population, distance from water, distance from railway, distance from grade highway, and distance from government. This model quantifies the influence intensity of each driving factor on forest change. The main findings are as follows: (1) From 1985 to 2024, the forest cover rate in the Wumeng Mountain area significantly increased from 54.7% to 60.2%, exhibiting a “high-low-high” heterogeneous spatial distribution pattern along the northeast-southwest axis; (2) Forest increase primarily originated from the conversion of cropland and grassland, with contribution rates reaching 93.58% and 5.9%, respectively, indicating an overall trend of “increase in low-value areas and decrease in high-value areas”; (3) Forest cover change is driven by both natural and anthropogenic factors, with dominant driving factors exhibiting phased replacement over time. Overall, this is manifested as long-term stable constraints exerted by natural background factors, alongside strong disturbances from anthropogenic factors such as social-economic, and transportation-related activities. Natural factors remain the primary driving force behind changes in forest cover. The core findings of this study elucidate the complex driving factors of forest change in karst mountainous areas, thereby providing scientific support for the precise management of regional forest resources, the planning of ecological restoration projects, and the implementation of sustainable development strategies. Full article

Forest, shrubland, and grassland exhibit highly overlapping characteristics, and single-modal remote sensing data cannot simultaneously capture both spectral and structural information. Moreover, multimodal fusion learning of optical and SAR data faces challenges such as the lack of high-quality samples and difficulties in effective cross-modal feature fusion. Therefore, a high-resolution multimodal remote sensing feature dataset (GF23FSG) is constructed for the fine classification of forest, shrubland, and grassland, and a Cross-modal Adaptive Structure Fusion Network (CASFNet) is proposed. In response to the feature heterogeneity of optical and SAR, a cross-modal adaptive fusion module based on spatial alignment and a dynamic weight allocation strategy is proposed, which effectively enhances the learning of spectral–spectrum heterogeneous features. In addition, a multi-level auxiliary supervision mechanism is introduced to strengthen feature representation learning. Gradient constraints are further imposed on deep-level features to improve the model’s ability to capture and learn deep cross-modal representations, thereby effectively mitigating representation degradation during the feature fusion process. Experiments on the self-constructed GF23FSG dataset and the publicly available SEN12MS dataset achieve OA of 77.38% and 71.84%, respectively, demonstrating superior classification performance compared with SOTA methods. In addition, comparative analysis with public land cover products and field samples further confirm the reliability and generalization performance of the proposed dataset and model for the fine classification of forest, shrubland, and grassland. This study provides a new solution for the fine classification of forest, shrubland, and grassland from multimodal remote sensing images from the perspectives of dataset construction and methodological design. Full article

Digital elevation models (DEMs) are key fundamental inputs in hydrological modelling, yet the influence of spatial resolution on basin delineation and process representation remains insufficiently understood, particularly in small catchments. This study investigates the influence of DEM resolution on topographic characterization and hydrological response in a small agroforestry basin in central Portugal. Three DEMs with resolutions of 5 m, 10 m, and 30 m were generated from contour data and satellite sources and processed using the TOPAZ-based TopAGNPS delineation framework. The sensitivity of basin structure to delineation parameters—critical source area (CSA) and minimum source channel length (MSCL)—was assessed, and the resulting configurations were used as inputs to the AnnAGNPS model. Results show that DEM resolution strongly influences the representation of hydrological cells and stream reaches. Increasing resolution from 30 m to 5 m leads to a nearly doubling of average cell slope and increases reach slope by more than four times, with corresponding changes in drainage network density and connectivity. Log-linear relationships were identified between slope and contributing area, as well as between slope and reach length, consistent with established geomorphic scaling laws. Hydrological simulations further indicate that resolution-dependent delineation significantly influences runoff, erosion, and peak discharge estimates, with finer resolutions increasing sensitivity to parametrization. Among land-cover scenarios, desertified conditions generate substantially higher runoff and peak flows compared to naturalized and forested conditions. Overall, the findings demonstrate that DEM resolution, together with preprocessing and delineation choices, exerts a critical control on hydrological model outputs. These effects are particularly pronounced in low-relief, human-influenced catchments, where coarse-resolution DEMs may lead to systematic underestimation of hydrological responses. The study highlights the need for resolution-aware modelling strategies and careful parametrization to improve the reliability and transferability of hydrological simulations. Full article

Human–elephant conflict (HEC) has emerged as a major conservation and socio-economic challenge across Asia, largely driven by habitat degradation and increasing human pressure within elephant ranges. In India, expanding agriculture, mining activities, and infrastructure development have progressively altered forest landscapes, restricting elephant movement and intensifying interactions with human settlements. This study examines the relationship between landscape dynamics and HEC in the West Singhbhum district, Jharkhand, India. A three-year field investigation (2018–2020) across four forest divisions—Porahat, Chaibasa, Kolhan, and Saranda—was integrated with multi-temporal land-use and land-cover (LULC) analysis from 2000 to 2020 to evaluate habitat changes and their influence on conflict patterns. During the study period, 157 human casualties and extensive crop and property losses were recorded, indicating the severity of the conflict in the region. Landscape analysis revealed a substantial decline in dense forest cover and a reduction of large core forest areas (>500 acres), accompanied by increasing agricultural expansion and forest perforation. NDVI trends further indicated widespread deterioration in vegetation condition, reflecting declining habitat quality. These structural landscape changes have fragmented elephant habitats and displaced movement routes toward human-dominated landscapes and are thus associated with a spatial clustering of conflict events, particularly in the Chaibasa Forest Division. In contrast, the Saranda Forest Division retains relatively intact forest cores and supports more stable elephant habitat conditions. The findings demonstrate that HEC in the region is strongly linked to habitat fragmentation and declining vegetation quality rather than random elephant behaviour. Maintaining large contiguous forest blocks, restoring landscape connectivity, and implementing targeted mitigation strategies are therefore essential for sustaining elephant populations while reducing conflict with local communities. Full article

Surface solar radiation at the ground is affected by aerosols, clouds, and atmospheric moisture, as well as by circulation-related conditions that influence cloud formation and pollutant transport. In daily observations, these influences are mixed, which makes pollution-related variability difficult to interpret. We analyzed data from Beijing station 54511 (2015–2019), including daily integrated radiation components and collocated meteorological and pollution variables. We used wavelet coherence, pollution-stratified association analysis, and gray relational analysis, and compared two meteorological normalization methods: multiple linear regression (MLR) and random forest (RF). The results show that meteorological–radiation relationships vary systematically across pollution levels, indicating substantial meteorological confounding in daily radiation analyses. Among the radiation components, DR shows the clearest pollution-dependent shift in its relationship with RH, while several direct components become less sensitive to cloud cover under heavier pollution. RF reproduced daily radiation components with strong predictive performance (R² = 0.83–0.88), and the meteorologically adjusted anomalies from RF were consistent with those from MLR (r = 0.63–0.78 across components). These findings suggest that both MLR and RF can be effectively used to normalize meteorological effects in daily station records. The analysis supports routine interpretation of day-to-day surface radiation variability and can be extended to multi-site studies and finer temporal resolution. Full article