Search Results (5,213)

Bumblebees are among the most important wild pollinators; however, their populations are declining worldwide due to factors such as climate change, habitat loss, and pesticide use. For their conservation, it is important to understand the community structure at the local scale and the drivers responsible for their assemblages. However, little is known about bumblebee community assemblages and their drivers in Yunnan Province, China. In this study, we mapped bumblebee community assemblages across 125 counties in Yunnan Province using field-collected and published data. We also quantified the climatic and land use/land cover (LULC) drivers shaping these assemblages. The climatic habitat suitability for 21 bumblebee species was assessed at the county level across Yunnan using species distribution modeling. The biogeographic zones (groups of counties) were identified using Ward’s agglomerative cluster analysis, and the impacts of 12 bioclimatic and LULC drivers on the zonation pattern were assessed using Canonical Correspondence Analysis (CCA). Results indicated that more than 70% of bumblebee species showed their highest climatic suitability in the northern region of Yunnan. Among climatic factors, temperature-related bioclimatic variables were identified as dominant drivers influencing the spatial distribution of 15 out of 21 bumblebee species within the counties of Yunnan. In contrast, five species, B. grahami, B. impetuosus, B. lepidus, B. picipies, and B. securus, showed the highest contribution from precipitation-related factors. Six biogeographic zones (I, II, III, IV, V, and VI) were identified using Ward’s agglomerative cluster analysis. All 12 drivers were found to play critical roles in shaping the community assemblages of bumblebee species. This study provides essential insights for devising targeted conservation strategies at a local scale to maintain bumblebee populations in Yunnan. Full article

This study investigates the relationship between land use and soil organic carbon (SOC) storage in tropical Andean ecosystems, introducing a socio-ecological perspective to assess how community conservation perceptions influence SOC storage and contribute to climate change mitigation strategies. Background and Objectives: Land-use change reduces carbon stocks in tropical ecosystems. Focusing on the Las Piedras River basin (Popayan, Colombia), we evaluated SOC storage under four plant cover types—riparian forests (RFs), ecological restoration (ER), natural regeneration (NR), and livestock pastures (LSs)—and examined its association with local conservation perceptions. Materials and Methods: SOC storage at 30 cm depth, carbon inputs and outputs, and soil physicochemical properties were measured across land-use types. Conservation perceptions were assessed through 65 community surveys. Data analyses included ANOVA, principal component analysis, and multinomial logistic regression. Results: SOC storage was highest in RFs (148.68 Mg ha⁻¹), followed by ER and LSs, and lowest in NR (97.30 Mg ha⁻¹). A positive relationship was observed between high conservation perception and greater SOC content. Conclusions: SOC storage is strongly influenced by land use and community conservation values. Active restoration efforts, coupled with environmental education, are essential for enhancing the socio-ecological resilience of these ecosystems. Full article

The Yellow River Delta region is not only a concentrated area of human activities in coastal zones, but also a zone strongly influenced by regional environmental changes, where land cover changes are significantly affected by natural factors. Current historical LUCC datasets overlook the importance of partitioning to obtain accurate information on the potential maximum distribution range, which may lead to uncertainties in climate and environmental predictions. This study aims to reconstruct historical cropland changes in the Yellow River Delta via a region-adapted allocation model, supporting improved LUCC data accuracy and related research. Based on historical river course, settlement, and cropland survey data, this study identifies natural factors using historical settlement density through correlation analysis. Subsequently, a reclamation suitability model conforming to regional characteristics was constructed, and it obtains the cropland changes in the Yellow River Delta Basin at a spatial resolution of 0.5′ × 0.5′ over five time periods since 1855. The research indicates the following: (1) Through the method of analyzing the correlation between historical settlement density and natural factors, it is found that elevation (−), soil pH (+), soil organic carbon density (−), and NDVI (+) are the primary natural factors influencing the distribution of farmland in the Yellow River Delta. (2) The amount of farmland in the Yellow River Delta increased initially and then decreased after 1885; the average reclamation rate increased from 5.65%, peaked at 23.46% in the early 20th century, and then fell back to 7.68%. Spatially, the reclamation area expanded from scattered local areas along the Yellow River towards the sea, with a distinct coastal distribution. (3) Evaluation through absolute difference analysis shows that, compared with the HYDE 3.2 data, our reconstruction reflects the impacts of coastal changes, river distribution, and regional policy history on the allocation results. Based on the findings of this study, relevant issues can be improved from two aspects: first, by correlating settlement density with natural factors to identify key regional natural factors, which can then be applied to the update of LUCC data in small spatial units and similar regions to enhance data accuracy; second, by referring to the historical laws of cropland reclamation and suitability conditions, to optimize the current land planning of the Yellow River Delta and balance cropland utilization with ecological protection. Full article

To promote sustainable development and guide the responsible use of natural ecosystems, the United Nations introduced the concept of ecosystem accounting. Ecosystem services are key components of ecosystem accounting. Water-related ecosystem services (ES) are of primary importance for Armenia due to relatively dry climate, and dependence on irrigation water for agriculture. This study aims to conduct a pilot-level quantitative scoping assessment and mapping of key water-related regulating ES in accordance with the SEEA-EA guidelines, and to offer recommendations to initiate their accounting in Armenia. We used three Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models—Seasonal Water Yield, Sediment Delivery Ratio, and Urban Flood Risk Mitigation. Input data for these models were sourced from global and national databases, as well as ESRI land cover datasets for 2017 and 2023. Government-reported data on river flow and water consumption were used to assess the ES supply–use balance. The results show that natural ecosystems contribute between 11% and 96% of the modeled ES, with the strongest impact on baseflow supply and erosion prevention. The average current erosion is estimated at 2.3 t/ha/year, and avoided erosion at 46.4 t/ha/year. Ecosystems provide 93% of baseflow, with an average baseflow index of 34%, while on bare ground it is only 3%. Changes in land cover from 2017 to 2023 have resulted in alterations across all assessed ES. Comparison of total water flow and baseflow with water consumption revealed water-deficient provinces. InVEST models show their general operability at the scoping phase of ecosystem accounting planning. Advancing ES accounting in Armenia requires model calibration and validation using local data, along with the integration of InVEST and hydrological and meteorological models to account for the high diversity of natural conditions in Armenia, including terrain, geological structure, soil types, and regional climatic differences. Full article

Land use and land cover change (LUCC) is central to regulating human–land relationships and crucial for urban planning and sustainable development in arid oasis cities. As a typical oasis city in Xinjiang, Shihezi City faces the triple challenges of agricultural protection, urban expansion, and ecological conservation. Taking Shihezi City as the research object, this study used the 30 m resolution China Land Cover Dataset and applied the land use dynamic degree, comprehensive index of land use degree, transfer matrix, Geodetector, and PLUS model to analyse the spatiotemporal dynamics of LUCC from 2002 to 2022, identify driving mechanisms, and predict the land use pattern from 2027 to 2032. The results showed that (1) from 2002 to 2022, farmland decreased by 86.1075 km², man-made surfaces increased by 63.7389 km² (annual expansion rate of 2.86%), grassland slightly increased by 24.5592 km², and other land types remained stable; (2) the dynamics of land use showed a phased characteristic of “growth–equilibrium–acceleration”, and the land use degree index rose to 2.8639; natural factors (elevation, soil, temperature) dominated LUCC, and most interactions among factors showed enhancement effects; (3) the PLUS model predicted that by 2032, farmland would decrease to 224.347 km² and man-made surfaces would increase to 111.941 km². This study clarifies the laws of LUCC in Shihezi, demonstrates driving analysis and simulation prediction, and provides scientific support for balancing urban development, agricultural protection, and ecological security in arid oasis regions. Full article

Urban livability and sustainable development remain major global challenges, yet the interplay between urban planning layouts and actual human activities has not been sufficiently examined. This study investigates this relationship in Beijing by integrating multi-source spatiotemporal data, including point of interest (POI), Land Use Cover Change (LUCC), remote sensing data, and the railway network. Defining urban functional units as “street + railway network”, we analyze the spatial–temporal evolution within the 6th Ring Road over the past four decades and propose targeted strategies for the urban functional layout. The results reveal the following: (1) The evolution of Beijing’s urban functions can be divided into four stages (1980–1990, 1990–2005, 2005–2015, and 2015–2020), with continuous population growth (+142%) driving the over-concentration of functions in central districts. (2) Between 2010 and 2020, the POI densities of medical services (+133.6%) and transport services (+130.48%) increased most rapidly, subsequently stimulating the expansion of other urban functions. (3) High-density functional facilities and construction land (+179.10%) have expanded significantly within the 6th Ring Road, while green space (cropland, forestland and grassland) has decreased by 86.97%, resulting in a severe imbalance among land use types. To address these issues, we recommend the following: redistributing high-intensity functions to sub-centers such as Tongzhou and Xiongan New Area to alleviate population pressure, expanding high-capacity rail transit to reinforce 30–50 km commuting links between the core and periphery, and establishing ecological corridors to connect green wedges, thereby enhancing carbon sequestration and environmental quality. This integrated framework offers transferable insights for other megacities, providing guidance for sustainable functional planning that aligns human activity patterns with urban spatial structures. Full article

The ornate dog tick Dermacentor reticulatus is a key vector of several pathogens and has been expanding its range across Europe, raising concerns about the associated veterinary and public health risks. This study aimed to assess the current distribution and local-scale expansion of D. reticulatus in southwestern Poland, particularly in and around the city of Wrocław. In 2024, host-seeking ticks were collected using the flagging method at 80 sites, including 30 previously monitored locations and 50 newly designated ones, selected based on land cover analysis and field verification. Spatial statistics and kriging method were applied to evaluate changes in the tick’s range compared to data from 2014–2019. The presence of D. reticulatus was confirmed at 68 sites, including 13 located beyond the previously estimated range. A shift in the mean center of tick occurrence toward the southeast was observed, along with an increase in the compact area of occurrence. The results indicate a continued expansion of D. reticulatus in the region, with urbanization and landscape structure likely influencing its spread. These findings underscore the importance of local-scale surveillance and spatial modeling in assessing the risk of tick-borne diseases. Full article

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities. Full article

The transformation of natural ecosystems into agroecosystems due to changes in land use/land cover (LULC) has been shown to significantly affect soil characterization and classification. The impact of LULC on soil taxonomy was assessed in a primary forest located in central–eastern Honduras, which had been deforested approximately forty years prior to the study. Morphological, physical, and physicochemical analyses were performed by describing 10 representative profiles, applying the Soil Taxonomy (ST) and World Reference Base for Soil Resources (WRB) nomenclatures. LULC resulted in physical degradation in agricultural areas, as evidenced by lighter-colored horizons (P02), reduced granular structure (P01, P02, P05), higher bulk densities (≤1.73 Mg m⁻³), and surface crusting (P02, P05); this phenomenon was also observed in pastures (P06–P09). SOC loss was 62% in croplands, 47–53% in agroforestry systems (P03) and fruit tree plantations (P04), and 25% in pastures. All profiles exhibited pH values between 6.5 and 8.4 and complete base saturation (BS), except for P08 and P09, which had pH values below 5.5, high levels of Al³⁺, and reduced BS (50–60%). Mollic epipedons and variability in the endopedons were also observed. According to the ST of the System of Soil Classification (SSC), the soils were classified as Mollisols, Entisols, Vertisols, and Alfisols; and as Phaeozems, Fluvisols, Gleysols, Anthrosols, Gypsisols, and Plinthosols by the WRB. We advocate for the inclusion of Anthropogenic Soils as a distinct Order within Soil Taxonomy (ST). The implementation of sustainable agricultural practices, in conjunction with the formulation of regulatory frameworks governing land use based on capacity and suitability, is imperative, particularly within the context of fragile tropical systems. Full article

Urban economic development together with the concentration of population acts as a major stimulus for changes in land-use configurations, thereby reshaping local ecosystems and influencing habitat quality. Conducting a rigorous evaluation of the temporal–spatial dynamics and the mechanisms underlying these changes is crucial for refining spatial management strategies, improving urban livability, and steering cities toward sustainable pathways. In this research, we established a comprehensive analytical framework that integrates the PLUS model, the InVEST model, and the GeoDetector model to examine shifts in land-use patterns and habitat quality in Busan Metropolitan City during 1988–2019 to pinpoint the principal influencing factors and to project possible trajectories for 2029–2049 under multiple climate change scenarios. The key findings can be summarized as follows: (1) during the last thirty years, the city’s land-use structure underwent substantial transformation, with forested areas and built-up zones becoming the primary categories, indicating continuous urban encroachment and the reduction in ecological land; (2) the average habitat quality dropped by 18.23%, displaying a distinct spatial gradient from low values in plains and coastal areas to higher values in mountainous and inland zones; (3) results from the GeoDetector revealed that variations in land-use type and NDVI exerted the greatest influence on habitat quality differences, reflecting the combined impacts of environmental conditions and socio-economic pressures; (4) scenario projections show that the SSP1-2.6 pathway supports ecological land growth and leads to a notable improvement in habitat quality, while SSP5-8.5 causes ongoing deterioration driven by the expansion of construction land. The SSP2-4.5 pathway demonstrates a relatively moderate pattern, balancing urban development needs with ecological preservation and thus is more consistent with the long-term sustainability objectives of Busan. This study provides a robust scientific basis for understanding historical and projected changes in land cover and habitat quality in Busan and offers theoretical guidance for optimizing land-use structures, strengthening ecological protection, and fostering sustainable development in Busan and other coastal mountainous cities. Full article

Quantitative analysis of ecosystem services (ESs) supply–demand dynamics, and identifying its dominant drivers and the spatial non-stationarity of driving mechanisms, is a crucial prerequisite for effective regional ESs management and the formulation of scientific ecological conservation plans. Previous related studies have primarily focused on the supply–demand balance of specific ESs and the driving analysis of ESs supply. Comprehensive analysis of ESs supply–demand dynamics and research on their spatially heterogeneous response mechanisms remain relatively scarce. In this study, we assessed the supply, demand, and supply–demand matching relationships of ESs on the Loess Plateau (LP) from 1990 to 2023 using a land-cover-based ESs supply–demand quantitative matrix. We then employed Geodetector and Geographically weighted regression model to explore the dominant driving factors and their spatially varying effects on ESs supply–demand relationships. The results revealed that over the past three decades, the continuous decline in ESs supply coupled with the annual increase in ESs demand has led to a worsening trend in ESs supply–demand relationships towards deficit. Fortunately, the LP still maintained a supply-surplus state at present. The proportion of construction land, population density, GDP density, and the proportion of forestland and grassland were identified as key drivers of changes in ESs supply–demand relationships. The expansion of construction land was the most crucial driver of the deterioration in ESs supply–demand relationships on the LP, exhibiting a universally negative inhibitory effect. The proportion of forestland and grassland exerted a regionally wide positive spatial effect, highlighting the critical role of vegetation restoration in improving ESs relationships. The influences of population density and GDP density exhibited a coexistence of positive promoting and negative inhibitory effects across space. Our results emphasize that ESs management policies on the LP must account for the spatial heterogeneity of driving mechanisms, requiring more localized and targeted land use strategies and management policies to enhance ESs sustainability. Full article

Avocado orchards are among the most profitable and fastest-growing commodity crops in Mexico, especially in the area known as the “Avocado Belt”. Several efforts have been made to monitor their expansion; however, there is currently no method that can be easily updated to track this expansion. The main objective of this study was to monitor the expansion of avocado orchards from 1993 to 2024, using the Continuous Change Detection and Classification (CCDC) algorithm and Landsat 5, 7, 8, and 9 imagery. Presence/absence maps of avocado orchards corresponding to 1 January of each year were used to perform a trajectory analysis, identifying eight possible change trajectories. Finally, maps from 2020 to 2023 were verified using reference data and very-high-resolution images. The maps showed a level of agreement = 0.97, while the intersection over union for the avocado orchard class was 0.62. The main results indicate that the area occupied by avocado orchards more than tripled from 1993 to 2024, from 64,304.28 ha to 200,938.32 ha, with the highest expansion occurring between 2014 and 2024. The trajectory analysis confirmed that land conversion to avocado orchards is generally permanent and happens only once (i.e., gain without alternation). The method proved to be a robust approach for monitoring avocado orchard expansion and could be an attractive alternative for regularly updating this information. Full article

Rapid urbanization within ecologically fragile mountainous regions exacerbates tensions between development needs and land use sustainability, yet few studies have systematically quantified long-term land use/land cover (LULC) dynamics in large-scale mountainous urban agglomerations. Focusing on the Chengdu–Chongqing Urban Agglomeration (CCUA) in Southwest China—an archetypal mountainous megaregion undergoing accelerated development—this study analyzed LULC evolution from 1985 to 2019 using multi-period data, identified dominant driving factors through logistic regression, and projected future LULC patterns under various scenarios via the Future Land Use Simulation (FLUS) model. The outcomes indicate that (1) over the past decades, construction land expanded by over 4000 km², an increase of about 318%, while cultivated land decreased by nearly 8600 km², a reduction of 6.86%; (2) the dominant transformation type was the conversion of cultivated land to forest, followed by its conversion to construction land; (3) elevation, slope, and average annual temperature emerged as significant predictors of LULC change, highlighting the critical influence of topographical and climatic conditions; and (4) natural development scenarios (NDS) and ecology and cultivated protection scenarios (ECPS) represent suitable development pathways. These findings contribute to evidence-based spatial governance and provide policy guidance for ecological protection in the CCUA and other similarly vulnerable areas. Full article

Heat-related morbidity and mortality are increasing due to climate change, emphasizing the need to identify vulnerable areas and people exposed to extreme temperatures. To improve heat stress impact assessment, we developed a replicable machine learning model that integrates remote sensing, ground station, and geospatial data to estimate daily air temperature at a spatial resolution of 100 m × 100 m across the region of Tuscany, Italy. Using a two-stage approach, we first imputed missing land surface temperature data from MODIS using gradient-boosted trees and spatio-temporal predictors. Then, we modeled daily maximum and minimum air temperatures by incorporating monitoring station observations, satellite-derived data (MODIS, Landsat 8), topography, land cover, meteorological variables (ERA5-land), and vegetation indices (NDVI). The model achieved high predictive accuracy, with R² values of 0.95 for Tmax and 0.92 for Tmin, and root mean square errors (RMSE) of 1.95 °C and 1.96 °C, respectively. It effectively captured both temporal (R²: 0.95; 0.94) and spatial (R²: 0.92; 0.72) temperature variations, allowing for the creation of high-resolution maps. These results highlight the potential of integrating Earth Observation and machine learning to generate high-resolution temperature maps, offering valuable insights for urban planning, climate adaptation, and epidemiological studies on heat-related health effects. Full article

Land use/land cover (LULC) data serve as a critical information source for understanding the complex interactions between human activities and global environmental change. The subtropical karst region, characterized by fragmented terrain, spectral confusion, topographic shadowing, and frequent cloud cover, represents one of the most challenging natural scenes for remote sensing classification. This study reviews the evolution of multi-source data acquisition (optical, SAR, LiDAR, UAV) and preprocessing strategies tailored for subtropical regions. It evaluates the applicability and limitations of various methodological frameworks, ranging from traditional approaches and GEOBIA to machine learning and deep learning. The importance of uncertainty modeling and robust accuracy assessment systems is emphasized. The study identifies four major bottlenecks: scarcity of high-quality samples, lack of scale awareness, poor model generalization, and insufficient integration of geoscientific knowledge. It suggests that future breakthroughs lie in developing remote sensing intelligent models that are driven by few samples, integrate multi-modal data, and possess strong geoscientific interpretability. The findings provide a theoretical reference for LULC information extraction and ecological monitoring in heterogeneous geomorphic regions. Full article