Search Results (64)

Land consolidation (LC) is a sustainability-oriented policy tool designed to address land fragmentation, inefficient spatial organization, and ecological degradation in rural areas. This research proposes a Production–Living–Ecological (PLE) spatial utilization efficiency evaluation system, based on an integrated methodological framework combining Principal Component Analysis (PCA), Entropy Weight Method (EWM), Attribute-Weighting Method (AWM), Linear Weighted Sum Method (LWSM), Threshold-Verification Coefficient Method (TVCM), Jenks Natural Breaks (JNB) classification, and the Obstacle Degree Model (ODM). The framework is applied to Qian County, located in the Guanzhong Plain in Shaanxi Province. The results reveal three key findings: (1) PLE efficiency exhibits significant spatial heterogeneity. Production efficiency shows a spatial pattern characterized by high values in the central region that gradually decrease toward the surrounding areas. In contrast, the living efficiency demonstrates higher values in the eastern and western regions, while remaining relatively low in the central area. Moreover, ecological efficiency shows a marked advantage in the northern region, indicating a distinct south–north gradient. (2) Integrated efficiency consolidation potential zones present distinct spatial distributions. Preliminary consolidation zones are primarily located in the western region; priority zones are concentrated in the south; and intensive consolidation zones are clustered in the central and southeastern areas, with sporadic distributions in the west and north. (3) Five primary obstacle factors hinder land use efficiency: intensive utilization of production land (PC1), agricultural land reutilization intensity (PC2), livability of living spaces (PC4), ecological space security (PC7), and ecological space fragmentation (PC8). These findings provide theoretical insights and practical guidance for formulating tar-gated LC strategies, optimizing rural spatial structures, and advancing sustainable development in similar regions. Full article

This study focuses on the Dongting Lake region in China and evaluates ecological vulnerability using the Sensitivity–Resilience–Pressure (SRP) framework, integrated with Spatial Principal Component Analysis (SPCA) to calculate the Ecological Vulnerability Index (EVI). The EVI values were classified into five levels using the Natural Breaks (Jenks) method, and spatial autocorrelation analysis was applied to reveal spatial differentiation patterns. The Geodetector model was used to analyze the driving mechanisms of natural and socioeconomic factors on EVI, identifying key influencing variables. Furthermore, the LightGBM algorithm was used for feature optimization, followed by the construction of six machine learning models—Multilayer Perceptron (MLP), Extremely Randomized Trees (ET), Decision Tree (DT), Random Forest (RF), LightGBM, and K-Nearest Neighbors (KNN)—to conduct multi-class classification of ecological vulnerability. Model performance was assessed using ROC–AUC, accuracy, recall, confusion matrix, and Kappa coefficient, and the best-performing model was interpreted using SHAP (SHapley Additive exPlanations). The results indicate that: ① ecological vulnerability increased progressively from the core wetlands and riparian corridors to the transitional zones in the surrounding hills and mountains; ② a significant spatial clustering of ecological vulnerability was observed, with a Moran’s I index of 0.78; ③ Geodetector analysis identified the interaction between NPP (q = 0.329) and precipitation (PRE, q = 0.268) as the dominant factor (q = 0.50) influencing spatial variation of EVI; ④ the Random Forest model achieved the best classification performance (AUC = 0.954, F1 score = 0.78), and SHAP analysis showed that NPP and PRE made the most significant contributions to model predictions. This study proposes a multi-method integrated decision support framework for assessing ecological vulnerability in lake wetland ecosystems. Full article

The interaction between atmospheric moisture condensation (AMC) on leaf surfaces and vegetation health is an emerging area of research, particularly relevant for advancing our understanding of water–vegetation dynamics in the contexts of remote sensing and hydrology. AMC, particularly in the form of dew, plays a vital role in both hydrological and ecological processes. The presence of AMC on leaf surfaces serves as an indicator of leaf water potential and overall ecosystem health. However, the large-scale assessment of AMC on leaf surfaces remains limited. To address this gap, we propose a leaf area index (LAI)-derived condensation potential (LCP) index to estimate potential dew yield, thereby supporting more effective land management and resource allocation. Based on psychrometric principles, we apply the nocturnal condensation potential index (NCPI), using dew point depression (ΔT = T_a − T_d) and vapor pressure deficit derived from field meteorological data. Kriging interpolation is used to estimate the spatial and temporal variations in the AMC. For management applications, we develop a management suitability score (MSS) and prioritization (MSP) framework by integrating the NCPI and the LAI. The MSS values are classified into four MSP levels—High, Moderate–High, Moderate, and Low—using the Jenks natural breaks method, with thresholds of 0.15, 0.27, and 0.37. This classification reveals cases where favorable weather conditions coincide with low ecological potential (i.e., low MSS but high MSP), indicating areas that may require active management. Additionally, a pairwise correlation analysis shows that the MSS varies significantly across different LULC types but remains relatively stable across groundwater potential zones. This suggests that the MSS is more responsive to the vegetation and micrometeorological variability inherent in LULC, underscoring its unique value for informed land use management. Overall, this study demonstrates the added value of the LAI-derived AMC modeling for monitoring spatiotemporal micrometeorological and vegetation dynamics. The MSS and MSP framework provides a scalable, data-driven approach to adaptive land use prioritization, offering valuable insights into forest health improvement and ecological water management in the face of climate change. Full article

River channels are fundamental geomorphological and hydrological features that play a critical role in regulating the Earth’s water cycle and ecosystems and influencing human activities. This study utilized Digital Elevation Model (DEM) data and multi-source remote sensing imagery (including GF-1 WFV, Sentinel-1, and Sentinel-2) to determine river channel dimensions. River water masks were obtained from multiple remote sensing imagery sources and processed through triangulation and segmentation to generate river reach results. Based on these segmented river reaches, buffer analysis was conducted. The buffer analysis results were then used to refine and clip the 5 m DEM and 12.5 m DEM datasets. Finally, river channels were extracted from the clipped DEM data using the natural breaks classification method. The classification accuracy was assessed using a confusion matrix. Experimental results demonstrate a high overall classification accuracy, reaching or exceeding 0.985, with classification consistency (Kappa coefficient) ranging from 0.78 to 0.81. The 5 m resolution DEM exhibited superior performance compared to the 12.5 m resolution DEM in river channel extraction, especially regarding the classification consistency (Kappa coefficient), with the 5 m resolution model outperforming the latter. This approach effectively delineates the river channel boundaries, transcends the constraints of a singular data source, enhances the precision and resilience of river extraction, and possesses several practical applications. The extracted data can support analyses of river evolution, facilitate hydrological modeling at the basin scale, improve flood disaster monitoring, and contribute to various other research domains. Full article

Congenital cytomegalovirus (cCMV) is the most common infectious cause of birth defects worldwide, affecting approximately 1 in every 200 live-born infants globally. Recent work has identified potential immune correlates of protection against cCMV transmission including maternal and placentally transferred antibody levels and their function, which may inform the development of maternal active (vaccine) and passive (mono/polyclonal antibody) immunizations. However, these correlates need to also be assessed in diverse cohorts, including women living with HIV who have increased risk of cCMV transmission. Using a case–control design, we investigated whether the magnitude, specificity, function and placental transfer of maternal IgG responses are associated with protection against and/or risk of cCMV transmission in HIV/HCMV co-infection. Within 3 historical cohorts of pregnant women with HIV/HCMV co-infection, we identified 16 cCMV transmitting cases that were matched to 29 cCMV non-transmitting controls. Using a systems serology approach, we found that normalized HCMV-specific IgG binding to FcγR1α was higher in non-transmitting dyads, whereas HCMV-neutralizing antibody responses were higher in transmitting dyads. These findings suggest that engagement of FcγR1α by HCMV-specific IgG may help confer protection against cCMV transmission. Building upon previous research, our study reinforces the critical role of validating maternal humoral immune correlates of cCMV transmission risk across diverse seropositive cohorts, providing essential insights to inform and accelerate the development of effective HCMV vaccines. Full article

Gastrodia elata has been a valuable medicinal resource in the East for approximately 3000 years. In South Korea, G. elata is cultivated in open-fields or greenhouses near residential areas. However, due to severe continuous damage, cultivation sites need to be frequently relocated, leading to a shortage of available cultivation areas. Alternatively, farmers are focusing on mountain cultivation. This study analyzed suitable cultivation sites for G. elata in mountainous areas using a geographic information system (GIS) and applied various classification methods to identify their characteristics and similarities. The analysis showed that the Natural Breaks (Jenks) classification method maximized the differences between grades, whereas the Quantile method reclassified the area of suitable sites to a relatively high proportion. In contrast, the Equal Interval method reclassified the areas of suitable and unsuitable sites to a lower proportion, whereas the Geometric Interval method best demonstrated extreme-temperature regions as unsuitable sites. Among the classification methods, the Natural Breaks (Jenks) and Geometric Interval methods yielded the most similar results. These findings provide critical methodological outcomes for G. elata cultivation and sustainable agriculture and forestry. Future empirical research and the application of climate change scenarios are necessary to enhance the sustainability of the G. elata cultivation industry. Full article

The Soil and Water Assessment Tool (SWAT) was applied to investigate agricultural non-point source pollution in the Shitun River Basin (54.87 km²), China, where intensive agriculture dominates. This study analyzed spatiotemporal pollutant distribution from January 2021 to September 2023 and identified key pollution sources. The basin was divided into 46 sub-basins and 268 hydrological response units (HRUs). Model calibration and validation using runoff, total phosphorus, and ammonia nitrogen data demonstrated high accuracy (R² ≥ 0.6, Ens ≥ 0.5), confirming its applicability for area-source pollution assessment in agricultural regions. Agricultural area-source pollution was particularly concentrated from June to October, aligning with the high-flow period. Conversely, pollution levels saw a significant reduction during the medium- and low-flow periods. Severe pollution was mainly observed along the river and in the eastern part of the basin. By means of unit area load index method and Jenks natural fracture point method, it was determined that the key source areas of surface source pollution are mainly distributed in the upper reaches of the basin. The results can provide an adjusting basis and a theoretical basis for the control of agricultural surface source pollution in the watershed. Full article

A change in the understanding of rural policy took place in the 1980s when there was a transition from a sectoral to a territorial perception of rural areas. Rural areas are no longer identified only with agriculture. There has been a recent decentralization of rural policy to the regional and local levels, which requires the recognition of the diversity of rural areas and the changes taking place in them. Therefore, it is important to recognize the state of sustainable development and disparities at the local level so that the targeted support is appropriate. The aim of the research is to measure sustainable development, including spatial diversification. The measurement of sustainable development was carried out using synthetical measures, and measures for economic, social, and environmental domains were constructed. The Jenks method was applied to group entities into classes characterized by similar levels of development. The results of the research proved the significance of the differences in the scope of balanced development for each of the domains. The resulting spatial systems are characterized by the line of the center–periphery. The obtained research results are valuable for central and local authorities in the process of planning local and regional development. Full article

Ecotourism is becoming more and more significant because it aids in environmental protection and maintaining the sustainable growth of a region. Mizoram is known for its potentially varied landscapes, which draw visitors from many nations and territories. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach was used in this study to evaluate land suitability for ecotourism initiatives in Mizoram spatially. The study also focused on role weighting (subjective, objective, and intergraded) on the decision-making process. In this regard, the weightage of twelve ecotourism influencing factors was determined to integrate with the TOPSIS model and the Geographical Information System (GIS) environment. As a result, five hierarchical ecotourism zones, including very high to very low, have been classified using Jenks’s natural breaking classification. The model’s accuracy based on the area under the curve (AUC) and receiver operating characteristic (ROC) curve revealed that all models successfully predict potential ecotourism in the marginal hilly region. As a result, the intergrade weighting combined TOPSIS model showed that 25.18% of the study region has very highly suitable for ecotourism. The results of this study may be used as a foundation for assessing the feasibility of resources suitable for ecotourism development by government officials and planners. Full article

Precision agriculture (PA) utilizes spatial and temporal variability to improve the sustainability and efficiency of farming practices. This study used high-resolution imagery from UAS to evaluate maize yield variability across three fields in Ghana: Sombolouna, Tilli, and Yendi, exploiting the potential of UAS technology in PA. Initially, excess green index (EGI) classification was used to differentiate between bare soil, dead vegetation, and thriving vegetation, including maize and weeds. Thriving vegetation was further classified into maize and weeds, and their corresponding rasters were developed. Normal difference red edge (NDRE) was applied to assess maize health. The Jenks natural breaks algorithm classified maize rasters into low, medium, and high differential yield zones (DYZs). The percentage of bare spaces, maize, weed coverages, and total maize production was determined. Significant variations in field conditions showed Yendi had 34% of its field as bare, Tilli had the highest weed coverage at 22%, and Sombolouna had the highest maize crop coverage at 73.9%. Maize yields ranged from 860 kg ha⁻¹ in the low DYZ to 4900 kg ha⁻¹ in the high DYZ. Although yields in Sombolouna and Tilli were similar, both fields significantly outperformed Yendi. Scenario analysis suggested that enhancing management practices to elevate low DYZs to medium levels could increase production by 2.1%, while further improvements to raise low and medium DYZs to high levels could boost productivity by up to 20%. Full article

This work proposes a new unsupervised method to evaluate the behavior of urban green areas in the presence of heatwave scenarios by analyzing three indices extracted from satellite data: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST). The aim of this research is to analyze the behavior of urban vegetation types during heatwaves through the analysis of these three indices. To evaluate how these indices characterize urban green areas during heatwaves, an unsupervised classification method of the three indices is proposed that uses the Elbow method to determine the optimal number of classes and the Jenks classification algorithm. Each class is assigned a Gaussian fuzzy set and the green urban areas are classified using zonal statistics operators. The membership degree of the corresponding fuzzy set is calculated to assess the reliability of the classification. Finally, for each type of greenery, the frequencies of types of green areas belonging to NDVI, NDMI, and LST classes are analyzed to evaluate their behavior during heatwaves. The framework was tested in an urban area consisting of the city of Naples (Italy). The results show that some types of greenery, such as deciduous forests and olive groves, are more efficient, in terms of health status and cooling effect, than other types of urban green areas during heatwaves; they are classified with NDVI and NDMI values of mainly High and Medium High, and maximum LST values of Medium Low. Conversely, uncultivated areas show critical behaviors during heatwaves; they are classified with maximum NDVI and NDMI values of Medium Low and maximum LST values of Medium High. The research results represent a support to urban planners and local municipalities in designing effective strategies and nature-based solutions to deal with heat waves in urban settlements. Full article

This paper presents an optimal scheduling problem for coordinating pressure and self-cleaning operations in dynamically adaptive water networks. Our problem imposes a set of time-coupling constraints to manage pressure variations during the transition between operational modes. Solving this time-coupled, nonlinear optimization problem poses challenges for off-the-shelf nonlinear solvers due to its high memory demands. We compare the performance of a decomposition method using the alternating direction method of multipliers (ADMM) with a gradient-based sequential convex programming (SCP) monolithic solver. Solution quality and computational efficiency are evaluated using a model of a large-scale network in the UK. Full article

Machine learning models such as artificial neural networks (ANNs) are becoming increasingly popular in short-term water demand forecasting. This is because, despite their lack of interpretability, ANNs are able to capture complex interactions between explanatory variables and water consumption better than a traditional time series analysis or simple linear regression. In this work, we forecast the hourly water demand of ten operational district metered areas using optimal trees, a machine learning model which has been shown to combine the interpretability of regression approaches and the accuracy of ANNs. We show that, compared to existing water demand forecasting models, optimal trees offer valuable insights without sacrificing predictive or computational performance. Full article

As population growth and urbanization continue to accelerate, city dwellers are increasingly conscious of the demand for urban green space (UGS) and the ecosystem services (ESs) it provides. Great efforts are made for the supply of certain ESs in UGS. However, less is known about the residents’ preferences and the supply–demand matching of UGS types, as well as the various ESs it provides at different spatial scales. Given this, our research establishes a research framework to reveal the heterogeneity of USG types and the supply–demand matching degree (SDM) of ESs from municipal, provincial, and national spatial scales, and examines the correlation between the influencing factors and demands of residents for UGS. This study mainly used the Gini coefficient, the Lorenz curve, Z-scores, the Jenks natural breaks classification method, Pearson correlation analysis, and spatial analysis. The main findings are that (1) the Gini coefficients are 0.433 and 0.137 at the municipal and provincial scales, respectively, indicating that the supply of UGS is more unequal at the municipal scale than provincial scale; (2) the multi-scale demand for ESs between residents has no significant difference. At the provincial scale, the area with low demand is larger than that of high demand, while at the municipal scale, the contrary is the case; (3) the SDM was in a deficit at both the provincial and municipal scales. And as the scaling-up occurred, the spatial heterogeneity of the SDM decreased; (4) the number of influencing factors that significantly affected the UGS type and ESs grew as the scale increased. Among them, the impact of age and COVID-19 on three scales deserves attention. These results identify regions with deficits and surpluses in ESs provided by UGS in China at different scales. This research also advises that attention should be paid to the distribution of UGS between cities within provinces, and future UGS planning should focus on building regional green spaces to promote the well-being of an aging society. The findings in this study would offer insights for managers to improve UGS construction and urban forestry planning in the future. Full article

(1) Background: With increasing international travel and mass population displacement due to war, famine, climate change, and immigration, pathogens, such as Staphylococcus aureus (S. aureus), can also spread across borders. Methicillin-resistant S. aureus (MRSA) most commonly causes skin and soft tissue infections (SSTIs), as well as more invasive infections. One clonal strain, S. aureus USA300, originating in the United States, has spread worldwide. We hypothesized that S. aureus USA300 would still be the leading clonal strain among US-born compared to non-US-born residents, even though risk factors for SSTIs may be similar in these two populations (2) Methods: In this study, 421 participants presenting with SSTIs were enrolled from six community health centers (CHCs) in New York City. The prevalence, risk factors, and molecular characteristics for MRSA and specifically clonal strain USA300 were examined in relation to the patients’ self-identified country of birth. (3) Results: Patients born in the US were more likely to have S. aureus SSTIs identified as MRSA USA300. While being male and sharing hygiene products with others were also significant risks for MRSA SSTI, we found exposure to animals, such as owning a pet or working at an animal facility, was specifically associated with risk for SSTIs caused by MRSA USA300. Latin American USA300 variant (LV USA300) was most common in participants born in Latin America. Spatial analysis showed that MRSA USA300 SSTI cases were more clustered together compared to other clonal types either from MRSA or methicillin-sensitive S. aureus (MSSA) SSTI cases. (4) Conclusions: Immigrants with S. aureus infections have unique risk factors and S. aureus molecular characteristics that may differ from US-born patients. Hence, it is important to identify birthplace in MRSA surveillance and monitoring. Spatial analysis may also capture additional information for surveillance that other methods do not. Full article