Search Results (16)

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

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

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

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

Tangible cultural heritage is vulnerable to various risks, particularly those stemming from criminal activity. Through analyzing the distribution and flow of crime risks from a spatial perspective based on quantitative methods, risks can be better managed to contribute to the protection of cultural heritage. This paper explores and summarizes the spatial characteristics of crime risks from 2011 to 2019 in China. Firstly, the average nearest neighbor (ANN) and the Jenks Natural Breaks Classification method showed that the national key protected heritage sites (NPS) and crime risks exhibit clustering features in space, and most of the NPS were located in the middle and lower reaches of the Yangtze River and the Yellow River. Secondly, the economy has no impact on crime risks in the spatial statistical analysis. However, the population density, distribution of NPS, and tourism development influenced specific types of crime risks. Finally, Global Moran’s I was used to examine the strong sensitivity between crime risks and cultural relics protection policies. The quantitative results of this study can be applied to improve strategies for crime risk prevention and the effectiveness of heritage security policy formulation. Full article

A scientific and comprehensive effectiveness evaluation is a prerequisite for clarifying the guiding direction of rural socio-economic development and environmental protection. By using the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method and weight combination based on game theory, this paper systematically assessed the integrated effectiveness of rural socio-economic development and environmental protection (IERSE) of Wenchuan County in 2018 from the administrative village scale perspective. Results showed that: (1) VIKOR with combined weight and Jenks Natural Breaks Classification is both comprehensive and feasible for large-sample-size evaluation, such as IERSE assessment. (2) The general IERSE of Wenchuan demonstrated considerable positive outcomes. The villages with favorable scores were located along the northwest-central-southeast, whereas unfavorable ones were principally distributed in the northeast and south-central regions. Local spatial agglomeration of favorable IERSE was found in Miansi, Wolong, and Sanjiang Town, whereas the agglomeration of unfavorable IERSE was seen in Yingxiu and Xuankou Town. (3) The IERSE of Wenchuan is mainly constrained by ecological conservation and villagers’ autonomy from the village-scale perspective. Villages with favorable IERSE are chiefly constrained by the education level of the village heads or Party secretaries, while villages with unfavorable IERSE are restricted by ecological conservation. To improve the IERSE in rural Wenchuan, thoroughly taking into account the restrictive factors of local IERSE is an essential step for putting forward differentiated and targeted recommendations connected with ecological environment management, as well as social development initiatives. Full article

The river border positioning is an important part of river surveys, which is crucial for water conservation project development, water resource use, water disasters, river regime monitoring, and many other applications related to water resources. Currently, satellite images or field measurements are used to extract river channels. However, satellite images are insufficiently precise, and field measurement requires significant manpower and cost. In this paper, a new method for river channel extraction is proposed, which is based on the combination of Jenks natural breaks classification method and digital elevation model (DEM), and then the river channel range is complemented by using the water range monitored by GF-1(Gaofen-1 satellite) in flood season. The overall precision is greater than 85%, and the Kappa values achieve moderate stability (0.41–0.60). Using this method, the extraction of river range is practicable and achievable, and the higher the DEM resolution, the better the extraction result. Full article

Coastal zones are very dynamic natural systems that experience short-term and long-term morphological changes. Their highly dynamic behavior requires frequent monitoring. Tidal flat topography for a large spatial coverage has been generated mainly by the waterline extraction method from multitemporal remote sensing observations. Despite the efficiency and robustness of the waterline extraction method, the waterline-based digital elevation model (DEM) is limited to representing small scale topographic features, such as localized tidal tributaries. Tidal flats show a rapid increase in SAR backscattering coefficients when the tide height is lower than the tidal flat topography compared to when the tidal flat is covered by water. This leads to a tidal flat with a distinct statistical behavior on the temporal variability of our multitemporal SAR backscattering coefficients. Therefore, this study aims to suggest a new method that can overcome the constraints of the waterline-based method by using a pixel-based DEM generation algorithm. Jenks Natural Break (JNB) optimization was applied to distinguish the tidal flat from land and ocean using multitemporal Senitnel-1 SAR data for the years 2014–2020. We also implemented a logistic model to characterize the temporal evolution of the SAR backscattering coefficients along with the tide heights and estimated intertidal topography. The Sentinel-1 DEM from the JNB classification and logistic function was evaluated by an airborne Lidar DEM. Our pixel-based DEM outperformed the waterline-based Landsat DEM. This study demonstrates that our statistical approach to intertidal classification and topography serves to monitor the near real-time spatiotemporal distribution changes of tidal flats through continuous and stable SAR data collection on local and regional scales. Full article

The aim of this study was to identify the areas with different levels of riverine flood potential (RFP) in the Nitra river basin, Slovakia, using multi-criteria evaluation (MCE)-analytical hierarchical process (AHP), geographic information systems (GIS), and seven flood conditioning factors. The RFP in the Nitra river basin had not yet been assessed through MCE-AHP. Therefore, the methodology used can be useful, especially in terms of the preliminary flood risk assessment required by the EU Floods Directive. The results showed that classification techniques of natural breaks (Jenks), equal interval, quantile, and geometric interval classified 32.03%, 29.90%, 41.84%, and 53.52% of the basin, respectively, into high and very high RFP while 87.38%, 87.38%, 96.21%, and 98.73% of flood validation events, respectively, corresponded to high and very high RFP. A single-parameter sensitivity analysis of factor weights was performed in order to derive the effective weights, which were used to calculate the revised riverine flood potential (RRFP). In general, the differences between the RFP and RRFP can be interpreted as an underestimation of the share of high and very high RFP as well as the share of flood events in these classes within the RFP assessment. Therefore, the RRFP is recommended for the assessment of riverine flood potential in the Nitra river basin. Full article

In recent years, with the rapid development of urban transportation network in China, many problems have been exposed, especially in the Beijing–Tianjin–Hebei (BTH) region. Under the call of sustainable development, it is of great significance to evaluate the economic, social, and ecological (ESE) impact of transportation network in BTH urban agglomeration for promoting the sustainable development of transportation ESE in BTH urban agglomeration. In this paper, 12 indicators in the field of transportation are selected to build the evaluation index system of ESE effects of transportation network in BTH urban agglomeration. By using entropy weight TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model and the Jenks natural breaks classification method, the ESE impacts of transportation network in 13 cities of BTH from 2013 to 2017 are analyzed from the temporal and spatial dimensions. The research shows that: (1) From 2013 to 2017, the economic impact degree of traffic network shows an annual fluctuation trend, the social impact degree increases year by year, and the ecological impact degree decreases year by year; (2) For the cities of BTH, the ESE impact assessment results of transportation network from 2013 to 2017 can be divided into seven clusters. Except Handan City, the ESE impact assessment categories of other cities’ transportation network have been improved, but the proportion of cities in the transition period is still large, especially the “Low-Low-Low” cities. The types of cities in the transitional period need to be focused. It is still a heavy burden to realize the ESE coordination and sustainable development of BTH urban agglomeration transportation network. Full article

This paper examines the relationship between pedestrian density and space syntax measures in a university campus using ordinal logistic regression analysis. The pedestrian density assumed as the dependent variable of regression analysis was categorised in low, medium, and high classes by using Jenks natural break classification. The data elements of groups were derived from pedestrian counts performed in 22 gates 132 times. The counting period grouped in nominal categories was assumed as an independent variable. Another independent was one of the 15 derived measures of axial analysis and visual graphic analysis. The statistically significant model results indicated that the integration of axial analysis was the most reasonable measure that explained the pedestrian density. Then, the changes in integration values of current and master plan datasets were analysed using paired sample t-test. The calculated p-value of t-test proved that the master plan would change the campus morphology for pedestrians. Full article

Ecological environment evaluation is of great significance to achieve the Sustainable Development Goals (SDGs) and promote the harmonious development of economy, society, and environment. To evaluate environmental SDGs, single environmental indicators have been analyzed at national or large regional scale in some literature, while the urban integrated environment is ignored. Therefore, it is necessary to systematically and quantically evaluate the sustainability of ecological environment integrating the water, soil, and air environment at the urban scale. This study aims to construct the Integrated Perception Ecological Environment Indicator (IPEEI) based on the Driver-Pressure-State-Impact-Response (DPSIR) framework to solve the above-mentioned problems. The IPEEI model was proposed based on the three-level association mechanism of the Domain-Theme-Element, and the DPSIR framework conforming to the relevant standards for indicator determination. Moreover, the multi-dimensional, multi-thematic, and multi-urban quantitative evaluations were conducted using the entropy weight method, and the comprehensive evaluation grades by the Jenks natural breaks classification method of the geospatial analysis. Nine cities in the Wuhan Metropolitan Area were selected as the experimental areas. The results were consistent with the Ecological Index and local government’s planning and measures, which demonstrated that IPEEI can be effectively verified and applied for the evaluation of urban ecological environment sustainability. Full article

Gully erosion has become one of the major environmental issues, due to the severity of its impact in many parts of the world. Gully erosion directly and indirectly affects agriculture and infrastructural development. The Golestan Dam basin, where soil erosion and degradation are very severe problems, was selected as the study area. This research maps gully erosion susceptibility (GES) by integrating four models: maximum entropy (MaxEnt), artificial neural network (ANN), support vector machine (SVM), and general linear model (GLM). Of 1042 gully locations, 729 (70%) and 313 (30%) gully locations were used for modeling and validation purposes, respectively. Fourteen effective gully erosion conditioning factors (GECFs) were selected for spatial gully erosion modeling. Tolerance and variance inflation factors (VIFs) were used to examine the collinearity among the GECFs. The random forest (RF) model was used to assess factors’ effectiveness and significance in gully erosion modeling. An ensemble of techniques can provide more accurate results than can single, standalone models. Therefore, we compared two-, three-, and four-model ensembles (ANN-SVM, GLM-ANN, GLM-MaxEnt, GLM-SVM, MaxEnt-ANN, MaxEnt-SVM, ANN-SVM-GLM, GLM-MaxEnt-ANN, GLM-MaxEnt-SVM, MaxEnt-ANN-SVM and GLM-ANN-SVM-MaxEnt) for GES modeling. The susceptibility zones of the GESMs were classified as very-low, low, medium, high, and very-high using Jenks’ natural break classification method (NBM). Subsequently, the receiver operating characteristics (ROC) curve and the seed cell area index (SCAI) methods measured the reliability of the models. The success rate curve (SRC) and predication rate curve (PRC) and their area under the curve (AUC) values were obtained from the GES maps. The results show that the ANN model combined with two and three models are more accurate than the other combinations, but the ANN-SVM model had the highest accuracy. The rank of the others from best to worst accuracy is GLM, MaxEnt, SVM, GLM-ANN, GLM-MaxEnt, GLM-SVM, MaxEnt-ANN, MaxEnt-SVM, GLM-ANN-SVM-MaxEnt, GLM-MaxEnt-ANN, GLM-MaxEnt-SVM and MaxEnt-ANN-SVM. The resulting gully erosion susceptibility models (GESMs) are efficient and powerful and could be used to improve soil and water conservation and management. Full article