Search Results (3,051)

New challenges will be experienced by the agriculture sector in the near future, especially due to the effects of climate change. For example, rising temperatures could result in increased evapotranspiration demand, causing difficulties in the management of irrigation practices. Generally, an important predictor of plant water status to be taken into account for irrigation monitoring and management is the stem water potential. However, it requires a huge amount of time-consuming fieldwork, particularly when an adequate data amount is necessary to fully investigate the spatial and temporal variability of large areas under monitoring. In this study, the integration of machine learning and satellite remote sensing (Sentinel-2) was investigated to obtain a model able to predict the stem water potential in viticulture using multispectral imagery. Vine water status data were acquired within a Montepulciano vineyard in the south of Italy (Puglia region), under semi-arid conditions; data were acquired over two years during the irrigation seasons. Different machine learning algorithms (lasso, ridge, elastic net, and random forest) were compared using vegetation indices and spectral bands as predictors in two independent analyses. The results show that it is possible to remotely estimate vine water status with random forest from vegetation indices (R² = 0.72). Integrating machine learning techniques and satellite remote sensing could help farmers and technicians manage and plan irrigation, avoiding or reducing fieldwork. Full article

Resistance spot-welded joints are crucial parts in contemporary manufacturing technology due to their ubiquitous use in the automobile industry. The necessity of improving manufacturing efficiency and quality at an affordable cost requires deep knowledge of the resistance spot welding (RSW) process and the development of artificial neural network (ANN)- and machine learning (ML)-based modelling techniques, apt for providing essential tools for design, planning, and incorporation in the welding process. Tensile shear force and nugget diameter are the most crucial outputs for evaluating the quality of a resistance spot-welded specimen. This study uses ML and ANN models to predict shear force and nugget diameter responses to RSW parameters. The RSW analysis was executed on similar and dissimilar AISI 304 and grade 2 titanium alloy joints with equal and unequal thicknesses. The input parameters included welding current, pressure, welding duration, squeezing time, holding time, pulse welding, and sheet thickness. Linear regression, Decision tree, Support vector machine (SVM), Random forest (RF), Gradient-boosting, CatBoost, K-Nearest Neighbour (KNN), Ridge, Lasso, and ElasticNet machine learning algorithms, along with two different structures of Multilayer Perceptron, were utilized for studying the impact of the RSW parameters on the shear force and nugget diameter. Different validation metrics were applied to assess each model’s quality. Two equations were developed to determine the shear force and nugget diameter based on the investigation parameters. The current research also presents a prediction of the Relative Importance (RI) of RSW factors. Shear force and nugget diameter predictions were examined using SHapley (SHAP) Additive Explanations for the first time in the RSW field. Trainbr as the training function and Logsig as the transfer function delivered the best ANN model for predicting shear force in a one-output structure. Trainrp with Tansig made the most accurate predictions for nugget diameter in a one-output structure and for shear force and diameter in a two-output structure. Depending on validation metrics, the Random forest model outperformed the other ML algorithms in predicting shear force or nugget diameter in a one-output model, while the Decision tree model gave the best prediction using a two-output structure. Linear regression made the worst ML predictions for shear force, while ElasticNet made the worst nugget diameter forecasts in a one-output model. However, in two-output models, Lasso made the worst predictions. Full article

Forests provide a variety of ecosystem services (ESs) that contribute to a society’s wellbeing. ES provision depends on the structure and evolution of forest ecosystems and is influenced by forest management. Society’s increasing need for ESs requires these complex ecological dynamics to be understood and integrated in forest management and planning. We present the decision support system (DSS) Multisilva for multifunctional forest management. The Multisilva DSS is a web-based application that comprises two tools: the Mapping tool and the Simulation tool. The first tool provides spatial statistics and maps of the current provision of ESs at the forest property level. The Simulation tool compares two alternative, user-defined management scenarios over time and returns the biophysical estimations of ESs and the economic costs for each alternative. Multisilva is calibrated for Luxembourg, though it can be adapted for other temperate forest regions. Full article

Economic corridors play a crucial role in promoting economic growth and facilitating coordinated regional development. However, land use changes associated with the development of emerging economic corridors have become a prominent source of conflict in regional integration in the Global South. This study takes the Laos Economic Corridor as a case study to explore the characteristics and driving mechanisms of land use changes in emerging economic corridor regions. Using global land cover data from 2000 to 2020 (GlobeLand30) and employing spatial statistical analysis, the Random Forest (RFC) algorithm, and the CA-Markov model, the study follows a Pattern–Process–Mechanism–Trend analytical framework to reveal the spatial distribution characteristics and transformation paths of land use within the corridor. The study results indicate that (1) The land use pattern in the Laos Economic Corridor has gradually shifted from a “single-core radial” structure to a “dumbbell-shaped” structure, promoting coordinated regional economic development. (2) A significant unidirectional flow of land use has been established, with forestland being converted into cultivated land and cultivated land being further converted into artificial surfaces. (3) In addition to the natural geographical constraints, the transport infrastructure and the spatial layout of industries are the main drivers for the expansion of ecological land, agricultural land, and built-up land. (4) Spatial planning interventions are essential and urgent: the establishment of land management rules based on the principles of forest conservation and intensive development can effectively control the uncontrolled expansion of artificial areas, significantly reduce the loss of forestland, and ensure the rational allocation of land resources for long-term development. The findings of this study offer valuable insights and reference points for the Global South, enhancing understanding of the spatial development dynamics of economic corridors, informing the optimization of land-use policies, and supporting efforts to promote regional integration and sustainable development. Full article

With the intensification of socioeconomic activities and climate change, land use conflicts are becoming more and more serious, posing major obstacles to the sustainable use of territorial space. This study conducted research on land use conflict and zoning control with a view to contributing new ideas for the prevention and resolution of land use risks. By analyzing the positioning and drawing upon fundamental theories, a novel research paradigm was proposed. An empirical study was conducted in the Gan River Basin in Jiangxi Province by applying the comprehensive evaluation method and geographical detector, and the basin was divided into six types of zones according to the intensity of land use conflict and the hierarchy of ecosystem service values. The results of the empirical study showed that the areas of intense conflict, low conflict and weak conflict accounted for 1.57%, 29.16% and 69.26% of the basin area, respectively. Of the intense conflict areas, 4.42% of the areas in the lower Gan River Basin were in intense conflict, while only 0.37% of the right bank of the middle reaches was in intense conflict. The driving factor analysis showed that precipitation, the population density and policy planning had a greater influence on land use conflict and that land use conflict was more likely to occur with the interaction of precipitation and the nighttime light index, population density and NDVI. The superimposed image analysis revealed that the land use conflict was intense at the junctions of urban areas and cropland and at the junctions of cropland and forests in the middle and upper reaches of the basin, which were mainly caused by the demand for urban expansion and the spread of agricultural production areas. The results of this empirical study are in agreement with the actual situation in the Gan River Basin, proving that the research paradigm proposed in this study is scientific and applicable. Moreover, we emphasize that this paradigm can be adapted in its application according to different research objects and continuously improved in response to the evolution of the territorial spatial management system. This study is of positive significance for the implementation of territorial spatial planning and provides a scientific basis for the further enhancement of the system of territorial spatial governance. Full article

The alterations in runoff resulting from changes in land use and land cover (LULC) were the primary influencing factors contributing to non-point source pollution (NPS). In order to evaluate the long-term hydrological consequences of LULC for the purposes of land use optimization in the Hulan River Basin, Northeast China, the validated Long-term Hydrological Impact Assessment (L-THIA) model was employed to simulate the spatiotemporal distribution of total nitrogen (TN) and total phosphorus (TP) non-point source (NPS) loads from 2000 to 2020. Additionally, the load per unit area index (LPUAI) method was utilized to identify critical source areas. The findings indicated that the regions with elevated pollution levels were predominantly situated in areas designated for agricultural and construction activities. The greatest contributor to nitrogen and phosphorus loads was agricultural land. There were clear increases in both TN and TP during the study period, with increases of 51.73% and 55.56%, respectively. As a consequence of the process of urbanization in the basin, the area of land devoted to construction activities increased, reaching a coverage of 5.02%. Nevertheless, the contribution of construction land to the total basin NPS load exceeded 10% in 2020. This was the primary factor contributing to the observed increase in pollution loads despite a reduction in agricultural land area over the past two decades. TN and TP loads were markedly higher during the flood season than the non-flood season, accounting for over 80% of the NPS load. The sub-watersheds in the southwest and northeast have been identified as significant sources of nitrogen and phosphorus loss, contributing to the overall burden of NPS pollution. Implementing measures such as fertilizer reduction and conversion of farmlands to forests and grasslands can effectively mitigate NPS pollution, particularly TN pollution. This study proposes that the integration of L-THIA with GIS can serve as a valuable tool for local planners to consider potential pollution risks during future planning and development activities. Full article

Linear programming models have been used in forest management planning since the 1960s. These models have been formulated in three basic ways: Models I, II, and III, which are defined by the sequences of management unit states represented by the variables. In Model I, variables represent sequences of states from the beginning of the planning horizon to the end. In Model II, variables represent sequences of states from one intervention to the next. Finally, in Model III, variables represent a single arc in a management unit’s decision tree, i.e., two states. The objectives of this paper are to clarify the definitions of these model variations and evaluate the advantages and disadvantages of each model. This second objective is to test the hypothesis that the relative performance of these models varies with the increasing number of ecosystem services (ES) incorporated into the models. This objective was achieved by formulating a case study problem using each model type. The case study includes three increasingly complex scenarios, each incorporating additional ecosystem services. Results show that despite having more variables and constraints, Model III requires the least time to formulate due to its less dense parameter matrix. Model II has the shortest solution times, followed closely by Model III, while Model I requires the longest times for both formulation and solution. These results are increasingly apparent in more complex scenarios. Full article

Global warming and landscape fragmentation significantly affect the spatial distribution pattern of bamboo forests. This study used high-resolution data and an optimized MaxEnt model to predict the distribution of Phyllostachys edulis in China under current and future climatic conditions in three climate scenarios (SSP126, SSP370, SSP585), and analyzed its land use landscape fragmentation using landscape indices. The results indicate that Phyllostachys edulis currently has potentially suitable habitats majorly distributed in East China, Southwest China, and Central South China. The precipitation of the driest month (BIO14) and the precipitation seasonality (BIO15) are the key environmental factors affecting the distribution of Phyllostachys edulis. In the next three scenarios, the adaptive distribution area of Phyllostachys edulis is generally expanding. With an increase in CO₂ concentration, the adaptive distribution of Phyllostachys edulis in the 2050s migrates towards the southeast direction, and in the 2070s, the suitable habitat of Phyllostachys edulis migrates northward. In the suitable habitat area of Phyllostachys edulis, cropland and forests are the main land use types. With the passage of time, the proportion of forest area in the landscape pattern of the high-suitability area for Phyllostachys edulis continues to increase. Under SSP370 and SSP585 scenarios, the cropland in the Phyllostachys edulis high-suitability area gradually becomes fragmented, leading to a decrease in the distribution of cropland. In addition, it is expected that the landscape of high-suitability areas will become more fragmented and the quality of the landscape will decline in the future. This research provides a scientific basis for understanding the response of Phyllostachys edulis to climate change, and also provides theoretical guidance and data support for the management and planning of bamboo forest ecosystems, which will help in managing bamboo forest resources rationally and balancing carbon sequestration and biodiversity conservation. Full article

The relationship between coronary heart disease (CHD) and complex urban built environments remains a subject of considerable uncertainty. The development of predictive models via machine learning to explore the underlying mechanisms of this association, as well as the formulation of intervention policies and planning strategies, has emerged as a pivotal area of research. A cross-sectional dataset of hospital admissions for CHD over the course of a year from a hospital in Dalian City, China, was assembled and matched with multi-source built environment data via residential addresses. This study evaluates five machine learning models, including decision tree (DT), random forest (RF), eXtreme gradient boosting (XGBoost), multi-layer perceptron (MLP), and support vector machine (SVM), and compares them with multiple linear regression models. The results show that DT, RF, and XGBoost exhibit superior predictive capabilities, with all R² values exceeding 0.70. The DT model performed the best, with an R² value of 0.818, and the best performance was based on metrics such as MAE and MSE. Additionally, using explainable AI techniques, this study reveals the contribution of different built environment factors to CHD and identifies the significant factors influencing CHD in cold regions, ranked as age, Digital Elevation Model (DEM), house price (HP), sky view factor (SVF), and interaction factors. Stratified analyses by age and gender show variations in the influencing factors for different groups: for those under 60 years old, Road Density is the most influential factor; for the 61–70 age group, house price is the top factor; for the 71–80 age group, age is the most significant factor; for those over 81 years old, building height is the leading factor; in males, GDP is the most influential factor; and in females, age is the most influential factor. This study explores the feasibility and performance of machine learning in predicting CHD risk in the built environment of cold regions and provides a comprehensive methodology and workflow for predicting cardiovascular disease risk based on refined neighborhood-level built environment factors, offering scientific support for the construction of sustainable healthy cities. Full article

A comprehensive scientific assessment of the dynamic changes in land cover and landscape patterns in Myanmar, considering both human activities and natural factors such as climate change, is essential for a thorough understanding of the transformations in the country’s ecological environment. This assessment also provides data-driven insights into the complex interactions between humans, climate, and the environment. This study aims to examine the dynamic changes in land cover in Myanmar over a thirty-year period from a comprehensive perspective. This paper, based on the MLC30 land cover dataset for Myanmar from 1990 to 2020, employs land use dynamic degree and land use transition matrix to analyze the extent and process of land cover changes in Myanmar. Furthermore, using landscape pattern indicators, the paper explores the changes in the spatial structural characteristics of land cover in Myanmar at both the patch scale and the landscape scale. The results indicate the following: (a) Areas with significant land cover changes are primarily located in the eastern, southeastern, and southwestern regions bordering China, Laos, and Thailand, as well as the coastal areas, with the change intensity from 2000 to 2020 being notably higher than before 2000. (b) Myanmar’s cultivated land, artificial surfaces, and water bodies show an expanding trend, with cultivated land expansion mainly at the expense of forests, while the increase in artificial surfaces and water bodies is through the conversion of the existing cultivated land. (c) Myanmar’s landscape patterns remained stable from 1990 to 2000. However, after 2000, the land cover has shown a clear trend towards fragmentation and spatial distribution dispersion, especially for the dominant forest and cultivated land types. Despite Myanmar’s rapid economic development, the trend toward the fragmentation and irregularization of cultivated land patches indicates a lack of attention to cultivated land use and planning. The reduction and fragmentation of forest areas have led to a decline in ecological connectivity, posing risks of ecological environment deterioration. Consequently, Myanmar must prioritize scientific land use planning and the rational allocation of land resources to foster the sustainable development of agriculture and the protection of natural ecosystems. Full article

Background/Objectives: Yellow fever (YF) outbreaks continue to affect populations that are not reached by routine immunization services, such as workers at a high risk of occupational exposure to YF. In the Central African Republic (CAR), YF cases were detected in districts characterized by the presence of workers in forest areas. We developed an innovative approach based on a local partnership with private companies of the extractive industry to administer YF vaccine to workers in remote areas during the response to an outbreak. Methods: The planning stage of the campaign included the mapping of forestry and mining companies through the involvement of national and/or local representatives of companies from both the formal and informal sectors. Information sessions and mobilization targeted the heads of operating companies. Advanced and mobile strategies were used to target workers on their work site. Companies provided logistical support including transportation and communication and set up temporary vaccination posts. Results: Using this local partnership, it was possible to vaccinate over 70,000 workers (5.8% of the entire vaccinated population) in hard-to-reach areas, protecting them from YF. This represented around 47% of the estimated number of workers and dependents. The partnership with the private sector also contributed to increasing knowledge on the risk of YF and means of protection among a high-risk community. Conclusions: Private companies represent potentially useful actors that can contribute to the protection of high-risk workers and to the prevention and control YF outbreaks. The experience in the CAR has demonstrated that it is possible to obtain support from private companies, including informal ones, for a vaccination campaign. Full article

Potential evapotranspiration (PET) is a significant factor contributing to water loss in hydrological systems, making it a critical area of research. However, accurately calculating and measuring PET remains challenging due to the limited availability of comprehensive data. This study presents a detailed sustainable model for predicting PET using the Thornthwaite equation, which requires only mean monthly temperature (Tmean) and latitude, with calculations performed using R-Studio. A geographic information system (GIS) was employed to interpolate meteorological data, ensuring coverage of all sub-basins within the Murat River basin, the study area. Additionally, Python libraries were utilized to implement artificial intelligence-driven models, incorporating both machine learning and deep learning techniques. The study harnesses the power of artificial intelligence (AI), applying deep learning through a convolutional neural network (CNN) and machine learning techniques, including support vector machine (SVM) and random forest (RF). The results demonstrate promising performance across the models. For CNN, the coefficient of determination (R²) varied from 96.2 to 98.7%, the mean squared error (MSE) ranged from 0.287 to 0.408, and the root mean squared error (RMSE) was between 0.541 and 0.649. For SVM, the R² varied from 94.5 to 95.6%, MSE ranged between 0.981 and 1.013, and RMSE ranged from 0.990 to 1.014. RF showed the best performance, achieving an R² of 100%, MSE values of 0.326 and 0.640, and corresponding RMSE values of 0.571 and 0.800. The climate and topography data used for all algorithms were consistent, and the results indicate that the RF model outperforms the others. Consequently, The RF model’s superior accuracy highlights its potential as a reliable tool for sustainable PET prediction, supporting informed decision-making in water resource planning. By leveraging GIS, AI, and machine learning, this study enhances PET modeling methodologies, addressing critical water management challenges and promoting sustainable hydrological practices in the face of climate change and resource limitations. Full article

Amid global environmental degradation, understanding the spatiotemporal dynamics and trade-offs of ecosystem services (ESs) under varying land-use scenarios is critical for advancing the sustainable development of social–ecological systems. This study analyzed the Chaohu Lake Basin (CLB), focusing on four scenarios: natural development (ND), economic priority (ED), ecological protection (EP), and sustainable development (SD). Using the PLUS model and multi-objective genetic algorithm (MOGA), land-use changes for 2030 were simulated, and their effects on ESs were assessed quantitatively and qualitatively. The ND scenario led to significant declines in cropland (3.73%) and forest areas (0.18%), primarily due to construction land expansion. The EP scenario curbed construction land growth, promoted ecosystem recovery, and slightly increased cropland by 0.05%. The SD scenario achieved a balance between ecological and economic goals, maintaining relative stability in ES provision. Between 2010 and 2020, construction land expansion, mainly concentrated in central Hefei City, led to a marked decline in habitat quality (HQ) and landscape aesthetics (LA), whereas water yield (WY) and soil retention (SR) improved. K-means clustering analysis identified seven ecosystem service bundles (ESBs), revealing significant spatial heterogeneity. Bundles 4 through 7, concentrated in mountainous and water regions, offered high biodiversity maintenance and ecological regulation. In contrast, critical ES areas in the ND and ED scenarios faced significant encroachment, resulting in diminished ecological functions. The SD scenario effectively mitigated these impacts, maintaining stable ES provision and ESB distribution. This study highlights the profound effects of different land-use scenarios on ESs, offering insights into sustainable planning and ecological restoration strategies in the CLB and comparable regions. Full article

Timely and accurate land use information in open-pit mines is essential for environmental monitoring, ecological restoration planning, and promoting sustainable progress in mining regions. This study used high-resolution unmanned aerial vehicle (UAV) imagery, combined with object-oriented methods, optimal segmentation algorithms, and machine learning algorithms, to develop an efficient and practical method for classifying land use in open-pit mines. First, six land use categories were identified: stope, restoration area, building, vegetation area, arterial road, and waters. To achieve optimal scale segmentation, an image segmentation quality evaluation index is developed, emphasizing both high intra-object homogeneity and high inter-object heterogeneity. Second, spectral, index, texture, and spatial features are identified through out-of-bag (OOB) error of random forest and recursive feature elimination (RFE) to create an optimal multi-feature fusion combination. Finally, the classification of open-pit mines was executed by leveraging the optimal feature combination, employing the random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) classifiers in a comparative analysis. The experimental results indicated that classification of appropriate scale image segmentation can extract more accurate land use information. Feature selection effectively reduces model redundancy and improves classification accuracy, with spectral features having the most significant effect. The RF algorithm outperformed SVM and KNN, demonstrating superior handling of high-dimensional feature combinations. It achieves the highest overall accuracy (OA) of 90.77%, with the lowest misclassification and omission errors and the highest classification accuracy. The disaggregated data facilitate effective monitoring of ecological changes in open-pit mining areas, support the development of mining plans, and help predict the quality and heterogeneity of raw clay in some areas. Full article

The core of the cultural services of ecosystems (CESs) is the spiritual connection between humans and nature, and participatory mapping from the stakeholder perspective is an effective method for perceiving and protecting hotspot CES areas. This study used participatory mapping combined with 184 interviews and questionnaires, completed on 10 December 2023, to investigate the perceptions of CESs in Taibai Mountain National Forest Park by different stakeholder groups; spatial and correlation analyses were used to comparatively analyze the characteristics of the differences in the perceptions of CESs among different stakeholders, the influencing factors, and their spatial distribution patterns. The results show that (1) there is a positive correlation between the literacy level of external stakeholders and the perception of CESs, and there is no significant difference between the differences in the other demographic characteristics of stakeholders (gender, age, occupation, and literacy level) in the perception of CESs. (2) Different stakeholders have convergent perceptions of spiritual and religious values, cultural heritage values, educational values, and inspirational values, whereas there are greater differences in the perceptions of aesthetic values, ecological and recreational values, and local identity values. (3) Different stakeholders of the same CES are strongly correlated, and there is no correlation between spiritual and religious values and other values and no correlation between recreational and ecotourism values and educational and cultural heritage values; however, there is a correlation between all other subcultural services. It is important to fully identify and consider the characteristics of the differences in the perceptions of different stakeholders in CESs to enhance the regional planning and scenic area service function in the study area. Full article