Search Results (8,790)

Aiming at the saline soil in Binhai New Area, which is solid and sclerotic, and addressing the problem of poor quality and low efficiency of traditional rotary tillage, this research designed a high-speed rotary tiller that can realize the high-speed rotation of knife rollers to cut. The average operating speed is higher than that of the ordinary rotary tiller. We analyzed the rotary tiller operating conditions and rotary tiller knife cutting process and conducted a movement trajectory theoretical analysis to determine the rotary tiller’s high-speed operating speed relationship. The working process of a high-speed rotary tiller was simulated using EDEM software. The experimental indicators included the soil-crushing rate and surface smoothness after tilling. The experimental factors included the forward speed of the machine, the rotational speed of the blade roller, and the tilling depth. An orthogonal experiment was performed to establish regression equations for the soil-crushing rate and surface smoothness. Using Design-Expert analysis software, we obtained the following optimal combination of parameters: a knife roller speed at 310 r/min, tillage depth of 13.2 cm, and machine forward speed of 4.8 km/h. At this time, the simulation values of the soil fragmentation rate and surface flatness were 90.6% and 18.2 mm, respectively. When determining the optimal knife roller speed of 310 r/min, a transient structural simulation under the mesh bevel gear transient was conducted. The simulation analysis showed that the maximum equivalent stress value was 584.57 MPa, which was smaller than the permissible stress of 695.8 MPa, meeting the bevel gear meshing strength requirements. Under the optimal combination determined by a field comparison test, the results show that the values of the high-speed rotary tiller operation after the soil-breaking rate, tillage depth, the tillage depth stability coefficient, and vegetation cover were 89.3%, 14.2 cm, 92.8%, and 90.3%. The land surface flatness was 16.4 mm, which is superior to the ordinary rotary tiller operation effects, meeting the agronomic requirements for pre-sowing land preparation for peanuts in the saline land of Binhai New Area. Full article

During the last six decades, Lebanon’s landscapes have undergone significant regime shifts whose causes are under-investigated. Using land cover maps from 1962 and satellite imagery from 2014 and 2023 in five randomly selected villages across Lebanon’s major agroecological zones (AEZs), we identified salient trends in the urbanization-driven transformation of land use and land cover (LULC). Household socio-economic characteristics and environmental pressures were analyzed as independent variables influencing land use decisions. Logistic regression (LR) was employed to assess the significance of these variables in shaping farmers’ choices to transition toward “perennialization”—namely fruit tree monocropping or protected agriculture. The LR results indicate that education reduced the likelihood of “perennialization” by 45% (p < 0.001). Farm size positively influenced “perennialization” (p < 0.01), suggesting that land availability encourages this agricultural practice. In contrast, water availability negatively affects “perennialization” (p < 0.01), though farmers may still opt to irrigate by purchasing water during shortages. Our findings underline the complex interplay of socio-economic and environmental dynamics and historical events in shaping Lebanon’s rural landscapes and they offer insights into similar transformations across the Middle East and North Africa (MENA) region. Full article

Heavy metal soil contamination is a global environmental issue that poses serious threats to human health, agricultural advancement, and ecosystem systems. Thirty-five soil samples from various parts of Jazan, Southwest Saudi Arabia, were collected. To create spatial pattern maps for nine potentially toxic elements (PTEs) (As, Co, Cr, Cu, Fe, Ni, Pb, V, and Zn), Ordinary Kriging (OK) was utilized. The variability of the soil metal concentration was estimated using multivariate analysis, including principal component analysis (PCA) and cluster analysis. In addition, the levels of soil contamination in the research area were assessed using contaminations indices, namely, Enrichment Factor (EF), Contamination Factor (CF), and geoaccumulation index (Igeo), and modified contamination degree (mC_d). Normalized Difference Vegetation Index (NDVI) and land use/land cover (LULC) were assessed to evaluate vegetation density and identify different forms of land cover and land use. The results showed that the Gaussian model fitted As well, whereas the spherical model fitted Co, Cr, Cu, Ni, and Zn. An exponential model was fitted to Fe and V. Pb also suited the Stable model. In each of the selected metals, the root mean square standardized error (RMSSE) values were close to one, and the mean standardized error (MSE) values were almost zero for each fitted model. Moreover, the findings showed that there was a tendency for the concentration of heavy metals in the research area to rise from west to east. The cluster analysis divided the data in this investigation into two clusters. Significant alterations in Co, Cr, Cu, Fe, Ni, V, and Zn were revealed by the acquired data. However, the total As and Pb concentrations in the two clusters did not differ significantly. The mC_d value of the research region often fell into one of three classes, with areas of 148.20 km² (nil to very low degree of contamination), 26.16 km² (low degree of contamination), and 0.495 km² (moderate degree of contamination). The findings indicated that the minerals connected to the Arabian Shield’s basement rocks are the main source of these PTEs. It is crucial to monitor PTEs contamination because the research region is highly cultivated, as shown by the NDVI and LULC status. Given the potential for future pollution due to human activity, PTEsPTEs decision-makers may use the findings of the spatial distribution maps of pollutants and their concentrations as a basis for future monitoring of PTEs concentrations in the study area. Full article

The mountainous Samian Watershed hosts important rivers recently, significantly triggered by fast and unplanned urbanization, population growth, environmentally hazardous industrialization, and inappropriate dam construction. Nonetheless, this watershed has not yet been evaluated through the lens of river restoration. Therefore, this study aims (1) to apply the River Restoration Index (RRI), (2) to assess the significance of each river restoration criterion and sub-index, and (3) to identify priority hotspots for immediate restoration efforts across 27 sub-watersheds in this case study. First, we built a database containing meteorological, hydrological, land use, physiographic, soil, and economic data. Then, we calculated the general state of the watershed (GSW), connectivity (Con), riverbank conditions (RbC), and hydraulic risk reduction (HRR) sub-indices to develop a multi-domain RRI. Finally, the MEREC-ORESTE hybrid method supported sustainable government planning. The findings reveal significant environmental issues, notably in sanitation conditions, transversal connectivity, and urban encroachment on riverbanks. Sanitation risks were high throughout the watershed, while other eco-environmental risks varied across regions. The weights of 0.36, 0.16, 0.32, and 0.16 were assigned for GSW, Con, RbC, and HRR, respectively, highlighting the importance of GSW and RbC in river restoration activities. Priority management areas (with RRI below 0.50) cover 78% of the watershed. Full article

Freshwater scarcity remains a pressing global issue, exacerbated by inefficiencies in stormwater management during rainy seasons. Strategic stormwater harvesting offers a sustainable solution through runoff utilization for irrigation and livestock support. However, challenges such as limited farmer knowledge, difficult terrain, financial constraints, unpredictable weather, and scarce meteorological data hinder the accuracy of optimum stormwater harvesting sites. This study employs a GIS-based SCS-CN hydrological approach to address these issues, identifying suitable stormwater harvesting locations, estimating runoff volumes, and recommending site-specific storage structures. Using spatial datasets of daily rainfall (20 years), land use/land cover (LULC), digital elevation models (DEM), and soil data, the study evaluated 80 watersheds in Uganda’s cattle corridor. Annual runoff estimates within watersheds ranged from 62 million to 557 million m³, with 56 watersheds (70%) identified for multiple interventions such as farm ponds, check dams, and gully plugs. These structures are designed to enhance stormwater harvesting and utilization, improving water availability for livestock and crop production in a region characterized by water scarcity and erratic rainfall. The findings provide practical solutions for sustainable water management in drought-prone areas with limited meteorological data. This approach can be scaled to similar regions to enhance resilience in water-scarce landscapes. By offering actionable insights, this research supports farmers and water authorities in effectively allocating stormwater resources and implementing tailored harvesting strategies to bolster agriculture and livestock production in Uganda’s cattle corridor. Full article

This study focuses on assessing flash flood risks in Northeastern Thailand, particularly within the Lam Saphung, Phrom, and Chern River Basins, which are highly susceptible to flash floods and debris flows. Using the HEC-RAS hydraulic model integrated with GIS tools, the research analyzes historical and scenario-based flood events to evaluate the impact of land use changes and hydrological dynamics. The model was calibrated and validated with statistical metrics such as R² values ranging from 0.745 to 0.994 and NSE values between 0.653 and 0.893, indicating strong agreement with the observed data. This study also identified high-risk areas, with up to 5.49% and 5.50% increases in flood-prone areas in the Phrom and Chern River Basins, respectively, from 2006 to 2019. Key findings highlight the critical role of proactive risk management and targeted mitigation strategies in enhancing community resilience. The integration of advanced hydraulic modeling with detailed datasets enables precise flood hazard mapping, including flood depths exceeding 1.5 m in certain areas and high-risk zones covering up to 105.2 km² during severe flood events. These results provide actionable insights for emergency response and land use planning. This research significantly contributes to hydrological risk assessments by advancing modeling techniques and delivering practical recommendations for sustainable flood management. The outcomes are particularly relevant for stakeholders, including urban planners, emergency management officials, and policymakers, who aim to strengthen resilience in vulnerable regions. By addressing the complexities of flash flood risk assessments with robust quantitative evidence, this study not only enhances the understanding of flood dynamics, but also lays the groundwork for developing adaptive strategies to mitigate the adverse impacts of flash floods, safeguarding both communities and infrastructure in the region. Full article

Reducing food loss can improve environmental sustainability, resource use, and food security. Agricultural machinery services have considerable advantages in enhancing the adaptability and competitiveness of farms, but little is known about its potential for addressing food loss. Here, this work attempts to reveal a strong yet under-discussed connection between agricultural machinery services and food loss. Using survey data covering 483 corn farmers in the Heilongjiang, Jilin, and Liaoning provinces of China from October to December 2024, this study examined the extent to which participation in agricultural machinery services reduced food loss. Our results confirmed the existence of this significant causal effect and estimated 0.864% and 0.862% reductions in weight and value losses in response to a 1% increase in the purchase of agricultural machinery services. The possible mechanisms driving this relationship, including factor allocation optimization and technology introduction, were further verified. A variety of robustness tests were conducted to validate the strength and reliability of the empirical results and address endogeneity issues. Also, to better contextualize the heterogeneous effects of agricultural machinery services on food loss, the differences across production links, land fragmentation, and service quality were explored. By highlighting the important roles of agricultural machinery services in reducing food loss, our analysis contributed to contemporary debates about the long-term linkage between the wide popularization of agricultural machinery services and achieving food security, particularly providing insights for developing countries. Full article

Investigating the historical patterns of urban growth and their drivers is crucial to informing sustainable urban planning policies, especially in cities of the Global South. In Vietnam, most studies focus primarily on city extents, offering little insight into urban growth across various provinces. This study, therefore, combined categorical land use and land cover change detection, Random Forest classification and expert interviews to quantify the urban growth between 2000 and 2020, assess urban encroachment upon other land uses, and identify key drivers shaping this growth in Thua Thien Hue province. Findings show that the urban land areas were 27.94 km², 82.97 km², and 209.80 km² in 2000, 2010, and 2020, respectively. Urban encroachment upon other land use types, especially cropland, barren land, rice paddies, shrubs, and forests, was observed in these periods. Additionally, accessibility to built-up areas, DEM, proximity to rice paddies, slope, proximity to street roads, accessibility to social areas, and proximity to cropland are the major spatial drivers of urban growth in the province. The study concludes that rapid urban expansion is evident in the province at the expense of other land use types, especially agricultural land use types, which may impact food security and livelihoods in the province. Full article

Groundwater in northwest Tunisia plays a vital role in supporting the domestic, agriculture, industry, and tourism sectors. However, climate change and over-exploitation have led to significant degradation in groundwater quality and quantity. Traditional spatial analysis techniques such as Geographic Information Systems (GIS) and Remote Sensing (RS) are frequently used for assessing groundwater potential and water quality. Yet, these methods are limited by data availability. The integration of Geospatial Artificial Intelligence (Geo-AI) offers improved precision in groundwater potential zone (GWPZ) delineation. This study compares the effectiveness of the Analytical Hierarchy Process (AHP) and advanced Geo-AI techniques using deep learning to map GWPZ in the Majerda transboundary basin, shared between Tunisia and Algeria. By incorporating thematic layers such as rainfall, slope, drainage density, land use/land cover (LU/LC), lithology, and soil, a comprehensive analysis was conducted to assess groundwater recharge potential. The results revealed that both methods effectively delineated GWPZ; however, the Geo-AI approach demonstrated superior accuracy with a classification accuracy rate of approximately 92%, compared to 85% for the AHP method. This indicates that Geo-AI not only enhances the quality of groundwater potential assessments but also offers a reliable alternative to traditional methods. The findings underscore the importance of adopting innovative technologies in groundwater exploration efforts in this critical region, ultimately contributing to more effective and sustainable water resource management strategies. Full article

Variations in vegetation indices derived from multispectral images and digital terrain models from satellite imagery have been successfully used for reclamation and hazard management in former mining areas. However, low spatial resolution and the lack of sufficiently detailed information on surface morphology have restricted such studies to large sites. This study investigates the application of small, unmanned aerial vehicles (UAVs) equipped with multispectral sensors for land cover classification and vegetation monitoring. The application of UAVs bridges the gap between large-scale satellite remote sensing techniques and terrestrial surveys. Photogrammetric terrain models and orthoimages (RGB and multispectral) obtained from repeated mapping flights between November 2023 and May 2024 were combined with an ALS-based reference terrain model for object-based image classification. The collected data enabled differentiation between natural forests and areas affected by former mining activities, as well as the identification of variations in vegetation density and growth rates on former mining areas. The results confirm that small UAVs provide a versatile and efficient platform for classifying and monitoring mining areas and forested landslides. Full article

The Urban Heat Island (UHI) phenomenon, combined with reduced vegetation and heat generated by human activities, presents a major environmental challenge for many European urban areas. The UHI effect is especially concerning in hot and temperate climates, like the Mediterranean region, during the summer months as it intensifies the discomfort and raises the risk of heat-related health issues. As a result, assessing urban heat dynamics and steering sustainable land management practices is becoming increasingly crucial. Analyzing the relationship between land cover and Land Surface Temperature (LST) can significantly contribute to achieving this objective. This study evaluates the spatial correlations between various land cover types and LST trends in Thessaloniki, Greece, using data from the Coordination of Information on the Environment (CORINE) program and advanced vegetation index techniques within Google Earth Engine (GEE). Our analysis revealed that there has been a gradual increase in average surface temperature over the past five years, with a more pronounced increase observed in the last two years (2022 and 2023) with mean annual LST values reaching 26.07 °C and 27.09 °C, respectively. By employing indices such as the Normalized Difference Vegetation Index (NDVI) and performing correlation analysis, we further analyzed the influence of diverse urban landscapes on LST distribution across different land use categories over the study area, contributing to a deeper understanding of UHI effects. Full article

We measured the spatiotemporal dynamics of habitat quality (HQ) in Sicily in two different reference years, 2018 and 2050, assuming a business-as-usual scenario. To estimate HQ and related vulnerability, we used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) Habitat Quality model and data on land use/land cover provided by the Esri Land Cover 2050 project. We also implemented a Coarse–Filter approach to validate the reliability of HQ measures and detect biodiversity hotspots that require priority conservation. Further, we used spatial statistic tools for identifying clusters or hotspot/coldspot areas and uncovering spatial autocorrelation in HQ values. Finally, we implemented a geographically weighted regression (GWR) model for explaining local variations in the effects on HQ estimates. The findings reveal that HQ in Sicily varies across space and time. The highest HQ values occur in protected areas and forests. In 2018, the average HQ value was higher than it was in 2050. On average, HQ decreased from 0.29 in 2018 to 0.25 in 2050. This slight decline was mainly due to an increase in crop and urbanized areas at the expense of forests, grasslands, and bare lands. We found the existence of a positive spatial autocorrelation in HQ, demonstrating that areas with higher or lower HQ tend to be clustered, and that clusters come into contact randomly more often in 2050 than in 2018, as the overall spatial autocorrelation moved from 0.28 in 2018 to 1.30 in 2050. The estimated GWR model revealed the sign and the significance effect of population density, compass exposure, average temperature, and patch richness on HQ at a local level, and that such effects vary either in space and time or in significance level. Across all variables, the spatial extent of significant effects intensifies, signaling stronger localized influences in 2050. The overall findings of the study provide useful insights for making informed decisions about conservation and land planning and management in Sicily. Full article

This paper aims to solve the problem of erosion sediment that negatively affects the quality of fallowed soil through the development of a new type of agricultural machinery. The transported erosion sediment will be quantified locally to evaluate the danger of these negative effects on the fallowed soil and on the functionality of the grass cover. Subsequently, a new type of machinery will be proposed for the remediation of eroded sediment and conservation of the fallowed soil. In various fallow research areas with different management methods (such as biobelts, grassed valleys, and grassed waterways), agricultural land affected by eroded sediment was examined, and appropriate machinery was designed to rehabilitate the stands after erosion events. By identifying the physical and mechanical properties of the soil, as well as the eroded and deposited sediment/colluvium, the shape, material, attachment method, and assembly of the working tool for the relevant mobile energy device were designed. The developed tool, based on a plow–carry system using a tractor, features flexible tools that separate the eroded sediment from the fallow land surface, transfer it over a short distance, and accumulate it in a designated area to facilitate subsequent removal with minimal damage to the herbaceous vegetation. The calculated erosion event was 196.9 m³ (179.0 m³ ha⁻¹), corresponding to 295 tons (268.5 t ha⁻¹) deposited from the area of 90 ha. Afterward, the proposed machinery was evaluated for the cost of the removal of the eroded sediment. Based on experience from the field, we calculated that 174 m³ per engine hour results in EUR 0.22 m⁻³. From the performed experiment, it is evident that the proposed machinery offers a suitable solution for eroded sediment removal locally, which prevents further erosion and subsequent sediment deposition in water bodies where the costs for sediment removal are higher. Moreover, we have proven the potential negative impact of invasive plant species because their seeds were stored in the sediment. Finally, it is credible to state that the proposed agricultural machinery offers an effective solution for the eroded sediment relocation, which subsequently can be used for other purposes and monetized. This results in an increase in the profitability of the erosion sediment removal process, which is already in place at the source before further transportation to aquatic systems where the costs for removal are significantly higher. Full article

Remote sensing (RS) spectral time series provide a substantial source of information for the regular and cost-efficient monitoring of the Earth’s surface. Important monitoring tasks include land use and land cover classification, change detection, forest monitoring and crop type identification, among others. To develop accurate solutions for RS-based applications, often supervised shallow/deep learning algorithms are used. However, such approaches usually require fixed-length inputs and large labeled datasets. Unfortunately, RS images acquired by optical sensors are frequently degraded by aerosol contamination, clouds and cloud shadows, resulting in missing observations and irregular observation patterns. To address these issues, efforts have been made to implement frameworks that generate meaningful representations from the irregularly sampled data streams and alleviate the deficiencies of the data sources and supervised algorithms. Here, we propose a conceptually and computationally simple representation learning (RL) approach based on autoencoders (AEs) to generate discriminative features for crop type classification. The proposed methodology includes a set of single-layer AEs with a very limited number of neurons, each one trained with the mono-temporal spectral features of a small set of samples belonging to a class, resulting in a model capable of processing very large areas in a short computational time. Importantly, the developed approach remains flexible with respect to the availability of clear temporal observations. The signal derived from the ensemble of AEs is the reconstruction difference vector between input samples and their corresponding estimations, which are averaged over all cloud-/shadow-free temporal observations of a pixel location. This averaged reconstruction difference vector is the base for the representations and the subsequent classification. Experimental results show that the proposed extremely light-weight architecture indeed generates separable features for competitive performances in crop type classification, as distance metrics scores achieved with the derived representations significantly outperform those obtained with the initial data. Conventional classification models were trained and tested with representations generated from a widely used Sentinel-2 multi-spectral multi-temporal dataset, BreizhCrops. Our method achieved $77.06\%$ overall accuracy, which is $\sim 6\%$ higher than that achieved using original Sentinel-2 data within conventional classifiers and even $\sim 4\%$ better than complex deep models such as OmnisCNN. Compared to extremely complex and time-consuming models such as Transformer and long short-term memory (LSTM), only a $3\%$ reduction in overall accuracy was noted. Our method uses only $6.8k$ parameters, i.e., $\sim 400x$ fewer than OmnicsCNN and $\sim 27x$ fewer than Transformer. The results prove that our method is competitive in terms of classification performance compared with state-of-the-art methods while substantially reducing the computational load. Full article

The urban heat island effect (UHI) has become a major challenge for sustainable urban development. In recent decades, the significant development of urban agglomerations has intensified the complex interaction and comprehensive impact of the UHI effect, but the spatiotemporal pattern of regional heat islands has been poorly understood. Based on the land surface temperature (LST) from 2001 to 2020, this study uses the relative land surface temperature (RLST) method to quantify the regional heat island (RHI) of the Central Yunnan Urban Agglomeration (CYUA) beyond a single city, combines a variety of spatial analysis tools to identify the multi-scale spatiotemporal pattern, and explores the multidimensional driving factors of RHIs. The combined effects of indicators such as urbanization intensity, blue–green space intensity (2D), and building height characteristics (3D) on the mitigation or exacerbation of RHIs are included. The results are as follows: (1) The RHI was significantly enhanced, especially during 2011–2014, when the heat island intensity and influence range expanded rapidly, especially in the core areas such as Kunming and Qujing. (2) The main urban areas of prefecture-level cities have a greater contribution to the RHI, and the intercity heat interaction further intensifies the heat island effect on county-level regions. (3) Different land cover types have different effects on RHI. The human and social factors have a positive effect on the RHI, the blue–green intensity has a strong inhibitory effect, and the cooling effect of blue space is better than that of green space. Topographic and meteorological factors have little influence. To effectively address the challenge of UHI, the CYUA must strengthen the construction of green infrastructure, optimize urban planning, promote energy conservation and emission reduction, and improve climate adaptation planning. This paper discusses the spatiotemporal variation in the heat island effect and the influencing factors from a new regional perspective, which enriches the research content of urban agglomeration thermal environment and improves the research system of the heat island effect. Full article