Search Results (97)

Remote sensing technology provides an efficient and low-cost approach for acquiring large-scale soil information, offering notable advantages for soil organic matter (SOM) mapping. However, in recent years, the bare soil period of cultivated land in Northeast China has significantly shortened, posing serious challenges to traditional SOM prediction and mapping methods that rely on optical imagery. Meanwhile, current approaches that integrate optical imagery, radar imagery, and environmental covariates have yet to fully exploit the potential of remote sensing data in SOM mapping. To address this, this study focuses on the typical black soil region in Northeastern China, acquiring median synthetic images from different time periods (crop sowing, growing, and harvest stages) along with vegetation and radar indices. Six data groups were created by integrating environmental covariate data. Four machine learning models—XGBoost, BRT, ET, and RF—were used to analyze the SOM prediction accuracy of different groups. The group and model with the highest prediction accuracy were selected for SOM mapping in cultivated land. The results show that: (1) in the same model, incorporating radar images and their related indices significantly improves SOM prediction accuracy; (2) when using four machine learning models for SOM prediction, the RF model, which integrates optical images, radar images, vegetation indices, and radar indices from the crop sowing and growing periods, achieves the highest accuracy (R² = 0.530, RMSE = 6.130, MAE = 4.822); (3) in the optimal SOM prediction model, temperature, precipitation, and elevation are relatively more important, with radar indices showing greater importance than vegetation indices; (4) uncertainty analysis and accuracy verification at the raster scale confirm that the SOM mapping results obtained in this study are highly reliable. This study made significant progress in SOM prediction and mapping by employing a radar–optical image fusion strategy combined with crop growth information. It helped address existing research gaps and provided new approaches and technical solutions for remote sensing-based SOM monitoring in regions with short bare soil periods. Full article

Batteries are used as energy storage devices in various equipment. Today, research is focused on solid-state batteries (SSBs), replacing the liquid electrolyte with a solid separator. The solid separators provide electrolyte stability, no leakage, and provide mechanical strength to the battery. Separators are mostly manufactured by either traditional processes or 3D printing technologies. These processes involve making a slurry of plastic, active and conductive material and usually adding a plasticizer when making thin films or filaments for 3D printing. This study investigates the additive manufacturing of solid-state electrolytes (SSEs) by employing fused deposition modeling (FDM) with recyclable, bio-derived polylactic acid (PLA) filaments. Precise control of macro-porosity is achieved by systematically varying key process parameters, including raster orientation, infill percentage, and interlayer adhesion conditions, thereby enabling the formation of tunable, interconnected pore networks within the polymer matrix. Following 3D printing, these engineered porous frameworks are infiltrated with lithium hexafluorophosphate (LiPF₆), which functions as the active ionic conductor. A tailored thermal sintering protocol is then applied to promote solid-phase fusion of the embedded salt throughout the macro-porous PLA scaffold, resulting in a mechanically robust and ionically conductive composite separator. The electrochemical ionic conductivity and structural integrity of the sintered SSEs are characterized through electrochemical impedance spectroscopy (EIS) and standardized mechanical testing to assess their suitability for integration into advanced solid-state battery architectures. The solid-state separator achieved an average ionic conductivity of 2.529 × 10⁻⁵ S·cm⁻¹. The integrated FDM-sintering process enhances ion exchange at the electrode–electrolyte interface, minimizes material waste, and supports cost-efficient, fully recyclable component fabrication. Full article

The environment is being affected by the great development of human activities, which is why, in recent years, the need to protect the environment has increased, through the carrying out of a Strategic Environmental Assessment (SEA). Within this assessment, environmental geology constitutes an instrument for territorial and urban planning based on the analysis of conservation and the integral analysis of risks, obtaining cartography that can be useful in territorial and regional planning strategies. The methodology carried out in this article consists of applying a multi-criteria analysis in territorial planning, combining vector and raster data. This novel, low-cost, and effective methodology assesses conservation areas and risks, using map algebra and network analysis to identify priority areas and facilitate decision-making in a precise and quantitative manner. This analysis has been carried out in the Arribes del Duero Natural Park, which stands out as a place where numerous environmental values coexist, i.e., geological, geomorphological, and edaphological, forming unique landscapes. With regard to the results obtained, the cartography of conservation quality classifies the territory into four categories according to its degree of conservation: very high, high, low, and very low quality. The integral risk cartography identifies the areas with the greatest geological risks, such as erosion and landslides, and establishes limitations for land use. Also, by integrating both cartographies, it is determined which activities are compatible with each zone, considering both conservation and risks. Finally, it can be concluded that the cartographies obtained are useful for efficient land management, protecting the environment, and allowing human development in a controlled manner. Full article

Wetlands provide the world with important ecosystem services (ES) including carbon (C) storage. The Ramsar Convention (RC) is the only global treaty on wetlands outside of the United Nations (UN) with 172 contracting parties across the world as of 2025. The goals of the convention are to promote the wise use and conservation of wetlands, designation of suitable wetlands as wetlands of international importance, and international cooperation. The problem is that there is no consensus for standard global analysis, which is needed to ensure wetlands conservation. The novelty of this study is the use of methodology that combines satellite-based land cover change analysis with high-resolution spatial databases to help understand the change in wetlands area over time and identify potential hotspots for C loss. Greenhouse gas (GHG) emissions from wetland conversions represent “transboundary” damages. Therefore, C loss from wetlands conversions can be expressed through the “realized” social cost of C (SC-CO₂) which is a conservative estimate of the damages caused by carbon dioxide (CO₂) release. A case study of the contiguous United States of America (USA) using raster analysis within ArcGIS Pro showed key findings that almost 53% of the wetlands area was lost between 1780 and 1980, starting with 894,880.7 km² in 1780 and falling to 422,388.2 km² in 1980. This net loss generated damages including midpoint total soil C loss (6.7 × 10¹³ kg of C) with associated midpoint “realized” social costs of C (SC-CO₂) value of $11.4T (where T = trillion = 10¹², $ = United States dollars, USD). Recent analysis of the contiguous USA (2001–2021) revealed wetlands area losses and damages in all states. The newly demonstrated method for rapid monitoring of wetlands changes over time can be integrated into systems for worldwide monitoring to support the RC wise use concept. Full article

Evacuation routing in wildland areas is an important aspect during various emergencies, including fire incidents. A review of the literature found a lack of research on vector routing systems for evacuations from wildland areas. This article aims to address the issue of determining evacuation routes using vector object database technology with various optimization methods. To this end, the author developed a novel algorithm for network creation and optimization through heuristic data aggregation. Case studies were conducted in a wooded area of the Bieszczady Mountains, where the potential of determining evacuation routes in the proprietary geodatabase (SQLite SpatiaLite) was examined, and the results were compared with traditional methods based on raster least-cost path analyses. The analyses confirmed the feasibility of creating a network of connections in the database within an area of 3.74 km² with undefined roads. Through the implementation of optimizations, the determination of evacuation routes in wildland areas was reduced to less than 1 s. Additionally, the possibility of the system operating for areas covering 40 km² was presented. The use of optimized vector data and database technology enabled the development of a comprehensive forest area management system, encompassing points of rescue units situated at significant distances from the area. This facilitated the establishment of flexible evacuation routes or rescue missions, particularly allowing for the establishment of multimodal routes using different means of transportation to reach the destination. Full article

This study explores the experimental and theoretical optimization of process parameters to improve the quality of 3D-printed parts produced using the Fused Deposition Modeling technique. To ensure the cost-effective production of high-quality components, advancements in printing strategies are essential. This research identifies optimal 3D printing strategies to enhance the quality of finished products. Form and dimensional tolerances were assessed using a 3D Coordinate Measuring Machine, and the resulting data were analyzed via Design Expert software version 9.0.6.2. Design Expert for experimental design was utilized and an Analysis of Variance was conducted to validate the models’ accuracy. The results indicate that a 45° raster angle, combined with internal raster values between 0.5048 and 0.726, minimizes flatness, cylindricity, and dimensional deviations by optimizing deposition patterns and thermal dynamics. Internal raster values below 0.308 resulted in insufficient support and greater deviations, while higher values enhanced stability through improved interlayer adhesion. Experimental validation confirmed these parameter settings as optimal for producing precise and consistent 3D-printed parts. Full article

Planning for a green–blue infrastructure system around big cities, having the shape of a belt, to connect natural areas—such as green spaces, water, and agricultural land—is a solution for mitigating the challenges of climate change and urban sprawl. In this context, this study presents an innovative information technology solution for assessing the connectivity of the green and blue areas in the metropolitan area of Bucharest, Romania. The solution is to try to stop the sprawl of Bucharest into the adjacent rural areas and answer the need for a green infrastructure providing ecosystem services. The methodology uses datasets compatible with the European databases on environmental issues, CORINE Land Cover 2018 and Urban Atlas, and two tools in the ArcGIS PRO 2.9 software package, namely Cost Raster and Cost Connectivity. Based on the results, we developed a framework for implementing a strategy for the green–blue infrastructure for the Bucharest metropolitan area. Our methodology is a starter for planning a green–blue belt for the metropolitan area of Bucharest and a model of good practice in terms of making green–blue infrastructure part of urban and territorial planning. Full article

Extreme spread events (ESEs), often characterized by high intensity and rapid rates of spread, can overwhelm fire suppression and emergency response capacity, threaten responder and public safety, damage landscapes and communities, and result in high socioeconomic costs and losses. Advances in remote sensing and geospatial analysis provide an improved understanding of observed ESEs and their contributing factors; however, there is a need to improve anticipatory and predictive capabilities to better prepare, mitigate, and respond. Here, leveraging individual-fire day-of-arrival raster outputs from the FSim fire modeling system, we prototype and evaluate methods for the simulation and categorization of ESEs. We describe the analysis of simulation outputs on a case study landscape in Colorado, USA, summarize daily spread event characteristics, threshold and probabilistically benchmark ESEs, spatially depict ESE potential, and describe limitations, extensions, and potential applications of this work. Simulation results generally showed strong alignment with historical patterns of daily growth and the proportion of cumulative area burned in the western US and identified hotspots of high ESE potential. Continued analysis and simulation of ESEs will likely expand the horizon of uses and grow in salience as ESEs become more common. Full article

The socioeconomic sector increasingly relies on accessible and cost-effective tools for predicting climatic conditions. This study employs a straightforward decision tree classifier model to identify similar monthly ENSO (El Niño Southern Oscillation) conditions from December 2000 to November 2023, using historically monthly ENSO Indices data from December 1950 to November 2000 as a reference. The latter is to construct monthly precipitation hindcasts for Mexico spanning from December 2000 to November 2023 through historically high-resolution monthly precipitation rasters. The model’s performance is evaluated at a global and local scale across seasonal periods (winter, spring, summer, and fall). Assessment using global Hansen–Kuiper Skill Score and Heidkee Skill Score metrics indicates skillful performance across all seasons (>0.3) nationwide. However, local metrics reveal a higher spatial percent of corrects (>0.40) in winter and spring, corresponding to dry seasons, while a lower percent of corrects (<0.40) are observed in more extensive areas during summer and fall, indicative of rainy seasons, due to increased variability in precipitation. The choice of averaging method influences the degree of underestimations and overestimations, impacting the model’s variability. Spearman correlations highlight regions with significant model performance, revealing potential misinterpretations of high hit rates during winter and spring. Notably, during the fall, the model demonstrates spatial skill across most of Mexico, while in the spring, it performs well in the southern and northeastern regions and, in the summer, in the northwestern areas. Integration of accurate forecasts of ENSO Indices to predict precipitation months ahead is crucial for the operational efficacy of this model, given its heavy reliance on anticipating ENSO behavior. Overall, the empirical method exhibits great promise and potential for application in other developing countries directly impacted by the El Niño phenomenon, owing to its low resource costs. Full article

Raster data represent one of the fundamental data formats utilized in GIS. As the technology used to observe the Earth continues to evolve, the spatial and temporal resolution of raster data is becoming increasingly refined, while the data scale is expanding. One of the key issues in the development of GIS technology is to determine how to make large-scale raster data better to provide computation, visualization, and analysis services in the Internet environment. This paper proposes a decentralized COG-pyramid-based map service method (DCPMS). In comparison to traditional raster data online service technology, such as GIS servers and static tiles, DCPMS employs virtual mapping to reduce data storage costs and combines tile technology with a cloud-native storage scheme to enhance the concurrency of supportable requests. Furthermore, the band calculation process is shifted to the client, thereby effectively resolving the issue of efficient customized band calculation and data rendering in the context of a large-scale raster data online service. The results indicate DCPMS delivers commendable performance. Its decentralized architecture significantly enhances performance in high concurrency scenarios. With a thousand concurrent requests, the response time of DCPMS is reduced by 74% compared to the GIS server. Moreover, this service exhibits considerable strengths in data preprocessing and storage, suggesting a novel pathway for future technical improvement of large-scale raster data map services. Full article

Wildfires cause great losses to the ecological environment, economy, and people’s safety and belongings. As a result, it is crucial to establish wildfire susceptibility models and delineate fire risk levels. It has been proven that the use of remote sensing data, such as meteorological and topographical data, can effectively predict and evaluate wildfire susceptibility. Accordingly, this paper converts meteorological and topographical data into fire-influencing factor raster maps for wildfire susceptibility prediction. The continuous convolutional neural network (CCNN for short) based on coordinate attention (CA for short) can aggregate different location information into channels of the network so as to enhance the feature expression ability; moreover, for different patches with different resolutions, the improved CCNN model does not need to change the structural parameters of the network, which improves the flexibility of the network application in different forest areas. In order to reduce the annotation of training samples, we adopt an active learning method to learn positive features by selecting high-confidence samples, which contributes to enhancing the discriminative ability of the network. We use fire probabilities output from the model to evaluate fire risk levels and generate the fire susceptibility map. Taking Chongqing Municipality in China as an example, the experimental results show that the CA-based CCNN model has a better classification performance; the accuracy reaches 91.7%, and AUC reaches 0.9487, which is 5.1% and 2.09% higher than the optimal comparative method, respectively. Furthermore, if an accuracy of about 86% is desired, our method only requires 50% of labeled samples and thus saves about 20% and 40% of the labeling efforts compared to the other two methods, respectively. Ultimately, the proposed model achieves the balance of high prediction accuracy and low annotation cost and is more helpful in classifying fire high warning zones and fire-free zones. Full article

Improving urban mobility, especially pedestrian mobility, is a current challenge in virtually every city worldwide. To calculate the least-cost paths and safer, more efficient routes, it is necessary to understand the geometry of streets and their various elements accurately. In this study, we propose a semi-automatic methodology to assess the capacity of urban spaces to enable adequate pedestrian mobility. We employ various data sources, but primarily point clouds obtained through a mobile laser scanner (MLS), which provide a wealth of highly detailed information about the geometry of street elements. Our method allows us to characterize preferred pedestrian-traffic zones by segmenting crosswalks, delineating sidewalks, and identifying obstacles and impediments to walking in urban routes. Subsequently, we generate different displacement cost surfaces and identify the least-cost origin–destination paths. All these factors enable a detailed pedestrian mobility analysis, yielding results on a raster with a ground sampling distance (GSD) of 10 cm/pix. The method is validated through its application in a case study analyzing pedestrian mobility around an educational center in a purely urban area of A Coruña (Galicia, Spain). The segmentation model successfully identified all pedestrian crossings in the study area without false positives. Additionally, obstacle segmentation effectively identified urban elements and parked vehicles, providing crucial information to generate precise friction surfaces reflecting real environmental conditions. Furthermore, the generation of cumulative displacement cost surfaces allowed for identifying optimal routes for pedestrian movement, considering the presence of obstacles and the availability of traversable spaces. These surfaces provided a detailed representation of pedestrian mobility, highlighting significant variations in travel times, especially in areas with high obstacle density, where differences of up to 15% were observed. These results underscore the importance of considering obstacles’ existence and location when planning pedestrian routes, which can significantly influence travel times and route selection. We consider the capability to generate accurate cumulative cost surfaces to be a significant advantage, as it enables urban planners and local authorities to make informed decisions regarding the improvement of pedestrian infrastructure. Full article

Addressing within-field spatial variability for nitrogen (N) management to avoid over and under-use of nitrogen is crucial for optimizing crop productivity and ensuring environmental sustainability. In this study, we investigated the economic, environmental, and agronomic benefits of variable rate nitrogen application in potato (Solanum tuberosum L.). An online visible and near-infrared (vis-NIR) spectroscopy sensor was utilized to predict soil moisture content (MC), pH, total organic carbon (TOC), extractable phosphorus (P), potassium (K), magnesium (Mg), and cation exchange capacity (CEC) using a partial least squares regression (PLSR) models. The crop’s normalized difference vegetation index (NDVI) from Sentinel-2 satellite images was incorporated into online measured soil data to derive fertility management zones (MZs) maps after homogenous raster and clustering analyses. The MZs maps were categorized into high fertile (VR-H), medium–high fertile (VR-MH), medium–low fertile (VR-ML), and low fertile (VR-L) zones. A parallel strip experiment compared variable rate nitrogen (VR-N) with uniform rate (UR) treatments, adjusting nitrogen levels based on fertility zones as 50% less for VR-H, 25% less for VR-MH, 25% more for VR-ML, and 50% more for VR-L zones compared to the UR treatment. The results showed that the VR-H zone received a 50% reduction in N fertilizer input and demonstrated a significantly higher crop yield compared to the UR treatment. This implies a potential reduction in negative environmental impact by lowering fertilizer costs while maintaining robust crop yields. In total, the VR-N treatment received an additional 1.2 Kg/ha of nitrogen input, resulting in a crop yield increase of 1.89 tons/ha. The relative gross margin for the VR-N treatment compared to the UR treatment is 374.83 EUR/ha, indicating substantial profitability for the farmer. To further optimize environmental benefits and profitability, additional research is needed to explore site-specific applications of all farm resources through precision agricultural technologies. Full article

With the rapid development of the intelligent driving technology, achieving accurate path planning for unmanned vehicles has become increasingly crucial. However, path planning algorithms face challenges when dealing with complex and ever-changing road conditions. In this paper, aiming at improving the accuracy and robustness of the generated path, a global programming algorithm based on optimization is proposed, while maintaining the efficiency of the traditional A* algorithm. Firstly, turning penalty function and obstacle raster coefficient are integrated into the search cost function to increase the adaptability and directionality of the search path to the map. Secondly, an efficient search strategy is proposed to solve the problem that trajectories will pass through sparse obstacles while reducing spatial complexity. Thirdly, a redundant node elimination strategy based on discrete smoothing optimization effectively reduces the total length of control points and paths, and greatly reduces the difficulty of subsequent trajectory optimization. Finally, the simulation results, based on real map rasterization, highlight the advanced performance of the path planning and the comparison among the baselines and the proposed strategy showcases that the optimized A* algorithm significantly enhances the security and rationality of the planned path. Notably, it reduces the number of traversed nodes by 84%, the total turning angle by 39%, and shortens the overall path length to a certain extent. Full article

This study presents a comprehensive approach for single sensor placement optimization in two-dimensional and three-dimensional spaces. A traditional exhaustive search technique and a novel method called recursive exhaustive search are used to place a sensor in a way that maximizes the area coverage metric. Exhaustive search provides a baseline by methodically evaluating all potential placements, while recursive exhaustive search innovates by segmenting the search process into more manageable, recursive steps. Our findings highlight the significant impact of two key parameters, the number of evaluations and the rasterization value, on the achieved coverage and computation time. The results show that the right choice of parameters can significantly reduce the computational effort without compromising the quality of the solution. This underlines the critical need for a balanced approach that considers both computational complexity and placement efficacy. We show that exhaustive search is not feasible for three-dimensional environment models and propose to establish a modified exhaustive search method as a ground truth for the single sensor placement problem. We then explore nature-inspired genetic algorithms and the impact of the number of evaluations of the optimization function for these algorithms on both accuracy and computational cost. Full article