Search Results (2,338)

Soil Moisture (SM) plays a crucial role in agricultural production, ecology, and sustainable development. The prevailing resolution of microwave-based SM products is notably coarse, typically spanning from 10 to 50 km, which might prove inadequate for specific applications. In this research, various single-model machine learning algorithms have been employed to study SM downscaling, each with its own limitations. In contrast to existing methodologies, our research introduces a pioneering algorithm that amalgamates diverse individual models into an integrated Stacking framework for the purpose of downscaling SM data within the Shandian River Basin. This basin spans the southern region of Inner Mongolia and the northern area of Hebei province. In this paper, factors exerting a profound influence on SM were comprehensively integrated. Ultimately, the surface variables involved in the downscaling process were determined to be Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Surface Reflectance (SR), Evapotranspiration (ET), Digital Elevation Model (DEM), slope, aspect, and European Space Agency-Climate Change Initiative (ESA-CCI) product. The goal is to generate a 1 km SM downscaling dataset for a 16-day period. Two distinct models are constructed for the SM downscaling process. In one case, the downscaling is followed by the inversion of SM, while in the other case, the inversion is performed after the downscaling analysis. We also employ the Categorical Features Gradient Boosting (CatBoost) algorithm, a single model, for analytical evaluation in identical circumstances. According to the results, the accuracy of the 1 km SM obtained using the inversion-followed-by-downscaling model is higher. Furthermore, it is observed that the stacking algorithm, which integrates multiple models, outperforms the single-model CatBoost algorithm in terms of accuracy. This suggests that the stacking algorithm can overcome the limitations of a single model and improve prediction accuracy. We compared the predicted SM and ESA-CCI SM; it is evident that the predicted results exhibit a strong correlation with ESA-CCI SM, with a maximum Pearson correlation coefficient (PCC) value of 0.979 and a minimum value of 0.629. The Mean Absolute Error (MAE) values range from 0.002 to 0.005 m³/m³, and the Root Mean Square Error (RMSE) ranges from 0.003 to 0.006 m³/m³. Overall, the results demonstrate that the stacking algorithm based on multi-model integration provides more accurate and consistent retrieval and downscaling of SM. Full article

Fire emergencies pose significant risks to occupants in high-rise buildings, particularly individuals with functional limitations who struggle with conventional evacuation facilities like stairs. The objective of the study was to survey current literature to identify safe fire evacuation solutions for functionally limited groups. A systematic analysis of 156 journal articles (2000–2024) was conducted to identify factors affecting the evacuation, and their impact on evacuation efficiency. The findings were categorized into four main types: (1) human behavior during fire emergencies, (2) architectural and environmental factors, (3) fire and smoke risk calculation and control, and (4) evacuation models and tools. Additionally, our findings highlight the limitations of current research for individuals with disabilities, including evacuation methods, building design, fire risk calculation and control, evacuation models, and elevator operation strategies. The study concludes with recommendations for future research to address the identified gaps. This study underscores the need for further research on expanding solutions for different emergencies (e.g., earthquakes), addressing special building environments (e.g., hospitals), and leveraging digital technologies to improve evacuation processes for vulnerable populations. Future efforts will focus on incorporating rescuers and rescue methodologies into the evacuation framework to further enhance the safety and protection of vulnerable populations. Full article

This paper presents an assessment of potential losses and damage costs to rare coastal plant species of Lake Baikal (UNESCO World Heritage Site) as a result of inundation at high water levels. The lake’s ecosystem is characterized by an exceptional diversity of rare and endemic animal and plant species. The construction of a hydroelectric power plant caused an increase in the water level of Lake Baikal, resulting in the inundation of low-lying coastal areas, the destruction of the coastline, alterations to the hydrological regime, etc. However, there are practically no works devoted to water-level modeling and the assessment of its impact on riparian vegetation, including rare species. We conducted fieldwork to determine the abundance of four vulnerable species and identified inundation zones at different high water levels on the basis of digital elevation models based on aerial photography data. The analysis revealed that at the maximum level of inundation, the number of plant species affected would total 5164, amounting to a financial loss of biodiversity estimated at 3098.4 thousand rubles. To mitigate the projected losses, it is imperative to implement measures that restrict water-level fluctuations above the 457.00 m threshold. The absence of flora as an object of state environmental monitoring, which is not specified in the regulatory legal document, must be rectified in a timely manner. Full article

The dynamic loads of fluid impact and static loads, such as the gravity of a rock mass during the formation of debris flows, exhibit a coupled effect of mutual influence. Under this coupling effect, surface monitoring points in disaster areas experience displacement. However, existing methods do not consider the dynamic–static coupling effects of debris flows on the surface. Instead, they rely on GNSS or InSAR technology for dynamic or static single-scale monitoring, leading to high Mean Absolute Percentage Error (MAPE) values and low warning accuracy. To address these limitations and improve debris flow warning accuracy, a multi-scale warning method was proposed based on Global Navigation Satellite System (GNSS) and Synthetic Aperture Radar Interferometry (InSAR) technology. GNSS technology was utilized to correct coordinate errors at monitoring points, thereby enhancing the accuracy of monitoring data. Surface deformation images were generated using InSAR and Small Baseline Subset (SBAS) technology, with time series calculations applied to obtain multi-scale deformation data of the surface in debris flow disaster areas. A debris flow disaster morphology classification model was developed using a support vector mechanism. The actual types of debris flow disasters were employed as training labels. Digital Elevation Model (DEM) files were utilized to extract datasets, including plane curvature, profile curvature, slope, and elevation of the monitoring area, which were then input into the training model for classification training. The model outputted the classification results of the hidden danger areas of debris flow disasters. Finally, the dynamic and static coupling variables of surface deformation were decomposed into valley-type internal factors (rock mass static load) and slope-type triggering factors (fluid impact dynamic load) using the moving average method. Time series prediction models for the variable of the dynamic–static coupling effects on surface deformation were constructed using polynomial regression and particle swarm optimization (PSO)–support vector regression (SVR) algorithms, achieving multi-scale early warning of debris flows. The experimental results showed that the error between the predicted surface deformation results using this method and the actual values is less than 5 mm. The predicted MAPE value reached 6.622%, the RMSE value reached 8.462 mm, the overall warning accuracy reached 85.9%, and the warning time was under 30 ms, indicating that the proposed method delivered high warning accuracy and real-time warning. Full article

The semi-automated extraction of landforms using GIS analysis is one of the main topics in computer analyses. The use of digital elevation models (DEMs) in GIS applications makes the extraction and classification procedure of landforms easier and faster. In the present paper, we assess the accuracy of semi-automated landform maps by means of a comparison with hand-made landform maps realized in the Pleistocene Agri intermontane basin (southern Italy). In this study, landform maps at three different scales of 1:50,000, 1:25,000, and 1:10,000 were used to ensure a good level of detail in the spatial distribution of landforms. The semi-automated extraction and classification of landforms was performed using a GIS-related toolbox, which identified ~48 different landform types. Conversely, the hand-made landform map identified ~57 landforms pertaining to various morphogenetic groups, such as structural, fluvial, karst landforms, etc. An overlap of the two landform maps was produced using GIS applications, and a 3D block diagram visualization was realized. A visual inspection of the overlapping maps was conducted using different spatial scales of patch frames and then analyzed to provide information on the accuracy of landform extraction using the implemented tools. Full article

The swift advancement of digital technologies under Industry 4.0 has significantly transformed business operations and supply chain management. These advancements hold the potential to improve efficiency, reduce waste, and foster sustainable development; however, they also create challenges due to the uneven adoption of digital technologies across enterprises. (1) Background: The adoption of digital technologies across supply chains is uneven, resulting in a digital divide between enterprises. This disparity disrupts supply chain collaboration and alignment with sustainable practices. (2) Methods: This research examines how the corporate digital divide affects the supply–demand imbalance by employing a quantitative method to identify obstacles and strategies for improving collaboration. This research employs a quantitative approach, specifically multiple regression analysis, to investigate how the digital divide among enterprises affects the supply–demand imbalance and to identify strategies for overcoming collaboration barriers. The research utilizes firm-level data from the Chinese stock market and accounting research databases and performs robustness checks, including methods such as the instrumental variable approach and the Heckman two-stage model, to ensure the validity of the findings. (3) Results: The study finds that the corporate digital divide exacerbates imbalances in both upstream and downstream chains. Elevating supply chain resilience has effectively alleviated this relationship. Specifically, the strengthening of resource resilience and process resilience has effectively alleviated the impact of the corporate digital divide on the supply–demand imbalance in the upstream supply chain, while the enhancement of system resilience and product resilience has effectively mitigated the impact of the corporate digital divide on the supply–demand imbalance in the downstream supply chain. Heterogeneity analysis indicates that the impact of the digital divide in supply chain enterprises on supply–demand imbalance varies under different conditions of network centrality, supply chain concentration, government digital focus, and enterprise nature. (4) Conclusions: To foster sustainability in Industry 4.0, enterprises must bridge the corporate digital divide and enhance supply chain collaboration. It is recommended to mitigate upstream supply chain disruptions caused by the digital divide by improving resource and process resilience while alleviating downstream impacts through strengthened system and product resilience. Furthermore, fostering collaborative digital development among enterprises is essential for optimizing supply chain sustainability. Full article

As the contradiction between vegetation growth and soil moisture (SM) demand in arid zones gradually expands, accurately obtaining SM data is crucial for ecological construction. Remote sensing products limit small-scale studies due to the low resolution, and the emergence of downscaling solves this problem. This study proposes an improved semi-physical SM downscaling method. The effects of environmental factors on SM in different geographical zones (Windy Sand Hills, Flood Plains, Loess Yuan, Hilly Loess, Earth-rock Hills and Rocky Mountain) were analyzed using Random Forests. Vegetation and topographic factors were incorporated into the traditional downscaling algorithm based on the Mualem–van Genuchten model by setting weights, yielding 250 m resolution SM data for the Loess Plateau. This study found the following: (1) The Normalized Difference Vegetation Index (NDVI) was the most important environmental factor in all divisions except the Flood Plain, and the Digital Elevation Model (DEM) was second only to the NDVI in the overall importance evaluation, both of which positively influenced SM. (2) SM variability increased and then decreased when SM was below 0.4 cm³/cm³, but showed a quadratic growth trend when exceeding this threshold. The Rocky Mountain division exhibited the highest variability under the same SM. (3) Validation showed that the improved algorithm, based on geographic divisions to analyze factors importance and interpolation of coarse-scale SM and variability, had the highest accuracy, with an average R of 0.753 and an average ubRMSE of 0.042 cm³/cm³. The improved algorithm produced higher resolution, more accurate SM data, and offered insights for downscaling studies in arid regions, meeting the region’s high-resolution SM needs. Full article

Aboveground biomass (AGB) serves as an important indicator for assessing the productivity of forest ecosystems and exploring the global carbon cycle. However, accurate estimation of forest AGB remains a significant challenge, especially when integrating multi-source remote sensing data, and the effects of different feature combinations for AGB estimation results are unclear. In this study, we proposed a method for estimating forest AGB by combining Gao Fen 7 (GF-7) stereo imagery with data from Sentinel-1 (S1), Sentinel-2 (S2), and the Advanced Land Observing Satellite digital elevation model (ALOS DEM), and field survey data. The continuous tree height (TH) feature was derived using GF-7 stereo imagery and the ALOS DEM. Spectral features were extracted from S1 and S2, and topographic features were extracted from the ALOS DEM. Using these features, 15 feature combinations were constructed. The recursive feature elimination (RFE) method was used to optimize each feature combination, which was then input into the extreme gradient boosting (XGBoost) model for AGB estimation. Different combinations of features used to estimate forest AGB were compared. The best model was selected for mapping AGB distribution at 30 m resolution. The outcomes showed that the forest AGB model was composed of 13 features, including TH, topographic, and spectral features extracted from S1 and S2 data. This model achieved the best prediction performance, with a determination coefficient (R²) of 0.71 and a root mean square error (RMSE) of 18.11 Mg/ha. TH was found to be the most important predictive feature, followed by S2 optical features, topographic features, and S1 radar features. Full article

This paper presents a novel methodology to generate Digital Elevation Models (DEMs) in flat areas, incorporating river channels from relatively coarse initial data. The technique primarily utilizes filtered dense point clouds derived from SfM-MVS (Structure from Motion-Multi-View Stereo) photogrammetry of available crewed aerial imagery datasets. The methodology operates under the assumption that the aerial survey was carried out during low-flow or drought conditions so that the dry (or almost dry) riverbed is detected, although in an imprecise way. Direct interpolation of the detected elevation points yields unacceptable river channel bottom profiles (often exhibiting unrealistic artifacts) and even distorts the floodplain. In our Fluvial Domain Method, channel bottoms are represented like “highways”, perhaps overlooking their (unknown) detailed morphology but gaining in general topographic consistency. For instance, we observed an 11.7% discrepancy in the river channel long profile (with respect to the measured cross-sections) and a 0.38 m RMSE in the floodplain (with respect to the GNSS-RTK measurements). Unlike conventional methods that utilize active sensors (satellite and airborne LiDAR) or classic topographic surveys—each with precision, cost, or labor limitations—the proposed approach offers a more accessible, cost-effective, and flexible solution that is particularly well suited to cases with scarce base information and financial resources. However, the method’s performance is inherently limited by the quality of input data and the simplification of complex channel morphologies; it is most suitable for cases where high-resolution geomorphological detail is not critical or where direct data acquisition is not feasible. The resulting DEM, incorporating a generalized channel representation, is well suited for flood hazard modeling. A case study of the Ranchería river delta in the Northern Colombian Caribbean demonstrates the methodology. Full article

Lakes are critical resources that support the ecological balance and provide essential services for human and environmental well-being. However, their quality is being increasingly threatened by both natural and anthropogenic processes. This study aimed to assess the water quality and the presence of heavy metals in 15 lakes in the Vidarbha and Marathwada regions of Maharashtra, India. To understand the extent of pollution and its sources, the physico-chemical parameters were analyzed which included pH, turbidity, total hardness, orthophosphate, residual free chlorine, chloride, fluoride, and nitrate, as well as heavy metals such as iron, lead, zinc, copper, arsenic, chromium, manganese, cadmium, and nickel. The results revealed significant pollution in several lakes, with the Lonar Lake showing a pH value of 12, exceeding the Bureau of Indian Standards’ (BIS) limit. The Lonar Lake also showed elevated levels of fluoride having a value of 2 mg/L, nitrate showing a value of 45 mg/L, and orthophosphate showing a concentration up to 2 mg/L. The Rishi Lake had higher concentrations of nickel having a value of 0.2 mg/L and manganese having a value of 0.7 mg/L, crossing permissible BIS limits. The Rishi Lake and the Salim Ali Lake exhibited higher copper levels than other lakes. Cadmium was detected in most of the lakes ranging from values of 0.1 mg/L to 0.4 mg/L, exceeding BIS limits. The highest turbidity levels were observed in Rishi Lake and Salim Ali Lake at 25 NTU. The total hardness value observed in the Kharpudi Lake was 400 mg/L, which is highest among all the lakes under study. The spatial analysis, which utilized remote sensing and GIS techniques, including Sentinel-2 multispectral imagery for land use and land cover mapping and Digital Elevation Model (DEM) for watershed delineation, provided insights into the topography and drainage patterns affecting these lakes. The findings emphasize the urgent need for targeted management strategies to mitigate pollution and protect these vital freshwater ecosystems, with broader implications for public health and ecological sustainability in regions reliant on these water resources. Full article

In Antarctica, SAR interferometry has largely been used in coastal glacial areas, while in rare cases this method has been used on the Antarctic plateau. In this paper, the authors present a digital elevation and ice flow map based on SAR interferometry for an area encompassing Talos Dome (TD) and the Frontier Mountain (FM) meteorite site in North Victoria Land/Antarctica. A digital elevation model (DEM) was calculated using a double SAR interferometry method. The DEM of the region was calculated by extracting approximately 100 control points from the Reference Elevation Model of Antarctica (REMA). The two DEMs differ slightly in some areas, probably due to the penetration of the SAR-C band signal into the cold firn. The largest differences are found in the western area of TD, where the radar penetration is more pronounced and fits well with the layer structures calculated by the georadar and the snow accumulation observations. By differentiating a 70-day interferogram with the calculated DEM, a displacement interferogram was calculated that represents the ice dynamics. The resulting ice flow pattern clearly shows the catchment areas of the Priestley and Rennick Glaciers as well as the ice flow from the west towards Wilkes Basin. The ice velocity field was analysed in the area of FM. This area has become well known due to the search for meteorites. The velocity field in combination with the calculated DEM confirms the generally accepted theories about the accumulation of meteorites over the Antarctic Plateau. Full article

SAR data from the lunar spaceborne Reconnaissance Orbiter’s (LRO) Mini-RF and Chandrayaan-1’s Mini-SAR provide valuable insights into the properties of the lunar surface. However, public lunar SAR data products are not properly registered and are limited by localization issues. Existing registration methods for Earth SAR have proven to be inadequate in their robustness for lunar data registration. And current research on methods for lunar SAR has not yet focused on producing globally registered datasets. To solve these problems, this article introduces a robust automatic registration method tailored for S-band Level-1 Mini-RF and Mini-SAR data with the assistance of lunar DEM. A simulated SAR image based on real lunar DEM data is first generated to assist the registration work, and then an offset calculation approach based on normalized cross-correlation (NCC) and specific processing, including background removal, is proposed to achieve the registration between the simulated image, and the real image. When applying Mini-RF images and Mini-SAR images, high robustness and good accuracy are exhibited, which produces fully registered datasets. After processing using the proposed method, the average error between Mini-RF images and DEM references was reduced from approximately 3000 m to about 100 m. To further explore the additional improvement of the proposed method, the registered lunar SAR datasets are used for further analysis, including a review of the circular polarization ratio (CPR) characteristics of anomalous craters. Full article

Urban areas serve as critical habitats for numerous plant species. Existing studies suggest that, due to human-mediated introductions, urban environments often harbor a greater variety of plant species compared to suburban areas, potentially becoming focal points for biodiversity. Consequently, investigating the driving forces and complex mechanisms by which urban environmental factors influence plant species distribution is essential for establishing the theoretical foundation for urban biodiversity conservation and future urban planning and management. Solar radiation, among these factors, is a critical determinant of plant growth, development, and reproduction. However, there is a notable lack of research on how this factor affects the distribution of urban plant species and influences species’ richness and composition within plant communities. We present for the first time an analysis of how solar radiation drives the spatial distribution of plant species within the built-up areas of Nanchang City, China. Based on three years of monitoring and survey data from experimental sites, this study employs three evaluation models—Species Richness Index (R), Simpson’s Diversity Index (D), and Shannon–Wiener Index (H)—to analyze and validate the survey results. Additionally, MATLAB and ArcGIS Pro software are utilized for the numerical simulation and visualization of spatial data. Our study shows that areas with low solar radiation exhibit higher plant species richness, while plots with high plant diversity are primarily concentrated in regions with strong solar radiation. Moreover, the Diversity Index D proves to be more sensitive than the Shannon–Wiener Index (H) in evaluating the spatial distribution of plant species, making it a more suitable metric for studying urban plant diversity in our study area. Among the 18 plant species analyzed, Mulberry and Dandelion are predominantly dispersed by birds and wind, showing no significant correlation with solar radiation. This finding indicates that the spatial distribution of urban plant species is influenced by multiple interacting factors beyond solar radiation, highlighting the critical need for long-term observation, monitoring, and analysis. This study also suggests that shaded urban areas may serve as hubs of high species richness, while regions with relatively strong solar radiation can sustain greater plant diversity. These findings underscore the practical significance of this research, offering essential insights to guide urban planning and management strategies. Additionally, this study offers valuable insights for the future predictions of plant species distribution and potential areas of high plant diversity in various urban settings by integrating computational models, building data, Digital Elevation Models (DEMs), and land cover data. Full article

Topographical data are essential for hydrological analysis and can be gathered through on-site surveys, UAVs, or remote sensing methods such as Digital Elevation Models (DEMs). These tools are crucial in hydrological studies for accurately modeling basin morphology and surface stream network patterns. Two different DEMs with resolutions of 0.13 m and 5 m were used, as well as tools which carry out urban basin delineation by analyzing their morphometric parameters to process the hydrography of the study area, using three Geographic Information Systems (GIS): ArcGIS, GlobalMapper, and SAGA GIS. Each piece of software uses different algorithms for the pre-processing of DEMs in the calculation of morphometric parameters of the study area. The results showed variations in the quantity of delineated stream networks between the different GIS tools used, even when using the same DEM. Similarly, the morphometric parameters varied between GIS tools and DEMs, which tells us that the tools and topographic data used are important. The stream network generated using ArcGIS and the DEM obtained with UAV offered a more precise description of surface flow behavior in the study area. Concerning ArcGIS, it can be observed that between the resolutions of the INEGI DEM and the UAV DEM, the delimited area of micro-basin 1 presented a minimum difference of 0.03 km². In contrast, micro-basin 2 had a more significant difference of 0.16 km². These discrepancies in results are attributed to the different algorithms used by each piece of software and the resolution of each DEM. Although some studies claim to have obtained the same results using different software and algorithms, in this research, different results were obtained, and emphasize the importance of establishing procedural standards, as they can significantly impact the design of stormwater drainage systems. These comparisons will allow decision-makers to consider these aspects to standardize the tools and topographic data used in urban hydrological analyses. Full article

Due to the high-density coverage of vegetation, the complexity of terrain, and occlusion issues, ground point extraction faces significant challenges. Airborne Light Detection and Ranging (LiDAR) technology plays a crucial role in complex mountainous areas. This article proposes a method for constructing fine terrain in high vegetation coverage areas based on implicit neural representation. This method consists of data preprocessing, multi-scale and multi-feature high-difference point cloud initial filtering, and an upsampling module based on implicit neural representation. Firstly, preprocess the regional point cloud data is preprocessed; then, K-dimensional trees (K-d trees) are used to construct spatial indexes, and spherical neighborhood methods are applied to capture the geometric and physical information of point clouds for multi-feature fusion, enhancing the distinction between terrain and non-terrain elements. Subsequently, a differential model is constructed based on DSM (Digital Surface Model) at different scales, and the elevation variation coefficient is calculated to determine the threshold for extracting the initial set of ground points. Finally, the upsampling module using implicit neural representation is used to finely process the initial ground point set, providing a complete and uniformly dense ground point set for the subsequent construction of fine terrain. To validate the performance of the proposed method, three sets of point cloud data from mountainous terrain with different features are selected as the experimental area. The experimental results indicate that, from a qualitative perspective, the proposed method significantly improves the classification of vegetation, buildings, and roads, with clear boundaries between different types of terrain. From a quantitative perspective, the Type I errors of the three selected regions are 4.3445%, 5.0623%, and 5.9436%, respectively. The Type II errors are 5.7827%, 6.8516%, and 7.3478%, respectively. The overall errors are 5.3361%, 6.4882%, and 6.7168%, respectively. The Kappa coefficients of the measurement areas all exceed 80%, indicating that the proposed method performs well in complex mountainous environments. Provide point cloud data support for the construction of wind and photovoltaic bases in China, reduce potential damage to the ecological environment caused by construction activities, and contribute to the sustainable development of ecology and energy. Full article