Search Results (1,433)

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

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

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

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

Soil, a crucial natural resource and the cornerstone of agriculture, profoundly impacts crop growth, quality, and yield. However, soil degradation affects over one-third of global land, with topsoil loss emerging as a significant form of this degradation, posing a grave threat to agricultural sustainability and socio-economic development. Therefore, accurate monitoring of topsoil-loss distribution is essential for formulating effective soil protection and management strategies. Traditional survey methods are limited by time-consuming and labor-intensive processes, high costs, and complex data processing. These limitations make it particularly challenging to meet the demands of large-scale research and efficient information processing. Therefore, it is imperative to develop a more efficient and accurate extraction method. This study focuses on the Heshan Farm in Heilongjiang Province, China, as the research subject and utilizes remote sensing technology and machine learning methods. It introduces multi-source data, including Sentinel-2 satellite imagery and Digital Elevation Model (DEM) data, to design four extraction schemes. (1) spectral feature extraction; (2) spectral feature + topographic feature extraction; (3) spectral feature + index extraction; (4) spectral feature + topographic feature + index extraction. Models for topsoil loss identification based on Random Forest (RF) and Support Vector Machine (SVM) algorithms are developed, and the Particle Swarm Optimization (PSO) algorithm is introduced to optimize the models. The performance of the models is evaluated using overall accuracy and Kappa coefficient indicators. The results show that Scheme 4, which integrates spectral features, topographic features, and various indices, performs the best in extraction effects. The RF model demonstrates higher classification accuracy than the SVM model. The optimized PSO-RF and PSO-SVM models show significant improvements in extraction accuracy, especially the PSO-RF model, with an overall accuracy of 0.97 and a Kappa coefficient of 0.94. The PSO-RF model using Scheme 4 improves OA by 34.72% and Kappa by 38.81% compared to the RF model in Scheme 1. Topsoil loss has a significant negative impact on crop growth, severely restricting the normal growth and development of crops. This study provides an efficient technical means for monitoring soil degradation in black-soil regions and offers a scientific basis for formulating effective agricultural ecological protection strategies, thereby promoting the sustainable management of soil resources. Full article

The Critical Raw Materials Act (CRMA), enacted by the European Union (EU) in May 2024, represents a strategic framework that aims to address the growing demand for critical raw materials (CRMs) and reduce dependency on non-EU sources. The present study explores the potential of CRMs recovery from granite extractive waste (EW) at a granite quarry in Buddusò (Sardinia, Italy). A significant quantity of granite EW, stored in piles within designated disposal areas at the quarry under study, is estimated in terms of mass and volume using GISs and digital elevation models (DEMs). Analysis performed using a scanning electron microscope attached to an energy-dispersive spectrometer (SEM-EDS) reveals the presence of allanite, a rare-earth-bearing mineral with substantial light rare-earth elements (LREEs), which can potentially be exploited for LREEs recovery. A combined working process including gravity and magnetic separations yields CRMs-enriched fractions with concentrations of REEs, Sc, and Ga, reaching levels of potential economic interest for different industrial applications. Despite promising concentrations, limited knowledge of allanite processing represents significant challenges for CRMs recovery from this waste. Therefore, the present study was conducted to assess the efficiency of these gravity and magnetic separation methods in order to concentrate CRMs from granite EW. Economic evaluations, including potential market value estimates, suggest that CRMs recovery from granite EW can be very profitable under optimized processing conditions. Expanding studies to other quarries in the region can provide valuable insights into the feasibility of establishing a recycling hub, offering a sustainable supply chain solution for CRMs within the EU’s strategic framework. Full article

In recent years, Vision-Based Navigation (VBN) techniques have emerged as a fundamental component to enable autonomous spacecraft operations, particularly in challenging environments such as planetary landings, where ground control may be limited or unavailable. Developing and testing VBN algorithms requires the availability of a large number of realistic images of the application scenario; however, these are rarely available. This paper presents a novel rendering software tool to generate accurate synthetic optical images of the lunar surface by leveraging high-resolution Digital Terrain Models (DTMs). Unlike traditional ray-tracing algorithms, the method iteratively propagates camera rays to determine their intersection with the terrain surface defined by a Digital Elevation Model (DEM). The color information is then retrieved from the corresponding Digital Orthophoto Model (DOM) through the knowledge of the ray impact points, bypassing the need for the costly computation of shadows, reflections, and refractions effects. The rendering performance is demonstrated through a comprehensive selection of images of the lunar surface under different illumination conditions and camera orientations. 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

Grassland ecosystems provide a range of services in semi-arid and arid regions. However, they have significantly declined due to overgrazing and desertification. In the current study, we employed Landsat time series data (TM, OLI, OLI-2) spanning from 1990 to 2024, combined with vegetation indices such as NDVI and SAVI, along with NDWI and digital elevation models (DEMs), to analyze land cover dynamics in the Ugii Lake watershed area, Mongolia. By integrating multisource remote sensing data into the advanced XGBoost (extreme gradient boosting) machine learning algorithm, we achieved high classification accuracy, with overall accuracies exceeding 94% and Kappa coefficients greater than 0.92. The results revealed a decline in montane grasslands (−6.2%) and an increase in other grassland types, suggesting ecosystem redistribution influenced by climatic and anthropogenic factors. Cropland exhibited resilience, recovering from a significant decline in the 1990s to moderate growth by 2024. Our findings highlight the stability of barren land and underscore pressures from ecological degradation and human activities. This study provides up-to-date statistical data to support decision-making in the conservation and sustainable management of grassland ecosystems in Mongolia under changing climatic conditions. Full article