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This paper proposes the use of check surfaces or 'patches' for the assessment of Digital Elevation Model (DEM) elevations and derived variables (e.g. slope and aspect). We work with grid-type DEMs, and the patches are implemented through square windows of different sizes. Since the data included in a patch are autocorrelated, a trial is performed under controlled conditions of simulation, and then the same simulation method is applied to a DEM data set. Using the Kolmogorov–Smirnov statistic, the observed distribution functions of the results for different sample sizes (20, 50, 100) and different patch sizes (3 × 3, 5 × 5, 9 × 9, 11 × 11, 15 × 15, 19 × 19) and for a sample of points are compared against the true population distribution function. The trial based on autocorrelated synthetic data created following a geostatistical model (exponential law) outputs a clear result regarding the influence of intra-patch autocorrelation. The lower this autocorrelation is, the better the patch performs in terms of sampling efficiency. The same simulation process applied to synthetic data has also been applied to the case of a DEM product. The results obtained allow us to establish an equivalence between sample size when using control patches and control points. The main result of this study is that it allows us to understand the behaviour of samples based on patches and offers us a guide for determining the size of patch-based samples. The most relevant contribution of this proposal is that it opens the possibility of analysing variables derived from elevation data, such as slope and aspect. [ABSTRACT FROM AUTHOR]

The current digital elevation model super-resolution (DEM SR) methods are unstable in regions with significant spatial heterogeneity. To address this issue, this study proposes a regional DEM SR method based on an ensemble learning strategy (ELSR). Specifically, we first classified geographical regions into 10 zones based on their terrestrial geomorphologic conditions to reduce spatial heterogeneity; we then integrated the global terrain features with local geographical zoning for terrain modeling; finally, based on ensemble learning theory, we integrated the advantages of different networks to improve the stability of the generated results. The approach was tested for 46,242 km2 in Sichuan, China. The total accuracy of the regional DEM (stage 3) improved by 2.791 % compared with that of the super-resolution convolutional neural network (SRCNN); the accuracy of the geographical zoning strategy results (stage 2) increased by 1.966 %, and that of the baseline network results (stage 1) increased by 0.950 %. Specifically, the improvement in each stage compared with the previous stage was 110.105 % (in stage 2) and 41.963 % (in stage 3). Additionally, the accuracy of the 10 terrestrial geomorphologic classes improved by at least 2.000 %. In summary, the strategy proposed herein is effective for improving regional DEM resolution, with an improvement in relative accuracy related to terrain relief. This study creatively integrated geographical zoning and ensemble learning ideas to generate a stable, high-resolution regional DEM. [ABSTRACT FROM AUTHOR]

The article discusses the study of complex paleo-landslides in the south of France, focusing on the Mont Mayres and Lamerallède landslides. The research investigates the geometry, typology, and potential triggering mechanisms of these landslides using field investigations, remote sensing surveys, and exposure ages obtained through the cosmogenic nuclide method. The findings suggest that these landslides exhibit rock spreading processes, with similar ages indicating synchronous triggering, potentially linked to climatic factors and lithologic contrasts. The study highlights the role of fractures, water percolation, and structural inheritance in the development of these landslides. [Extracted from the article]

The steep right slope of the Jiu River (a tributary of the Danube River in the Romanian Plain) in its lower course is one of the hotspots for landslides in Southwestern Romania, constantly facing instability issues due to landslide reactivations and slope-related active deformations. In our study, we aimed to analyze the behavior of the Breasta landslide. The 16-year monitoring data set (2006-2022) contributes to a better understanding of the movement mechanisms associated with triggering factors. Following GNSS monitoring of the profile line since 2006, it became obvious that the most significant morphological changes occurred in the median and final sectors of the landslide, where the slope retreated by 6 to 19 meters. In terms of results, a digital terrain model of the central sector of the Breasta landslide was generated using 5000 GPS-measured points. Using the Kriging method, this sector was enclosed within a rectangle covering an area of 313.40 square meters, with an average height of 108 meters. This sector emphasizes the morphology of the landslide from 2022 in one of the 'amphitheaters' that developed after the 2006 reactivation. This paper provides insights into the dynamics of the landslide, helping to discover possible triggering factors of mass movement and periodic changes in the landslide morphology. [ABSTRACT FROM AUTHOR]

The article discusses the need for smoothing digital elevation models (DEMs) derived from lidar data to reduce noise and improve accuracy. Three methods for smoothing DEMs are outlined: decimating the lidar data, filling the DEM, and using DEM smoothing techniques. Specific tools in ArcGIS Pro and WhiteBox are highlighted for implementing these smoothing techniques. The article provides detailed instructions and examples for each method, emphasizing the importance of empirical experimentation to achieve desired results. [Extracted from the article]

The objective of this study is to provide a thorough assessment of soil erosion in the Hirshabelle state from 2020 to 2023, utilizing the Revised Universal Soil Loss Equation (RUSLE) and advanced geospatial technologies, particularly Google Earth Engine, to guide sustainable land management strategies. The study integrates multiple datasets, including CHIRPS for rainfall measurement, MODIS for land use analysis, and a digital elevation model for slope calculation, to offer a comprehensive understanding of the factors contributing to soil erosion. The rainfall erosivity (R) factor is calculated using CHIRPS data, while the soil erodibility (K-factor) is derived from the soil dataset. The topographic condition (LS-factor) is computed using the digital elevation model, and the cover-management (C) and support practice (P) factors are determined from the NDVI and land use data, respectively. The findings reveal considerable spatial variation in soil erosion across the Hirshabelle state. The results are categorized into five levels based on the severity of soil loss: very low (<5), low (5-10), moderate (10-20), high (20-40), and very high (≥40). While areas classified under “very low” soil loss are dominant, indicating relatively stable soils, regions under “very high” soil loss signal potential land degradation and the need for immediate intervention. Furthermore, the study revealed the intricate interplay of slope, vegetation, and land use in influencing soil erosion. Areas with steeper slopes and less vegetation were more susceptible to soil loss, emphasizing the need for targeted soil conservation measures in these regions. The land use factor played a crucial role, with certain land uses contributing more to soil erosion than others. [ABSTRACT FROM AUTHOR]

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Urban cover-collapse sinkholes pose a significant global challenge due to their destructive impacts. Previous studies have identified groundwater fluctuations, subsurface soil conditions, pipeline leakage, precipitation, and subterranean construction activities as key contributors to these phenomena. However, unique geological settings across different urban environments lead to variations in the primary factors influencing sinkhole formation. This study focuses on Shanghai, a city notable for its extensive urbanization and rich historical context, to explore the dynamics of sinkholes within urbanized areas worldwide. We employ spatial analysis and statistical methods to examine data on sinkholes recorded in the past two decades in Shanghai, correlating these events with the city's shallow sand layer, ground elevation, and proximity to surface water. Our goal is to identify the dominant factors governing sinkhole occurrence in Shanghai and to lay the groundwork for their effective scientific management and prevention. Key findings indicate that most sinkholes in the area are associated with a thin shallow sand layer, low to moderate ground elevations, and the absence of nearby rivers. Additionally, many sinkholes correlate with subterranean voids within the confined aquifer beneath the cohesive soil layer. The lack of historical river channels, obscured by urban development, also indirectly contributes to sinkhole formation. We recommend enhancing urban river management and drainage systems to mitigate potential damage from water accumulation. [ABSTRACT FROM AUTHOR]

Uncrewed aerial vehicles (UAVs) have transformed remote sensing, offering unparalleled flexibility and spatial resolution across diverse applications. Many of these applications rely on mapping flights using snapshot imaging sensors for creating 3D models of the area or for generating orthomosaics from RGB, multispectral, hyperspectral, or thermal cameras. Based on a literature review, this paper provides comprehensive guidelines and best practices for executing such mapping flights. It addresses critical aspects of flight preparation and flight execution. Key considerations in flight preparation covered include sensor selection, flight height and GSD, flight speed, overlap settings, flight pattern, direction, and viewing angle; considerations in flight execution include on-site preparations (GCPs, camera settings, sensor calibration, and reference targets) as well as on-site conditions (weather conditions, time of the flights) to take into account. In all these steps, high-resolution and high-quality data acquisition needs to be balanced with feasibility constraints such as flight time, data volume, and post-flight processing time. For reflectance and thermal measurements, BRDF issues also influence the correct setting. The formulated guidelines are based on literature consensus. However, the paper also identifies knowledge gaps for mapping flight settings, particularly in viewing angle pattern, flight direction, and thermal imaging in general. The guidelines aim to advance the harmonization of UAV mapping practices, promoting reproducibility and enhanced data quality across diverse applications. [ABSTRACT FROM AUTHOR]

This study explores the potential of repurposing historical aerial photographs to produce high-accuracy digital surface models (DSMs) at regional scales. A novel methodology is introduced, incorporating road points for quality control and refinement to enhance the precision and comparability of multitemporal DSMs. The method consists of two phases. The first is the photogrammetric phase, where DSMs are generated using photogrammetric and structure from motion (SfM) techniques. The second is the refinement phase, which uses a large number (millions) of points extracted from road centrelines to evaluate altimetric residuals—defined as the differences between photogrammetric DSMs and a reference DSM. These points are filtered to ensure that they represent stable positions. The analysis shows that the initial residuals exhibit geographical trends, rather than random behaviour, that are removed after the refinement. An application example covering the whole coast of the Valencian region (Eastern Spain, 518 km of coastline) shows the obtention of a series composed of six DSMs. The method achieves levels of accuracy (0.15–0.20 m) comparable to modern LiDAR techniques, offering a cost-effective alternative for three-dimensional characterisation. The application to the foredune and coastal environment demonstrated the method's effectiveness in quantifying sand volumetric changes through comparison with a reference DSM. The achieved accuracy is crucial for establishing precise sedimentary balances, essential for coastal management. At the same time, this method shows significant potential for its application in other dynamic landscapes, as well as urban or agricultural monitoring. [ABSTRACT FROM AUTHOR]

The advent of geomatic techniques and novel sensors has opened the road to new approaches in mapping, including morphological ones. The evolution of a land portion and its graphical representation constitutes a fundamental aspect for scientific and land planning purposes. In this context, new paradigms for geomorphological mapping, which are useful for modernizing traditional, geomorphological mapping, become necessary for the creation of scalable digital representation of processes and landforms. A fully remote mapping approach, based on multi-source and multi-sensor applications, was implemented for the recognition of landforms and processes. This methodology was applied to a study site located in central Italy, characterized by the presence of 'calanchi' (i.e., badlands). Considering primarily the increasing availability of regional LiDAR products, an automated landform classification, i.e., Geomorphons, was adopted to map landforms at the slope scale. Simultaneously, by collecting and digitizing a time-series of historical orthoimages, a multi-temporal analysis was performed. Finally, surveying the area with an unmanned aerial vehicle, exploiting the high-resolution digital terrain model and orthoimage, a local-scale geomorphological map was produced. The proposed approach has proven to be well capable of identifying the variety of processes acting on the pilot area, identifying various genetic types of geomorphic processes with a nested hierarchy, where runoff-associated landforms coexist with gravitational ones. Large ancient mass movement characterizes the upper part of the basin, forming deep-seated gravity deformation, highly remodeled by a set of widespread runoff features forming rills, gullies, and secondary shallow landslides. The extended badlands areas imposed on Plio-Pleistocene clays are typically affected by sheet wash and rill and gully erosion causing high potential of sediment loss and the occurrence of earth- and mudflows, often interfering and affecting agricultural areas and anthropic elements. This approach guarantees a multi-scale and multi-temporal cartographic model for a full-coverage representation of landforms, representing a useful tool for land planning purposes. [ABSTRACT FROM AUTHOR]

On a global scale, a considerable amount of life, property, and resources are lost because of the increasing frequency and severity of flooding occurrences. This necessitates worldwide development, thorough flood risk assessments and urbanization policies. This work used sophisticated remote sensing data and hydraulic models to create an effective and appropriate methodology for flood risk assessment in a coastal city located in western Algeria. In the first stage, Sentinel-2 optical data was classified using deep learning and machine learning methods for land cover and land use (LULC). The CNN deep model based on LULC was selected for its outstanding overall accuracy. Then, a 1-D HEC-RAS hydraulic model was performed, integrating LULC maps with a higher precision, topography using a digital surface model (DSM) derived from Pléiades tri-stereo data, and another digital elevation model (12 m). Flood hydrographs were also constructed for four scenarios (10, 20, 50, and 100 years) using hydrometric data. The 1D flood model was indeed validated using a flood event data. Flood hazard, LULC and flood risk maps were derived. Results show the high flood hazard areas are concentrated on the left bank of the Oued Allala River and urban cities near the coast. According to the results of the flood hazard simulation of 100 years, built-up areas and roads are the LULC classes most affected by flood hazard, with more than 94.4 % and 69.34 % for DSM tri-stereo and DEM models, respectively. As well, results of flood risk assessment by combining hazard risk and LULC vulnerability show that for the DSM model, 0.48 %, 44.55 %, and 53.11 %, and 54.04 % of flooded areas are in low, medium, and high flood risk, respectively. For the DEM model, 3.14 %, 45 %, and 51.04 % of flooded areas are in low, medium, and high flood risk, respectively. Results confirmed that topographic resolution models and LULC accuracy of CNN models can highly affect hydraulic simulation output results. Based on the obtained results, Ténès City needs necessary planning for flood risk management, particularly in the coastal area. Derived maps can serve as valuable information for regional and national decision-making. [ABSTRACT FROM AUTHOR]

Detecting micro-terrain is essential for the effective layout and maintenance of transmission lines. To address the issues of detection incompleteness, classification ambiguity, and inefficiency in traditional methods, particularly the challenge of distinguishing between saddle and canyon micro-terrain, this paper optimizes the calculation of micro-terrain features and the strategy of micro-terrain detection, and explores a detection method of micro-terrain around transmission lines based on the GPU parallel random forest. This paper employs the GPU parallel random forest model as the extraction framework, leveraging the computational speed advantage of GPU parallel technology for handling large datasets and the robustness inherent in the ensemble approach of random forests. The DEM data of 49 transmission lines in the study area was used for micro-terrain detection experiments. Most of these 49 routes are situated in mountainous regions with complex terrain and contain diverse micro-terrain categories along their paths, rendering them highly representative. The experimental results demonstrate that the proposed method effectively identifies atypical micro-terrain types and four typical micro-terrain types—saddle, canyon, alpine watershed, and uplift—with a classification accuracy of 97.96% and a Kappa coefficient of 0.974. Compared to the traditional method, which achieves a classification accuracy of 75.19% and a Kappa coefficient of 0.642, the proposed method demonstrates a clear improvement in performance. Moreover, by employing the parallel model, the acceleration ratios for training and classification reach 50.57 and 109.06, respectively, significantly improving the efficiency of micro-terrain detection for large-scale regions. These findings could significantly enhance transmission line maintenance and layout planning by providing more accurate micro-terrain data, enabling better decision-making and resource allocation for infrastructure development and disaster risk mitigation. [ABSTRACT FROM AUTHOR]

Peatlands are prevalent across northern regions, including bogs, fens, marshes, meadows, and select tundra wetlands that all vary in size (e.g., 0.01 s to 10 s km2) and shape (e.g., circular to elongated). However, our best remotely sensed products describing the regional-scale distribution of peatland extents are constrained to 1 km2 pixels, often representing notable sub-pixel heterogeneity and local-scale uncertainties. Here we develop a new 20 m spatial resolution wall-to-wall ~1.5 million km2 peatland map of Alaska, using peat cores, ground observations, and sub-meter resolution image interpretation. Ground-data were used to train machine learning classifiers to detect peatlands using a fusion of Sentinel-1 (Dual-polarized Synthetic Aperture Radar), Sentinel-2 (Multi-Spectral Imager), and derivatives from the Arctic Digital Elevation Model (ArcticDEM), that were spatially constrained by a peatland suitability model. Statewide peatland mapping (overall agreement:85%) identified peatlands to cover 4.6, 10.4, and 5.3% of polar, boreal, and maritime ecoregions, respectively, and 7.3% of the total terrestrial land area. This new dataset will improve the representation of peatland carbon, nutrient, and fire dynamics across Alaska. [ABSTRACT FROM AUTHOR]

The northwest part of Hubei Province, China, is characterized by steep topography, complex geological structures, and intense precipitation, providing ideal natural conditions for landslide disasters. To address the lack of integration of synthetic aperture radar interferometry (InSAR) and geological data for the identification of latent landslide hazards, in this study, we incorporated InSAR technology and geological data to identify potential landslides in Fang County, northwest Hubei Province. With the aid of 10 ALOS-2 data scenes and high-precision digital elevation models of the study area, a displacement rate map with a maximum value of −70.6 mm/a was extracted. Then, according to the displacement rate and optical images, the suspected latent landslide area was delineated, and a comprehensive analysis of the slope map and fault and watershed buffer zone map was performed to obtain the final results. Compared to the existing latent landslide recognition method, the proposed method integrating InSAR and geological data can eliminate areas where landslides are geologically unlikely to occur, thereby enhancing the efficiency and accuracy of latent landslide hazard identification. The results were verified using geological and optical image features, which confirmed its effectiveness for identifying latent landslide hazards. The results of this research can contribute to the prediction and early warning of landslides and guide field investigations of geological disasters. [ABSTRACT FROM AUTHOR]

The escalating impacts of climate change have intensified slope instability and increased landslide occurrences in the rapidly urbanizing Klang Valley, Malaysia. With more intense rainfall and rising temperatures, the region faces unprecedented challenges to soil and slope stability due to rapid urbanization. This study evaluates landslide susceptibility by analyzing rainfall and temperature as primary triggering factors, alongside parameters such as elevation, slope angle, aspect, curvature, lithology, land use, soil properties, and NDVI. Key findings highlight that land use, particularly in commercial, industrial, and infrastructure areas with high FR (9.44) and LSI (2.627), significantly influences landslide susceptibility due to construction and terrain alterations. Steep slopes are especially vulnerable as they accelerate runoff, while areas with low NDVI, indicative of sparse vegetation, are more prone to slope failures due to the stabilizing role of vegetation. Regions characterized by vein quartz (FR=6.31; LSI=0.801), known for its brittle structure, and mined lands disturbed by human activities, also exhibit heightened geological vulnerabilities. Utilizing bivariate regression and the Weighted Frequency Ratio (WFR) method in ArcGIS, the study integrates high-resolution LiDAR and digital terrain models (DTMs) to develop a detailed and accurate landslide susceptibility map. These findings offer critical insights for disaster risk reduction strategies and climate-resilient urban planning in the Klang Valley, aligning with United Nations Sustainable Development Goal (SDG) 13. [ABSTRACT FROM AUTHOR]

Tidal flooding represents a natural phenomenon that presents a significant risk to coastal regions in Indonesia. It occurs when sea tides inundate an area with a topography that is below sea level, such as Bandar Lampung City's coastal areas. In order to mitigate the adverse effects of tidal floods in these regions, it is essential to develop a comprehensive model of the tidal flood hazard, coupled with effective mitigation strategies. In order to evaluate the potential for tidal flooding, inundation modelling was conducted using the minimum height of tidal flooding (10 cm), the value of the low tide level (43 cm), and the highest tide during full moon conditions (160 cm) with the Digital Elevation Model (DEM). The ArcGIS software was employed to create a model of inundation through the use of the raster calculator tool, resulting in the generation of a map that delineates the extent of tidal flood hazard levels and the areas that would be affected. The modeling analysis indicates that the tidal flood hazard affects an area of 172.95 hectares. The land uses most susceptible to impact are shrubs and settlements. Panjang Sub-district experienced the most extensive inundation area due to its residential, industrial, trade, and port activities in the 160 cm inundation scenario. In order to mitigate the risk of tidal flooding, various measures can be implemented, such as raising house floors, constructing multi-storey houses, implementing residential conservation, and developing mitigation plans through spatial planning and control strategies. The level of tidal flood hazard, existing adaptation and mitigation measures, and characteristics of tidal flooding can be used as a reference in the formulation of coastal management strategies to mitigate the impact of tidal flooding in coastal regions. [ABSTRACT FROM AUTHOR]

Research on flood hazard and risk mapping has been widely conducted and is crucial for flood disaster management and mitigation. These studies often involve flood modeling using HEC-RAS, which has been applied to major rivers in the Special Region of Yogyakarta Province. This modeling analyses the area and depth of inundation using parameters such as flood hydrographs, Manning's coefficient, and Digital Elevation Model (DEM) data. This study aims to compare the discharge simulated by the GAMA I Synthetic Unit Hydrograph (HSS GAMA I) model with observed discharges using loggers and to evaluate flood modeling with HEC-RAS in Sewon District, Bantul Regency. The results of flood modeling predictions using two different discharge inputs are not too different in terms of discharge and inundation area. Peak discharge values using HSS Gama I are determined as 29.70 m³/s, 52.11 m³/s, and 64.20 m³/s for 2, 5, and 10-year return periods respectively, while values obtained using loggers are 26.16 m³/s, 41.53 m³/s, and 49.56 m³/s. It can be concluded that discharge data from the HSS GAMA I method can still be considered relevant to be used as an alternative method if discharge data is not obtained from direct measurements. Geometry data obtained from the results of aerial photography that is regressed with terrestrial data is also important in this study because it can adjust the basic data and the latest data into one good geometry data. At least the data produced can represent the geometry of the modeled river as closely as possible to the conditions in the field and the results of this study can be a new reference for further research to be able to apply the model and adjust it to the character of each region, so that flood modeling using HEC-RAS becomes more innovative and adaptable to the modeled area. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Sinkholes are significant geohazards in karst regions that pose risks to landscapes and infrastructure by disrupting geological stability. Usually, sinkholes are mapped by field surveys, which is very cost-intensive with regard to vast coverages. One possible solution to derive sinkholes without entering the area is the use of high-resolution digital terrain models, which are also expensive with respect to remote areas. Therefore, this study focusses on the mapping of sinkholes in arid regions from open-access remote sensing data. The case study involves data from the Sentinel missions over the Mangystau region in Kazakhstan provided by the European Space Agency free of cost. The core of the technique is a multi-scale curvature filter bank that highlights sinkholes (and takyrs) by their very special illumination pattern in Sentinel-2 images. Marginal confusions with vegetation shadows are excluded by consulting the newly developed Combined Vegetation Doline Index based on Sentinel-1 and Sentinel-2. The geospatial analysis reveals distinct spatial correlations among sinkholes, takyrs, vegetation, and possible surface discharge. The generic and, therefore, transferable approach reached an accuracy of 92%. However, extensive reference data or comparable methods are not currently available. [ABSTRACT FROM AUTHOR]

Landslides can cause severe damage to property and human life. Identifying their locations and characteristics is crucial for emergency rescue and disaster risk assessment. However, existing methods need help in accurately detecting landslides because of their diverse characteristics and scales, as well as the differences in spectral features and spatial heterogeneity of remote sensing images. To overcome these challenges, a multiscale feature fusion landslide-detection network (MFLD-Net) is proposed. This network utilizes reflectance difference images from pre- and post-landslide Sentinel-2A images, along with digital elevation model (DEM) data. Moreover, a multichannel differential landslide dataset was constructed through spectral analysis of Sentinel-2A images, which facilitates network training and enables differentiation between landslides and other objects with similar spectral features, such as bare soil and buildings. The proposed MFLD-Net was tested in Shuzheng Valley and Detuo town in Sichuan, China, where earthquakes have occurred. The experimental results revealed that compared with advanced deep learning models, MFLD-Net has promising landslide detection performance. This study provides suggestions for selecting optimal deep learning methods and spectral band combinations for landslide detection and offers a publicly available landslide dataset for further research. [ABSTRACT FROM AUTHOR]

The 2021–2024 Nordic Waste Landslide, located near the village of Ølst in East Jutland, Denmark, was a significant geohazard event, occurring within a former clay pit that had been repurposed as a landfill for polluted soil. This study provides a first analysis of the landslide's development, characteristics, and causative factors. The slow-to-moderate-moving landslide gained public attention in December 2023 when it protruded beyond the landfill area, threatening to reach Ølst and dispense pollutants to large downstream watercourses. We analyzed the landslide's evolution and causative factors utilizing aerial imagery, digital elevation models (DEMs), satellite data, and field observation. The landslide's evolution can be categorized into two distinct phases driven by two modes of soil deposition. In Phase 1, spanning 2021 to spring 2023, the landslide developed due to gradual vertical soil aggradation on the gently sloping surface of the former clay pit. In Phase 2, from spring 2023 to January 2024, the landslide developed rapidly due to substantial soil dumping on the western slope of the clay pit, forming two earthflows that moved east, forcing acceleration in most of the Phase 1 landslide. The Phase 2 landslide encompassed approximately 1.2 million cubic meters of soil, accounting for over half of the total soil deposited at Nordic Waste between 2015 and 2023. After the practice of dumping soil at the edge of the clay pit ended the landslide slowed down, eventually stopping by late January 2024. This was while the water table was at a record high and still increasing, pointing to soil deposition, and not elevated water table, as the main preconditioning factor. Runout modelling indicated the landslide was best replicated using a Voellmy friction angle of atan(μ) = 2.9°. However, even in a worst-case modelling scenario, assuming an unrealistically low atan(μ) of 1.7°, the landslide did not reach inhabited areas in Ølst. From a landslide point of view, our findings emphasize the need for land-use planning and regulatory frameworks of landfills to take slope instability into account. Furthermore, they highlight the need for increased public awareness, and for educating decision-makers and oversight authorities into the risks associated with landslides. [ABSTRACT FROM AUTHOR]

Utilizing remote sensing and deep learning methods can greatly enhance efficiency and accuracy in landslide identification. However, the presence of vegetation cover and human activities pose challenges to identify landslides using optical images alone. In addition, the limited availability of landslide samples affects the generalization and adaptability of identification models. This study introduces a novel feature fusion model called SAMLS, which is adapted from the vision foundation model Segment Anything Model (SAM). The SAMLS is designed with two-branch encoders, namely the RGB branch and the digital elevation model (DEM) branch, which retains the strong feature extraction abilities from the original SAM encoder but utilizes adapter approach to finetune on landslide datasets. The SAMLS utilizes a cross-branch attention mechanism to dynamically fuse terrain features from DEM branch with optical features from RGB branch based on different types of landslides. The SAMLS also follows the prompt learning approach for an automatic landslide identification process. Experiment results on the Bijie dataset demonstrate the effectiveness of SAMLS in identifying visually distinct landslides with an improvement of 0.820 and 0.831 to 0.843 in terms of F1-score compared with Mask R-CNN and Mask2Former. Furthermore, experiment results on the Zhejiang dataset indicate that SAMLS is capable to identify both visually distinct and occluded landslides. The combination of RGB and color relief image map (CRIM) data achieves the best F1-score of 0.592, primarily due to its enhanced visibility of terrain characteristics of landslides in CRIM images. The proposed method holds promise for applications in landslide mapping and risk management in the future. [ABSTRACT FROM AUTHOR]

As the domain of artificial intelligence has advanced, the integration of deep learning techniques into terrain and landform analysis has become more prevalent. Nevertheless, many existing methods are fully supervised and designed for specific tasks; thus, their transferability is limited and massive annotated samples are required. This study introduces a geomorphic pretrained model (GeomorPM) capable of performing multiple tasks. First, an architecture was designed that combined a convolution-based Vector Quantised-Variational Autoencoder (VQVAE) with a Transformer-based masked autoencoder (MAE) framework, allowing it to autonomously learn local details and global patterns from large-scale digital elevation model (DEM) data. Subsequently, GeomorPM, based on the VQMAE architecture, was pretrained on massive DEM data and fine-tuned for three specific tasks: DEM void filling, DEM superresolution, and landform classification. GeomorPM outperformed the traditional and other deep learning methods in all three tasks, demonstrating the superior learning ability and transferability of the model. This study provides a practical framework for developing pretrained models based on DEMs that can be expanded to other continuous geoscientific data. [ABSTRACT FROM AUTHOR]

Deep learning-based super-resolution methods have been successfully applied to digital elevation model (DEM) downscaling studies by designing structures and loss functions of the model. However, little attention has been paid to the design of super-resolution models that can maintain the hydrological characteristics of the DEM, which is important for hydrological studies. This study introduces a super-resolution model that integrates hydrologic knowledge (HKSRCGAN), with the aim to effectively maintain topographic features as well as the hydrologic connectivity of the DEM. The hydrological knowledge derived from surface flow direction and hydrological features are integrated into a deep learning algorithm to guide model training. The 30 m spatial resolution FABDEM is used to demonstrate the utility of the proposed method. Results show that the HKSRCGAN outperforms the bicubic interpolation, SRCNN, SRGAN, SRResNet and TfaSR methods in reducing topographic errors and maintaining hydrologic characteristics. In the test area, the entropy difference analysis shows that the DEM generated by HKSRCGAN is similar to the information contained in the reference DEM. Furthermore, super-resolution models integrating hydrological knowledge are valuable for modeling terrain primarily shaped by gravity and surface water flows. In the future, deep learning-based models integrating hydrologic knowledge are expected to be applied in DEM upscaling to maintain consistent hydrological characteristics. [ABSTRACT FROM AUTHOR]

The goal of terrain viewshed point selection is to identify multiple viewpoints within a specific area that offer optimal visibility. However, as the resolution of terrain data becomes finer, the number of data points to be processed grows significantly, leading to a sharp rise in computational demands for viewshed point selection. This paper introduces the Efficient Precise Viewshed Point Selection (EPVPS) algorithm, which provides an important reference for multi-viewpoint planning. First, the Empty Circles based K-means (ECKM) algorithm is utilized to determine the initial centers for candidate viewpoint clustering. Second, a new viewpoint evaluation metric, Weighted Coverage Overlap Metric (WCOM), is proposed. This metric divides the viewshed of candidate points into coverage contribution points and overlap contribution points, computes WCOM based on this division, and stores the values in a min-heap. Finally, viewpoints are added and deleted from clusters by adjusting the sets of coverage contribution points and overlap contribution points. The EPVPS algorithm is compared with the Candidate Viewpoints Filtering (CVF) algorithm and the Simulated Annealing (SA) algorithm. Experimental results demonstrate that the EPVPS algorithm outperforms the others in computational efficiency, coverage rate, and overlap rate. [ABSTRACT FROM AUTHOR]

The increasingly serious urban heat island (UHI) effect is unfavorable to urban development. This study utilized land cover data and land surface temperature (LST) data of China in 2020 by using correlation analysis and spatial regression models to analyze the relationships between LST and two influencing factors (land cover and digital elevation model (DEM)). The results showed the following: (1) The correlation between LST and forest was highest in the Northeast China Plain (NCP), Huang-Huai-Hai Plain (HHP), Qinghai Tibet Plateau (QTP), and Loess Plateau (LP). DEM mean displayed its highest correlation in the Northern arid and semiarid region (NAR), Sichuan Basin and surrounding regions (SCR), Yunnan-Guizhou Plateau (YGP), and Middle-lower Yangtze Plain (MYP). Southern China (SC) had the highest correlation between LST and construction land. (2) There was spatial heterogeneity between land cover and LST. Unused land in LP had larger impact on LST. For every 1 % increase in the proportion of unused land area, the LST increased by 0.250 °C. LST in some central and western regions of China (the NAR, the LP, the SCR, and the YGP) was mainly affected by local land cover; LST in eastern coastal regions (the HHP, MYP, NCP, SC) and QTP was not only affected by local land cover, but also by LST or land cover of neighboring regions. The warming effect of construction land on LST was more significant, with LST increasing by 0.079 °C to 0.338 °C for every 1 % increase in the proportion of construction land area. Coordination of land use planning and synergistic remediation in different regions and rational planning of construction land are essential to mitigate the UHI effect. (3) Water bodies in the NCP, NAR, and MYP had the greatest cooling impact on LST, with LST decreasing by 0.277 °C, 0.246 °C, and 0.079 °C, respectively, for every 1 % increase in the proportion of water bodies area. Forest in the QTP, LP, SC, and YGP had the greatest cooling impact on LST, and for every 1 % increase in the proportion of forest area, LST decreased by 0.144 °C, 0.089 °C, 0.086 °C, and 0.038 °C, respectively. Actively planting trees and increasing the area of forests and water bodies are of positive significance in alleviating the UHI effect and improving the ecological environment. [ABSTRACT FROM AUTHOR]

Over the course of NASA's Dawn Discovery mission, the onboard framing camera mapped Ceres across a wide wavelength spectrum at varying polar science orbits and altitudes. With increasing resolution, the uniqueness of the 92 km wide, young Occator crater became evident. Its central cryovolcanic dome, Cerealia Tholus, and especially the associated bright carbonate and ammonium chloride deposits—named Cerealia Facula and the thinner, more dispersed Vinalia Faculae—are the surface expressions of a deep brine reservoir beneath Occator. Understandably, this made this crater the target for future sample return mission studies. The planning and preparation for this kind of mission require the characterization of potential landing sites based on the most accurate topography and orthorectified image data. In this work, we demonstrate the capabilities of the freely available and open-source USGS Integrated Software for Imagers and Spectrometers (ISIS 3) and Ames Stereo Pipeline (ASP 2.7) in creating high-quality image data products as well as stereophotogrammetric (SPG) and multi-view shape-from-shading (SfS) digital terrain models (DTMs) of the aforementioned spectroscopically challenging features. The main data products of our work are four new DTMs, including one SPG and one SfS DTM based on High-Altitude Mapping Orbit (HAMO) (CSH/CXJ) and one SPG and one SfS DTM based on Low-Altitude Mapping Orbit (LAMO) (CSL/CXL), along with selected Extended Mission Orbit 7 (XMO7) framing camera (FC) data. The SPG and SfS DTMs were calculated to a GSD of 1 and 0.5 px, corresponding to 136 m (HAMO SPG), 68 m (HAMO SfS), 34 m (LAMO SPG), and 17 m (LAMO SfS). Finally, we show that the SPG and SfS approaches we used yield consistent results even in the presence of high albedo differences and highlight how our new DTMs differ from those previously created and published by the German Aerospace Center (DLR) and the Jet Propulsion Laboratory (JPL). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Mapping the terrain surrounding concentrations of documented Manteño (ca. a.d. 650–1700) stone architecture within 1.2 km² of the cloud forests of Bola de Oro in southern Manabí, Ecuador using an Uncrewed Aerial Vehicle (UAV)-based lidar Mobile Mapping System (MMS) revealed a modified landscape of agricultural systems. The resulting Digital Terrain Models (DTMs), subsequent ground-truthing, and excavation show a modified Manteño landscape of agricultural terraces, drainage channels, and water retention ponds tailored to the storage and distribution of water brought by seasonal rains and by marine layers precipitating at these higher elevations. Such a massive human investment in this challenging landscape was most likely a result of the Chongón-Colonche Mountains being the most resistant environments to the extreme climate changes brought by El Niño-Southern Oscillation (ENSO) during the climatic shifts of the Medieval Climate Anomaly (MCA, ca. a.d. 950–1250) and the Little Ice Age (LIA, ca. a.d. 1400–1700). [ABSTRACT FROM AUTHOR]

Reconstructing urban areas in 3D from satellite raster images has been a long-standing problem for both academical and industrial research. While automatic methods achieving this objective at a Level Of Detail (LOD) 1 are mostly efficient today, producing LOD2 models is still a scientific challenge. In particular, the quality and resolution of satellite data is too low to infer accurately the planar roof sections in 3D by using traditional plane detection algorithms. Existing methods rely upon the exploitation of both strong urban priors that reduce their applicability to a variety of environments and multi-modal data, including some derived 3D products such as Digital Surface Models. In this work, we address this issue with KIBS (Keypoints Inference By Segmentation), a method that detects planar roof sections in 3D from a single-view satellite image. By exploiting large-scale LOD2 databases produced by human operators with efficient neural architectures, we manage to both segment roof sections in images and extract keypoints enclosing these sections in 3D to form 3D-polygons with a low-complexity. The output set of 3D-polygons can be used to reconstruct LOD2 models of buildings when combined with a plane assembly method. While conceptually simple, our method manages to capture roof sections as 3D-polygons with a good accuracy, from a single satellite image only by learning indirect 3D information contained in the image, in particular from the view inclination, the distortion of facades, the building shadows, roof peak and ridge perspective. We demonstrate the potential of KIBS by reconstructing large urban areas in a few minutes, with a Jaccard index for the 2D segmentation of individual roof sections of approximately 80%, and an altimetric error of the reconstructed LOD2 model of less than to 2 meters. [ABSTRACT FROM AUTHOR]

The crown area is a critical metric for evaluating tree growth and supporting various ecological and forestry analyses. This study compares three approaches, i.e., unsupervised clustering, region-based, and deep learning, to estimate the crown area of Pinus eldarica Medw. using UAV-acquired RGB imagery (2 cm ground sampling distance) and high-density point clouds (1.27 points/cm3). The first approach applied unsupervised clustering techniques, such as Mean-shift and K-means, to directly estimate crown areas, bypassing tree top detection. The second employed a region-based approach, using Template Matching and Local Maxima (LM) for tree top identification, followed by Marker-Controlled Watershed (MCW) and Seeded Region Growing for crown delineation. The third approach utilized a Convolutional Neural Network (CNN) that integrated Digital Surface Model layers with the Visible Atmospheric Resistance Index for enhanced segmentation. The results were compared against field measurements and manual digitization. The findings reveal that CNN and MCW with LM were the most effective, particularly for small and large trees, though performance decreased for medium-sized crowns. CNN provided the most accurate results overall, with a relative root mean square error (RRMSE) of 8.85%, a Nash–Sutcliffe Efficiency (NSE) of 0.97, and a bias score (BS) of 1.00. The CNN crown area estimates showed strong correlations (R2 = 0.83, 0.62, and 0.94 for small, medium, and large trees, respectively) with manually digitized references. This study underscores the value of advanced CNN techniques for precise crown area and shape estimation, highlighting the need for future research to refine algorithms for improved handling of crown size variability. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of long traversal paths, low efficiency, high fuel consumption, and costs in the collaborative harvesting of corn by harvesters and grain transport vehicles in hilly areas. A path-planning and collaborative scheduling method is proposed, combining Dijkstra's algorithm with the Improved Harris Hawk Optimization (IHHO) algorithm. A field model based on Digital Elevation Model (DEM) data is created for full coverage path planning, reducing traversal path length. A field transfer road network is established, and Dijkstra's algorithm is used to calculate distances between fields. A multi-objective collaborative scheduling model is then developed to minimize fuel consumption, scheduling costs, and time. The IHHO algorithm enhances search performance by introducing quantum initialization to improve the initial population, integrating the slime mold algorithm for better exploration, and applying an average differential mutation strategy and nonlinear energy factor updates to strengthen both global and local search. Non-dominated sorting and crowding distance techniques are incorporated to enhance solution diversity and quality. The results show that compared to traditional HHO and HHO algorithms, the IHHO algorithm reduces average scheduling costs by 4.2% and 14.5%, scheduling time by 4.5% and 8.1%, and fuel consumption by 3.5% and 3.2%, respectively. This approach effectively reduces transfer path costs, saves energy, and improves operational efficiency, providing valuable insights for path planning and collaborative scheduling in multi-field harvesting and transportation in hilly areas. [ABSTRACT FROM AUTHOR]

In the last decades, natural and anthropogenic pressures have caused observable changes in the argan landscape despite its significance in Morocco. Remote sensing data can be used to monitor these changes over time and provide information on vegetation health and land cover changes. This study assesses the performance of supervised methods (support vector machine, maximum likelihood, and minimum distance) and unsupervised classification method (Isodata) for mapping the argan forest in the Smimou area of Essaouira province using remote sensing data from Landsat-5 and Landsat-8 (1985 and 2019). Additionally, the impact of the resampling method and the digital elevation model (DEM) integration on the classification results have been examined. The ground truth data were collected and randomly divided into two categories: 234 samples to calibrate the classification algorithms and 340 samples for validation. Maximum likelihood supervised classification achieved an overall accuracy (OA) of 89.62% (kappa = 0.84) and 87.58% (kappa = 0.81) in 1985 and 2019, respectively. Using resampling techniques on normalized difference vegetation index (NDVI) products, aiming for a 10 m resolution, the NDVI results yielded an OA of 91.60% in 1985 and 88.85% in 2019. Further integration of DEM (30-m resolution) with NDVI, which was resampled to a 10 m resolution, achieved an OA of 92.27% and 92.37% for 1985 and 2019, respectively. [ABSTRACT FROM AUTHOR]

Light Detection and Ranging (LiDAR), with its ability to capture vegetation vertical profile, could be a unique technique for deriving Leaf Area Index (LAI). A global LAI product at 25-m spatial resolution was derived from the Global Ecosystem Dynamics Investigation (GEDI) LiDAR data since 2019, but it was often significantly underestimated in dense forests. Here we explored the potential for improving the estimation of the underestimated GEDI LAI in dense forests by using the Digital Elevation Model (DEM) as auxiliary data to separate ground and canopy returns in the received waveform. Dense forests were defined as forests with high vegetation greenness (annual maximum NDVI ≥ 0.8). The newly estimated GEDI footprint LAI was first validated with the ground-measured LAI at two sites in Fujian, China, and the results showed that the underestimation was significantly reduced compared to the original GEDI LAI product (r increased from −0.55 to 0.81, RMSE decreased from 3.94 to 1.43, Bias decreased from 3.17 to 0.48). To evaluate whether the improvement was applicable to other areas and forest types, the newly estimated GEDI footprint LAI for the entire Fujian and Contiguous US (CONUS) was then compared to the consistent LAI among three widely used global LAI products. The comparison results demonstrated that the use of DEM as auxiliary data could largely reduce the underestimation of GEDI footprint LAI (In Fujian, RMSE decreased from 4.75 to 2.52, and Bias decreased from 4.61 to 0.58; in CONUS, RMSE decreased from 5.24 to 1.96, and Bias decreased from 5.1 to 0.73). Overall, this study demonstrates the effectiveness of correcting the large underestimation of GEDI footprint LAI in dense forests by utilizing DEM, which has an important influence on the results, as auxiliary data. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

Climate change triggering extreme weather events and the fact that settlements are at risk have made flood disaster analysis a more critical issue. The economic, social, and environmental risk areas of Karakoyun, Sırrın, and Cavsak streams located in Şanlıurfa city center were determined using flood risk analysis. Thus, it aims to determine low, medium, high, and very high probability flood areas in the area, to collect the necessary data for establishing early warning systems and precaution packages, and to create an infrastructure for similar studies. While performing the risk analysis, we used Q5, Q10, Q25, Q50, Q100, Q500, and Q1000 flood recurrence flows of the mentioned streams. Many criteria were considered, including stream routes, natural water retention capacities, land topography, and general hydrological and geological features. In the light of the data provided, digital elevation models of the streams were created in the ArcGIS 10.3 program, all information was transferred to the hydraulic modeling HEC-RAS 5.07 software, and risky areas were determined by performing a 1D flood analysis. At Q2 flow rate, Karakoyun has 7.09, Cavsak 2.67, Sırrın 6.603 m3 s− 1, and at Q1000 flow rate, Karakoyun has 167.550, Cavsak 90.77, Sırrın 151.298 m3 s− 1 hydrograph peak values. As a result of the analysis, it was appointed that there was a flood risk in many parts of the stream sections. The flooding phenomenon has happened to be one of the most devastating floods for economic and environmental damages that occurred in Şanlıurfa City in 2023. People lost their lives, and many were injured during flooding; also, the urban economy affected nearly $15 million in the region. The risk of residences, shopping malls, and commercial areas in the city is revealed spatially. [ABSTRACT FROM AUTHOR]

Coastal landslides pose significant hazards to populated areas and infrastructure, necessitating accurate assessment and mitigation strategies. In this study, landslide susceptibility maps for rockfalls/topples, rotational/translational slides, complex phenomena and rapid flows were developed in the Liguria region (Italy) from the coast to 2 km inland using the inventory of Italian landslides and the maximum entropy model that allows to develop, in a limited computational time, studies at regional scale. Sixteen environmental variabilities derived from the digital elevation model, geological map, CORINE land cover and topographic map of the region were used in the models. Only the variables found to be most significant for each model were used for each landslide type. The landslide occurrences were divided randomly into training (80%) and test set (20%). The accuracy of the models was evaluated by the receiver operating characteristic curves and the area under the curve. The rockfall/topple model and the rapid flow model showed high accuracy although this latter model was only evaluated on the training data due to an insufficient number of landslides for a split into test and training datasets. The rotational/translational slides model and the complex landslides model also performed well. We found that variables contributing most significantly to the models are the slope, lithology, land cover, distance from the shoreline and elevation. Susceptibility maps were created for each type of landslide and combined into a final map providing a comprehensive overview of the landslide hazard at the regional level. [ABSTRACT FROM AUTHOR]

Doline susceptibility mapping (DSM) in karst aquifer is important in terms of estimating the vulnerability of the aquifer to pollutants, estimating the infiltration rate, and infrastructures exposed to the development of dolines. In this research, doline susceptibility map was prepared in Saldaran mountain by generalized linear model (GLM) using 14 affecting parameters extracted from satellite images, digital elevation model, and geology map. Only 8 parameters have been inputted to the model which had correlation with dolines. In this regards, 306 dolines were identified by the photogrammetric Unmanned Aerial Vehicles (UAV) method in 600 hectares of Salderan lands and then, these data were divided into the training (70%) and testing (30%) data for modelling. The results of DSM modeling showed that classified probability of doline occurrences in the Saldaran mountain were as follow: 16.5% of the area high to very high, 72% in the class of low to very low, and 5% in the moderate class. Also, locally, in Saldaran mountain, the Pirghar aquifer has the highest potential for the doline development, followed by Bagh Rostam and Sarab aquifers. Also, the precipitation, digital elevation model, Topographic Position Index, drainage density, slope, TRASP (transformed the circular aspect to a radiation index), Snow-Covered Days and vegetation cover index are of highest importance in the DSM modeling, respectively. Accurate evaluation of the model using the Receiver Operating Characteristics (ROC) curve represents a very good accuracy (AUC=0.953) of the DSM model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]