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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]

Presenting real-world paths in property graphs is a complex challenge of identifying and representing the properties of routes and their environments. These property graphs serve as foundational datasets for generating smart sports training routes, where route features such as terrain, bends, and hills critically influence the route design. This paper outlines a method for identifying key parameters of real-world paths and encoding them into property graphs. The proposed method has significant implications for sports event planning, particularly in designing route-based training that meets specific athletic challenges. The research concludes by presenting a case study in which a property graph that enables cycling route generation was created for the country of Slovenia, and a sample training route was generated. [ABSTRACT FROM AUTHOR]

To evaluate the performance of the global geopotential models (GGMs) in a more unbiased way, ground-based gravity and GNSS/levelling datasets are highly required. In this study, the eight latest releases of the satellite-only and combined GGMs are evaluated on the regional scale using the available terrestrial gravity and GNSS/Levelling data over Sudan, considering the spectral consistency issue by applying the spectral enhancement method (SEM). The evaluation process consists of three stages: firstly, the eight GGMs are evaluated globally with each other by using different degree variances in terms of geoid heights, gravity anomalies, and signal-to-noise ratio (SNR); secondly, the GGMs are compared against the Earth Gravitational Model 2008 (EGM2008) on a regional scale over Sudan; thirdly, apply the SEM strategy by incorporating high (SEM_WITHOUT_RTM technique) and very-high (SEM technique) frequencies of the gravity field spectrum from the EGM2008 and high-resolution residual terrain model (RTM), respectively. For reliable robustness of the latter evaluation process, three different DEMs are used, namely, SRTM30, ASTER30, and GTOPO30. Our findings on the evaluation process using SEM_WITHOUT_RTM technique show improved gravity anomalies solutions regarding differences of standard deviations (STD) from 19–20.7 mGal to about 14 mGal. When applying the SEM technique, more improvements are achieved, providing STD differences in gravity anomalies and geoid heights of about 12 mGal and 45 cm, respectively. Among the three applied DEMs, it has been found that despite the slight refinements, the ASTER30 and GTOPO30 models show better performance than the SRTM30 model. [ABSTRACT FROM AUTHOR]

Active Faults in urban and densely populated areas are of great importance to understand, as these faults may be of high risk to life and property. Peshawar Basin covers Peshawar, the capital of Khyber Pakhtunkhwa, and other populated areas of the Swabi, Mardan, Nowshera, and Charsadda districts. This research article focuses on active faulting and seismic activity in the Peshawar Basin. Fault causes a sudden release of energy in the form of an earthquake when stress exceeds the Strength of a rock. More than 12,000 earthquakes have been reported in the Peshawar Basin, ranging from 1 to 5.7 on the Richter scale. The shallow nature of these earthquakes is a clear indication of ongoing tectonic activity in the region, resulting in active faults in the Peshawar Basin. Intense Field observations and detailed Digital Elevation Model (DEM) analysis analysis confirm active faults in the Peshawar Basin. Field observations of Peshawar Basin cover the study of infrastructures along the active faults where only 10% of the rocks are exposed. Fracture data was collected from infrastructure and this data was carefully analyzed as well. Based on this analysis six active faults are marked. Charsada-Takhbhai Fault, Mardan Fault, Pir Piai Fault, and Swabi Fault are marked as normal, whereas Pir Sabak and Ghorghushti Fault are thrust faults in nature. Normal faults and tilting were also observed in the alluvial deposits, on a small scale, but it is a manifestation of the same phenomenon on a larger scale in the Peshawar Basin. In the Southern part of the Peshawar Basin, active faults were identified and recognized in the near past. This research is significant because it covers the whole Peshawar Basin and this study was never done before. This research shows that these active faults particularly the Swabi fault may be of high risk to the world's largest earth-filled Tarbela dam and surrounding areas. Subsidence of more than 2 cm per year is observed in the area along the Swabi and Ghoghasti faults. [ABSTRACT FROM AUTHOR]

We propose a lightweight method for constructing a global digital elevation and terrain database in consideration of the satellite flight path. First, a global maximum/minimum elevation database with a grid size of 0.05° × 0.05° was generated using Shuttle Radar Topography Mission data and the Global Multi-resolution Terrain Elevation Data along with other multi-source digital elevation model (DEM) data. A global database of maximum terrain fluctuation with a grid size of 0.05°× 0.05° and distances of 140 and 700 m was then constructed on the basis of the ICESat-2 satellite flight path and the main frame/super frame distance criterion. The databases, which achieve global coverage while occupying a small storage space, can be used to assist in the in-orbit preprocessing of onboard data. The database accuracy was evaluated using ICESat-2 data. In the 10 validation areas, the global DEM database had an error of only 3 m in two validation areas, and the error was less than the accuracy range of each DEM data source. The global terrain database can characterize the degree of surface relief in each verification area, confirming the effectiveness of the method used in this study. [ABSTRACT FROM AUTHOR]

The objective of our research is to produce a digital elevation model (DEM) in a real-time domain. For this purpose, GNSS measurements are obtained from a kinematic trajectory in a clear location in New Aswan City, Egypt. Different real-time processing solutions are employed, including real-time precise point positioning (RT-PPP) and real-time kinematics (RTK); additionally, the widely used post-processed precise point positioning (PPP) processing scenario is used. Thereafter, the acquired positioning estimates are compared with the traditional kinematic differential GNSS solution counterparts. To achieve the RT-PPP mode, the instantaneous products from the Centre National d'Etudes Spatiales (CNES) are utilized. Our proposed models are validated for both kinematic positioning and DEM accuracies. For kinematic positioning accuracy validation, the findings indicate that the three-dimensional position is about 0.480 m, 0.101 m, and 0.628 for RT-PPP, RTK, and PPP solutions, respectively. Furthermore, the DEM accuracy investigation shows that the produced DEMs have accuracies within 0.249 m, 0.005 m, and 0.264 m for RT-PPP, RTK, and PPP solutions, respectively. [ABSTRACT FROM AUTHOR]

Activities, particularly harbor construction, often exert significant and non-negligible impacts on coastal environments. Therefore, it is of great practical importance to quantitatively assess the effects of such construction on the surrounding topography, such as tidal flats. This study focuses on the coast of Jiangsu Binhai Harbor. Using multi-source and multi-temporal remote sensing images, digital elevation models of tidal flats surrounding Binhai Harbor were generated for the years 2013, 2015, and 2017 through the waterline method. A quantitative analysis was conducted utilizing GIS spatial analysis techniques to examine erosion–deposition patterns, contour changes, and typical cross-sectional comparisons. The findings reveal that, although the overall coastline is in a state of erosion, the localized impacts of harbor construction are evident. Between 2013 and 2017, the northern tidal flats experienced overall erosion, whereas deposition occurred near the harbor's root areas. Compared to 2013–2015, there was a significant decrease in erosion between 2015 and 2017, indicating that the construction of the project had a significant impact on the northern tidal flats. Throughout the five-year study period, the tidal flats within the breakwater underwent continuous adjustment, shifting from being close to the shoreline to being concentrated on both sides of the breakwater. Significant siltation was observed on the inner side of the breakwater at Binhai Harbor between 2015 and 2017, with an increase of 0.86 km2 in the area above −2 m. This study demonstrates that remote sensing technology is highly effective in monitoring changes in coastal topography, especially under the influence of human activities. [ABSTRACT FROM AUTHOR]

Sumatra-Java megathrust has been a host of six earthquake-triggered tsunamis in recent decades, which caused severe damage. Cilegon industrial area is a tsunami-prone zone since it is located on the Sunda Strait coast, facing a possibility of large-magnitude earthquake occurrence in the transition zone of Sumatra and Java megathrust. In this study, we assess the tsunami hazard in this area using a deterministic approach from a worst-case earthquake scenario (Mw 8.9). The assessment was done using numerical tsunami modeling, considering various fault source models and digital elevation model (DEM) datasets to accommodate the uncertainty of those factors in the modeling. Two source models (non-uniform and uniform fault slips) and three DEM datasets (global, regional, and local data) were employed. Uniform slip affected the smaller maximum tsunami amplitude than the non-uniform slip, but the travel time from these two source models is similar. Differences in water depth and slope bathymetry profile also strongly influence the tsunami propagation characteristics, particularly in the finest layer model. Generally, the Cilegon coast is consistently hit by up to 9 m of tsunami height from all used scenarios. The estimated tsunami arrival time is more than 60 min, providing enough time for the coastal community to evacuate to the higher ground level. However, the Cilegon industrial area is still categorized as highly hazardous since the tsunami strike can damage industrial buildings, infrastructure, and factory equipment, leading to economic losses. [ABSTRACT FROM AUTHOR]

Lunar craters are important geomorphological features, that provide valuable insights into lunar morphology, geology, and impact processes. However, the current understanding of lunar craters of different sizes, especially smaller craters (diameter

A digital elevation model (DEM) is widely recognized as the most effective digital representation of the Earth’s surface and serves as the fundamental platform for simulating various Earth systems. Extensive efforts have been devoted to exploring methods for generating high-fidelity DEM datasets that are computationally efficient for diverse applications. However, the existing methods do not guarantee the optimal digital representation of the Earth’s surface. This study proposed a novel curvature-based geodesic centroidal Voronoi tessellation method for generating a topographic triangulated irregular network (TIN) DEM based on optimal transport theory. This study is the first to present a globally optimized digital representation of the Earth’s surface with a predetermined number of vertices, which is crucial for computational feasibility. This study achieves the optimal TIN by measuring mean curvature and introducing geodesic distances on the topographic surface. Representative vertices that best adapt to the topography are identified through an optimal surface approximation process. Experimental results confirm that the proposed method effectively generates the optimal digital representation of the topographic surface with the lowest elevation errors and minimal deviations from the original topographic features. By generating optimal TIN DEM with any desired number of vertices, the proposed method not only balances high-precision representation and computational efficiency but also offers a novel approach to deepening the understanding of topographic structures. Furthermore, it provides an effective solution for compressing extensive topographic data and facilitating multiscale representation of the Earth’s surface.

Accurate measurement and spatial extension of soil properties are essential in geoinformatics and precision agriculture for effective resource management, particularly irrigation planning. This study addresses the challenge of extending soil moisture data and related soil water regime variables in heterogeneous agricultural landscapes by integrating geomorphological variables (GVs) derived from high-resolution digital elevation models (DEM). In digital soil mapping, machine learning and geostatistical models often struggle with validation due to data scarcity and variability across space through many geographical regions that come from the point readings of soil properties. A different approach was developed in the form of a new methodology combining two hourly Sentek soil moisture measurements from the topsoil with DEM-derived GVs to model and extend soil water regime variables. The research was conducted on an agricultural field in a hilly area with diverse geomorphological variability. The model’s performance was validated using cross-validation techniques. The monitoring and spatial extension results indicate that GVs enhance the spatial prediction of soil moisture, capturing periodic fluctuations in the upper soil layer more effectively by using in-situ, time series soil moisture sensor readings rather than traditional, on field, one time reading approaches. We observed that certain GVs, such as the slope, both type of curvatures and the convergence, were strong predictors of soil moisture variation, enabling the model to produce more accurate irrigation recommendations for agricultural areas with similar geomorphological areas. One of the soil water regime variables was validated during the preliminary validation with mixed results. The main issue was coming from the field use and spatial scarcity of the measurements. Our approach not only provides a different method for spatially extending the current soil water regime data but also offers a framework for improving irrigation decision-making with the help of other value rates and limit related soil regime variables derived from the time series readings from the soil moisture sensors. With its variables, the model allows for forecasts of soil moisture changes, which can inform better irrigation scheduling and water resource management, all based on data from the soil monitoring sensor system.

Glacial landforms, created by the continuous movements of glaciers over millennia, are crucial topics in geomorphological research. Their systematic analysis affords invaluable insights into past climatic oscillations and augments understanding of long-term climate change dynamics. The classification of these types of terrain traditionally depends on labor-intensive manual or semi-automated methods. However, the emergence of automated techniques driven by deep learning and neural networks holds promise for enhancing efficiency of terrain classification workflows. This study evaluated the effectiveness of Convolutional Neural Network (CNN) architectures, particularly Residual Neural Network (ResNet) and VGG in comparison with Vision Transformer (ViT) architecture in the glacial landform classification task. By using preprocessed input data from Digital Elevation Model (DEM) which covers regions such as the Lubawa Upland and Gardno-Leba Plain in Poland, as well as the Elise Glacier in Svalbard, Norway, comprehensive assessments of those methods were conducted. The final results highlight the unique ability of deep learning methods to accurately classify glacial landforms. Classification process presented in this study can be the efficient, repeatable and fast solution for automatic terrain classification.

The fine modeling of the lunar gravity field is a fundamental task for establishing lunar altitude references, conducting research on lunar interior structure, and other scientific endeavors. The lunar gravity field model constructed solely based on spacecraft orbit data has limited spatial resolution, making it challenging to meet the demands of fine gravity field research. The acquisition of high-resolution terrain models presents new opportunities for the study of precise lunar gravity fields. This paper reviews the current status and advancements in fine lunar gravity field modeling, including research efforts and evaluation methods for global lunar gravity field models, principles and technologies for fine gravity field modeling, as well as related research outcomes. On this basis, this paper systematically summarizes the key theoretical issues and technical challenges in the theory of lunar gravity field modeling based on high-resolution terrain models. These include data updating, error correction in high-frequency gravity field modeling, calculation efficiency, and the series divergence problem in spherical harmonic analysis. In addition, future research directions are discussed. This work will provide references for gravity field research on the Moon and other terrestrial planets.

This research aims to cost-effectively identify inundation due to combined rainfall with tidal surges by evaluating the existing urban drainage system. The Storm Water Management Model (SWMM) applied two scenarios based on different Digital Elevation Model (DEM) sources. These are scenario A, i.e. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and scenario B, i.e. Shuttle Radar Topography Mission (SRTM). Then, catchment delineation and SWMM parameterization in the low-lying urban region of Chattogram City were executed. In contrast to scenario A, i.e. ASTER DEM, identified inundated nodes in the northwest part of the city, scenario B, i.e. SRTM DEM, showed more inundated nodes. In both scenarios, drainage capacity was observed in the field at two outlet points of Ispahani Khal (R2 = 0.91 and 0.85) and Chaktai Khal (R2 = 0.90 and 0.92), which reasonably mimics the model outcome. With an intensive field dataset, this model could provide relevant information for betterment. [ABSTRACT FROM AUTHOR]

The article focuses on analyzing the effects of Digital Elevation Model (DEM) characteristics, specifically resolution and preprocessing, on the Topographic Wetness Index (TWI) within the Painganga Wildlife Sanctuary in India. It highlights how variations in DEM quality influence hydrological modeling outcomes, spatial wetness patterns, and ecosystem management decisions.

Orbit error is one of the primary error sources of interferometric synthetic aperture radar (InSAR) and differential InSAR (D-InSAR) measurements, arising from inaccurate orbit determination of SAR platforms. Typically, orbit error in the interferogram can be estimated using polynomial models. However, correcting for orbit errors with significant time-varying characteristics presents two main challenges: (1) the complexity and variability of the azimuth time-varying orbit errors make it difficult to accurately model them using a set of polynomial coefficients; (2) existing patch-based polynomial models rely on empirical segmentation and overlook the time-varying characteristics, resulting in residual orbital error phase. To overcome these problems, this study proposes an automated block adjustment framework for estimating time-varying orbit errors, incorporating the following innovations: (1) the differential interferogram is divided into several blocks along the azimuth direction to model orbit error separately; (2) automated segmentation is achieved by extracting morphological features (i.e., peaks and troughs) from the azimuthal profile; (3) a block adjustment method combining control points and connection points is proposed to determine the model coefficients of each block for the orbital error phase estimation. The feasibility of the proposed method was verified by repeat-pass L-band spaceborne and P-band airborne InSAR data, and finally, the InSAR digital elevation model (DEM) was generated for performance evaluation. Compared with the high-precision light detection and ranging (LiDAR) elevation, the root mean square error (RMSE) of InSAR DEM was reduced from 18.27 m to 7.04 m in the spaceborne dataset and from 7.83~14.97 m to 3.36~6.02 m in the airborne dataset. Then, further analysis demonstrated that the proposed method outperforms existing algorithms under single-baseline and single-polarization conditions. Moreover, the proposed method is applicable to both spaceborne and airborne InSAR data, demonstrating strong versatility and potential for broader applications. [ABSTRACT FROM AUTHOR]

Landslide is the most common natural risk in mountainous regions on all five continents and they can pose a serious threat in these areas. Strong earthquakes, unusual weather events such as storms and eruptions of volcanoes, and human-caused events such as creating roadways that crossed the slopes are the main causes of landslides and they cause significant dangers to residential properties and society as a whole. The Landslide4sense dataset is used for identifying landslides, which contains 3799 training samples and 245 testing samples. These image patches are taken from the Sentinel-2 sensor, while the slope and Digital Elevation Model (DEM) are from the ALOS PALSAR sensor. Data was gathered from four distinct geographical areas namely Kodagu, Iburi, Taiwan, and Gorkha. We use Deep Learning (DL) models such as ResNet18, U-Net, and VGG16 to predict the landslide. By comparing the above models with the evaluation metrics like loss, precision, recall, F1 score and accuracy, ResNet18 model is selected as the best model for landslide identification. [ABSTRACT FROM AUTHOR]

Landslides are complex geohazards responsible for damage to life, the natural environment, and essential infrastructures like buildings, roads, and transmission lines in mountainous regions. The modeling of topographic input parameters for landslide-related investigations is often based on Digital Elevation Models (DEMs), which serve as a crucial geospatial data source. The present study attempts to analyze the effects of DEMs, obtained from different sources and varying in spatial resolution, on terrain feature estimation and spatial characterization of landslide-affected areas in the Indian Himalayas. Carto-DEM version 3R1 and ALOS PALSAR DEM are used to generate two geodatabases of DEM-derived landslide causative factors, each including digital maps of Elevation, Slope, Aspect, Curvature, Terrain Ruggedness Index, and Distance to Drainage. The generated geodatabases are utilized for conducting a spatial frequency distribution analysis to characterize the selected area into spatial bins with similar topographic characteristics. A comparative study of this analysis reveals that both the DEMs exhibited comparable topographic characteristics on a general level. However, considerable variations are observed when both the geodatabases are scrutinized closely. The results of this study highlight that the quality of the DEM used may affect its usability in a specific investigation and hope to add to the scientific discourse on the effects of DEM on landslide-related studies. [ABSTRACT FROM AUTHOR]

Combined with the structure from motion (SfM) technique, unmanned aerial vehicles (UAVs) are powerful tools for generating high-resolution digital elevation models (DEMs) for application in hazard assessments. During our field observations in October 2015 at Langtang Village, which was destroyed by the Gorkha earthquake in April 2015, three different UAVs with mounted cameras were operated to evaluate the volume of the avalanche deposit covering the village. This study evaluated the performance of DEMs created from the different cameras on board those UAVs. Multiple DEMs for the different cameras, including Sony-α7R (PA7), Ricoh-GR (XGR), and Canon-IXUS (EIX), were created using SfM software. All DEMs were compared with a base DEM created from differential global positioning system survey data, which was obtained simultaneously with the UAV campaigns. The results show that the elevation difference of PA7-, XGR-, and EIX-DEMs are within ±0.14 m; the standard deviations of elevation difference range from 0.33 to 0.40 m. Although there were slightly larger differences in elevation on the southwest-to-west sides of the XGR- and EIX-DEMs, which can be attributed mainly to the flight paths and ground control point network, our DEMs are still of high enough quality to be used in hazard assessments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate measurement and spatial extension of soil properties are essential in geoinformatics and precision agriculture for effective resource management, particularly irrigation planning. This study addresses the challenge of extending soil moisture data and related soil water regime variables in heterogeneous agricultural landscapes by integrating geomorphological variables (GVs) derived from high-resolution digital elevation models (DEM). In digital soil mapping, machine learning and geostatistical models often struggle with validation due to data scarcity and variability across space through many geographical regions that come from the point readings of soil properties. A different approach was developed in the form of a new methodology combining two hourly Sentek soil moisture measurements from the topsoil with DEM-derived GVs to model and extend soil water regime variables. The research was conducted on an agricultural field in a hilly area with diverse geomorphological variability. The model's performance was validated using cross-validation techniques. The monitoring and spatial extension results indicate that GVs enhance the spatial prediction of soil moisture, capturing periodic fluctuations in the upper soil layer more effectively by using in-situ, time series soil moisture sensor readings rather than traditional, on field, one time reading approaches. We observed that certain GVs, such as the slope, both type of curvatures and the convergence, were strong predictors of soil moisture variation, enabling the model to produce more accurate irrigation recommendations for agricultural areas with similar geomorphological areas. One of the soil water regime variables was validated during the preliminary validation with mixed results. The main issue was coming from the field use and spatial scarcity of the measurements. Our approach not only provides a different method for spatially extending the current soil water regime data but also offers a framework for improving irrigation decision-making with the help of other value rates and limit related soil regime variables derived from the time series readings from the soil moisture sensors. With its variables, the model allows for forecasts of soil moisture changes, which can inform better irrigation scheduling and water resource management, all based on data from the soil monitoring sensor system. [ABSTRACT FROM AUTHOR]

To address the accurate extraction of sparse and low mangroves perplexed by the periodic change of tide level, we take the Beibu Gulf of Guangxi as the research area to construct a decision tree model for mangrove identification using Landsat8 OLI images at low and high tidal levels and DEM (Digital Elevation Model) data, which is then evaluated by comparing with SVM (Support Vector Machine). The research results show that difference exists in the spectra of mangroves with different heights and canopy densities or under different tide levels, while the sparse and low mangroves share the same spectrum with shady slope forest and water-terrestrial vegetation mixed pixel. The SVM approach classifies the mangroves as high-dense type and low-sparse type, and improves the overall accuracy by 4. 65, 4. 41 and 7. 22 percentage points for low-tide, high-tide and multi-tide images, respectively. The proposed approach reaches 98. 80％ of overall accuracy and 0. 973 of Kappa coefficient, which are 1. 62 percentage points and 0. 035 higher than the best values of SVM approach. It can be concluded that considering the mangrove height, density, tide level and DEM can significantly improve the identification accuracy of mangroves from remote sensing images. [ABSTRACT FROM AUTHOR]

Increased public awareness, stricter measures, and new laws in the field of water resources have made the use of modern computer technology indispensable in the fields of science and technology. Thus, geographic information systems (GISs) are most in demand as they can be used as effective tools for storing, managing, and presenting spatial hydrological data and are essential in water resources management tasks. The application of GIS technology to water resources is steadily expanding. Recently, a lot of software provided by modern GIS has appeared. These software products solve various hydrological tasks, including hydrological modeling of surface water, groundwater, water supply, and sewerage systems, as well as rainwater pollution simulation. This article reviews and analyzes modern GIS software products that are significant in the analysis of hydrological data. The study aims to highlight the differences between various programs and use the results to determine which software is the best, regardless of whether it is paid or open source. [ABSTRACT FROM AUTHOR]

Copyright of Reviews of Geophysics & Planetary Physics is the property of Editorial Office of Reviews of Geophysics & Planetary Physics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ABSTRACTDeep learning-based super-resolution is an essential technique for acquiring high-resolution digital elevation models (DEMs) by enhancing the spatial resolution of low-resolution DEMs. However, current deep learning-based approaches for DEM super-resolution lack comprehensiveness in terrain information reconstruction, resulting in the need to strengthen the rationality of terrain representation. Furthermore, the limited adaptability and extension potential of these approaches restrict their practical applicability and scope, hindering further advancement. As a solution, we introduce a broadly scalable detrending-based deep learning (DTDL) spatially explicit framework for DEM super-resolution. The framework aims to improve DEM reconstruction through data processing and augmentation. It employs detrending to distinguish between large-scale terrain trends and small-scale residuals in DEMs, thereby enhancing the neural network's capacity to learn terrain information. We integrate DTDL with classical super-resolution methods (SRCNN, EDSR, and SRGAN) and conduct experiments in the Alps, Himalayas, and Rockies. The experimental results indicate that the fusion of DTDL with deep learning-based methods enhances the accuracy of terrain reconstruction and the rationality of terrain feature representation, demonstrating strong compatibility and robustness.

This study assesses sediment erosion rates in the Zhoukou River Basin in southern Taiwan over the past three decades, focusing on the impact of extreme rainfall events. While various established methods determine erosion rates over different temporal scales, we employ an independent approach for decadal-scale erosion rate calculation by utilizing open-source global and regional digital elevation models (DEMs) data for two distinct periods. We introduce a new method that applies Fourier analysis to vertically register the DEMs, significantly minimizing their vertical bias. Through spectral analysis, we identify long wavelength topography crucial for correcting vertical offsets. Erosion rates, computed through DEMs of Difference (DoD), exhibit a similar trend of significant reduction in sediment export rates from 1990–2010 to 2011–2020 due to decreased extreme rainfall events, aligning with erosion rate estimates derived from mean suspended load data at gauge stations. Over the entire period from 1990 to 2020, the calculated denudation rate was 14.19 mm/yr, whereas in the recent decade (2011–2020), it decreased to 10.46 mm/yr. Our study suggests that the improved DoD method can effectively estimate sediment transport rates by leveraging underutilized DEMs captured at distinct points in time, especially when the erosional signal dominates data noise.

Imagery obtained from unmanned aerial vehicle (UAV) is widely used for land surface modelling. Recent research prove that digital elevation models (DEMs) created from UAV imagery are characterized by a high rate of accuracy and reliability. Most of these studies are focused on assessing absolute elevation accuracy of the UAV DEMs, but the accuracy of relative elevations (i.e., accuracy of reproducing of local elevation differences within DEM) also should be considered. In this paper, we focus on the precision of replicating relative elevations in DEMs derived from imagery captured via UAVs without precise coordinate reference. To evaluate this accuracy, we use datasets of aerial images processed in two different methods: one with on-board coordinates obtained from a GNSS receiver, and the other based on precise coordinates calculated with the Post-Processing Kinematic (PPK) method. The sites selected for assessment are not look like each other in terms of terrain and forest cover characteristics to track the difference of modelling in the divergent areas. Constructed DEMs were compared with reference fragments of global DEMs by the statistical indices for the difference fields. The findings indicate that the absence of an accurate coordinate reference does not have a substantial impact on the precision of reproducing relative elevations in the DEM. This makes it possible to use UAV materials without precise coordinate reference for modelling in most geographical studies, where the error of terrain steepness values of 0.9° can be considered acceptable.

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]

Copyright of Journal of Xinyang Normal University Natural Science Edition is the property of Journal of Xinyang Normal University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Terrain feature points, such as peaks, pits, and saddles, represent the macro-structure of the landform. Conventional techniques for extracting these points from digital elevation models (DEMs) often grapple with issues of inaccuracy, omission and redundancy, largely due to the problematic necessity of setting threshold values. This paper proposes an innovative approach for the automatic detection of terrain feature points based on the topological relationships of contours and the inherent constraints of terrain shape characteristics. The study provides a robust mathematical model of terrain feature points and an effective algorithm for their extraction. Comparing with manually reference data, the accuracy metrics including completeness, correctness, and quality of our extracted results demonstrate a high level, significantly surpassing those obtained through existing algorithms. This proposed approach not only avoids the spurious feature points produced by the local window method, but also prevents the omission of valid points and the creation of redundant ones. Moreover, by utilizing the contour interval as its only variable, our approach eliminates the need for various threshold settings, streamlining the extraction process. [ABSTRACT FROM AUTHOR]

This research conducts a comparative analysis of the vertical accuracies of satellite-derived Digital Elevation Models (DEMs) in the Akure South Local Government Area, Ondo State, Nigeria. The DEMs assessed were obtained from three open-source satellite missions: Sentinel-1A, ALOS PALSAR, and TanDEM-X. The reference Ground Control Points (GCPs) used in the evaluations comprised 133 GCPs of 2nd and 3rd-order accuracy acquired from the Office of the Surveyor General, Ondo State, and through field surveys using the South Galaxy-1 Differential GPS. These obtained GCPs provided extensive coverage of the study area. The evaluation was undertaken based on the land cover, which was established by dividing the study area into open (built-up and bare land) and vegetation-covered (agricultural and forested) areas. The vertical accuracies of the satellite-based DEM collected within the study area were then compared and analyzed. The results revealed that Sentinel-1A and TanDEM-X were the most suitable in both open and vegetation-covered parts of the study area, with Sentinel-1A exhibiting the best performance. The effect of vegetation cover was mostly felt by ALOS PALSAR. Sentinel-1A and TanDEMX displayed commendable vertical accuracies, deemed highly suitable for applications in topographic mapping, as shown by an RMS of less than 2m. This research recommends the open-source satellite DEMs Sentinel-1A and TanDEM-X for planning and engineering applications because they offer the highest accuracy among their counterparts within Akure and its environs. [ABSTRACT FROM AUTHOR]

ABSTRACTHigh-quality height reference data are embedded in the accuracy verification processes of most remote sensing terrain applications. The Ice, Cloud, and Land elevation Satellite 2 (ICESat-2)/ATL08 terrain product has shown promising results for estimating ground heights, but it has not been fully evaluated. Hence, this study aims to assess and enhance the accuracy of the ATL08 terrain product as a height reference for the newest versions of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the Shuttle Radar Topography Mission (SRTM), and TanDEM-X (TDX) DEMs over vegetated mountainous areas. We used uncertainty-based filtering method for the ATL08 strong and weak beams to enhance their accuracy. Then, the results were evaluated against a reference airborne LiDAR digital terrain model (DTM), by selecting 10,000 points over the entire area and comparing the accuracy of ASTER, SRTM, and TDX DEMs assessed by the LiDAR DTM to the accuracy of the ASTER, SRTM, and TDX DEMs assessed by the ATL08 strong beams, weak beams, and all beams. We also detected the impact of the terrain aspect, slope, and land cover types on the accuracy of the ATL08 terrain elevations and their relationship with height errors and uncertainty. Our findings show the accuracy of the ATL08 strong beams was enhanced by 43.91%; while the weak beams accuracy was enhanced by 74.05%. Furthermore, slope strongly influenced ATL08 height errors and height uncertainty; especially on the weak beams. The errors induced by the slope significantly decreased when the uncertainty levels were reduced to

Activities, particularly harbor construction, often exert significant and non-negligible impacts on coastal environments. Therefore, it is of great practical importance to quantitatively assess the effects of such construction on the surrounding topography, such as tidal flats. This study focuses on the coast of Jiangsu Binhai Harbor. Using multi-source and multi-temporal remote sensing images, digital elevation models of tidal flats surrounding Binhai Harbor were generated for the years 2013, 2015, and 2017 through the waterline method. A quantitative analysis was conducted utilizing GIS spatial analysis techniques to examine erosion–deposition patterns, contour changes, and typical cross-sectional comparisons. The findings reveal that, although the overall coastline is in a state of erosion, the localized impacts of harbor construction are evident. Between 2013 and 2017, the northern tidal flats experienced overall erosion, whereas deposition occurred near the harbor’s root areas. Compared to 2013–2015, there was a significant decrease in erosion between 2015 and 2017, indicating that the construction of the project had a significant impact on the northern tidal flats. Throughout the five-year study period, the tidal flats within the breakwater underwent continuous adjustment, shifting from being close to the shoreline to being concentrated on both sides of the breakwater. Significant siltation was observed on the inner side of the breakwater at Binhai Harbor between 2015 and 2017, with an increase of 0.86 km2 in the area above −2 m. This study demonstrates that remote sensing technology is highly effective in monitoring changes in coastal topography, especially under the influence of human activities.

TanDEM-X bistatic interferometric system successfully generated a high-precision, high-resolution global digital elevation model (DEM). However, in forested areas, two core problems make it difficult to obtain sub-canopy topography: (1) the penetrability of short-wave signals is limited, and the DEM obtained in dense forest areas contains a significant forest signal, that is, the scattering phase center (SPC) height; and (2) the single-baseline and single-polarization TanDEM-X interferometric synthetic aperture radar (InSAR) data cannot provide sufficient observations to make the existing physical model reversible for estimating the real surface phase, whereas the introduction of optical data makes it difficult to ensure data synchronization and availability of cloud-free data. To overcome these problems in accurately estimating sub-canopy topography from TanDEM-X InSAR data, this study proposes a practical method of sub-canopy topography estimation based on the following innovations: (1) An orthogonal polynomial model was established using TanDEM-X interferometric coherence and slope to estimate the SPC height. Interferometric coherence records forest height and dielectric property information from an InSAR perspective and has spatiotemporal consistency with the InSAR-derived DEM. (2) Introduce Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data to provide more observational information and automatically screen ICESat-2 control points with similar forest and slope conditions in the local area to suppress forest spatial heterogeneity. (3) A weighted least squares criterion was used to solve this model to estimate the SPC height. The results were validated at four test sites using high-precision airborne light detection and ranging (LiDAR) data as a reference. Compared to the InSAR-derived DEM, the accuracy of the sub-canopy topography was improved by nearly 60%, on average. Furthermore, we investigated the necessity of local modeling, confirming the potential of the proposed method for estimating sub-canopy topography by relying only on TanDEM-X and ICESat-2 data. [ABSTRACT FROM AUTHOR]

An on-site InSAR imaging method carried out with unmanned aerial vehicles (UAVs) is proposed to monitor terrain changes with high spatial resolution, short revisit time, and high flexibility. To survey and explore a specific area of interest in real time, a combination of a least-square phase unwrapping technique and a mean filter for removing speckles is effective in reconstructing the terrain profile. The proposed method is validated by simulations on three scenarios scaled down from the high-resolution digital elevation models of the US geological survey (USGS) 3D elevation program (3DEP) datasets. The efficacy of the proposed method and the efficiency in CPU time are validated by comparing with several state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

High-quality height reference data are embedded in the accuracy verification processes of most remote sensing terrain applications. The Ice, Cloud, and Land elevation Satellite 2 (ICESat-2)/ATL08 terrain product has shown promising results for estimating ground heights, but it has not been fully evaluated. Hence, this study aims to assess and enhance the accuracy of the ATL08 terrain product as a height reference for the newest versions of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the Shuttle Radar Topography Mission (SRTM), and TanDEM-X (TDX) DEMs over vegetated mountainous areas. We used uncertainty-based filtering method for the ATL08 strong and weak beams to enhance their accuracy. Then, the results were evaluated against a reference airborne LiDAR digital terrain model (DTM), by selecting 10,000 points over the entire area and comparing the accuracy of ASTER, SRTM, and TDX DEMs assessed by the LiDAR DTM to the accuracy of the ASTER, SRTM, and TDX DEMs assessed by the ATL08 strong beams, weak beams, and all beams. We also detected the impact of the terrain aspect, slope, and land cover types on the accuracy of the ATL08 terrain elevations and their relationship with height errors and uncertainty. Our findings show the accuracy of the ATL08 strong beams was enhanced by 43.91%; while the weak beams accuracy was enhanced by 74.05%. Furthermore, slope strongly influenced ATL08 height errors and height uncertainty; especially on the weak beams. The errors induced by the slope significantly decreased when the uncertainty levels were reduced to <20 m. The evaluations of ASTER, SRTM, and TDX DEMs by ATL08 strong and weak beams are close to those assessed by LiDAR DTM points within 0.6 m for the strong beams. These findings indicate that ATL08 strong beams can be used as a height reference over vegetated mountainous regions. [ABSTRACT FROM AUTHOR]

This paper presents a method for extracting the digital elevation model (DEM) of forested areas from polarimetric interferometric synthetic aperture radar (PolInSAR) data. The method models the ground phase as a Von Mises distribution, with a mean of the topographic phase computed from an external DEM. By combining the prior distribution of the ground phase with the complex Wishart distribution of the observation covariance matrix, we derive the maximum a posterior (MAP) inversion method based on the RVoG model and analyze its Cramer–Rao Lower Bound (CRLB). Furthermore, considering the characteristics of the objective function, this paper introduces a Four-Step Optimization (FSO) method based on gradient optimization, which solves the inefficiency problem caused by exhaustive search in solving ground phase using the MAP method. The method is validated using spaceborne L-band repeat-pass SAOCOM data from a test forest area. The test results for FSO indicate that it is approximately 5.6 times faster than traditional methods without compromising accuracy. Simultaneously, the experimental results demonstrate that the method effectively solves the problem of elevation jumps in DEM inversion when modeling the ground phase with the Gaussian distribution. ICESAT-2 data are used to evaluate the accuracy of the inverted DEM, revealing that our method improves the root mean square error (RMSE) by about 23.6% compared to the traditional methods. [ABSTRACT FROM AUTHOR]