High-quality Digital Elevation Models Research Articles

Multiphase photoclinometry as applied to the lunar photometry with LROC NAC data

An original method for photometric and geometric correction of LROC NAC data with high resolution (up to 0.5 m/pix) has been developed. The technique is based on photogrammetry and multiphase photoclinometry and allows us to obtain a longitudinal slope map and digital elevation model (DEM) with the resolution of LROC NAC CDR input images as well as to map parameters of model phase function. Obtained DEMs, requiring only LROC NAC images and LRO SPICE-kernels, exhibit significantly fewer defects and artifacts compared to existing DEMs, because they are derived from the same images they correct (self-orthorectification).This method was used to study the area of the photometric anomaly, the Irregular Mare Patch (IMP) formation Ina. A zoning map of the correlation diagram of phase ratio vs. equigonal albedo shows significant differences in the optical (photometric) properties of the regolith for hummocky and blocky formations of the IMP Ina, compared to the surrounding areas, which indicates the different nature of their formation, age, and surface roughness, contradicting some models of the IMP formation. The analysis reveals the incompleteness or inconsistencies of previously proposed models of the IMP's formation mechanisms. The high-quality DEM of the Ina formation and surrounding area with a resolution of 0.5 m/pixel was constructed.

Simulation of the Hydraulic Model HEC-RAS Coupled with GIS and Remote Sensing to Study the Effect of River Cross-section Width in Detecting Flood-prone Areas

Abstract Flooding is one of the extreme hydrological phenomena. It is a recurring natural disaster that causes loss of life and property in many parts of the world, particularly during the monsoon season. It is important to address such issues for local government and policymakers to manage the flood properly. One such flood management activity is to develop a flood-prone area that depicts the spatial and temporal extent of flood accurately. The integration of the Hydrologic Engineering Centre - River Analysis System (HEC-RAS) model and geospatial tools have emerged as a crucial approach for identifying and mapping flood-prone areas. The successful application of the HEC-RAS model generally depends on the topographical data, which represents the channel and floodplain geometry. In floodplain geometry, discrete cross-sections play a vital role to develop the floodplain map, particularly in the flat topographical region. To extract these data it needs a high-quality digital elevation model (DEM), such as light detection and ranging (LiDAR). However, due to a lack of high-resolution topographical data, flood hazard mapping in developing countries is rare. In common practice, the centerline of the river is considered the flow path for the channel. The orientation of the cross-sections is perpendicular to this line and extends to reach the limits of the floodplain. But, it is difficult to define the limits and it may depend on the magnitude of the flood. Hence, in this study, the HEC RAS model coupled with ArcGIS has been applied to the Ganga River, which traverses through the Bihar state of India to study the effect of cross-sectional width to define the floodplain. The Bihar state is facing substantial hardships from annual flooding events with approximately 16.5% of India’s flood-prone area. The extreme flood values for 5, 10, and 25 years of return period have been determined and the influence of the three different cross-sectional widths to mapping the floodplain has been investigated. This novel perspective adds dimension to the understanding of flood dynamics and its implications for flood risk assessment. In this analysis, it has been observed that, with an increase in the width of the cross-section, the floodplain area also getting increased. In this topographical region, keeping a fixed flow path will underestimate the flood-prone area. The width of the flow path depends on the topography of the region and the river flow. The outcomes of this analysis provide valuable insights into the flood-inundated areas for the specified return periods, enabling the identification and prioritization of flood-prone zones.

Construction of a High-Quality Digital Elevation Model of the Amundsen Crater and Landing Area Selection for Future Lunar Missions

Construction of a High-Quality Digital Elevation Model of the Amundsen Crater and Landing Area Selection for Future Lunar Missions

Vertical accuracy assessment for open-source digital elevation models using GPS control points and watershed basins delineation using GIS

Digital elevation models (DEMs) are crucial for numerous assessments of the Earth's surface, so this research aim to evaluate the quality of several open source DEMs like advanced spaceborne thermal emission and reflection radiometer global DEM (ASTER GDEM), shuttle radar topography mission DEM (SRTM DEM), SENTINEL-1, and TerraSAR-X for digital elevation measurement (TanDEM-X). TanDEM-X is one of the newest DEM models; however, ASTER and SRTM are still the most used. To evaluate the qualities of these DEMs, the vertical accuracies of ASTER, STRM (all 30 m), SRTM, TanDEM-X (90 m), and SENTINEL 1 (range from 5 to 12.5 m) were tested using GPS reference points and The RMSE gave the values of ± 3.56 m ± 57.68 m, ±5.89 m, ±10.21 m and ± 20.25 mfor used SRTM1, SENTINEL-1, SRTM3, TanDEM-X, and ASTER DEMs respectively. As is well known, obtaining a high-quality DEM is exceedingly challenging since it is time-consuming, expensive, and often constrained, therefore this study examined how to enhance SRTM1 as it gave the best result and assessed the effects of increasing the number of points on digital elevation model accuracy by using raster calculator and merge model in ArcGIS. The results showed that the merge model method gave the best result and, by increasing the number of reference points, the accuracy increased. The DEM produced by the enhanced SRTM (iSRTM) demonstrated noticeably better outcomes than the baseline SRTM DEM, with around 3.56 m RMSE.In this research, STRM-1 DEM is used to determine watershed basin delineation. The topographic map showed significant variations in the level of the Earth's surface, as the heights of the area ranged between 20 m and 120 m. The number of watersheds extracted in the Benban is 132 with different areas, the large area is about 72.14 km2, while the small area is about 1.13 km2, and between those areas, there are various areas. The largest recorded watershed circumference reached 48.94 km in area (72.14 km2), while the lowest recorded perimeter is 5.40 km for the area (1.13 km2).

Evaluation of the effect of MLS density level data on DEM

Mobile laser scanning (MLS) systems are quickly establishing themselves as the primary source of high-quality digital elevation models (DEM). Data density is the key element that determine the quality of DEMs. Moreover, point density acts a vital role in MLS operation planning and MLS project's cost. This study's goal was to evaluate the impact of point density from MLS on creating DEMs and the accuracy of computed volume in different interpolation methods. The finding demonstrated that the DEM's quality and the volume are more impacted by density level and interpolation method. Interpolation methods are affected by point density. There is no single interpolation approach that is appropriate for all data sources. From this study, the different interpolation methods are suitable for use with dense sample data. In this study, the difference between interpolation approaches is very small because the surface is roughly flat.

Historical Structure from Motion (HSfM): Automated processing of historical aerial photographs for long-term topographic change analysis

Precisely measuring the Earth’s changing surface on decadal to centennial time scales is critical for many science and engineering applications, yet long-term records of quantitative landscape change are often temporally and geographically sparse. Archives of scanned historical aerial photographs provide an opportunity to augment these records with accurate elevation measurements that capture the historical state of the Earth surface. Structure from Motion (SfM) photogrammetry workflows produce high-quality digital elevation models (DEMs) and orthoimage mosaics from these historical images, but time-intensive tasks like manual image preprocessing (e.g., fiducial marker identification) and ground control point (GCP) selection impede processing at scale. We developed an automated method to process historical images and generate self-consistent time series of high-resolution (0.5–2 m) DEMs and orthomosaics, without manual GCP selection. The method relies on SfM to correct camera interior and exterior orientation and a robust multi-stage co-registration approach using modern reference terrain datasets for geolocation refinement. We demonstrate the method using scanned images from the North American Glacier Aerial Photography (NAGAP) archive collected between 1967 and 1997. We present results for two sites with variable photo acquisition geometry and overlap — Mount Baker and South Cascade Glacier in Washington State, USA. The automated method corrects initial camera position errors of several kilometers and produces accurately georeferenced, high-resolution DEMs and orthoimages, regardless of camera configuration, acquisition geometry, terrain characteristics, and reference DEM properties. The average RMS reprojection error following bundle adjustment optimization was 0.67 px (0.15 m) for the 261 images contributing to 10 final DEM mosaics between 1970 and 1992 at Mount Baker, and 0.65 px (0.13 m) for the 243 images contributing to 18 individual DEMs between 1967 and 1997 at South Cascade Glacier. The relative accuracy of elevation values in the historical time series stacks was 0.68 m at Mount Baker and 0.37 m at South Cascade Glacier. Our products have reduced systematic error and improved accuracy compared to DEM products generated using SfM with manual GCP selection. Final elevation change measurement precision was ∼0.7–1.0 m over a 30-year period, enabling the study of processes with rates as low as ∼1-3 cm/yr. Our results demonstrate the potential of this scalable method to rapidly process archives of historical imagery and deliver new quantitative insights on long-term geodetic change and Earth surface processes.

A Neural Network-Based Fusion Approach for Improvement of SAR Interferometry-Based Digital Elevation Models in Plain and Hilly Regions of India

Interferometry Synthetic Aperture Radar (InSAR) is an advanced remote sensing technique for studying the earth’s surface topography and deformations; it is used to generate high-quality Digital Elevation Models (DEMs). DEMs are a crucial and primary input to various topographical quantification and modelling applications. The quality of input DEMs can be further improved using fusion methods, which combine multi-sensor or multi-temporal datasets intelligently to retrieve the best information from the input data. This research study is based on developing a Neural Network-based fusion approach for improving InSAR-based DEMs in plain and hilly terrain parts of India. The study areas comprise relatively plain terrain from Ghaziabad and hilly terrain of Dehradun and their surrounding regions. The training dataset consists of DEM elevations and derived topographic attributes like slope, aspect, topographic position index (TPI), terrain ruggedness index (TRI), and vector roughness measure (VRM) in different land use land cover classes of the study areas. The spaceborne altimetry ICESat-2 ATL08 photon data are used as a reference elevation. A Feed Forward Neural Network with a backpropagation algorithm is trained based on the prepared training samples. The trained model produces fused DEMs by learning the relationship between the input and target samples; this is used to predict elevations for the test areas. The accuracy of results from the models is assessed with TanDEM-X 90 m DEM. The fused DEMs show significant improvement in terms of RMSE (Root Mean Square Error) over the input DEMs with an improvement factor of 94.65% in plain areas and 82.62% in hilly areas. The study concludes that the ANN with its universal approximation property can significantly improve the fused DEM.

Glacier-wide seasonal and annual geodetic mass balances from Pléiades stereo images: application to the Glacier d'Argentière, French Alps

AbstractThe increased availability of high-resolution and high-quality digital elevation models (DEMs) allows for the investigation of small-scale glaciological changes and improved precision in geodetic mass-balance estimates. However, high precision and careful methodological choices are required to retrieve glacier-wide mass changes at annual to seasonal time scales. Here, we used a 7-year time series of 12 DEMs of the Glacier d'Argentière, in the French Alps, derived from the Pléiades optical satellites to assess the ability of sub-metre stereoscopic satellite images to retrieve annual-to-seasonal mass balances. We found good agreement between the five annual and the four winter mass-balance values estimated using a geodetic method and those of in situ glaciological measurements: mean values via the geodetic method are −0.66 m w.e. and 1.47 m w.e. for annual and winter balances, respectively; mean absolute discrepancies are 0.25 m w.e. (annual) and 0.36 m w.e. (winter). Our study identified three main limitations of this methodology: (i) the intrinsic DEM precision; (ii) the lack of control over the satellite acquisition dates; and (iii) the density assumption. The consistency between the methods demonstrates the potential of short time-scale glacier mass-balance monitoring using very high-resolution satellite images.

Glacier Mass Balance in the Manas River Using Ascending and Descending Pass of Sentinel 1A/1B Data and SRTM DEM

Mountain glaciers monitoring is important for water resource management and climate changes but is limited by the lack of a high-quality Digital Elevation Model (DEM) and field measurements. Sentinel 1A/1B satellites provide alternative data for glacier mass balance. In this study, we tried to generate DEMs from C-band Sentinel 1A/1B ascending and descending pass SLC images and evaluate the overall accuracy of INSAR DEMs based on Shuttle Radar Topography Mission (SRTM) DEM and ICESat/GLAS. The low Standard Deviation (STD)and Root Means Square Error (RMSE) displayed the feasibility of Sentinel 1A/1B satellites for DEM generation. Glacier elevation changes and glacier mass balance were estimated based on INSAR DEM and SRTM DEM. The results showed that the most glaciers have exhibited obvious thinning, and the mean annual glacier mass balance between 2000 and 2020 was −0.18 ± 0.1 m w.e.a−1. The south-facing and-east facing aspects, slope and elevation play an important role on glacier melt. This study demonstrates that ascending and descending orbit data of Sentinel-1A/1B satellites are promising for the detailed retrieval of surface elevation changes and mass balance in mountain glaciers.

Land-Surface Quantitative Analysis to Investigate the Spatial Distribution of Gravitational Landforms along Rocky Coasts

The increasing availability of high-quality digital elevation models (DEMs) has been associated with a growing interest in developing quantitative analyses aimed at taking advantage of these detailed, updated, and promising digital datasets. Land-surface quantitative (LSQ) analysis is valuable for describing the land-surface topography and performing measures of the signature of specific geomorphic processes, taking into account site-specific geological contexts and morphoclimatic settings, proving to be particularly effective in transitional environments, such as rocky coasts. This paper presents the results of research aimed at investigating the spatial distribution of gravitational landforms along rocky coasts, by means of LSQ analysis based on a DEM with a ground resolution of 2 m, derived from airborne LiDAR (light detection and ranging) surveys. The study area is at Mt. San Bartolo (Northern Marche, Italy) and characterized by a sea cliff diffusely affected by gravitational phenomena of different sizes and types. Geomorphological and geological field data, interpretations of remotely sensed datasets derived from ad hoc unmanned aerial vehicle (UAV) flights, and DEM-derived hillshades were also adapted to support LSQ analysis. In detail, four morphometric variables (slope, roughness, terrain ruggedness index, and elevation standard deviation) were computed and the outputs evaluated based on visual–spatial inspections of derived raster datasets, descriptive statistics, and joint comparison. Results reveal the best performing variables and how combined interpretations can support the identification and mapping of zones characterized by varying spatial distribution of gravitational landforms of different types. The findings achieved along the Mt. San Bartolo rocky coast confirm that an approach based on land-surface quantitative analysis can act as a proxy to efficiently investigate gravitational slope processes in coastal areas, especially those that are difficult to reach with traditional field surveys.

Satellite DEM Improvement Using Multispectral Imagery and an Artificial Neural Network

The digital elevation model (DEM) is crucial for various applications, such as land management and flood planning, as it reflects the actual topographic characteristic on the Earth’s surface. However, it is quite a challenge to acquire the high-quality DEM, as it is very time-consuming, costly, and often confidential. This paper explores a DEM improvement scheme using an artificial neural network (ANN) that could improve the German Aerospace’s TanDEM-X (12 m resolution). The ANN was first trained in Nice, France, with a high spatial resolution surveyed DEM (1 m) and then applied on a faraway city, Singapore, for validation. In the ANN training, Sentinel-2 and TanDEM-X data of the Nice area were used as the input data, while the ground truth observation data of Nice were used as the target data. The applicability of iTanDEM-X was finally conducted at a different site in Singapore. The trained iTanDEM-X shows a significant reduction in the root mean square error of 43.6% in Singapore. It was also found that the improvement for different land covers (e.g., vegetation and built-up areas) ranges from 20 to 65%. The paper also demonstrated the application of the trained ANN on Ho Chi Minh City, Vietnam, where the ground truth data are not available; for cases such as this, a visual comparison with Google satellite imagery was then utilized. The DEM from iTanDEM-X with 10 m resolution categorically shows much clearer land shapes (particularly the roads and buildings).

Φ-Net: Deep Residual Learning for InSAR Parameters Estimation

Nowadays, deep learning (DL) finds application in a large number of scientific fields, among which the estimation and the enhancement of signals disrupted by the noise of different natures. In this article, we address the problem of the estimation of the interferometric parameters from synthetic aperture radar (SAR) data. In particular, we combine convolutional neural networks together with the concept of residual learning to define a novel architecture, named Φ-Net, for the joint estimation of the interferometric phase and coherence. Φ-Net is trained using synthetic data obtained by an innovative strategy based on the theoretical modeling of the physics behind the SAR acquisition principle. This strategy allows the network to generalize the estimation problem with respect to: 1) different noise levels; 2) the nature of the imaged target on the ground; and 3) the acquisition geometry. We then analyze the Φ-Net performance on an independent data set of synthesized interferometric data, as well as on real InSAR data from the TanDEM-X and Sentinel-1 missions. The proposed architecture provides better results with respect to state-of-the-art InSAR algorithms on both synthetic and real test data. Finally, we perform an application-oriented study on the retrieval of the topographic information, which shows that Φ-Net is a strong candidate for the generation of high-quality digital elevation models at a resolution close to the one of the original single-look complex data.

Glacier Ice Thickness Estimation and Future Lake Formation in Swiss Southwestern Alps—The Upper Rhône Catchment: A VOLTA Application

Glacial lake formations are currently being observed in the majority of glacierized mountains in the world. Given the ongoing climate change and population increase, studying glacier ice thickness and bed topography is a necessity for understanding the erosive power of glacier activity in the past and lake formation in the future. This study uses the available information to predict potential sites for future lake formation in the Upper Rhône catchment located in the Southwestern Swiss Alps. The study integrates the latest available glacier outlines and high-quality digital elevation models (DEMs) into the Volume and Topography Automation (VOLTA) model to estimate ice thickness within the extent of the glaciers. Unlike the previous ice thickness models, VOLTA calculates ice thickness distribution based on automatically-derived centerlines, while optimizing the model by including the valley side drag parameter in the force equation. In this study, a total ice volume of 37.17 ± 12.26 km3 (1σ) was estimated for the Upper Rhône catchment. The comparison of VOLTA performance indicates a stronger relationship between measured and predicted bedrock, confirming the less variability in VOLTA’s results (r2 ≈ 0.92) than Glacier Bed Topography (GlabTop) (r2 ≈ 0.82). Overall, the mean percentage of ice thickness error for all measured profiles in the Upper Rhône catchment is around ±22%, of which 28 out of 42 glaciers are underestimated. By incorporating the vertical accuracy of free-ice DEM, we could identify 171 overdeepenings. Among them, 100 sites have a high potential for future lake formation based on four morphological criteria. The visual evaluation of deglaciated areas also supports the robustness of the presented methodology, as 11 water bodies were already formed within the predicted overdeepenings. In the wake of changing global climate, such results highlight the importance of combined datasets and parameters for projecting the future glacial landscapes. The timely information on future glacial lake formation can equip planners with essential knowledge, not only for managing water resources and hazards, but also for understanding glacier dynamics, catchment ecology, and landscape evolution of high-mountain regions.

Fusion of public DEMs based on sparse representation and adaptive regularization variation model

Global or quasi-global digital elevation model (DEM) datasets provide three-dimensional information on terrain surface, and they have been extremely useful in geoscience research and applications. However, the wide application of DEMs is constrained by differences in the means of observation and processing, and in the resolution of global public DEM datasets. An adaptive regularization variation model based on sparse representation is proposed to generate a high-quality DEM by fusing multi-source DEMs. First, since the sparse representation method has a powerful capability to reconstruct information based on a small amount of information, prior terrain information is extracted from the 90-m TanDEM-X DEM (TDM90) with unprecedented global accuracy using a so-called sparse representation. In this step, an intermediate DEM (termed STDM30) is first extracted from TDM90 that preserves maximum terrain details, thereby preventing the degradation of the DEM accuracy induced by resampling. Then, the designed regularization framework based on terrain slope can constrain the DEM spatial information during fusing multiple datasets. STDM30 is combined with the ALOS Global Digital Surface Model “ALOS World 3D 30 m” (AW3D30) and the 1 arc-second Shuttle Radar Topography Mission Digital Elevation Model (SRTM1) through the designed adaptive regularization variation model to generate a high-accuracy DEM product with a resolution of 30 m. The results of the proposed method were verified by a model-to-model comparison in South Dakota as well as by validation against GPS benchmarks in Southern California. The RMSE, MAE, and SD of the fused DEM are all lower than those of the existing public DEMs, especially in terms of removing topographic noise and refining terrain details. The GPS validation showed that the fused DEM has an RMSE of 3.04 m, with the highest absolute accuracy among the four studied DEMs, and its errors are almost equal to the normal distribution. These experimental results confirm that the multi-scale and multi-source DEM fusion strategy combining sparse representation and an adaptive regularization variation model can utilize existing public datasets and effectively improve the quality of global DEM products.

Multiscale Integration of High-Resolution Spaceborne and Drone-Based Imagery for a High-Accuracy Digital Elevation Model Over Tristan da Cunha

Very high-resolution (VHR) optical Earth observation (EO) satellites as well as low-altitude and easy-to-use unmanned aerial systems (UAS/drones) provide ever-improving data sources for the generation of detailed 3-dimensional (3D) data using digital photogrammetric methods with dense image matching. Today both data sources represent cost-effective alternatives to dedicated airborne sensors, especially for remote regions. The latest generation of EO satellites can collect VHR imagery up to 0.30 m ground sample distance (GSD) of even the most remote location from different viewing angles many times per year. Consequently, well-chosen scenes from growing image archives enable the generation of high-resolution digital elevation models (DEMs). Furthermore, low-cost and easy to use drones can be quickly deployed in remote regions to capture blocks of images of local areas. Dense point clouds derived from these methods provide an invaluable data source to fill the gap between globally available low-resolution DEMs and highly accurate terrestrial surveys. Here we investigate the use of archived VHR satellite imagery with approx. 0.5 m GSD as well as low-altitude drone-based imagery with average GSD of better than 0.03 m to generate high-quality DEMs using photogrammetric tools over Tristan da Cunha, a remote island in the South Atlantic Ocean which lies beyond the reach of current commercial manned airborne mapping platforms. This study explores the potentials and limitations to combine this heterogeneous data sources to generate improved DEMs in terms of accuracy and resolution. A cross-validation between low-altitude airborne and spaceborne data sets describes the fit between both optical data sets. No co-registration error, scale difference or distortions were detected, and a quantitative cloud-to-cloud comparison showed an average distance of 0.26 m between both data sets. Both point clouds were merged applying a conventional georeferenced approach. The merged DEM preserves the rich detail from the drone-based survey and provides an accurate 3D representation of the entire study area. It provides the most detailed model of the island so far, suitable to support practical and scientific applications. This study demonstrates that combination archived VHR satellite and low-altitude drone-based imagery provide inexpensive alternatives to generate high-quality DEMs.

Multi-resolution soil-landscape characterisation in KwaZulu Natal: Using geomorphons to classify local soilscapes for improved digital geomorphological modelling

Continual advances in quantitative modelling of surface processes, combined with new spatio-temporal and geo-computational algorithms, have revolutionised the auto-classification and mapping of landform components through the automated analysis of high-quality digital elevation models (DEMs). Digital geomorphic mapping (DGM) approaches that can simplify and translate the inclusion of “human knowledge” to automatic terrain classification across a broader spectrum of terrain morphological units as well as a range of spatial scales, therefore, offer great potential for improved topographic and landscape analysis. One such approach is the mapping of landform elements using the concept of the Geomorphon (geomorphological phonotypes). The output of the geomorphon approach is the stratification of the landscape into ten unique but recognisable landform elements: peak, ridge, shoulder, spur, and slope, hollow, foot slope, valley, depression and flat. Equally appealing is the way the model self-adapts to local topography using a line-of-sight principle enabling better matching of landform elements to computational spatial scale. The purpose of this paper is to observe the effects that different pixel resolution (grain size) and digital elevation model source (DEM) would have on the replication of observed geomorphic spatial patterns and representation of terrain selected parameters within the landscape. This paper provides a comprehensive exploratory assessment of digital terrain representation and relief classification using an automated geomorphometric mapping approach, by evaluating three different digital surface models (SUDEM, SRTM, ASTER GDEM2) and different spatial resolution (30 m & 90 m) for an 11,200 ha catchment in KwaZulu-Natal, South Africa. To test the self-adapting ability of the geomorphon approach under regional conditions, we use 4750 gridded terrain samples to quantitatively analyse how the choice of terrain model and scale influence the extraction, generalisation and representation of digitally-derived terrain attributes such as slope, elevation and terrain unit feature extent. We further show how the variation in resulting terrain unit representation is limited by spatial resolution discontinuities of selected elementary soil association distribution, soil texture and soil depth. We also introduce the results of a Similarity Index used to gauge the degree of recall and precision between the different geomorphic landscape features. Finally, the findings of the regional geomorphon-soil relationships are presented in a readily interpretable and qualitative manner, providing a “quasi-landscape signature” for potential localised geomorphons. The application of the study findings may be beneficial to practitioners looking to align or refine modelled terrain classification approaches with expert perception and formalised heuristic approaches.

Simple-Yet-Effective SRTM DEM Improvement Scheme for Dense Urban Cities Using ANN and Remote Sensing Data: Application to Flood Modeling

Digital elevation models (DEMs) are crucial in flood modeling as DEM data reflects the actual topographic characteristics where water can flow in the model. However, a high-quality DEM is very difficult to acquire as it is very time consuming, costly, and, often restricted. DEM data from a publicly accessible satellite, Shuttle Radar Topography Mission (SRTM), and Sentinel 2 multispectral imagery are selected and used to train the artificial neural network (ANN) to improve the quality of SRTM’s DEM. High-quality DEM is used as target data in the training of ANN. The trained ANN will then be ready to efficiently and effectively generate a high-quality DEM, at low cost, for places where ground truth DEM data is not available. In this paper, the performance of the DEM improvement scheme is evaluated over two dense urban cities, Nice (France) and Singapore; with the performance criteria using various matrices, e.g., visual clarity, scatter plots, root mean square error (RMSE) and flood maps. The DEM resulting from the improved SRTM (iSRTM) showed significantly better results than the original SRTM DEM, with about 38% RMSE reduction. Flood maps from iSRTM DEM show much more reasonable flood patterns than SRTM DEM’s flood map.

Automatic watershed delineation in the Tibetan endorheic basin: A lake-oriented approach based on digital elevation models

Watershed delineation from digital elevation models (DEMs) is an essential prerequisite for land-surface hydrology studies. Although existing automatic methods perform well in exorheic basins, finding a suitable watershed delineation approach in endorheic basins is still challenging. This study develops a lake-oriented approach given that lakes are important hydrological components in the Tibetan endorheic basin. The designed workflow of the proposed approach contains four steps: flow direction calculation, hydrological structure features extractions, classification of lake types, and watershed labeling. The experiments are conducted on a sub-region of the inner Tibetan Plateau, with an area of 119,055 km2. Extraction results prove that the proposed strategies, i.e., elevation-profile judgment and overflow-point-control correction can support the accurate delineation of watershed boundaries. In addition, the proposed approach can achieve two-level watershed boundaries in which the independences of all the endorheic basins can be maintained, meanwhile, further delineation within extracted exorheic basins satisfies various application requirements at different spatial scales. Unlike manual processing and existing datasets (e.g., HydroSHEDS), the proposed approach can fully explore the advantages of high-quality DEMs and can be expanded to much larger regions to handle scientific and engineering issues.

New types of boulder accumulations in the hyper-arid Atacama Desert

The accumulation of thousands of boulder-sized clasts into boulder fields in the Atacama Desert has been linked to seismic-driven downslope transport, a rare sedimentary process corroborated by this study. We surveyed boulder arrangements occurring in the Atacama Desert and identified three accumulation types for further investigation: a small circular boulder cluster (BC), a long channelized boulder stream (BS), and a wide convex-shaped boulder field (BF). Drone-based photogrammetric techniques and field observations were used to generate high-quality digital elevation models and orthophotos to determine boulder count, size, coverage, orientation, lithology and local topography. Our data shows that the arrangement of boulder accumulations corresponds with the shape of the accommodation space and the boulder input, where BCs occur at the center of completely confined topographic depressions, BSs occur along laterally confined and incised hill slopes with boulders stacked above each other, and BFs occur on largely unconfined shallow and low-relief slopes with a distinct boulder front. A general downslope increase of average boulder size and coverage was measured in all boulder accumulations and a long-axis orientation of boulders parallel to the transport direction was observed for the BS. Based on these results and the lack of fluvial transport indicators, we conclude that transport and arrangement of boulder accumulations are largely controlled by the interplay of topography and seismic-driven boulder transport, resulting in unique landscape features present in the hyper-arid Atacama Desert. Such sedimentary transport processes are rare on Earth but potentially play a greater role on other arid planetary surfaces that are covered by boulders and subject to sufficient seismic activity.

Optimizing Photogrammetric Techniques for Wetlands Monitoring: Southeast Texas

Environmentalists express concerns about the health of the planet and the vital role of wetlands on Earth. Sufficient knowledge of wetland changes is becoming more crucial as loss of wetland area increases. However, an ability to efficiently map and monitor the wetland topographies requires technology advancement. Producing high resolution and high quality digital elevation models (DEMs) requires substantial investments in personnel time, hardware, and software while increasing accessibility of three-dimensional imaging methods, such as digital photogrammetry. Further refinements have highly improved the method while preserving its convenience. This study introduces unmanned aerial system (UAS) coupled with with structure-from-motion (SfM) technology as a new method in wetland mapping. The contributions of this study are aimed at maximizing the efficiency and accuracy of the data collection process for mapping the Southeast Texas wetlands and other related applications. This paper serves as a summary and evaluation of various photogrammetric and data extraction techniques.