A scale-adaptive DEM for multi-scale terrain analysis

The scarcity of high-resolution urban digital elevation model (DEM) datasets, particularly in certain developing countries, has posed a challenge for many water-related applications such as flood risk management. A solution to address this is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current high-resolution DEM reconstruction approaches mainly focus on natural topography. Few attempts have been made for urban topography, which is typically an integration of complex artificial and natural features. This study proposed a novel multi-scale mapping approach based on convolutional neural network (CNN) to deal with the complex features of urban topography and to reconstruct high-resolution urban DEMs. The proposed multi-scale CNN model was firstly trained using urban DEMs that contained topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low-resolution equivalent. A two-level accuracy assessment approach was also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy. The proposed DEM reconstruction approach was applied to a 121 km2 urbanized area in London, United Kingdom. Compared with other commonly used methods, the current CNN-based approach produced superior results, providing a cost-effective innovative method to acquire high-resolution DEMs in other data-scarce regions.

High resolution digital elevation models (DEM) are becoming increasingly available for use as source data during the process of creating synthetic environments to support simulation systems. Several data sets that provide elevation points corresponding to 1 meter intervals on the earth surface are now available. However, multiple transformations must often be applied to the raw source data before it is suitable for use by any simulation system. These transformations have an impact on the fidelity of the final (simulation) synthetic environment that is difficult to quantify. Further, intuition alone now supports any claim that higher resolution source data necessarily results in generation of higher fidelity simulation data as a product of the transformation process. This paper documents an attempt to measure fidelity differences in final simulation synthetic environments that can be directly attributed to the resolution of the source data. Specifically, several lower resolution DEM are generated from a single high resolution (1 meter horizontal spacing) source DEM and all are used as source data for TIN construction. Automated planning software is applied to each and used as a metric to measure TIN quality.

A rapid high-resolution multi-sensory urban flood mapping framework via DEM upscaling

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.

Utilizing multispectral satellite data and digital elevation models (DEMs) has emerged as the primary approach for cartographically representing landforms. By using high-resolution satellite photos that capture spatial, temporal, spectral, and radiometric data, one may get a fresh comprehension of the geomorphology of a particular area by recognizing its landforms. In addition, a synergistic method is used by using data produced from digital elevation models (DEMs) such as Slope, Aspect, Hillshade, Curvature, Contour Patterns, and 3-D Flythrough Visuals. The increasing use of UAV (drone) technology for obtaining high-resolution digital images and elevation models has become an essential element in developing complete topographic models in landslide scars that are very unstable and prone to erosion. Comparison (differences in values) of seven (7) different DEMs between two algorithms used, i.e., QGIS and White Box Tool (WBT), were successfully attempted in the present research. The TLS, UAV and Satellite data of the study area—Kshetrapal Landslide, Chamoli (District), Uttarakhand (State), India was subjected to two different algorithms (QGIS and WBT) to evaluate and differentiate seven different DEMs (CARTOSAT, ASTER, SRTM, Alos 3D, TanDEM, MERIT, and FabDEM/FATHOM) taking into consideration various parameters viz. Aspect, Hillshade, Slope, Mean Curvature, Plan Curvature, Profile Curvature and Total Curvature. The different values of aforesaid parameters of various DEMs evaluated (using algorithms QIGS and WBT) reveal that only three parameters, i.e., Aspect, Hillshade, and Slope, show results. In contrast, the remaining ones do not show any meaningful results, and therefore, the comparison was possible only with regard to these three parameters. The comparison is drawn by comparing minimum, maximum, and elevation values (by subtracting WBT values from QGIS values) regarding Aspect, Hillshade, and Slope, arranging the differences in values as per their importance. (Increasing or decreasing order), assigning merit scores individually, and then cumulatively, and ascertaining the order of application suitability of various Dems, which stand in the order of (CARTOSAT, ASTER, SRTM, Alos 3D, TanDEM, and MERIT, and FabDEM/FATHOM).

Abstract. Digital Elevation Model (DEM) is an important input for geo-spatial analysis. For various applications like flood management, ortho rectification of remote sensing images, navigation, architectural works, defence, etc., high resolution DEM is required. TanDEM-X mission was launched in 2010 to obtain high resolution global DEM with HTRI-3 standard. SAR interferometry (InSAR) technique is used for DEM generation from TanDEM-X SAR data. The accuracy of DEM depends on many parameters like height ambiguity, incidence angle, polarization, etc. In this study, time series TanDEM-X data spanning over 3 years, had processed for generating DEM at the spatial resolution of 6 m and their accuracy had studied using DGPS elevation data and SRTM 90 m DEM. The products generated during DEM generation process are DEM, precision (or height error), coherence, layover and shadow images. Using weighted average fusion technique, ascending and descending DEMs are fused for improving the quality of DEM and to reduce invalid pixels corresponding to layover and shadow areas. Results from time series data were analysed and found RMSE error of fused DEMs is in the range of 2 m to 4 m, while individual DEM has accuracy of 3 m to 6 m with respect to DGPS elevation data. Fused DEMs are having high accuracy as well as less voids. The reduction of voids by fusion, ranges from 40 to 85 % in different combinations of data.

Satellite-derived Digital Elevation Model (DEM) selection, preparation and correction for hydrodynamic modelling in large, low-gradient and data-sparse catchments

Quantifying landscape form can provide crucial insight into the interactions between tectonics and climate. River long profile morphology, quantified by metrics such as channel steepness, is the most commonly used tool to investigate topographic form, with many studies relating long profile morphology to uplift rate, precipitation, sediment properties, or lithology, for example. River long profiles record the signal of external forcing over large spatial scales (i.e. tens of kilometres). This has many advantages: for example, it is a convenient scale for analysing variations in large-scale processes, such gradients in tectonic uplift. It also means that high resolution digital elevation models (DEMs) are not required and therefore river long profiles can be extracted globally. However, analysis of river long profiles over tens of kilometres can also result in signal smoothing and subsequent loss of finer scale tectonic or climatic signatures encoded into the landscape.Tectonic and climatic processes do not only leave their fingerprint in the long profiles of rivers. Hilltops, hillslopes, and valleys make up the majority of Earth’s landscapes by area, yet their morphology has received much less attention than that of rivers. This is in part due to the difficulty in accurately extracting hilltops and valley morphology from DEMs, especially on a global scale. Here, I show that we can now extract hilltop and valley metrics from high-resolution (< 15 m) DEMs over orogenic to continental scales using new topographic analysis techniques and high-performance computing facilities. I argue that by combining hilltop, hillslope, and valley metrics, we can obtain more information about tectonic and climatic processes than from river profiles alone.

To accurately assess landslide susceptibility and model debris flow paths, acquisition of high-resolution elevation data is essential. These data enable precise topographical analysis, considering factors like slope and curvature. But most of the time such high-resolution data will have inaccuracies due to vegetation, especially in tropical region. This study proposes a comprehensive approach that integrates field data and Structure from Motion (SfM) technology to rectify such DEM inaccuracies caused by dense vegetation. The goal is to enhance the accuracy of landslide simulations and volumetric analysis for effective post-disaster management. The Western Ghats, running parallel to India's western coast, has experienced a surge in rainfall-induced landslides, resulting in significant casualties in recent years. Notably, the Pettimudi village landslide in 2020 caused 70 deaths. The unique geomorphological features of the Western Ghats, such as concave curvature, colluvium deposits, and evidence of paleo landslides, contribute to the area's susceptibility. The study emphasizes the need for detailed assessment and mitigation strategies. The proposed method aims to improve the post-event high-resolution DEM accuracy by integrating field-collected elevation values and utilizing Agisoft Metashape software with the SfM algorithm. The rectification process involves combining elevation differences measured during a field study with photogrammetric elevation data. The field-collected elevation differences are crucial for rectifying these points, and enhancing the Digital Elevation Model (DEM) accuracy. The inaccessible source region is improved using a SfM-created DEM from drone footage, resulting in a more accurate post-event DEM. Correcting the 1 m DEM using an SfM-generated DEM proves challenging but significantly improves detail and accuracy, especially over the landslide source area. The impact of the rectification on accuracy is assessed by comparing volumes calculated from the initial DEM and the newly corrected DEM. The difference in volumes of debris depletion and accumulation, computed using initial and corrected DEMs, highlights variations, with depleted volumes being significantly larger due to the extensive depth increase over the landslide source region. These volumes are then utilized in Rapid Mass Movement Simulation (RAMMS) to validate the rectification process and enhance landslide impact predictions. Debris flow simulations in RAMMS, utilizing the rectified 1 m DEM, show specific outcomes at the landslide source region and toe of the landslide. The study emphasizes the importance of integrating SfM technology with field data to improve DEM accuracy, acknowledging the significance of additional field data for further refinement. The potential adoption of highly precise aerial imagery from Unmanned Aerial Vehicle (UAV) surveys is suggested to further enhance the SfM DEM. Debris flow modeling with RAMMS serves as a vital step in validating the accuracy and reliability of the rectified elevation model. Keywords: Landslides, Western Ghats, Pettimudi, Digital elevation model (DEM), Structure-from-Motion (SfM) technology, Rapid Mass Movement Simulation (RAMMS)

Voids caused by shadow, layover, and decorrelation usually occur in digital elevation models (DEMs) of mountainous areas that are derived from interferometric synthetic aperture radar (InSAR) datasets. The presence of voids degrades the quality and usability of the DEMs. Thus, void removal is considered as an integral part of the DEM production using InSAR data. The fusion of multiple DEMs has been widely recognized as a promising way for the void removal. Because the vertical accuracy of multiple DEMs can be different, the selection of optimum weights becomes a key problem in the fusion and is studied in this article. As a showcase, two high-resolution InSAR DEMs near Mt. Qilian in northwest China are created and then merged. The two pairs of InSAR data were acquired by TerraSAR-X from an ascending orbit and COSMO-SkyMed from a descending orbit. A maximum likelihood fusion scheme with the weights optimally determined by the height of ambiguity and the variance of phase noise is adopted to syncretize the two DEMs in our study. The fused DEM has a fine spatial resolution of 10 m and depicts the landform of the study area well. The percentage of void cells in the fused DEM is only 0.13 %, while 6.9 and 5.7 % of the cells in the COSMO-SkyMed DEM and the TerraSAR-X DEM are originally voids. Using the ICESat/GLAS elevation data and the Chinese national DEM of scale 1:50,000 as references, we evaluate vertical accuracy levels of the fused DEM as well as the original InSAR DEMs. The results show that substantial improvements could be achieved by DEM fusion after atmospheric phase screen removal. The quality of fused DEM can even meet the high-resolution terrain information (HRTI) standard.

Abstract. In this paper we describe how recent high-resolution digital elevation models (DEMs) can be used to extract glacier surface DEMs from old aerial photographs and to evaluate the uncertainty of the mass balance record derived from the DEMs. We present a case study for Drangajökull ice cap, NW Iceland. This ice cap covered an area of 144 km2 when it was surveyed with airborne lidar in 2011. Aerial photographs spanning all or most of the ice cap are available from survey flights in 1946, 1960, 1975, 1985, 1994 and 2005. All ground control points used to constrain the orientation of the aerial photographs were obtained from the high-resolution lidar DEM. The lidar DEM was also used to estimate errors of the extracted photogrammetric DEMs in ice- and snow-free areas, at nunataks and outside the glacier margin. The derived errors of each DEM were used to constrain a spherical semivariogram model, which along with the derived errors in ice- and snow-free areas were used as inputs into 1000 sequential Gaussian simulations (SGSims). The simulations were used to estimate the possible bias in the entire glaciated part of the DEM and the 95 % confidence level of this bias. This results in bias correction varying in magnitude between 0.03 m (in 1975) and 1.66 m (in 1946) and uncertainty values between ±0.21 m (in 2005) and ±1.58 m (in 1946). Error estimation methods based on more simple proxies would typically yield 2–4 times larger error estimates. The aerial photographs used were acquired between late June and early October. An additional seasonal bias correction was therefore estimated using a degree-day model to obtain the volume change between the start of 2 glaciological years (1 October). This correction was largest for the 1960 DEM, corresponding to an average elevation change of −3.5 m or approx. three-quarters of the volume change between the 1960 and the 1975 DEMs. The total uncertainty of the derived mass balance record is dominated by uncertainty in the volume changes caused by uncertainties of the SGSim bias correction, the seasonal bias correction and the interpolation of glacier surface where data are lacking. The record shows a glacier-wide mass balance rate of Ḃ = −0.26 ± 0.04 m w.e. a−1 for the entire study period (1946–2011). We observe significant decadal variability including periods of mass gain, peaking in 1985–1994 with Ḃ = 0.27 ± 0.11 m w.e. a−1. There is a striking difference when Ḃ is calculated separately for the western and eastern halves of Drangajökull, with a reduction of eastern part on average ∼ 3 times faster than the western part. Our study emphasizes the need for applying rigorous geostatistical methods for obtaining uncertainty estimates of geodetic mass balance, the importance of seasonal corrections of DEMs from glaciers with high mass turnover and the risk of extrapolating mass balance record from one glacier to another even over short distances.

River reach length and slope estimates for large-scale hydrological models based on a relatively high-resolution digital elevation model

Surface roughness is a land-surface parameter that is widely used in terrain analysis. Some typical roughness details, which have important effects on surface analysis, fail to be characterized on previous roughness maps. The objective of this paper is to provide a more accurate small-to-large scale roughness overview. The new roughness method is designed based on a complete orthogonal system called the V-system. The V-system roughness utilizes the special functions to detect and extract the roughness characteristics from high-resolution digital elevation models (DEMs). In this paper, Lunar Orbiter Laser Altimeter-derived DEMs are used as the source data for the roughness calculation. Compared with the global root-mean-square slope and Fourier-based roughness maps, the V-system roughness maps show that more typical roughness details have been added to clearly indicate the small roughness variations on the large map. Furthermore, the reliability and practicability of V-system roughness are demonstrated based on the multiresolution DEMs. As an example, the statistical parameters of the roughness characteristics in the lunar Maria and highlands identify the fact that the highlands are rougher at all scales than the Maria. And this difference corresponds to the basic roughness property.

Snow depth from ICESat laser altimetry — A test study in southern Norway

For areas of the world that do not have access to lidar, fine-scale digital elevation models (DEMs) can be photogrammetrically created using globally available high-spatial resolution stereo satellite imagery. The resultant DEM is best termed a digital surface model (DSM) because it includes heights of surface features. In densely vegetated conditions, this inclusion can limit its usefulness in applications requiring a bare-earth DEM. This study explores the use of techniques designed for filtering lidar point clouds to mitigate the elevation artifacts caused by above ground features, within the context of a case study of Prince William Forest Park, Virginia, USA. The influences of land cover and leaf-on vs. leaf-off conditions are investigated, and the accuracy of the raw photogrammetric DSM extracted from leaf-on imagery was between that of a lidar bare-earth DEM and the Shuttle Radar Topography Mission DEM. Although the filtered leaf-on photogrammetric DEM retains some artifacts of the vegetation canopy and may not be useful for some applications, filtering procedures significantly improved the accuracy of the modeled terrain. The accuracy of the DSM extracted in leaf-off conditions was comparable in most areas to the lidar bare-earth DEM and filtering procedures resulted in accuracy comparable of that to the lidar DEM.