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Perspectives on Digital Elevation Model Applications

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (1 March 2022) | Viewed by 51466

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Dr. Tomaž Podobnikar

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Faculty of Civil and Geodetic Engineering, University of Ljubljana, Jamova 2, SI-1000 Ljubljana, Slovenia
Interests: spatial analysis; geomorphometry; DEM; DTM; GIS; remote sensing; geovisual analytics; spatial data quality; image processing; spatial generalization; spatial data integration; spatial statistics; (palaeo)environmental analysis; landscape archaeology; natural hazard
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Dear Colleagues,

Imagine a digital elevation model (DEM) as a soft foil hugging every aspect of topography, which can coat all predefined objects upon it. More specifically, the DEM is a continuous surface model, usually recorded in a regular 2.5D grid, which consists of elevation values that describe the topographic surface—in various resolutions, or multitemporal surface changes, in 4D. A digital terrain model (DTM) portrays bare-earth surface (terrain). In contrast to a DTM, a digital surface model (DSM) includes the tops of buildings (e.g., houses, viaducts), vegetation cover, as well as natural terrain features (e.g., temporal snow cover, the 3D surface of caves). We can use the term DEM here as a generic one for the whole family of (geo)surface models. A DEM is, therefore, a rich model that inherently includes an unimaginable amount of information.

The DEM is considered to be among the most important data layers in the geoscience domain (geography, geodesy, cartography, geophysics), with outreach in natural science, engineering, environmental and social sciences (archaeology, urban dynamics), entertainment (movies), etc. There are already many publications on DEM applications. However, diverse users want to make applications that potentially require different kinds of DEMs. For example, for relatively flat flood zones, it is essential to use a geomorphologically correct DEM. It is important to consider the concept of DEM, definition and realization in the physical data model, various coordinate systems, availability of DEMs, various aspects of quality including DEM properties that are based on particular acquisition methods and sensors properties, aesthetics and other perspectives of the product. Different software and platforms used for DEM applications are based on GIS, remote sensing, CAD, virtual globes, computer graphics, game engines, BIM, IoT-software, or DEM-specific. There is, then, the added headache for users due to mismatches between metadata and issued DEM.

The basic questions regarding applicability are: Which solution is more reliable: highly standardized multiscale DEMs, unique universal DEM balanced for most demands, or something else? What are applications that include DEM, e.g., cartography, autonomous vehicle navigation, disaster/hazard management, precision agriculture, maritime, planning space missions, virtual reality, environmental studies, or even out of geodomains - medicine?

The research questions of the contributions should focus on contemporary and future applications of DEMs, on the reliability and usability of DEMs, or the more demanding relation between target applications and different properties of the selected DEM(s).

Dr. Tomaž Podobnikar
Guest Editor

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Keywords

Digital elevation model
DEM
DTM
DSM
Point cloud
Raster grid
TIN
Elevation
Terrain
Surface
Applications
Usability
Concept
Abstraction
Spatial data quality
Uncertainty
GIS
Remote sensing
Photogrammetry
Land surveying
Geodesy
Topography

Published Papers (11 papers)

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Research

20 pages, 6805 KiB

Open AccessArticle

The Impact of Digital Elevation Model Preprocessing and Detection Methods on Karst Depression Mapping in Densely Forested Dinaric Mountains

by Rok Ciglič, Špela Čonč and Mateja Breg Valjavec

Remote Sens. 2022, 14(10), 2416; https://doi.org/10.3390/rs14102416 - 18 May 2022

Cited by 5 | Viewed by 2404

Abstract

Karst landscapes have an abundance of enclosed depressions. Many studies have detected depressions and have calculated geomorphometric characteristics with computer techniques. These outcomes are somewhat determined by the methods and data used. We aim to highlight the applicability of high-resolution relief laser scanning data in geomorphological studies of karst depressions. We set two goals: geomorphometrically to characterize depressions in different karst plateaus and to examine the influence of data preprocessing and detection methods on the results. The study was performed in three areas of the Slovene Dinaric Karst using the following steps: preprocessing digital elevation models (DEMs), enclosed depression detection, calculating geomorphometric characteristics, and comparing the characteristics of selected areas. We discovered that different combinations of methods influenced the number and geomorphometric characteristics of depressions. The range of detected depressions in the three areas were 442–491, 364–403, and 366–504, and the share of the depressions’ area confirmed with all the approaches was 23%, 29%, and 47%, which resulted in different geomorphometric properties. Comparisons between the study areas were also influenced by the methods, which was confirmed by the Mann–Whitney test. We concluded that preprocessing of high-resolution relief data and the detection methods in karst environments significantly impact analyses and must be taken into account when interpreting geomorphometric results. Full article

(This article belongs to the Special Issue Perspectives on Digital Elevation Model Applications)

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26 pages, 8436 KiB

Open AccessArticle

A DEM Super-Resolution Reconstruction Network Combining Internal and External Learning

by Xu Lin, Qingqing Zhang, Hongyue Wang, Chaolong Yao, Changxin Chen, Lin Cheng and Zhaoxiong Li

Remote Sens. 2022, 14(9), 2181; https://doi.org/10.3390/rs14092181 - 2 May 2022

Cited by 7 | Viewed by 1967

Abstract

The study of digital elevation model (DEM) super-resolution reconstruction algorithms has solved the problem of the need for high-resolution DEMs. However, the DEM super-resolution reconstruction algorithm itself is an inverse problem, and making full use of the DEM a priori information is an effective way to solve this problem. In our work, a new DEM super-resolution reconstruction method is proposed based on the complementary relationship between internally learned super-resolution reconstruction methods and externally learned super-resolution reconstruction methods. The method is based on the presence of a large amount of repetitive information within the DEM. Using an internal learning approach to learn the internal prior of the DEM, a low-resolution dataset of the DEM rich in detailed features is generated, and based on this, the training of a constrained external learning network is constructed for the discrepancy data pair. Finally, it introduces residual learning based on the network model to accelerate the operation rate of the network and to solve the model degradation problem brought about by the deepening of the network. This enables the better transfer of learned detailed features in deeper network mappings, which in turn ensures accurate learning of the DEM prior information. The network utilizes the internal prior of the specific DEM as well as the external prior of the DEM dataset and achieves better super-resolution reconstruction results in the experimental results. The results of super-resolution reconstruction by the Bicubic method, Super-Resolution Convolutional Neural Networks (SRCNN), very deep convolutional networks (VDSR), ”Zero-Shot” Super-Resolution networks (ZSSR) and the new method in this paper were compared, and the average RMSE of the super-resolution reconstruction results of the five methods were 8.48 m, 8.30 m, 8.09 m, 7.02 m and 6.65 m, respectively. The mean elevation error at the same resolution is 21.6% better than that of the Bicubic method, 19.9% better than that of the SRCNN, 17.8% better than that of the VDSR method, and 5.3% better than that of the ZSSR method. Full article

(This article belongs to the Special Issue Perspectives on Digital Elevation Model Applications)

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36 pages, 64792 KiB

Open AccessArticle

Accuracy Assessment, Comparative Performance, and Enhancement of Public Domain Digital Elevation Models (ASTER 30 m, SRTM 30 m, CARTOSAT 30 m, SRTM 90 m, MERIT 90 m, and TanDEM-X 90 m) Using DGPS

by Kumari Preety, Anup K. Prasad, Atul K. Varma and Hesham El-Askary

Remote Sens. 2022, 14(6), 1334; https://doi.org/10.3390/rs14061334 - 9 Mar 2022

Cited by 14 | Viewed by 4303

Abstract

Publicly available Digital Elevation Models (DEM) derived from various space-based platforms (Satellite/Space Shuttle Endeavour) have had a tremendous impact on the quantification of landscape characteristics, and the related processes and products. The accuracy of elevation data from six major public domain satellite-derived Digital Elevation Models (a 30 m grid size—ASTER GDEM version 3 (Ast30), SRTM version 3 (Srt30), CartoDEM version V3R1 (Crt30)—and 90 m grid size—SRTM version 4.1 (Srt90), MERIT (MRT90), and TanDEM-X (TDX90)), as well as the improvement in accuracy achieved by applying a correction (linear fit) using Differential Global Positioning System (DGPS) estimates at Ground Control Points (GCPs) is examined in detail. The study area is a hard rock terrain that overall is flat-like with undulating and uneven surfaces (IIT (ISM) Campus and its environs) where the statistical analysis (corrected and uncorrected DEMs), correlation statistics and statistical tests (for elevation and slope), the impact of resampling methods, and the optimum number of GCPs for reduction of error in order to use it in further applications have been presented in detail. As the application of DGPS data at GCPs helps in the substantial reduction of bias by the removal of systematic error, it is recommended that DEMs may be corrected using DGPS before being used in any scientific studies. Full article

(This article belongs to the Special Issue Perspectives on Digital Elevation Model Applications)

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40 pages, 120842 KiB

Open AccessArticle

Granularity of Digital Elevation Model and Optimal Level of Detail in Small-Scale Cartographic Relief Presentation

by Timofey Samsonov

Remote Sens. 2022, 14(5), 1270; https://doi.org/10.3390/rs14051270 - 5 Mar 2022

Cited by 2 | Viewed by 2482

Abstract

One of the key applications of digital elevation models (DEMs) is cartographic relief presentation. DEMs are widely used in mapping, most commonly in the form of contours, hypsometric tints, and hill shading. Recent advancements in the coverage, quality, and resolution of global DEMs facilitate the overall improvement of the detail and reliability of terrain-related research. At the same time, geographic problem solving is conducted in a wide variety of scales, and the data used for mapping should have the corresponding level of detail. Specifically, at small scales, intensive generalization is needed, which is also true for elevation data. With the widespread accessibility of detailed DEMs, this principle is often violated, and the data are used for mapping at scales far smaller than what is appropriate. Small-scale relief shading obtained from fine-resolution DEMs is excessively detailed and brings an unclear representation of the Earth’s surface instead of emphasizing what is important at the scale of visualization. Existing coarse-resolution global DEMs do not resolve the issue, since they accumulate the maximum possible information in every pixel, and therefore also require reduction in detail to obtain a high-quality cartographic image. It is clear that guidelines and effective principles for DEM generalization at small scales are needed. Numerous algorithms have been developed for the generalization of elevation data represented either in gridded, contoured, or pointwise form. However, the answer to the most important question—When should we stop surface simplification?—remains unclear. Primitive error-based measures such as vertical distance are not effective for cartography, since they do not account for the landform structure of the surface perceived by the map reader. The current paper approached the problem by elaborating the granularity—a newly developed property of DEMs, which characterizes the typical size of a landform represented on the DEM surface. A methodology of estimating the granularity through a landform width measure was conceptualized and implemented as software. Using the developed program tools, the optimal granularity was statistically learned from DEMs reconstructed for multiple fragments of manually drawn 1:200,000, 1:500,000, and 1:1,000,000 topographic maps covering different relief types. It was shown that the relative granularity should be 5–6 mm at the mapping scale to achieve the clearness of relief presentation typical for manually drawn maps. We then demonstrate how the granularity measure can be used effectively as a constraint during DEM generalization. Experimental results on a combination of contours, hypsometric tints, and hill shading indicated clearly that the optimal level of detail in small-scale cartographic relief presentation can be achieved by DEM generalization constrained by granularity in combination with fine DEM resolution, which facilitates high-quality rendering. Full article

(This article belongs to the Special Issue Perspectives on Digital Elevation Model Applications)

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25 pages, 9685 KiB

Open AccessArticle

A Method for Obtaining a DEM with Curved Abscissa from MLS Data for Linear Infrastructure Survey Design

by Maurizio Barbarella, Alessandro Di Benedetto and Margherita Fiani

Remote Sens. 2022, 14(4), 889; https://doi.org/10.3390/rs14040889 - 12 Feb 2022

Cited by 5 | Viewed by 2243

Abstract

The sudden deterioration of the condition of linear infrastructure networks makes road management a complex task. Knowledge of the surface condition of the pavement is a requirement in order to estimate the causes of instabilities, select the appropriate action and identify all those sections that require urgent intervention. The mobile laser scanning (MLS) technique allows for a fast and safe diagnosis, thus making it possible to plan an early intervention program quickly and cost-effectively. This paper describes a methodology implemented with a twofold purpose: (i) the optimal definition, during the design phase, of the input parameters of the MLS survey (velocity of the vehicle and acquisition rate), defined through the study of the relationship between these parameters and the density of the scanned points and, therefore, with the resolution that allows the analysis of a certain type of pavement distress; (ii) the creation of a Digital Elevation Model with a curved abscissa (DEMc), specific for the analysis of road pavements. The field surveys made and the procedure developed allowed the velocity of the MLS to be associated with the resolution of the DEMc, and thus its capability to highlight distresses at different levels of severity. The creation of the road model is semiautomatic; the height value of each single node of the grid is estimated through spatial interpolation algorithms. Starting from experimental data, a few charts were created that relate the density of the point cloud to the variation of the acquisition rate, together with the minimum resolution. Depending on the type of distress analyzed, it is possible to infer the values to be respected of the parameters. In this way, it should be possible to draw up a few guidelines about MLS surveys addressing linear infrastructures focused on the optimization of the survey design, so as to identify strategies that can maximize benefits with the same available budget. Full article

(This article belongs to the Special Issue Perspectives on Digital Elevation Model Applications)

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21 pages, 5265 KiB

Open AccessArticle

Error Characteristics of Pan-Arctic Digital Elevation Models and Elevation Derivatives in Northern Sweden

by Martin Karlson, David Bastviken and Heather Reese

Remote Sens. 2021, 13(22), 4653; https://doi.org/10.3390/rs13224653 - 18 Nov 2021

Cited by 11 | Viewed by 2218

Abstract

Many biochemical processes and dynamics are strongly controlled by terrain topography, making digital elevation models (DEM) a fundamental dataset for a range of applications. This study investigates the quality of four pan-Arctic DEMs (Arctic DEM, ASTER DEM, ALOS DEM and Copernicus DEM) within the Kalix River watershed in northern Sweden, with the aim of informing users about the quality when comparing these DEMs. The quality assessment focuses on both the vertical accuracy of the DEMs and their abilities to model two fundamental elevation derivatives, including topographic wetness index (TWI) and landform classification. Our results show that the vertical accuracy is relatively high for Arctic DEM, ALOS and Copernicus and in our study area was slightly better than those reported in official validation results. Vertical errors are mainly caused by tree cover characteristics and terrain slope. On the other hand, the high vertical accuracy does not translate directly into high quality elevation derivatives, such as TWI and landform classes, as shown by the large errors in TWI and landform classification for all four candidate DEMs. Copernicus produced elevation derivatives with results most similar to those from the reference DEM, but the errors are still relatively high, with large underestimation of TWI in land cover classes with a high likelihood of being wet. Overall, the Copernicus DEM produced the most accurate elevation derivatives, followed by slightly lower accuracies from Arctic DEM and ALOS, and the least accurate being ASTER. Full article

(This article belongs to the Special Issue Perspectives on Digital Elevation Model Applications)

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19 pages, 3197 KiB

Open AccessArticle

Digital Elevation Models: Terminology and Definitions

by Peter L. Guth, Adriaan Van Niekerk, Carlos H. Grohmann, Jan-Peter Muller, Laurence Hawker, Igor V. Florinsky, Dean Gesch, Hannes I. Reuter, Virginia Herrera-Cruz, Serge Riazanoff, Carlos López-Vázquez, Claudia C. Carabajal, Clément Albinet and Peter Strobl

Remote Sens. 2021, 13(18), 3581; https://doi.org/10.3390/rs13183581 - 8 Sep 2021

Cited by 63 | Viewed by 18484

Abstract

Digital elevation models (DEMs) provide fundamental depictions of the three-dimensional shape of the Earth’s surface and are useful to a wide range of disciplines. Ideally, DEMs record the interface between the atmosphere and the lithosphere using a discrete two-dimensional grid, with complexities introduced by the intervening hydrosphere, cryosphere, biosphere, and anthroposphere. The treatment of DEM surfaces, affected by these intervening spheres, depends on their intended use, and the characteristics of the sensors that were used to create them. DEM is a general term, and more specific terms such as digital surface model (DSM) or digital terrain model (DTM) record the treatment of the intermediate surfaces. Several global DEMs generated with optical (visible and near-infrared) sensors and synthetic aperture radar (SAR), as well as single/multi-beam sonars and products of satellite altimetry, share the common characteristic of a georectified, gridded storage structure. Nevertheless, not all DEMs share the same vertical datum, not all use the same convention for the area on the ground represented by each pixel in the DEM, and some of them have variable data spacings depending on the latitude. This paper highlights the importance of knowing, understanding and reflecting on the sensor and DEM characteristics and consolidates terminology and definitions of key concepts to facilitate a common understanding among the growing community of DEM users, who do not necessarily share the same background. Full article

(This article belongs to the Special Issue Perspectives on Digital Elevation Model Applications)

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17 pages, 6678 KiB

Open AccessArticle

An Enhanced Double-Filter Deep Residual Neural Network for Generating Super Resolution DEMs

by Annan Zhou, Yumin Chen, John P. Wilson, Heng Su, Zhexin Xiong and Qishan Cheng

Remote Sens. 2021, 13(16), 3089; https://doi.org/10.3390/rs13163089 - 5 Aug 2021

Cited by 16 | Viewed by 2552

Abstract

High-resolution DEMs are important spatial data, and are used in a wide range of analyses and applications. However, the high cost to obtain high-resolution DEM data over a large area through sensors with higher precision poses a challenge for many geographic analysis applications. Inspired by the convolution neural network (CNN) excellent performance in super-resolution (SR) image analysis, this paper investigates the use of deep residual neural networks and low-resolution DEMs to generate high-resolution DEMs. An enhanced double-filter deep residual neural network (EDEM-SR) method is proposed, which uses filters with different receptive field sizes to fuse and extract features and reconstruct a more realistic high-resolution DEM. The results were compared with those generated with the bicubic, bilinear, and EDSR methods. The numerical accuracy and terrain feature preserving effects of the EDEM-SR method can generate reconstructed DEMs that better match the original DEMs, show lower MAE and RMSE, and improve the accuracy of the terrain parameters. MAE is reduced by about 30 to 50% compared with traditional interpolation methods. The results show how the EDEM-SR method can generate high-resolution DEMs using low-resolution DEMs. Full article

(This article belongs to the Special Issue Perspectives on Digital Elevation Model Applications)

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16 pages, 5678 KiB

Open AccessArticle

Comparison of Gridded DEMs by Buffering

by Francisco Javier Ariza-López and Juan Francisco Reinoso-Gordo

Remote Sens. 2021, 13(15), 3002; https://doi.org/10.3390/rs13153002 - 30 Jul 2021

Cited by 2 | Viewed by 1677

Abstract

Comparing two digital elevation models (DEMs), S1 (reference) and S2 (product), in order to get the S2 quality, has usually been performed on sampled points. However, it seems more natural, as we propose, comparing both DEMs using 2.5D surfaces: applying a buffer to S1 (single buffer method, SBM) or to both S1 and S2 (double buffer method, DBM). The SBM and DBM approaches have been used in lines accuracy assessment and, in this paper, we generalize them to a DEM surface, so that more area of the S2 surface (in the case of the SBM), or the area and volume (in the case of the DBM) that are involved, more similarly are S1 and S2. The results obtained show that across both methods, SBM recognizes the presence of outliers and vertical bias while DBM allows a richer and more complex analysis based on voxel intersection. Both methods facilitate creating observed distribution functions that eliminate the need for the hypothesis of normality on discrepancies and allow the application of quality control techniques based on proportions. We consider that the SBM is more suitable when the S1 accuracy is much greater than that of S2 and DBM is preferred when the accuracy of S1 and S2 are approximately equal. Full article

(This article belongs to the Special Issue Perspectives on Digital Elevation Model Applications)

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21 pages, 8833 KiB

Open AccessArticle

Airborne LiDAR-Derived Digital Elevation Model for Archaeology

by Benjamin Štular, Edisa Lozić and Stefan Eichert

Remote Sens. 2021, 13(9), 1855; https://doi.org/10.3390/rs13091855 - 10 May 2021

Cited by 44 | Viewed by 6515

Abstract

The use of topographic airborne LiDAR data has become an essential part of archaeological prospection, and the need for an archaeology-specific data processing workflow is well known. It is therefore surprising that little attention has been paid to the key element of processing: an archaeology-specific DEM. Accordingly, the aim of this paper is to describe an archaeology-specific DEM in detail, provide a tool for its automatic precision assessment, and determine the appropriate grid resolution. We define an archaeology-specific DEM as a subtype of DEM, which is interpolated from ground points, buildings, and four morphological types of archaeological features. We introduce a confidence map (QGIS plug-in) that assigns a confidence level to each grid cell. This is primarily used to attach a confidence level to each archaeological feature, which is useful for detecting data bias in archaeological interpretation. Confidence mapping is also an effective tool for identifying the optimal grid resolution for specific datasets. Beyond archaeological applications, the confidence map provides clear criteria for segmentation, which is one of the unsolved problems of DEM interpolation. All of these are important steps towards the general methodological maturity of airborne LiDAR in archaeology, which is our ultimate goal. Full article

(This article belongs to the Special Issue Perspectives on Digital Elevation Model Applications)

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16 pages, 5316 KiB

Open AccessArticle

Automated Mapping of Transportation Embankments in Fine-Resolution LiDAR DEMs

by Nigel Van Nieuwenhuizen, John B. Lindsay and Ben DeVries

Remote Sens. 2021, 13(7), 1308; https://doi.org/10.3390/rs13071308 - 30 Mar 2021

Cited by 4 | Viewed by 3355

Abstract

Fine-resolution LiDAR DEMs can represent surface features such as road and railway embankments with high fidelity. However, transportation embankments are problematic for several environmental modelling applications, and particularly hydrological modelling. Currently, there are no automated techniques for the identification and removal of embankments from LiDAR DEMs. This paper presents a novel algorithm for identifying embankments in LiDAR DEMs. The algorithm utilizes repositioned transportation network cells as seed points in a region-growing operation. The embankment region grows based on derived morphometric parameters, including road surface width, embankment width, embankment height, and absolute slope. The technique was tested on eight LiDAR DEMs representing subsections of four watersheds in southwestern Ontario, Canada, ranging in size from 16 million cells to 134 million cells. The algorithm achieved a recall greater than or equal to 90% for seven of the eight DEMs, while achieving a Pearson’s phi correlation coefficient greater than 80% for five of the eight DEMs. Therefore, the method has moderate to high accuracy for identifying embankments. The processing times associated with applying the technique to the eight study site DEMs ranged from 1.4 s to 20.3 s, which demonstrates the practicality of using the embankment mapping tool in applications with data set sizes commonly encountered in practice. Full article

(This article belongs to the Special Issue Perspectives on Digital Elevation Model Applications)

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