Synthetic Digital Elevation Models Research Articles

Use of GIS tools, enhanced by FFT filtering methods, to detect blurred craters in Synthetic Digital Elevation Models, to improve their location and morphological characterisation

Filtered Geomorphic Reference Models (FGRMs), extracted from High-Pass Filters based on the Fast Fourier Transform (HPF-FFT), provide a semi-automated and objective detection of a wide range of geoforms and geomorphic situations (flat areas, slopes, depressions, or valley bottoms). The method is sensitive enough to reveal numerous inflection points that enable the identification of subtle changes in relief polarity, resulting in accurate representations of real land depressions. The effectiveness of this method has been demonstrated in lunar landscapes, utilizing altimetry extracted from high-precision Planetary Digital Elevation Models (PDEMs) to construct crater inventories with well-defined FGRMs. However, when analysing lunar environments with closely-spaced geomorphic features, the FGRMs display blurring and generalization effects, reducing their capability to identify all existing crater depressions. This limitation controls the ability of FGRMs, to generate reliable crater inventories. This study examines such effects in three experiments. Using Synthetic Digital Relief Models (SDRMs) constructed by means of a MATLAB script two experiments are performed. In Experiment_1 different scenarios of proximity between aligned craters are analysed. The marks, or footprints, formed by craters of identical size and shape (i.e., circular bowl-shaped depressions with central peaks) are considered, resulting in simplified PDEMs. Various scenarios of crater centre separation (proximity) and the presence of internal features within craters are also analysed. Experiment_2 validates the method with different proximities, locations and crater sizes. Experiment_3 validates the approach in a sampled area of LRO lunar PDEM corresponding to Zelinskiy crater (Mare Ingenii area). To address these blurring effects, a hybrid methodology is employed, generating FGRMs extracted from HPF-FFT, in conjunction with other GIS and remote sensing tools, such as singular point zoning, aspect-slope zoning, and edge-limits zoning. As a result, the correspondence between the FGRMs and the synthetic forms is examined, aiding the comprehension of the graphical response provided by filtering in these cratered areas. The methodology demonstrates that FGRMs derived from HPF-FFT are sensitive to capturing connections between shapes as soon as the crater features start to merge, while blurring becomes evident when the craters are located at a proximity of <20 model units. Additionally, the results highlight the method's utility in accurately locating crater footprints and morphologically characterizing inner crater features, even when crater centres are closely spaced. This approach effectively mitigates blurring effects and enables the precise localization of crater marks and their internal features. The validations carried out in complex relief and high precise DEMs provide a use in real situation.

Automatic Mapping and Characterisation of Linear Depositional Bedforms: Theory and Application Using Bathymetry from the North West Shelf of Australia

Bedforms are key components of Earth surfaces and yet their evaluation typically relies on manual measurements that are challenging to reproduce. Several methods exist to automate their identification and calculate their metrics, but they often exhibit limitations where applied at large scales. This paper presents an innovative workflow for identifying and measuring individual depositional bedforms. The workflow relies on the identification of local minima and maxima that are grouped by neighbourhood analysis and calibrated using curvature. The method was trialed using a synthetic digital elevation model and two bathymetry surveys from Australia’s northwest marine region, resulting in the identification of nearly 2000 bedforms. The comparison of the metrics calculated for each individual feature with manual measurements show differences of less than 10%, indicating the robustness of the workflow. The cross-comparison of the metrics resulted in the definition of several sub-types of bedforms, including sandwaves and palaeoshorelines, that were then correlated with oceanic conditions, further corroborating the validity of the workflow. Results from this study support the idea that the use of automated methods to characterise bedforms should be further developed and that the integration of automated measurements at large scales will support the development of new classification charts that currently rely solely on manual measurements.

Generation of Synthetic Elevation Models and Realistic Surface Images of River Deltas and Coastal Terrains Using cGANs

Terrain generation aims to automatize the procedure of creating landscapes using a computer system. The generation models must follow different terrains’ topographical features, such as areas with river deltas and other regions where water bodies affect the natural landscapes. It is possible to generate more realistic terrains thanks to improvements in computer graphics techniques and deep learning models that use specific hardware. However, the advance on the generation of terrains that include river deltas, fjords, and waterfalls has not had the same pace as other more studied landscapes. Therefore, as a contribution to the advance of the research of terrain generation with water bodies using generative models, this paper presents the DRCA2020 dataset, which is useful for supervised training. The proposed dataset contains eight different types of real-world satellite images. These images are grouped by the same geographical location. There are 13,184 groups; each one has three RGB surface images, a water coverage map, three binarizations of water coverage, and a digital elevation model (DEM). Additionally, this paper proposes the use of a cGAN composite model, trained with the DRCA2020 dataset, for generating synthetic DEMs from water coverage images and therefore, to create realistic surface texture images with promising validation results.

Unsupervised detection of topographic highs with arbitrary basal shapes based on volume evolution of isocontours

In this work, an unsupervised isocontour based segmentation method is proposed, that is applied on the detection of topographic highs with arbitrary basal shapes on Digital Elevation Models (DEMs). A series of isocontour based segmentation maps is computed for decreasing altitude levels. During this process, the isocontours are gradually merged providing a topological hierarchy of highs in an inclusion tree structure. A novel formulation of a topographic high is given taking into account the volume evolution of an isocontour that starts from the top of a high and grows, as decreasing the altitude level of isocontour, until a high of higher altitude is reached. This formulation yields to a robust unsupervised algorithm that can be sequentially applied to automatically recognize and discriminate the topographic highs of a region according to the inclusion tree without any constraint on basal shapes. The proposed method is applied on real and synthetic DEMs, in order to automatically detect the exact shape of complex topographic highs and some geomorphological based features useful for high annotations, yielding high performance results, even if the highs are partially visible in the given DEM.

Perspective – synthetic DEMs: A vital underpinning for the quantitative future of landform analysis?

Abstract. Physical processes, including anthropogenic feedbacks, sculpt planetary surfaces (e.g. Earth's). A fundamental tenet of geomorphology is that the shapes created, when combined with other measurements, can be used to understand those processes. Artificial or synthetic digital elevation models (DEMs) might be vital in progressing further with this endeavour in two ways. First, synthetic DEMs can be built (e.g. by directly using governing equations) to encapsulate the processes, making predictions from theory. A second, arguably underutilised, role is to perform checks on accuracy and robustness that we dub "synthetic tests". Specifically, synthetic DEMs can contain a priori known, idealised morphologies that numerical landscape evolution models, DEM-analysis algorithms, and even manual mapping can be assessed against. Some such tests, for instance examining inaccuracies caused by noise, are moderately commonly employed, whilst others are much less so. Derived morphological properties, including metrics and mapping (manual and automated), are required to establish whether or not conceptual models represent reality well, but at present their quality is typically weakly constrained (e.g. by mapper inter-comparison). Relatively rare examples illustrate how synthetic tests can make strong "absolute" statements about landform detection and quantification; for example, 84 % of valley heads in the real landscape are identified correctly. From our perspective, it is vital to verify such statistics quantifying the properties of landscapes as ultimately this is the link between physics-driven models of processes and morphological observations that allows quantitative hypotheses to be tested. As such the additional rigour possible with this second usage of synthetic DEMs feeds directly into a problem central to the validity of much of geomorphology. Thus, this note introduces synthetic tests and DEMs and then outlines a typology of synthetic DEMs along with their benefits, challenges, and future potential to provide constraints and insights. The aim is to discuss how we best proceed with uncertainty-aware landscape analysis to examine physical processes.

TecLines: A MATLAB-Based Toolbox for Tectonic Lineament Analysis from Satellite Images and DEMs, Part 2: Line Segments Linking and Merging

Extraction and interpretation of tectonic lineaments is one of the routines for mapping large areas using remote sensing data. However, this is a subjective and time-consuming process. It is difficult to choose an optimal lineament extraction method in order to reduce subjectivity and obtain vectors similar to what an analyst would manually extract. The objective of this study is the implementation, evaluation and comparison of Hough transform, segment merging and polynomial fitting methods towards automated tectonic lineament mapping. For this purpose we developed a new MATLAB-based toolbox (TecLines). The proposed toolbox capabilities were validated using a synthetic Digital Elevation Model (DEM) and tested along in the Andarab fault zone (Afghanistan) where specific fault structures are known. In this study, we used filters in both frequency and spatial domains and the tensor voting framework to produce binary edge maps. We used the Hough transform to extract linear image discontinuities. We used B-spline as a polynomial curve fitting method to eliminate artificial line segments that are out of interest and to link discontinuous segments with similar trends. We performed statistical analyses in order to compare the final image discontinuities maps with existing references map.

Assessment of multiresolution segmentation for delimiting drumlins in digital elevation models

Mapping or “delimiting” landforms is one of geomorphology's primary tools. Computer-based techniques such as land-surface segmentation allow the emulation of the process of manual landform delineation. Land-surface segmentation exhaustively subdivides a digital elevation model (DEM) into morphometrically-homogeneous irregularly-shaped regions, called terrain segments. Terrain segments can be created from various land-surface parameters (LSP) at multiple scales, and may therefore potentially correspond to the spatial extents of landforms such as drumlins. However, this depends on the segmentation algorithm, the parameterization, and the LSPs. In the present study we assess the widely used multiresolution segmentation (MRS) algorithm for its potential in providing terrain segments which delimit drumlins. Supervised testing was based on five 5-m DEMs that represented a set of 173 synthetic drumlins at random but representative positions in the same landscape. Five LSPs were tested, and four variants were computed for each LSP to assess the impact of median filtering of DEMs, and logarithmic transformation of LSPs. The testing scheme (1) employs MRS to partition each LSP exhaustively into 200 coarser scales of terrain segments by increasing the scale parameter (SP), (2) identifies the spatially best matching terrain segment for each reference drumlin, and (3) computes four segmentation accuracy metrics for quantifying the overall spatial match between drumlin segments and reference drumlins. Results of 100 tests showed that MRS tends to perform best on LSPs that are regionally derived from filtered DEMs, and then log-transformed. MRS delineated 97% of the detected drumlins at SP values between 1 and 50. Drumlin delimitation rates with values up to 50% are in line with the success of manual interpretations. Synthetic DEMs are well-suited for assessing landform quantification methods such as MRS, since subjectivity in the reference data is avoided which increases the reliability, validity and applicability of results.

Testing 3D landform quantification methods with synthetic drumlins in a real digital elevation model

Metrics such as height and volume quantifying the 3D morphology of landforms are important observations that reflect and constrain Earth surface processes. Errors in such measurements are, however, poorly understood. A novel approach, using statistically valid ‘synthetic’ landscapes to quantify the errors is presented. The utility of the approach is illustrated using a case study of 184 drumlins observed in Scotland as quantified from a Digital Elevation Model (DEM) by the ‘cookie cutter’ extraction method. To create the synthetic DEMs, observed drumlins were removed from the measured DEM and replaced by elongate 3D Gaussian ones of equivalent dimensions positioned randomly with respect to the ‘noise’ (e.g. trees) and regional trends (e.g. hills) that cause the errors. Then, errors in the cookie cutter extraction method were investigated by using it to quantify these ‘synthetic’ drumlins, whose location and size is known. Thus, the approach determines which key metrics are recovered accurately. For example, mean height of 6.8m is recovered poorly at 12.5±0.6 (2σ) m, but mean volume is recovered correctly. Additionally, quantification methods can be compared: A variant on the cookie cutter using an un-tensioned spline induced about twice (×1.79) as much error. Finally, a previously reportedly statistically significant (p=0.007) difference in mean volume between sub-populations of different ages, which may reflect formational processes, is demonstrated to be only 30–50% likely to exist in reality. Critically, the synthetic DEMs are demonstrated to realistically model parameter recovery, primarily because they are still almost entirely the original landscape. Results are insensitive to the exact method used to create the synthetic DEMs, and the approach could be readily adapted to assess a variety of landforms (e.g. craters, dunes and volcanoes).

A synthetic Earth Gravity Model Designed Specifically for Testing Regional Gravimetric Geoid Determination Algorithms

A synthetic [simulated] Earth gravity model (SEGM) of the geoid, gravity and topography has been constructed over Australia specifically for validating regional gravimetric geoid determination theories, techniques and computer software. This regional high-resolution (1-arc-min by 1-arc-min) Australian SEGM (AusSEGM) is a combined source and effect model. The long-wavelength effect part (up to and including spherical harmonic degree and order 360) is taken from an assumed errorless EGM96 global geopotential model. Using forward modelling via numerical Newtonian integration, the short-wavelength source part is computed from a high-resolution (3-arc-sec by 3-arc-sec) synthetic digital elevation model (SDEM), which is a fractal surface based on the GLOBE v1 DEM. All topographic masses are modelled with a constant mass-density of 2,670 kg/m3. Based on these input data, gravity values on the synthetic topography (on a grid and at arbitrarily distributed discrete points) and consistent geoidal heights at regular 1-arc-min geographical grid nodes have been computed. The precision of the synthetic gravity and geoid data (after a first iteration) is estimated to be better than 30 μ Gal and 3 mm, respectively, which reduces to 1 μ Gal and 1 mm after a second iteration. The second iteration accounts for the changes in the geoid due to the superposed synthetic topographic mass distribution. The first iteration of AusSEGM is compared with Australian gravity and GPS-levelling data to verify that it gives a realistic representation of the Earth’s gravity field. As a by-product of this comparison, AusSEGM gives further evidence of the north–south-trending error in the Australian Height Datum. The freely available AusSEGM-derived gravity and SDEM data, included as Electronic Supplementary Material (ESM) with this paper, can be used to compute a geoid model that, if correct, will agree to in 3 mm with the AusSEGM geoidal heights, thus offering independent verification of theories and numerical techniques used for regional geoid modelling.