Quality Of Digital Elevation Models Research Articles

DIGITAL ELEVATION MODEL CORRECTION IN URBAN AREAS USING EXTREME GRADIENT BOOSTING, LAND COVER AND TERRAIN PARAMETERS

Abstract. The accuracy of digital elevation models (DEMs) in urban areas is influenced by numerous factors including land cover and terrain irregularities. Moreover, building artefacts in global DEMs cause artificial blocking of surface flow pathways. This compromises their quality and adequacy for hydrological and environmental modelling in urban landscapes where precise and accurate terrain information is needed. In this study, the extreme gradient boosting (XGBoost) ensemble algorithm is adopted for enhancing the accuracy of two medium-resolution 30-metre DEMs over Cape Town, South Africa: Copernicus GLO-30 and ALOS World 3D (AW3D). XGBoost is a scalable, portable and versatile gradient boosting library that can solve many environmental modelling problems. The training datasets are comprised of eleven predictor variables including elevation, urban footprints, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector roughness measure, forest cover and bare ground cover. The target variable (elevation error) was calculated with respect to highly accurate airborne LiDAR. After training and testing, the model was applied for correcting the DEMs at two implementation sites. The corrections achieved significant accuracy gains which are competitive with other proposed methods. There was a 46–53% reduction in the root mean square error (RMSE) of Copernicus DEM, and a 72–73% reduction in the RMSE of AW3D DEM. These results showcase the potential of gradient-boosted decision trees for enhancing the quality of global DEMs, especially in urban areas.

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).

Use of Generative Adversarial Network Algorithm in Super-Resolution Images to Increase the Quality of Digital Elevation Models Based on ALOS PALSAR Data

Digital elevation models are responsible for providing altimetric information on a surface to be mapped. While global models of low and medium spatial resolution are available open source by several space agencies, the high- resolution ones, which are utilized in scales 1:25,000 and larger, are scarce and expensive. Here we address this limitation by the utilization of deep learning algorithms coupled with Single Image Super-Resolution techniques in digital elevation models to obtain better spatial quality versions from lower resolution inputs. The development of a GAN-based (Generative Adversarial Network-based) methodology enables the improvement of the initial spatial resolution of low-resolution images. In the geospatial data context, for example, these algorithms can be used with digital elevation models and satellite images. The methodological approach uses a dataset with digital elevation models SRTM (Shuttle Radar Topography Mission) (30 meters of spatial resolution) and ALOS PALSAR (12.5 meters of spatial resolution), created with the objective of allowing the study to be carried out, promoting the emergence of new research groups in the area as well as enabling the comparison between the results obtained. It has been found that by increasing the number of iterations the performance of the generated model was improved and the quality of the generated image increased. Furthermore, the visual analysis of the generated image against the high- and low-resolution ones showed a great similarity between the first two.

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.

Accuracy Assessment of High-Resolution Globally Available Open-Source DEMs Using ICESat/GLAS over Mountainous Areas, A Case Study in Yunnan Province, China

The Open-Source Digital Elevation Model (DEM) is fundamental data of the geoscientific community. However, the variation of its accuracy with land cover type and topography has not been thoroughly studied. This study evaluates the accuracy of five globally covered and open-accessed DEM products (TanDEM-X90 m, SRTEM, NASADEM, ASTER GDEM, and AW3D30) in the mountain area using ICESat/GLAS data as the GCPs. The robust evaluation indicators were utilized to compare the five DEMs’ accuracy and explore the relationship between these errors and slope, aspect, landcover types, and vegetation coverage, thereby revealing the consistency differences in DEM quality under different geographical feature conditions. The Taguchi method is introduced to quantify the impact of these surface characteristics on DEM errors. The results show that the slope is the main factor affecting the accuracy of DEM products, accounting for about 90%, 81%, 85%, 83%, and 65% for TanDEM-X90, SRTM, NASADEM, ASTER GDEM, and AW3D30, respectively. TanDEM-X90 has the highest accuracy in very flat areas (slope < 2°), NASADEM and SRTM have the greatest accuracy in flat areas (2 ≤ slope < 5°), while AW3D30 accuracy is the best in other cases and shows the best consistency on slopes. This study makes a new attempt to quantify the factors affecting the accuracy of DEM, and the results can guide the selection of open-source DEMs in related geoscience research.

Elevation Model Mosaicking From Coregistration, Adjustment, and Median of Stacks (CAMS)

A large and growing volume of repeat Digital Elevation Models (DEMs) obtained from multiple spaceborne sensors enables high-resolution mapping of the Earth’s surface at continental scales. Mosaicking of individual DEMs to form a continuous surface is challenging due to variability data quality and positional accuracy, all of which can result in artifacts. Presented is a method for efficiently mosaicking sets of repeat, overlapping DEMs using their pairwise, translational offsets to remove poor quality DEMs and optimize their alignment prior to merging. The Coregistration, Adjustment and Median of Stacks (CAMS) approach is tested by mosaicking a set of 2-m resolution DEMs created from WorldView stereoscopic imagery and comparing the result to LiDAR data. CAMS produces a mosaic of substantially higher quality and accuracy than that obtained from the median of all overlapping DEMS, as commonly performed for mosaicking satellite derived DEMs. The method requires no sensor-specific information or ground control, making it applicable for large-area mosaic production using multiple datasets.

Image Texture as Quality Indicator for Optical DEM Generation: Geomorphic Applications in the Arid Central Andes

The generation of Digital Elevation Models (DEMs) through stereogrammetry of optical satellite images has gained great popularity across various disciplines. For the analysis of these DEMs, it is important to understand the influence of the input data and different processing steps and parameters employed during stereo correlation. Here, we explore the effects that image texture, as well as the use of different matching algorithms (Block Matching (BM) and More Global Matching (MGM)), can have on optical DEMs derived from the flexible, open-source Ames Stereo Pipeline. Our analysis relies on a ∼2700 km2 clip of a SPOT6 tristereo scene covering the hyperarid, vegetation-free Pocitos Basin and adjacent mountain ranges in the northwestern Argentine Andes. A large, perfectly flat salt pan (paleolake bed) that covers the center of this basin is characterized by strong contrasts in image texture, providing a unique opportunity to quantitatively study the relationship between image texture and DEM quality unaffected by topography. Our findings suggest that higher image texture, measured by panchromatic variance, leads to lower DEM uncertainty. This improvement continues up to ∼103 panchromatic variance, above which further improvements in DEM quality are independent of local image texture but instead may have sensor or geometric origins. Based on this behavior, we propose that image texture may serve as an important proxy of DEM quality prior to stereo correlation and can help to set adequate processing parameters. With respect to matching algorithms, we observe that MGM improves matching in low-texture areas and overall generates a smoother surface that still preserves complex, narrow (i.e., ridge and valley) features. Based on this sharper representation of the landscape, we conclude that MGM should be preferred for geomorphic applications relying on stereo-derived DEMs. However, we note that the correlation kernel selected for stereo-matching must be carefully chosen depending on local image texture, whereby larger kernels generate more accurate matches (less artifacts) at the cost of smoothing results. Overall, our analysis suggests a path forward for the processing and fusion of overlapping satellite images with suitable view-angle differences to improve final DEMs.

An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcán Copahue (Argentina & Chile)

Lahars are debris flows originating at volcanoes with very erosive and destructive capabilities. Lahars constitute a serious hazard for the communities close to the Volcán Copahue, lying at the Argentina-Chile border. This study presents a comprehensive analysis of the sensitivity of the geographic information system (GIS) assisted model LAHARZ to the spatial resolution and quality of the digital elevation models (DEMs) for mapping lahar inundation areas within the context of Volcán Copahue. In addition, critical lahar volumes required to reach the nearby settlements are investigated, following a literature review to characterize plausible triggering mechanisms. We compared lahar inundation areas at the four main catchments of Volcán Copahue resulting from five DEMs: three 30-m spatial resolution datasets (SRTM, ASTER and AW3D30), the 12-m WorldDEM™, and a 5-m DEM derived from SPOT-7 data. Our sensitivity analysis shows that DEM spatial resolution and quality precipitate into the LAHARZ results. The interaction between the spatial resolution and the channel confinement constitutes the main control of the output geometries. Higher-spatial resolution DEMs (i.e. smaller pixel size) better depict channels and better define confined channels zones resulting in narrower output areas and longer runouts. For the same volume, the inundation areas obtained using the 5- and 12-m pixel DEMs are narrower and 2–3 km longer than when derived from any of the 30-m DEMs. At unconfined channels, LAHARZ is less sensitive to the spatial resolution and inundation areas tend to show ragged edges regardless of the input DEM. The quality of the DEM controls the inundation trajectories. These results highlight the importance of evaluating topographic models for LAHARZ simulations before the implementation of the model for hazard mapping. Our results suggest that lahars of volumes of 105–106 m3 would reach the nearest villages within a radius of 15 km from the volcano. More work is required to improve the knowledge on lahar deposits and in turn hazard assessment.

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.

Assessing DEM quality and minimizing registration error in repeated geomorphic surveys with multi‐temporal ground truths of invariant features: Application to a long‐term dataset of beach topography and nearshore bathymetry

AbstractRemotely sensed digital elevation models (DEMs) and uncertainty‐based geomorphic change detection have become very practical tools for geoscientists, including for coastal research. Through the analysis of DEMs of differences (DoDs) and the provision of DEM quality, it allows monitoring complex landforms and confidently relating the changes observed to environmental forcing conditions.With continuing remote sensing advances, and as some monitoring programmes are reaching several decades of repeated data collection, it is timely to consider approaches that enable the reconciliation of DEMs of variable and potentially unknown quality before their subsequent geomorphic analyses. In this paper, we present an original workflow whereby composite data formed by fusing available measurements over invariant features serve as multi‐temporal ground truths for assessing repeated DEMs. Results of the evaluation enable identification of DEMs of lower quality (bias and precision) and correction of registration error (horizontal and vertical bias), and thus offer the in‐built capacity for estimating and improving change detection levels afforded by the data.The workflow was applied to a freely accessible multi‐sensor: RTK‐GNSS, terrestrial laser‐scanning, drone photogrammetry and multibeam echo‐sounding dataset of high‐resolution topographic and nearshore bathymetric DEMs collected at the macrotidal pocket beach of Porsmilin (France) over the period 2003–2019. Our results show that consistently high DEM precision can be achieved in a long‐term multi‐sensor dataset, but registration errors may be present and can be minimized through co‐registration with the purpose‐built ground truths. Although the study focuses primarily on measuring height discrepancies, which is directly relevant for DoD analysis, we show that the methods can also be used for dealing with horizontal error when high‐resolution imagery is available. Finally, the detailed DEM evaluations presented, in application to a rare dataset documenting beach and shoreface change for nearly two decades, provide original insights on the performance of usual topo‐bathymetric surveying techniques.

A Novel Topography Retrieval Algorithm Based on Single-Pass Polarimetric SAR Data and Terrain Dependent Error Analysis

Polarimetric synthetic aperture radar (PolSAR) data provide an alternative way for topography retrieval, especially when limited PolSAR data are available. This article proposes a novel topography retrieval algorithm based on the Lambertian backscatter model that further improves the vertical precision of digital elevation model (DEM) generation and requires only one flight. The key idea of the proposed algorithm is to avoid data fluctuations caused by the ratio of the azimuth slope angle to the polarimetric orientation angle (POA). The previous research has confirmed the feasibility of generating a DEM based on single-pass PolSAR data, but its effect on the quality of reference DEM has not been well-explained. To analyze this effect, a large number of experiments on DEM with different resolutions are conducted. In addition, an in-depth analysis of non-linear and terrain-dependent errors is performed. The L-band PolSAR data of NASA/JPL TOPSAR and ALOS-2 PALSAR-2 and interferometric SAR (InSAR) DEM data are used to verify the proposed algorithm. The experimental results show that PolSAR data can be used as an additional reliable information source for DEM fusion under certain conditions to improve the quality of public DEM.

Global DEMs vary from one to another: an evaluation of newly released Copernicus, NASA and AW3D30 DEM on selected terrains of China using ICESat-2 altimetry data

ABSTRACT A good understanding of the quality of digital elevation model (DEM) is a perquisite for various applications. This study investigates the accuracy of three most recently released 1-arcsec global DEMs (GDEMs, Copernicus, NASA and AW3D30) in five selected terrains of China, using more than 240,000 high-quality ICESat-2 (Ice, Cloud and land Elevation Satellite) ALT08 points. The results indicate the three GDEMs have similar overall vertical accuracy, with RMSE of 6.73 (Copernicus), 6.59 (NASA) and 6.63 m (AW3D30). While the accuracy varies considerably over study areas and among GDEMs. The results show a clear correlation between the accuracy and terrain slopes, and some relationship between the accuracy and land covers. Our analysis reveals the land cover exerts a greater impact on the accuracy than that of the terrain slope for the study area. Visual inspections of terrain representation indicate Copernicus DEM exhibits the greatest detail of terrain, followed by AW3D30, and then by NASADEM. This study has demonstrated that ICESat-2 altimetry offers an important tool for DEM assessment. The findings provide a timely and comprehensive understanding of the quality of newly released GDEMs, which are informative for the selection of suitable DEMs, and for the improvement of GDEM in future studies.

Atmospheric Correction for High-Resolution Shape from Shading on Mars.

Digital Elevation Models (DEMs) of planet Mars are crucial for many remote sensing applications and for landing site characterization of rover missions. Shape from Shading (SfS) is known to work well as a complementary method to greatly enhance the quality of photogrammetrically obtained DEMs of planetary surfaces with respect to the effective resolution and the overall accuracy. In this work, we extend our previous lunar shape and albedo from shading framework by embedding the Hapke photometric reflectance model in an atmospheric model such that it is applicable to Mars. Compared to previous approaches, the proposed method is capable of directly estimating the atmospheric parameters from a given scene without the need for external data, and assumes a spatially varying albedo. The DEMs are generated from imagery of the Context Camera (CTX) onboard the Mars Reconnaissance Orbiter (MRO) and are validated for clear and opaque atmospheric conditions. We analyze the necessity of using atmospheric compensation depending on the atmospheric conditions. For low optical depths, the Hapke model without an atmospheric component is still applicable to the Martian surface. For higher optical depths, atmospheric compensation is required to obtain good quality DEMs.

Adaptive digital elevation models construction method based on nonparametric regression

AbstractInterpolation is one of the most critical factors affecting the quality of digital elevation models (DEM) generated from sampling point data. However, existing methods have not taken into full account that the interpolation function should be consistent with the density and direction characteristics of sampling points. Therefore, an adaptive DEM construction method based on nonparametric regression (AdpNPR‐DEM) is proposed, which incorporates an adaptive kernel that changes according to local landform types. The method first estimates local terrain features, and second the dominant orientation of the local gradients. The orientation information adaptively “steers” the local kernel to produce elongated, elliptical contours that spread along the directions of local terrain features. The effectiveness of the AdpNPR‐DEM is verified based on directional features, the number of sampling points, and typical landform types. The results show that, compared with triangulated irregular network (TIN), natural neighbor interpolation (NNI), and the Australian National University digital elevation model (ANUDEM) interpolation method, AdpNPR‐DEM has the highest accuracy for all landform types and a better level of robustness. Our method improves the quality of DEM in areas with complex landforms. It could significantly promote the high‐quality production of DEMs, and also promisingly broaden and deepen its applications.

Point cloud does matter. Selected issues of using airborne LiDAR elevation data in geomorphometric studies of rugged sandstone terrain under forest – Case study from Central Europe

Contemporary airborne Light Detection And Ranging (LiDAR) elevation data provide a high-resolution input to quantitative studies within various branches of geomorphological research. Availability of such data played an especially significant role in the recent developments of geomorphometric studies focused on extremely rugged terrains, such as dissected sandstone plateaus. These are the areas of very complex topography, featuring structurally-controlled landforms such as tabular hills, canyons, cleft-and-valley systems, numerous high and near-vertical rock walls, isolated tors and dense boulder slope covers. In this paper we focus on issues of LiDAR data handling for geomorphometric research in Central European sandstone areas in Czechia, Poland and Germany, where the extremely rugged terrains are usually located in the forest and, thus, usability of LiDAR DEM (Digital Elevation Model) products for morphometric studies can be constrained. The impact of forest density on local decrease of ground point density may be considerable, adversely affecting the quality of DEMs, which become unsuitable to represent properly terrain features. Therefore, the role of DEM pre-processing in geomorphometric workflow is emphasized. Using the original point cloud data one can provide a much more detailed terrain representation that includes numerous convex landforms (tors, boulders) which are erroneously filtered-out from ready-to-use DEMs. Results of such procedure are shown explicitly in the form of Elevation Difference maps, as well as on examples of selected morphometric indices such as Topographic Wetness Index, Terrain Ruggedness Index and Morphometric Protection Index. The awareness of qualities of particular LiDAR datasets should be considered crucial while assessing LiDAR data applicability for morphometric studies on rugged erosional landscapes, especially if the study areas are transboundary.

Surface drainage features identification using LiDAR DEM smoothing in agriculture area: a study case of Kebumen Regency, Indonesia

ABSTRACT Digital Elevation Model (DEM) is the most vital data to generate drainage networks and to provide critical terrain factors and hydrologic derivatives, such as slope, aspect, and streamflow. The accuracy of generated drainage features is extensively dependent on the quality and resolution of DEM, such as LiDAR-derived DEM. Contrary, it has a high level of roughness and complexity. Thus, smoothing methods are sometimes employed to conquer the roughness. This paper presents feature-preserving DEM smoothing (FPDEM-S) and edge-preserving DEM smoothing (EPDEM-S) approaches to smooth surface complexity in kind of preserving small drainage features using the 0.5 m – resolution LiDAR DEM of the Kedungbener River area in Kebumen Regency, Indonesia. Entangling linear morphometric factors, those smoothing approaches delivered a slight difference of stream number, with the FPDEM-S stream length ratio performing 7% better tendencies. The FPDEM-S method perormed better than EPDEM-S in this study area to provide an optimal smoothed LiDAR DEM at certain parameter values. Summarising that two smoothing methods approaches performed similar characteristics of watershed as an oval structure close to the circular shape. Also, it can be revealed that the watershed did not reach maturity phase.

Application of Multi-Channel Convolutional Neural Network to Improve DEM Data in Urban Cities

A digital elevation model (DEM) represents the topographic surface of the Earth and is an indispensable source of data in many applications, such as flood modeling, infrastructure design and land management. DEM data at high spatial resolution and high accuracy of elevation data are not only costly and time-consuming to acquire but also often confidential. In this paper, we explore a cost-effective approach to derive good quality DEM data by applying a multi-channel convolutional neural network (CNN) to enhance free resources of available DEM data. Shuttle Radar Topography Mission (SRTM) data, multi-spectral imaging Sentinel-2, as well as Google satellite imagery were used as inputs to the CNN model. The CNN model was first trained using high-quality reference DEM data in a dense urban city—Nice, France—then validated on another site in Nice and finally tested in the Orchard Road area (Singapore), which is also an equally dense urban area in Singapore. The CNN model not only shows an impressive reduction in the root mean square error (RMSE) of 50% at validation site in Nice and 30% at the test site in Singapore, but also results in much clearer profiles of the land surface than input SRTM data. A comparison between CNN performance and that of an earlier conducted study using artificial neural networks (ANN) was conducted as well. The comparison within this limited study shows that CNN yields a more accurate DEM.

Evaluation of the Influence of Processing Parameters in Structure-from-Motion Software on the Quality of Digital Elevation Models and Orthomosaics in the Context of Studies on Earth Surface Dynamics

The fully automated Structure-from-Motion approach for developing digital elevation models and orthomosaics has been known and used in photogrammetry for at least 15 years. Years of practice and experience have allowed researchers to provide a solid description of the applicability and limitations of this method. That being said, the impact of input processing parameters in software on the quality of photogrammetric products has yet to be fully ascertained empirically. This study is aimed at identifying the most advantageous processing workflow to fill this research gap by testing 375 different setup variations in the Agisoft Metashape software for the same set of images acquired using an unmanned aerial vehicle in a proglacial area. The purpose of the experiment was to determine three workflows: (1) the fastest, which has the shortest calculation time; (2) the best quality, which is as accurate as possible, regardless of the time taken for the calculations; and (3) the optimal, which is a compromise between accuracy and calculation time. Each of the 375 processing setup variations was assessed based on final product accuracy, i.e., orthomosaics and digital elevation models. The three workflows were selected based on calculating the height differences between the digital elevation models and the control points that did not participate in their georeferencing. The analyses of the root mean square errors (RMSE) and standard deviations indicate that excluding some of the optimization parameters during the camera optimization stage results in high RMSE and an increase in the values of standard deviation errors. Furthermore, it was shown that increasing the detail of individual processing steps in software does not always positively affect the accuracy of the resulting models. The experiment resulted in the development of three different workflows in the form of Python scripts for Agisoft Metashape software, which will help users to process image sets efficiently in the context of earth surface dynamics studies.

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

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.

Evaluating low-cost topographic surveys for computations of conveyance

Abstract. Rapid modern technological advancements have led to significant improvements in river monitoring using unmanned aerial vehicles (UAVs), photogrammetric reconstruction software, and low-cost real-time kinematic Global Navigation Satellite System (RTK GNSS) equipment. UAVs allow for the collection of dry bathymetric data in environments that are difficult to access. Low-cost RTK GNSS equipment facilitates accurate measurement of wet bathymetry when combined with subaqueous measuring tools such as acoustic Doppler current profilers (ADCPs). Hydraulic models may be constructed from these data, which in turn can be used for various applications such as water management, forecasting, early warning and disaster preparedness by responsible water authorities, and construction of river rating curves. We hypothesise that the reconstruction of dry terrain with UAV-based photogrammetry combined with RTK GNSS equipment leads to accurate geometries particularly fit for hydraulic understanding and simulation models. This study sought to (1) compare open-source and commercial photogrammetry packages to verify if water authorities with low resource availability have the option to utilise open-source packages without significant compromise on accuracy; (2) assess the impact of variations in the number of ground control points (GCPs) and the distribution of the GCP markers on the quality of digital elevation models (DEMs), with a particular emphasis on characteristics that impact hydraulics; and (3) investigate the impact of using reconstructions based on different GCP numbers on conveyance and hydraulic slope. A novel method which makes use of a simple RTK tie line along the water edge measured using a low-cost but highly accurate GNSS is presented so as to correct the unwanted effect of lens distortion (“doming effect”) and enable the concatenation of geometric data from different sources. Furthermore, we describe how merging of the dry and wet bathymetry can be achieved through gridding based on linear interpolation. We tested our approach over a section of the Luangwa River in Zambia. Results indicate that the open-source software photogrammetry package is capable of producing results that are comparable to commercially available options. We determined that GCPs are essential for vertical accuracy, but also that an increase in the number of GCPs above a limited number of five only moderately increases the accuracy of results, provided the GCPs are well spaced in both the horizontal and vertical dimension. Furthermore, insignificant differences in hydraulic geometries among the various cross sections are observed, corroborating the fact that a limited well-spaced set of GCPs is enough to establish a hydraulically sound reconstruction. However, it appeared necessary to make an additional observation of the hydraulic slope. A slope derived merely from the UAV survey was shown to be prone to considerable errors caused by lens distortion. Combination of the photogrammetry results with the RTK GNSS tie line was shown to be essential to correct the slope and made the reconstruction suitable for hydraulic model setup.