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Leading Progress in Digital Terrain Analysis and Modeling

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A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 March 2018) | Viewed by 54730

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Special Issue Editors

Dr. Giulia Sofia

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Guest Editor

Department of Civil & Environmental Engineering, University of Connecticut, 261 Glenbrook Rd, Storrs, CT 06269, USA
Interests: LiDAR; geomorphology; geomorphometry; Matlab; hydrology; feature extraction; GIS training

Dr. Anette Eltner

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Guest Editor

Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden, Dresden, Germany
Interests: geomorphology; soil erosion; SfM photogrammetry; laser scanning; UAV; image processing

Dr. Efthymios Nikolopoulos

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Guest Editor

Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, USA
Interests: flood hydrology; prediction of hydrogeomorphic hazards; remote sensing of precipitation; uncertainty analysis

Mr. Christopher Crosby

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Guest Editor

UNAVCO / OpenTopography, Boulder, CO 80301-5394, USA
Interests: lidar remote sensing; geodetic imaging; cyberinfrastructure; terrestrial laser scanning
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Special Issue Information

Dear Colleagues,

Digital Terrain analysis and modeling has been a flourishing interdisciplinary field for years, with applications in hydrology, geomorphology, soil science, engineering projects and computer sciences. With the rapid growth of survey technologies and computing advances, in particular with the availability of high-resolution topography (i.e., sub-meter to meter resolution) at local or regional scale, and moderate resolution (5–30 m) global scale data, the challenge is now the interpretation of surface morphology for a better understanding of processes at a variety of scales, from micro, to local, to global. Increased ease of data acquisition from various sources (platforms and sensors) led to a vast data pool with unprecedented spatio-temporal range, density, and resolution, which requires for efficient data processing to extract sought-after information. This Special Issue focuses on terrain analysis applications that advance the fields of hydrology, geomorphology, soil science, GIS and computer science. Submissions related to new techniques in high-resolution terrain production and analysis, independent of the subject, as well as studies focused on associated error and uncertainty analyses, are also welcome.

Assist. Prof. Dr. Giulia Sofia
Dr. Anette Eltner
Dr. Efthymios Nikolopoulos
Mr. Christopher Crosby
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

DEM
SfM
GIS
high-resolution topography
Landscape evolution
Earth surface processes, point cloud
Geomorphometry
UAV
LiDAR

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Published Papers (9 papers)

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Editorial

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5 pages, 207 KiB

Open AccessEditorial

Leading Progress in Digital Terrain Analysis and Modeling

by Giulia Sofia, Anette Eltner, Efthymios Nikolopoulos and Christopher Crosby

ISPRS Int. J. Geo-Inf. 2019, 8(9), 372; https://doi.org/10.3390/ijgi8090372 - 27 Aug 2019

Cited by 5 | Viewed by 4319

Abstract

Digital Terrain analysis (DTA) and modeling has been a flourishing interdisciplinary field for decades, with applications in hydrology, geomorphology, soil science, engineering projects and computer sciences. Currently, DTA is characterized by a proliferation of multispectral data from new sensors and platforms, driven by regional and national governments, commercial businesses, and scientific groups, with a general trend towards data with higher spatial, spectral or temporal resolutions. Deriving meaningful and interpretable products from such a large pool of data sources sets new challenges. The articles included in this special issue (SI) focuses on terrain analysis applications that advance the fields of hydrology, geomorphology, soil science, geographic information software (GIS), and computer science. They showcase challenging examples of DTA tackling different subjects or different point of views on the same subject. Full article

(This article belongs to the Special Issue Leading Progress in Digital Terrain Analysis and Modeling)

Research

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14 pages, 3361 KiB

Open AccessArticle

Combining the Stock Unearthing Method and Structure-from-Motion Photogrammetry for a Gapless Estimation of Soil Mobilisation in Vineyards

by Alexander Remke, Jesús Rodrigo-Comino, Yeboah Gyasi-Agyei, Artemi Cerdà and Johannes B. Ries

ISPRS Int. J. Geo-Inf. 2018, 7(12), 461; https://doi.org/10.3390/ijgi7120461 - 27 Nov 2018

Cited by 19 | Viewed by 3651

Abstract

In vineyards, especially on steep slopes like the Ruwer-Mosel Valley, Germany, soil erosion is a well-known environmental problem. Unfortunately, some enterprises and farmers are not aware of how much soil is being lost and the long-term negative impacts of soil erosion. The non-invasive technique of the stock unearthing method (SUM) can be used for a quick assessment of soil erosion in vineyards. SUM uses the graft union as a reference elevation for soil surface changes since the time of plantation commencement, which is modelled with the help of a geographic information system. A shortcoming of SUM is that the areas between the pair-vine cross sections are not surveyed, hence it is not accurate enough to identify erosion hot-spots. A structure-from-motion (SfM) photogrammetric technique is adopted to complement SUM to fill this data gap. Combining SUM (only measuring the graft unions) and SfM techniques could lead to an improved, easy and low-cost method with a higher accuracy for estimation of soil erosion based on interpolation by projection, and contact and gapless measuring. Thus, the main aim of this paper was to map the current soil surface level and to improve the accuracy of estimation of long-term soil mobilisation rates in vineyards. To achieve this goal, the TEPHOS (TErrestrial PHOtogrammetric Scanner), a static five camera array, was developed on a 20 m² plot located in a steeply sloping vineyard of the Ruwer-Mosel Valley, Trier, Germany. A total soil mobilisation of 0.52 m³ (9.14 Mg ha yr⁻¹) with soil surface level differences in excess of 30 cm in the 40 years since plantation commencement were recorded. Further research is, however, needed to reduce the number of photos used for the point cloud without loss of accuracy. This method can be useful for the observation of the impacts of other factors in vineyards, such as tillage erosion, runoff pathway detection or the trampling effect on soil erosion in vineyards. Full article

(This article belongs to the Special Issue Leading Progress in Digital Terrain Analysis and Modeling)

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16 pages, 4904 KiB

Open AccessArticle

Accuracy Assessment of Point Clouds from LiDAR and Dense Image Matching Acquired Using the UAV Platform for DTM Creation

by Adam Salach, Krzysztof Bakuła, Magdalena Pilarska, Wojciech Ostrowski, Konrad Górski and Zdzisław Kurczyński

ISPRS Int. J. Geo-Inf. 2018, 7(9), 342; https://doi.org/10.3390/ijgi7090342 - 23 Aug 2018

Cited by 85 | Viewed by 10564

Abstract

In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and possibilities of penetration of the point cloud from the image-based approach, whilst referring to various types of land cover, was conducted based on Real Time Kinematic Global Navigation Satellite System (GNSS-RTK) measurements and was compared to archival airborne laser scanning data. The vertical accuracy of DTM was evaluated for uncovered and vegetation areas separately, providing information about the influence of the vegetation height on the results of the bare ground extraction and DTM generation. In uncovered and low vegetation areas (0–20 cm), the vertical accuracies of digital terrain models generated from different data sources were quite similar: for the UAV Laser Scanning (ULS) data, the RMSE was 0.11 m, and for the image-based data collected using the UAV platform, it was 0.14 m, whereas for medium vegetation (higher than 60 cm), the RMSE from these two data sources were 0.11 m and 0.36 m, respectively. A decrease in the accuracy of 0.10 m, for every 20 cm of vegetation height, was observed for photogrammetric data; and such a dependency was not noticed in the case of models created from the ULS data. Full article

(This article belongs to the Special Issue Leading Progress in Digital Terrain Analysis and Modeling)

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24 pages, 6926 KiB

Open AccessArticle

Study on Multi-Scale Window Determination for GLCM Texture Description in High-Resolution Remote Sensing Image Geo-Analysis Supported by GIS and Domain Knowledge

by Zeying Lan and Yang Liu

ISPRS Int. J. Geo-Inf. 2018, 7(5), 175; https://doi.org/10.3390/ijgi7050175 - 5 May 2018

Cited by 51 | Viewed by 6321

Abstract

Texture features based on the gray-level co-occurrence matrix (GLCM) can effectively improve classification accuracy in geographical analyses of optical remote sensing (RS) images, with the parameters of scale of the GLCM texture window greatly affecting the validity. By analyzing human visual attention characteristics for geo-texture cognition, it was found that there is a strong correlation between the texture scale parameters and the domain shape knowledge in a specified geo-scene. Therefore, a new approach for quickly determining the multi-scale parameters of the texture with the assistance of a geographic information system (GIS) and domain knowledge is proposed in this paper. First, the validity of domain knowledge from an existing GIS database is measured by spatial data mining algorithms, including spatial partitioning, image segmentation, and space-time system evaluation. Second, the general domain shape knowledge of each category is described by the GIS minimum enclosing rectangle indices and rectangular-degree indices. Then, the corresponding multi-scale texture windows can be quickly determined for each category by a correlation analysis with the shape indices. Finally, the Fisher function is used to evaluate the validity of the scale parameters. The experimental results show that the multi-scale value keeps a one-to-one relationship with the classified objects, and their value ranges are from a few to tens, instead of the smaller values of a traditional analysis; thus, effective texture features at such a scale can be built to identify categories in a geo-scene. In this way, the proposed method can increase the total number of categories for a certain geo-scene and reduce the classification uncertainty, as well as better meet the requirements of large-scale image geo-analysis. It also has as high a calculation efficiency and as good a performance as the traditional enumeration method. Full article

(This article belongs to the Special Issue Leading Progress in Digital Terrain Analysis and Modeling)

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22 pages, 6355 KiB

Open AccessArticle

Use of DEMs Derived from TLS and HRSI Data for Landslide Feature Recognition

by Maurizio Barbarella, Alessandro Di Benedetto, Margherita Fiani, Domenico Guida and Andrea Lugli

ISPRS Int. J. Geo-Inf. 2018, 7(4), 160; https://doi.org/10.3390/ijgi7040160 - 23 Apr 2018

Cited by 16 | Viewed by 4475

Abstract

This paper addresses the problems arising from the use of data acquired with two different remote sensing techniques—high-resolution satellite imagery (HRSI) and terrestrial laser scanning (TLS)—for the extraction of digital elevation models (DEMs) used in the geomorphological analysis and recognition of landslides, taking into account the uncertainties associated with DEM production. In order to obtain a georeferenced and edited point cloud, the two data sets require quite different processes, which are more complex for satellite images than for TLS data. The differences between the two processes are highlighted. The point clouds are interpolated on a DEM with a 1 m grid size using kriging. Starting from these DEMs, a number of contour, slope, and aspect maps are extracted, together with their associated uncertainty maps. Comparative analysis of selected landslide features drawn from the two data sources allows recognition and classification of hierarchical and multiscale landslide components. Taking into account the uncertainty related to the map enables areas to be located for which one data source was able to give more reliable results than another. Our case study is located in Southern Italy, in an area known for active landslides. Full article

(This article belongs to the Special Issue Leading Progress in Digital Terrain Analysis and Modeling)

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13 pages, 46319 KiB

Open AccessArticle

Saddle Position-Based Method for Extraction of Depressions in Fengcong Areas by Using Digital Elevation Models

by Xianwu Yang, Guoan Tang, Xin Meng and Liyang Xiong

ISPRS Int. J. Geo-Inf. 2018, 7(4), 136; https://doi.org/10.3390/ijgi7040136 - 1 Apr 2018

Cited by 9 | Viewed by 6611

Abstract

A karst depression is an important sign of the development stage of karst landforms. The morphological characteristics of depressions can help reflect the development and evolution process of such landforms. The accurate identification and extraction of depressions in Fengcong areas are the basis of this research on karst depressions. Previous studies on Fengcong depressions were primarily based on manual surveys, remote sensing image interpretation, and manual map plotting or GIS-based techniques. The extracted landform units of Fengcong depressions in these studies were not accurate and even inauthentic in certain cases. Thus, this work proposes a method for extracting Fengcong depressions in karst areas which is based on terrain saddle points and uses digital elevation models (DEMs). First, the surface morphology of the Fengcong karst area is analyzed. Second, saddles are detected from the intersection points, and spatial trend surfaces are generated by interpolating the elevations of these saddle points. The interface between pinnacles and depressions can be determined by the trend surface. We applied the method in a case Fengcong area of the Lijiang River in Guilin, China. Results showed that the proposed method successfully divided the positive terrain form of pinnacles and the negative terrain form of the depressions in the Fengcong karst area. A total of 188 surface depressions were extracted, whose average area was 0.14 km² and polygonal depression density was 2.5 km². Results also showed that most of the depressions were stable in terms of the morphological features of area and depth. A total of 94% of the depth measured less than 60 m, and the area was less than 0.5 km². This proposed method can accurately determine the boundary of depressions and provide an important reference for quantitative research on the Fengcong depression terrain in karst landforms. Full article

(This article belongs to the Special Issue Leading Progress in Digital Terrain Analysis and Modeling)

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22 pages, 9026 KiB

Open AccessArticle

Roughness Spectra Derived from Multi-Scale LiDAR Point Clouds of a Gravel Surface: A Comparison and Sensitivity Analysis

by Milutin Milenković, Camillo Ressl, Wilfried Karel, Gottfried Mandlburger and Norbert Pfeifer

ISPRS Int. J. Geo-Inf. 2018, 7(2), 69; https://doi.org/10.3390/ijgi7020069 - 22 Feb 2018

Cited by 7 | Viewed by 5053

Abstract

The roughness spectrum (i.e., the power spectral density) is a derivative of digital terrain models (DTMs) that is used as a surface roughness descriptor in many geomorphological and physical models. Although light detection and ranging (LiDAR) has become one of the main data sources for DTM calculation, it is still unknown how roughness spectra are affected when calculated from different LiDAR point clouds, or when they are processed differently. In this paper, we used three different LiDAR point clouds of a 1 m × 10 m gravel plot to derive and analyze the roughness spectra from the interpolated DTMs. The LiDAR point clouds were acquired using terrestrial laser scanning (TLS), and laser scanning from both an unmanned aerial vehicle (ULS) and an airplane (ALS). The corresponding roughness spectra are derived first as ensemble averaged periodograms and then the spectral differences are analyzed with a dB threshold that is based on the 95% confidence intervals of the periodograms. The aim is to determine scales (spatial wavelengths) over which the analyzed spectra can be used interchangeably. The results show that one TLS scan can measure the roughness spectra for wavelengths larger than 1 cm (i.e., two times its footprint size) and up to 10 m, with spectral differences less than 0.65 dB. For the same dB threshold, the ULS and TLS spectra can be used interchangeably for wavelengths larger than about 1.2 dm (i.e., five times the ULS footprint size). However, the interpolation parameters should be optimized to make the ULS spectrum more accurate at wavelengths smaller than 1 m. The plot size was, however, too small to draw particular conclusions about ALS spectra. These results show that novel ULS data has a high potential to replace TLS for roughness spectrum calculation in many applications. Full article

(This article belongs to the Special Issue Leading Progress in Digital Terrain Analysis and Modeling)

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6232 KiB

Open AccessArticle

An Automated Processing Algorithm for Flat Areas Resulting from DEM Filling and Interpolation

by Xingwei Liu, Ning Wang, Jingli Shao and Xuefeng Chu

ISPRS Int. J. Geo-Inf. 2017, 6(11), 376; https://doi.org/10.3390/ijgi6110376 - 21 Nov 2017

Cited by 17 | Viewed by 4978

Abstract

Correction of digital elevation models (DEMs) for flat areas is a critical process for hydrological analyses and modeling, such as the determination of flow directions and accumulations, and the delineation of drainage networks and sub-basins. In this study, a new algorithm is proposed for flat correction/removal. It uses the puddle delineation (PD) program to identify depressions (including their centers and overflow/spilling thresholds), compute topographic characteristics, and further fill the depressions. Three different levels of elevation increments are used for flat correction. The first and second level of increments create flows toward the thresholds and centers of the filled depressions or flats, while the third level of small random increments is introduced to cope with multiple threshold conditions. A set of artificial surfaces and two real-world landscapes were selected to test the new algorithm. The results showed that the proposed method was not limited by the shapes, the number of thresholds, and the surrounding topographic conditions of flat areas. Compared with the traditional methods, the new algorithm simplified the flat correction procedure and reduced the final elevation increments by 5.71–33.33%. This can be used to effectively remove/correct topographic flats and create flat-free DEMs. Full article

(This article belongs to the Special Issue Leading Progress in Digital Terrain Analysis and Modeling)

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52237 KiB

Open AccessFeature PaperArticle

Visualization of Features in 3D Terrain

by Steve Dübel and Heidrun Schumann

ISPRS Int. J. Geo-Inf. 2017, 6(11), 357; https://doi.org/10.3390/ijgi6110357 - 14 Nov 2017

Cited by 10 | Viewed by 7758

Abstract

In 3D terrain analysis, topographical characteristics, such as mountains or valleys, and geo-spatial data characteristics, such as specific weather conditions or objects of interest, are important features. Visual representations of these features are essential in many application fields, e.g., aviation, meteorology, or geo-science. However, creating suitable representations is challenging. On the one hand, conveying the topography of terrain models is difficult, due to data complexity and computational costs. On the other hand, depicting further geo-spatial data increases the intricacy of the image and can lead to visual clutter. Moreover, perceptional issues within the 3D presentation, such as distance recognition, play a significant role as well. In this paper, we address the question of how features in the terrain can be visualized appropriately. We discuss various design options to facilitate the awareness of global and local features; that is, the coarse spatial distribution of characteristics and the fine-granular details. To improve spatial perception of the 3D environment, we propose suitable depth cues. Finally, we demonstrate the feasibility of our approach by a sophisticated framework called TedaVis that unifies the proposed concepts and facilitates designing visual terrain representations tailored to user requirements. Full article

(This article belongs to the Special Issue Leading Progress in Digital Terrain Analysis and Modeling)

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