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Remote Sensing in Geomorphology

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 April 2014) | Viewed by 134201

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Dr. Richard Gloaguen

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Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Chemnitzer Straße 40, 09599 Freiberg, Germany
Interests: remote sensing; exploration; UAVs/drones; sensors; machine learning; spectroscopy; hyperspectral imaging; mineral exploration
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Dr. Louis Andreani

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Remote Sensing Group, Institut für Geologie, Technische Universität Bergakademie Freiberg, Bernhard-von-Cotta Straße 2, D-09599 Freiberg/Sachsen, Germany
Interests: earth sciences; remote sensing; tectonic geomorphology; geodynamics

Special Issue Information

Dear Colleagues,

Landscapes are the result of complex interactions between complex surface processes. The distribution of landmasses is modified by geological factors (such as tectonic activity or volcanism) or eustatic changes. Resulting topography is in turn shaped by erosional processes which are themselves controlled by climate. During the last decades, remote sensing has been extensively used in geomorphology due to its ability to provide critical informations regarding the distribution of landforms and their association with meaningful ground features (e.g., faults, drainage networks) or climatic variables (e.g., precipitations). The application of remote sensing to geomorphology is also closely related to the use of computational methods and analytical tools which allows to extract and interpret these parameters.

The aim of this special issue is to cover remote sensed analyses of landscapes and their application to the understanding of surface processes. We are mainly interested in research dealing with the interactions between tectonic, erosion and climate. Innovative papers on data processing or new computational methods for extracting geologic or geomorphic features are also greatly encouraged.

Dr. Richard Gloaguen
Dr. Louis Andreani
Guest Editors

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Keywords

surface processes
computational methods
tectonics
erosion
climate

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Published Papers (11 papers)

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Research

2836 KiB

Open AccessArticle

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

by Mehdi Rahnama and Richard Gloaguen

Remote Sens. 2014, 6(11), 11468-11493; https://doi.org/10.3390/rs61111468 - 18 Nov 2014

Cited by 37 | Viewed by 11506

Abstract

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. Full article

(This article belongs to the Special Issue Remote Sensing in Geomorphology)

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7410 KiB

Open AccessArticle

Detection of Gully-Affected Areas by Applying Object-Based Image Analysis (OBIA) in the Region of Taroudannt, Morocco

by Sebastian D'Oleire-Oltmanns, Irene Marzolff, Dirk Tiede and Thomas Blaschke

Remote Sens. 2014, 6(9), 8287-8309; https://doi.org/10.3390/rs6098287 - 2 Sep 2014

Cited by 56 | Viewed by 9026

Abstract

This study aims at the detection of gully-affected areas by applying object-based image analysis in the region of Taroudannt, Morocco, which is highly affected by gully erosion while simultaneously representing a major region of agro-industry with a high demand of arable land. As high-resolution optical satellite data are readily available from various sensors and with a much better temporal resolution than 3D terrain data, an area-wide mapping approach to extract gully-affected areas using only optical satellite imagery was developed. The methodology additionally incorporates expert knowledge and freely-available vector data in a cyclic object-based image analysis approach. This connects the two fields of geomorphology and remote sensing. The classification results show the successful implementation of the developed approach and allow conclusions on the current distribution of gullies. The results of the classification were checked against manually delineated reference data incorporating expert knowledge based on several field campaigns in the area, resulting in an overall classification accuracy of 62%. The error of omission accounts for 38% and the error of commission for 16%, respectively. Additionally, a manual assessment was carried out to assess the quality of the applied classification algorithm. The limited error of omission contributes with 23% to the overall error of omission and the limited error of commission contributes with 98% to the overall error of commission. This assessment improves the results and confirms the high quality of the developed approach for area-wide mapping of gully-affected areas in larger regions. In the field of landform mapping, the overall quality of the classification results is often assessed with more than one method to incorporate all aspects adequately. Full article

(This article belongs to the Special Issue Remote Sensing in Geomorphology)

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20458 KiB

Open AccessArticle

DEM-Based Analysis of Interactions between Tectonics and Landscapes in the Ore Mountains and Eger Rift (East Germany and NW Czech Republic)

by Louis Andreani, Klaus P. Stanek, Richard Gloaguen, Ottomar Krentz and Leomaris Domínguez-González

Remote Sens. 2014, 6(9), 7971-8001; https://doi.org/10.3390/rs6097971 - 26 Aug 2014

Cited by 77 | Viewed by 12823

Abstract

Tectonics modify the base-level of rivers and result in the progressive erosion of landscapes. We propose here a new method to classify landscapes according to their erosional stages. This method is based on the combination of two DEM-based geomorphic indices: the hypsometric integral, which highlights elevated surfaces, and surface roughness, which increases with the topographic elevation and the incision by the drainage network. The combination of these two indices allows one to produce a map of erosional discontinuities that can be easily compared with the known structural framework. In addition, this method can be easily implemented (e.g., in MATLAB) and provides a quick way to analyze regional-scale landscapes. We propose here an example of a region where this approach becomes extremely valuable: the Ore Mountains and adjacent regions. The lack of young stratigraphic markers prevents a detailed analysis of recent fault activity. However, discontinuities in mapped geomorphic indices coupled to the analysis of river longitudinal profiles suggest a tight relationship between erosional discontinuities and main tectonic lineaments. Full article

(This article belongs to the Special Issue Remote Sensing in Geomorphology)

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19817 KiB

Open AccessArticle

Small-Scale Surface Reconstruction and Volume Calculation of Soil Erosion in Complex Moroccan Gully Morphology Using Structure from Motion

by Andreas Kaiser, Fabian Neugirg, Gilles Rock, Christoph Müller, Florian Haas, Johannes Ries and Jürgen Schmidt

Remote Sens. 2014, 6(8), 7050-7080; https://doi.org/10.3390/rs6087050 - 29 Jul 2014

Cited by 139 | Viewed by 18638

Abstract

This study presents a computer vision application of the structure from motion (SfM) technique in three dimensional high resolution gully monitoring in southern Morocco. Due to impractical use of terrestrial Light Detection and Ranging (LiDAR) in difficult to access gully systems, the inexpensive SfM is a promising tool for analyzing and monitoring soil loss, gully head retreat and plunge pool development following heavy rain events. Objects with known dimensions were placed around the gully scenes for scaling purposes as a workaround for ground control point (GCP) placement. Additionally, the free scaling with objects was compared to terrestrial laser scanner (TLS) data in a field laboratory in Germany. Results of the latter showed discrepancies of 5.6% in volume difference for erosion and 1.7% for accumulation between SfM and TLS. In the Moroccan research area soil loss varied between 0.58 t in an 18.65 m² narrowly stretched gully incision and 5.25 t for 17.45 m² in a widely expanded headcut area following two heavy rain events. Different techniques of data preparation were applied and the advantages of SfM for soil erosion monitoring under complex surface conditions were demonstrated. Full article

(This article belongs to the Special Issue Remote Sensing in Geomorphology)

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11241 KiB

Open AccessArticle

Assessing Long-Term Changes in the Beach Width of Reef Islands Based on Temporally Fragmented Remote Sensing Data

by Thomas Mann and Hildegard Westphal

Remote Sens. 2014, 6(8), 6961-6987; https://doi.org/10.3390/rs6086961 - 25 Jul 2014

Cited by 40 | Viewed by 10060

Abstract

Atoll islands are subject to a variety of processes that influence their geomorphological development. Analysis of historical shoreline changes using remotely sensed images has become an efficient approach to both quantify past changes and estimate future island response. However, the detection of long-term changes in beach width is challenging mainly for two reasons: first, data availability is limited for many remote Pacific islands. Second, beach environments are highly dynamic and strongly influenced by seasonal or episodic shoreline oscillations. Consequently, remote-sensing studies on beach morphodynamics of atoll islands deal with dynamic features covered by a low sampling frequency. Here we present a study of beach dynamics for nine islands on Takú Atoll, Papua New Guinea, over a seven-decade period. A considerable chronological gap between aerial photographs and satellite images was addressed by applying a new method that reweighted positions of the beach limit by identifying “outlier” shoreline positions. On top of natural beach variability observed along the reweighted beach sections, we found that one third of the analyzed islands show a statistically significant decrease in reweighted beach width since 1943. The total loss of beach area for all islands corresponds to 44% of the initial beach area. Variable shoreline trajectories suggest that changes in beach width on Takú Atoll are dependent on local control (that is, human activity and longshore sediment transport). Our results show that remote imagery with a low sampling frequency may be sufficient to characterize prominent morphological changes in planform beach configuration of reef islands. Full article

(This article belongs to the Special Issue Remote Sensing in Geomorphology)

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1525 KiB

Open AccessArticle

TecLines: A MATLAB-Based Toolbox for Tectonic Lineament Analysis from Satellite Images and DEMs, Part 1: Line Segment Detection and Extraction

by Mehdi Rahnama and Richard Gloaguen

Remote Sens. 2014, 6(7), 5938-5958; https://doi.org/10.3390/rs6075938 - 25 Jun 2014

Cited by 55 | Viewed by 15373

Abstract

Geological structures, such as faults and fractures, appear as image discontinuities or lineaments in remote sensing data. Geologic lineament mapping is a very important issue in geo-engineering, especially for construction site selection, seismic, and risk assessment, mineral exploration and hydrogeological research. Classical methods of lineaments extraction are based on semi-automated (or visual) interpretation of optical data and digital elevation models. We developed a freely available Matlab based toolbox TecLines (Tectonic Lineament Analysis) for locating and quantifying lineament patterns using satellite data and digital elevation models. TecLines consists of a set of functions including frequency filtering, spatial filtering, tensor voting, Hough transformation, and polynomial fitting. Due to differences in the mathematical background of the edge detection and edge linking procedure as well as the breadth of the methods, we introduce the approach in two-parts. In this first study, we present the steps that lead to edge detection. We introduce the data pre-processing using selected filters in spatial and frequency domains. We then describe the application of the tensor-voting framework to improve position and length accuracies of the detected lineaments. We demonstrate the robustness of the approach in a complex area in the northeast of Afghanistan using a panchromatic QUICKBIRD-2 image with 1-meter resolution. Finally, we compare the results of TecLines with manual lineament extraction, and other lineament extraction algorithms, as well as a published fault map of the study area. Full article

(This article belongs to the Special Issue Remote Sensing in Geomorphology)

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1958 KiB

Open AccessArticle

Comparing Two Photo-Reconstruction Methods to Produce High Density Point Clouds and DEMs in the Corral del Veleta Rock Glacier (Sierra Nevada, Spain)

by Álvaro Gómez-Gutiérrez, José Juan De Sanjosé-Blasco, Javier De Matías-Bejarano and Fernando Berenguer-Sempere

Remote Sens. 2014, 6(6), 5407-5427; https://doi.org/10.3390/rs6065407 - 11 Jun 2014

Cited by 34 | Viewed by 9249

Abstract

In this paper, two methods based on computer vision are presented in order to produce dense point clouds and high resolution DEMs (digital elevation models) of the Corral del Veleta rock glacier in Sierra Nevada (Spain). The first one is a semi-automatic 3D photo-reconstruction method (SA-3D-PR) based on the Scale-Invariant Feature Transform algorithm and the epipolar geometry theory that uses oblique photographs and camera calibration parameters as input. The second method is fully automatic (FA-3D-PR) and is based on the recently released software 123D-Catch that uses the Structure from Motion and MultiView Stereo algorithms and needs as input oblique photographs and some measurements in order to scale and geo-reference the resulting model. The accuracy of the models was tested using as benchmark a 3D model registered by means of a Terrestrial Laser Scanner (TLS). The results indicate that both methods can be applied to micro-scale study of rock glacier morphologies and processes with average distances to the TLS point cloud of 0.28 m and 0.21 m, for the SA-3D-PR and the FA-3D-PR methods, respectively. The performance of the models was also tested by means of the dimensionless relative precision ratio parameter resulting in figures of 1:1071 and 1:1429 for the SA-3D-PR and the FA-3D-PR methods, respectively. Finally, Digital Elevation Models (DEMs) of the study area were produced and compared with the TLS-derived DEM. The results showed average absolute differences with the TLS-derived DEM of 0.52 m and 0.51 m for the SA-3D-PR and the FA-3D-PR methods, respectively. Full article

(This article belongs to the Special Issue Remote Sensing in Geomorphology)

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1419 KiB

Open AccessArticle

Karst Depression Detection Using ASTER, ALOS/PRISM and SRTM-Derived Digital Elevation Models in the Bambuí Group, Brazil

by Osmar Abílio De Carvalho, Júnior, Renato Fontes Guimarães, David R. Montgomery, Alan R. Gillespie, Roberto Arnaldo Trancoso Gomes, Éder De Souza Martins and Nilton Correia Silva

Remote Sens. 2014, 6(1), 330-351; https://doi.org/10.3390/rs6010330 - 27 Dec 2013

Cited by 82 | Viewed by 14249

Abstract

Remote sensing has been used in karst studies to identify limestone terrain, describe exokarst features, analyze karst depressions, and detect geological structures important to karst development. The aim of this work is to investigate the use of ASTER-, SRTM- and ALOS/PRISM-derived digital elevation models (DEMs) to detect and quantify natural karst depressions along the São Francisco River near Barreiras city, northeast Brazil. The study area is a karst landscape characterized by karst depressions (dolines), closed depressions in limestone, many of which contain standing water connected with the ground-water table. The base of dolines is typically sealed with an impermeable clay layer covered by standing water or herbaceous vegetation. We identify dolines by combining the extraction of sink depth from DEMs, morphometric analysis using GIS, and visual interpretation. Our methodology is a semi-automatic approach involving several steps: (a) DEM acquisition; (b) sink-depth calculation using the difference between the raw DEM and the corresponding DEM with sinks filled; and (c) elimination of falsely identified karst depressions using morphometric attributes. The advantages and limitations of the applied methodology using different DEMs are examined by comparison with a sinkhole map generated from traditional geomorphological investigations based on visual interpretation of the high-resolution remote sensing images and field surveys. The threshold values of the depth, area size and circularity index appropriate for distinguishing dolines were identified from the maximum overall accuracy obtained by comparison with a true doline map. Our results indicate that the best performance of the proposed methodology for meso-scale karst feature detection was using ALOS/PRISM data with a threshold depth > 2 m; areas > 13,125 m² and circularity indexes > 0.3 (overall accuracy of 0.53). The overall correct identification of around half of the true dolines suggests the potential to substantially improve doline identification using higher-resolution LiDAR-generated DEMs. Full article

(This article belongs to the Special Issue Remote Sensing in Geomorphology)

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1040 KiB

Open AccessArticle

Application of a Land Surface Model Using Remote Sensing Data for High Resolution Simulations of Terrestrial Processes

by Hyun Il Choi

Remote Sens. 2013, 5(12), 6838-6856; https://doi.org/10.3390/rs5126838 - 9 Dec 2013

Cited by 8 | Viewed by 8552

Abstract

Most current land surface models (LSMs) coupled to regional climate models (RCMs) have been implemented at the several tens of kilometer spatial scales. Modeling land surface processes in LSMs at a finer resolution is necessary for improvements in terrestrial water and energy predictions especially for small catchments. This study has therefore assessed the applicability of high-resolution simulations for terrestrial processes to a small study basin from the Common Land Model (CoLM) using 1-km surface boundary conditions (SBCs) based on remote sensing products. The performance of the CoLM simulations at finer (1-km) and coarser (30-km) resolutions are evaluated for daily runoff and land surface temperature results which have a significant influence on the terrestrial water and energy cycles. The daily stream water temperature is also estimated by a linear regression function of the 1-km daily land surface temperature prediction. The daily stream runoff and temperature results are compared with observations from a stream gauge station, and the daily land surface temperature prediction is compared with the 1-km remote sensing product. It is observed that the high-resolution CoLM results can reasonably capture seasonal variations in both daily runoff and temperatures crucial to the terrestrial water and energy budget. Full article

(This article belongs to the Special Issue Remote Sensing in Geomorphology)

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2744 KiB

Open AccessArticle

Quantitative Analysis of the Waterline Method for Topographical Mapping of Tidal Flats: A Case Study in the Dongsha Sandbank, China

by Yongxue Liu, Manchun Li, Minxi Zhou, Kang Yang and Liang Mao

Remote Sens. 2013, 5(11), 6138-6158; https://doi.org/10.3390/rs5116138 - 21 Nov 2013

Cited by 47 | Viewed by 8536

Abstract

Although the topography of tidal flats is important for understanding their evolution, the spatial and temporal sampling frequency of such data remains limited. The waterline method has the potential to retrieve past tidal flat topography by utilizing large archives of satellite images. This study performs a quantitative analysis of the relationship between the accuracy of tidal flat digital elevation models (DEMs) that are based on the waterline method and the factors that influence the DEMs. The three major conclusions of the study are as follows: (1) the coverage rate of the waterline points and the number of satellite images used to create the DEM are highly linearly correlated with the error of the resultant DEMs, and the former is more significant in indicating the accuracy of the resultant DEMs than the latter; (2) both the area and the slope of the tidal flats are linearly correlated with the error of the resultant DEMs; and (3) the availability analysis of the archived satellite images indicates that the waterline method can retrieve tidal flat terrains from the past forty years. The upper limit of the temporal resolution of the tidal flat DEM can be refined to within one year since 1993, to half a year since 2004 and to three months since 2009. Full article

(This article belongs to the Special Issue Remote Sensing in Geomorphology)

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7239 KiB

Open AccessArticle

GIS-Based Detection of Gullies in Terrestrial LiDAR Data of the Cerro Llamoca Peatland (Peru)

by Bernhard Höfle, Luisa Griesbaum and Markus Forbriger

Remote Sens. 2013, 5(11), 5851-5870; https://doi.org/10.3390/rs5115851 - 11 Nov 2013

Cited by 45 | Viewed by 13578

Abstract

Cushion peatlands are typical features of the high altitude Andes in South America. Due to the adaptation to difficult environmental conditions, they are very fragile ecosystems and therefore vulnerable to environmental and climate changes. Peatland erosion has severe effects on their ecological functions, such as water storage capacity. Thus, erosion monitoring is highly advisable. Erosion quantification and monitoring can be supported by high-resolution terrestrial Light Detection and Ranging (LiDAR). In this study, a novel Geographic Information System (GIS)-based method for the automatic delineation and geomorphometric description of gullies in cushion peatlands is presented. The approach is a multi-step workflow based on a gully edge extraction and a sink filling algorithm applied to a conditioned digital terrain model. Our method enables the creation of GIS-ready polygons of the gullies and the derivation of geomorphometric parameters along the entire channel course. Automatically derived boundaries and gully area values correspond to a high degree (93%) with manually digitized reference polygons. The set of methods developed in this study offers a suitable tool for the monitoring and scientific analysis of fluvial morphology in cushion peatlands. Full article

(This article belongs to the Special Issue Remote Sensing in Geomorphology)

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