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Geosciences
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Advances in Remote Sensing and GIS for Geomorphological Mapping
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Advances in Remote Sensing and GIS for Geomorphological Mapping

A special issue of Geosciences (ISSN 2076-3263).

Deadline for manuscript submissions: closed (31 May 2016) | Viewed by 35296

Special Issue Editors

Dr. Pavel Propastin

E-Mail Website
Guest Editor
Cartography, GIS and Remote Sensing Department, Institute of Geography, Goldschmidt str. 5, 37077 Göttingen, Germany
Interests: physical geography; cartography; natural resources management; applications of remote sensing, GIS and geospatial modelling in environmental monitoring and ecosystems science; land use and cover changes; ecosystem service assessment; ecosystem modelling at different spatial and temporal scales; integrated impact assessment of climate change
Prof. Dr. Yongwei Sheng

E-Mail Website
Guest Editor
Department of Geography, University of California, Los Angeles (UCLA), P.O. Box 951524, 1255 Bunche Hall, Los Angeles, CA 90095, USA
Interests: hydrology; lake dynamics; water resources; vegetation monitoring; glacier changes; remote sensing; geographic information systems (GIS); Tibetan Plateau; Arctic; Central Asia
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geomorphological mapping is the main method for providing data for the analysis of landforms. This method of mapping utilizes five fundamental concepts—morphology, morphometry, morphogenesis, morhpochronology and morphodynamics. The method plays a crucial role in understanding Earth's surface processes, relief configuration, landscape evolution, and subsurface composition. Geomorphological maps, at a variety of scales, are required, not only for geomorphological research and praxis, but also for other sectors of environmental research and for professionals dealing with landscapes and landforms, urban planners, construction engineers, soil and forest scientists, land conservation managers, and natural hazard and geological risk managers.

Traditionally, geomorphological mapping has been based upon using information from the field and the interpretation of photographs, satellite images, and topographic maps. Recent advances in remote sensing and geographic information systems (GIS) have led to a revolution in the field of geomorphological mapping and have placed remotely sensed data as a core geomorphological data source. A growing number of new airborne and spaceborne sensors are now delivering data on landform distribution, surface composition, land surface elevation, and subsurface characterization at increasingly higher spectral, temporal, and spatial resolutions. This, in addition to the extended capabilities of GIS and geospatial analysis, considerably enlarges the capacity of geomorphological mapping.

This Special Issue aims to review and synthesize the newest progress in applications of remote sensing and GIS in geomorphological mapping. The prospective authors are encouraged to submit articles with respect to the following topics:

  • New and improved techniques for remote sensing and GIS based mapping of geomorphological characteristics of landforms,
  • Enhanced algorithms of image analysis for geomorphological mapping,
  • Applications of airborne laser scanning (ALS) and terrestrial laser scanning (TLS) in geomorphological mapping,
  • Applications of Digital Elevation Models, including photogrammetric applications of satellite imagery, such as SPOT, ASTER data, and others,
  • Data fusion involving the integration of data with different spatial, spectral, and radiometric resolutions,
  • The issue of temporal and spatial scales in remote sensing and GIS applications in geomorphological mapping,
  • Uncertainty of remote sensing applications and its impact on the accuracies of geomorphological maps.

Dr. Pavel Propastin
Yongwei Sheng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Geosciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Cartography
  • Geomorphological mapping
  • Geomorphological maps
  • Remote sensing
  • Geographic information systems
  • Geospatial analysis
  • Airborne laser scanning
  • Terrestrial laser scanning
  • Digital elevation models
  • Data fusion
  • Spatial scale

Published Papers (5 papers)

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15046 KiB  
Article
Three-Dimensional Geological Model of Quaternary Sediments in Walworth County, Wisconsin, USA
by Jodi Lau, Jason F. Thomason, David H. Malone and Eric W. Peterson
Geosciences 2016, 6(3), 32; https://doi.org/10.3390/geosciences6030032 - 11 Jul 2016
Cited by 5 | Viewed by 7642
Abstract
A three-dimensional (3D) geologic model was developed for Quaternary deposits in southern Walworth County, WI using Petrel, a software package primarily designed for use in the energy industry. The purpose of this research was to better delineate and characterize the shallow glacial [...] Read more.
A three-dimensional (3D) geologic model was developed for Quaternary deposits in southern Walworth County, WI using Petrel, a software package primarily designed for use in the energy industry. The purpose of this research was to better delineate and characterize the shallow glacial deposits, which include multiple shallow sand and gravel aquifers. The 3D model of Walworth County was constructed using datasets such as the U.S. Geological Survey 30 m digital elevation model (DEM) of land surface, published maps of the regional surficial geology and bedrock topography, and a database of water-well records. Using 3D visualization and interpretation tools, more than 1400 lithostratigraphic picks were efficiently interpreted amongst 725 well records. The final 3D geologic model consisted of six Quaternary lithostratigraphic units and a bedrock horizon as the model base. The Quaternary units include in stratigraphic order from youngest to oldest: the New Berlin Member of the Holy Hill Formation, the Tiskilwa Member of the Zenda Formation, a Sub-Tiskilwa Sand/Gravel unit, the Walworth Formation, a Sub-Walworth Sand/Gravel unit, and a Pre-Illinoisan unit. Compared to previous studies, the results of this study indicate a more detailed distribution, thickness, and interconnectivity between shallow sand and gravel aquifers and their connectivity to shallow bedrock aquifers. This study can also help understand uncertainty within previous local groundwater-flow modeling studies and improve future studies. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Geomorphological Mapping)
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1298 KiB  
Article
Integrating Expert Knowledge with Statistical Analysis for Landslide Susceptibility Assessment at Regional Scale
by Christos Chalkias, Christos Polykretis, Maria Ferentinou and Efthimios Karymbalis
Geosciences 2016, 6(1), 14; https://doi.org/10.3390/geosciences6010014 - 01 Mar 2016
Cited by 15 | Viewed by 5739
Abstract
In this paper, an integration landslide susceptibility model by combining expert-based and bivariate statistical analysis (Landslide Susceptibility Index—LSI) approaches is presented. Factors related with the occurrence of landslides—such as elevation, slope angle, slope aspect, lithology, land cover, Mean Annual Precipitation (MAP) and Peak [...] Read more.
In this paper, an integration landslide susceptibility model by combining expert-based and bivariate statistical analysis (Landslide Susceptibility Index—LSI) approaches is presented. Factors related with the occurrence of landslides—such as elevation, slope angle, slope aspect, lithology, land cover, Mean Annual Precipitation (MAP) and Peak Ground Acceleration (PGA)—were analyzed within a GIS environment. This integrated model produced a landslide susceptibility map which categorized the study area according to the probability level of landslide occurrence. The accuracy of the final map was evaluated by Receiver Operating Characteristics (ROC) analysis depending on an independent (validation) dataset of landslide events. The prediction ability was found to be 76% revealing that the integration of statistical analysis with human expertise can provide an acceptable landslide susceptibility assessment at regional scale. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Geomorphological Mapping)
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5142 KiB  
Article
Magnitude-Frequency Distribution of Hummocks on Rockslide-Debris Avalanche Deposits and Its Geomorphological Significance
by Hidetsugu Yoshida
Geosciences 2016, 6(1), 5; https://doi.org/10.3390/geosciences6010005 - 19 Jan 2016
Cited by 6 | Viewed by 4837
Abstract
A magnitude-frequency analysis of rockslide-debris avalanche deposits was performed. Hummocks are conical mounds formed in debris avalanche deposits from the catastrophic sector collapse of a mountain (often volcanic) that represent relatively cohesive fragments of the mountain edifice. Examination of 17 debris avalanche deposits [...] Read more.
A magnitude-frequency analysis of rockslide-debris avalanche deposits was performed. Hummocks are conical mounds formed in debris avalanche deposits from the catastrophic sector collapse of a mountain (often volcanic) that represent relatively cohesive fragments of the mountain edifice. Examination of 17 debris avalanche deposits in Japan and the Philippines showed that, in general, the larger the magnitude of the hummocks, the smaller their frequency. Hummocks followed an exponential distribution: log10N(x) = a – bx, where N(x) is the cumulative number of hummocks with magnitude ≥ x and a and b are constants; x is equal to log10A, where A is the area of a hummock. The constants a and b were positively correlated. The value of b, which differs among avalanches and in this analysis ranged between 1 and 3, may be controlled by the mobility of the debris avalanche. Avalanches with higher mobility (relatively longer runout) have higher b and potentially produce more numerous fragments forming hummocks (i.e., higher a). From the above correlation, the magnitude-frequency relationship can be used to roughly estimate the original height of the collapsed volcanic body, if the runout distance of the rockslide–debris avalanche can be estimated with sufficient accuracy. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Geomorphological Mapping)
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3237 KiB  
Article
Evaluation of VIIRS Land Surface Temperature Using CREST-SAFE Air, Snow Surface, and Soil Temperature Data
by Carlos L. Pérez Díaz, Tarendra Lakhankar, Peter Romanov, Reza Khanbilvardi and Yunyue Yu
Geosciences 2015, 5(4), 334-360; https://doi.org/10.3390/geosciences5040334 - 15 Dec 2015
Cited by 3 | Viewed by 5324
Abstract
In this study, the Visible Infrared Imager Radiometer Suite (VIIRS) Land Surface Temperature (LST) Environmental Data Record (EDR) was evaluated against snow surface (T-skin) and near-surface air temperature (T-air) ground observations recorded at the Cooperative Remote Sensing Science and Technology Center—Snow Analysis and [...] Read more.
In this study, the Visible Infrared Imager Radiometer Suite (VIIRS) Land Surface Temperature (LST) Environmental Data Record (EDR) was evaluated against snow surface (T-skin) and near-surface air temperature (T-air) ground observations recorded at the Cooperative Remote Sensing Science and Technology Center—Snow Analysis and Field Experiment (CREST-SAFE), located in Caribou, ME, USA during the winters of 2013 and 2014. The satellite LST corroboration of snow-covered areas is imperative because high-latitude regions are often physically inaccessible and there is a need to complement the data from the existing meteorological station networks. T-skin is not a standard meteorological parameter commonly observed at synoptic stations. Common practice is to measure surface infrared emission from the land surface at research stations across the world that allow for estimating ground-observed LST. Accurate T-skin observations are critical for estimating latent and sensible heat fluxes over snow-covered areas because the incoming and outgoing radiation fluxes from the snow mass and T-air make the snow surface temperature different from the average snowpack temperature. Precise characterization of the LST using satellite observations is an important issue because several climate and hydrological models use T-skin as input. Results indicate that T-air correlates better than T-skin with VIIRS LST data and that the accuracy of nighttime LST retrievals is considerably better than that of daytime. Based on these results, empirical relationships to estimate T-air and T-skin for clear-sky conditions from remotely-sensed (RS) LST were derived. Additionally, an empirical formula to correct cloud-contaminated RS LST was developed. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Geomorphological Mapping)
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3416 KiB  
Article
Snow Depth Retrieval with UAS Using Photogrammetric Techniques
by Benjamin Vander Jagt, Arko Lucieer, Luke Wallace, Darren Turner and Michael Durand
Geosciences 2015, 5(3), 264-285; https://doi.org/10.3390/geosciences5030264 - 10 Jul 2015
Cited by 84 | Viewed by 9914
Abstract
Alpine areas pose challenges for many existing remote sensing methods for snow depth retrieval, thus leading to uncertainty in water forecasting and budgeting. Herein, we present the results of a field campaign conducted in Tasmania, Australia in 2013 from which estimates of snow [...] Read more.
Alpine areas pose challenges for many existing remote sensing methods for snow depth retrieval, thus leading to uncertainty in water forecasting and budgeting. Herein, we present the results of a field campaign conducted in Tasmania, Australia in 2013 from which estimates of snow depth were derived using a low-cost photogrammetric approach on-board a micro unmanned aircraft system (UAS). Using commercial off-the-shelf (COTS) sensors mounted on a multi-rotor UAS and photogrammetric image processing techniques, the results demonstrate that snow depth can be accurately retrieved by differencing two surface models corresponding to the snow-free and snow-covered scenes, respectively. In addition to accurate snow depth retrieval, we show that high-resolution (50 cm) spatially continuous snow depth maps can be created using this methodology. Two types of photogrammetric bundle adjustment (BA) routines are implemented in this study to determine the optimal estimates of sensor position and orientation, in addition to 3D scene information; conventional BA (which relies on measured ground control points) and direct BA (which does not require ground control points). Error sources that affect the accuracy of the BA and subsequent snow depth reconstruction are discussed. The results indicate the UAS is capable of providing high-resolution and high-accuracy (<10 cm) estimates of snow depth over a small alpine area (~0.7 ha) with significant snow accumulation (depths greater than one meter) at a fraction of the cost of full-size aerial survey approaches. The RMSE of estimated snow depths using the conventional BA approach is 9.6 cm, whereas the direct BA is characterized by larger error, with an RMSE of 18.4 cm. If a simple affine transformation is applied to the point cloud derived from the direct BA, the overall RMSE is reduced to 8.8 cm RMSE. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Geomorphological Mapping)
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