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GIS-Supported DEM Analysis for Characterization of Short to Long Term...
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GIS-Supported DEM Analysis for Characterization of Short to Long Term Landscape Dynamics

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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 18298

Special Issue Editors

Dr. Michele Santangelo

E-Mail Website
Guest Editor
CNR-IRPI (National Research Council, Research Institute for Geo-Hydrological Protection, 06128 Perugia, Italy
Interests: geomorphological mapping; landslide statistics, morphometry and modeling; landslide hazard and risk assessment; drainage network morphometry; DEM analysis; open-source GIS tools; photogrammetry; photo interpretation; landscape evolution
Dr. Dario Gioia

E-Mail Website
Co-Guest Editor
Consiglio Nazionale delle Ricerche—Istituto di Scienze del Patrimonio Culturale (ISPC), Tito Scalo, Potenza, Italy
Interests: tectonic geomorphology; landscape evolution; drainage network morphometry; geomorphological mapping; sediment yield; landslide analysis; geoarchaeology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The availability of digital elevation models globally is providing increasing capabilities of scaling up geomorphological analyses through the use of geographic information systems (GIS).

We are starting a Special Issue dedicated to current research in the characterization of landscape dynamics through GIS-supported analyses applied to digital elevation models. Contributions can explore methodological, conceptual, and technological aspects, as well as applications. We call for original papers, equally from researchers which focus on all topics involving the processing, analysis, and general use of digital elevation models within GIS systems. Short-term landscape dynamics may include soil erosion, landslides, and river network dynamics. Long-term landscape dynamics may include the evaluation of erosion/denudation/uplift rates.

Within this general framework, this Special Issue welcomes original and timely contributions especially focused on (but not necessarily limited to) the following topics:

  1. Development, application, and implementation of GIS tools for the extraction of geomorphic indices from digital elevation models.
  2. Development, comparison, and application of models and quantitative methods for slope stability analyses from the catchment to the global scale. Contributions on the analysis of source areas and ruonut definitions of fast and rapid moving landslides are also welcome.
  3. DEMs comparison and short-term landscape dynamics including landslide volume mobilization, soil erosion, and rills and gullies evolution.
  4. Application of models to estimate rates of geomorphic (i.e., fluvial, slope, and coastal) processes; erosion/deposition models, LEMs.
  5. DEM-supported extraction, classification, and mapping of landforms using different methods, including deep learning and AI, supported by morphometric characterization of high-standard maps of geomorphological features (e.g., erosion surfaces, terraces, landslides, alluvial fans, bedding, faults).
  6. Analysis of river profiles and drainage network analysis. Identification, interpretation, and analysis of the spatial distribution of knickpoints in relation to long-term landscape evolution. Additionally, contributions on tools and methods for the semi-automatic and automatic identification and mapping of stream captures are welcome.

In contributions, broad area approaches will be preferred over single-slope case studies.

Dr. Michele Santangelo
Dr. Dario Gioia
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. ISPRS International Journal of Geo-Information 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 1700 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

  • digital elevation model
  • landscape dynamics
  • river network
  • landslide
  • erosion
  • GIS
  • erosion rates
  • quantitative methods

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Published Papers (5 papers)

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Research

26 pages, 36367 KiB  
Article
Investigating Geomorphic Change Using a Structure from Motion Elevation Model Created from Historical Aerial Imagery: A Case Study in Northern Lake Michigan, USA
by Jessica D. DeWitt and Francis X. Ashland
ISPRS Int. J. Geo-Inf. 2023, 12(4), 173; https://doi.org/10.3390/ijgi12040173 - 20 Apr 2023
Cited by 2 | Viewed by 2118
Abstract
South Manitou Island, part of Sleeping Bear Dunes National Lakeshore in northern Lake Michigan, is a post-glacial lacustrine landscape with substantial geomorphic changes including landslides, shoreline and bluff retreat, and sand dune movement. These changes involve interrelated processes, and are influenced to different [...] Read more.
South Manitou Island, part of Sleeping Bear Dunes National Lakeshore in northern Lake Michigan, is a post-glacial lacustrine landscape with substantial geomorphic changes including landslides, shoreline and bluff retreat, and sand dune movement. These changes involve interrelated processes, and are influenced to different extents by lake level, climate change, and land use patterns, among other factors. The utility of DEM of Difference (DoD) and other terrain analyses were investigated as a means of understanding interrelated geomorphologic changes and processes across multiple decades and at multiple scales. A 1m DEM was developed from 1955 historical aerial imagery using Structure from Motion Multi-View Stereo (SfM-MVS) and compared to a 2016 lidar-based DEM to quantify change. Landslides, shoreline erosion, bluff retreat, and sand dune movement were investigated throughout South Manitou Island. While the DoD indicates net loss or gain, interpretation of change must take into consideration the SfM-MVS source of the historical DEM. In the case of landslides, where additional understanding may be gleaned through review of the timing of lake high- and lowstands together with DoD values. Landscape-scale findings quantified cumulative feedbacks between interrelated processes. These findings could be upscaled to assess changes across the entire park, informing future change investigations and land management decisions. Full article
(This article belongs to the Special Issue GIS-Supported DEM Analysis for Characterization of Short to Long Term Landscape Dynamics)
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23 pages, 50411 KiB  
Article
Landslide Susceptibility Mapping Using Machine Learning: A Danish Case Study
by Angelina Ageenko, Lærke Christina Hansen, Kevin Lundholm Lyng, Lars Bodum and Jamal Jokar Arsanjani
ISPRS Int. J. Geo-Inf. 2022, 11(6), 324; https://doi.org/10.3390/ijgi11060324 - 27 May 2022
Cited by 15 | Viewed by 5563
Abstract
Mapping of landslides, conducted in 2021 by the Geological Survey of Denmark and Greenland (GEUS), revealed 3202 landslides in Denmark, indicating that they might pose a bigger problem than previously acknowledged. Moreover, the changing climate is assumed to have an impact on landslide [...] Read more.
Mapping of landslides, conducted in 2021 by the Geological Survey of Denmark and Greenland (GEUS), revealed 3202 landslides in Denmark, indicating that they might pose a bigger problem than previously acknowledged. Moreover, the changing climate is assumed to have an impact on landslide occurrences in the future. The aim of this study is to conduct the first landslide susceptibility mapping (LSM) in Denmark, reducing the geographical bias existing in LSM studies, and to identify areas prone to landslides in the future following representative concentration pathway RCP8.5, based on a set of explanatory variables in an area of interest located around Vejle Fjord, Jutland, Denmark. A subset from the landslide inventory provided by GEUS is used as ground truth data. Three well-established machine learning (ML) algorithms—Random Forest, Support Vector Machine, and Logistic Regression—were trained to classify the data samples as landslide or non-landslide, treating the ML task as a binary classification and expressing the results in the form of a probability in order to produce susceptibility maps. The classification results were validated through the test data and through an external data set for an area located outside of the region of interest. While the high predictive performance varied slightly among the three models on the test data, the LR and SVM demonstrated inferior accuracy outside of the study area. The results show that the RF model has robustness and potential for applicability in landslide susceptibility mapping in low-lying landscapes of Denmark in the present. The conducted mapping can become a step forward towards planning for mitigative and protective measures in landslide-prone areas in Denmark, providing policy-makers with necessary decision support. However, the map of the future climate change scenario shows the reduction of the susceptible areas, raising the question of the choice of the climate models and variables in the analysis. Full article
(This article belongs to the Special Issue GIS-Supported DEM Analysis for Characterization of Short to Long Term Landscape Dynamics)
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13 pages, 2871 KiB  
Article
A Field Investigation on Gully Erosion and Implications for Changes in Sediment Delivery Processes in Some Tributaries of the Upper Yellow River in China
by Hui Yang, Changxing Shi and Jiansheng Cao
ISPRS Int. J. Geo-Inf. 2022, 11(5), 288; https://doi.org/10.3390/ijgi11050288 - 28 Apr 2022
Cited by 5 | Viewed by 2230
Abstract
Erosion and sediment delivery have been undergoing considerable variations in many catchments worldwide owing to climate change and human interference. Monitoring on-site erosion and sediment deposition is crucial for understanding the processes and mechanisms of changes in sediment yield from the catchments. The [...] Read more.
Erosion and sediment delivery have been undergoing considerable variations in many catchments worldwide owing to climate change and human interference. Monitoring on-site erosion and sediment deposition is crucial for understanding the processes and mechanisms of changes in sediment yield from the catchments. The Ten Kongduis (kongdui is the transliteration of ephemeral creeks in Mongolian) are 10 tributaries of the upper Yellow River. Severe erosion in the upstream hills and gullies and huge aeolian sand input in the middle reaches had made the 10 tributaries one of the main sediment sources of the Yellow River, but the gauged sediment discharge of the tributaries has decreased obviously in recent years. In order to find out the mechanisms of changes in the sediment load of the tributaries, topographic surveys of four typical gullies in 3 of the 10 tributaries were made repeatedly in the field with the terrestrial laser scanning (TLS) technique. The results show that all the monitored gullies were silted with a mean net rate of 587–800 g/m2 from November 2014 to June 2015 and eroded by a mean net rate of 185–24,800 g/m2 from June to November 2015. The monitoring data suggest that the mechanism of interseasonal and interannual sediment storage and release existed in the processes of sediment delivery in the kongduis. The contrast of the low gauged sediment load of the kongduis in recent years against the high surveyed gully erosion indicates the reduction in their sediment delivery efficiency, which can be attributed to the diminution in hyperconcentrated flows caused mainly by the increase in vegetation coverage on slopes and partly by construction of sediment-trapping dams in gullies. Full article
(This article belongs to the Special Issue GIS-Supported DEM Analysis for Characterization of Short to Long Term Landscape Dynamics)
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20 pages, 4984 KiB  
Article
Monitoring Coastal Vulnerability by Using DEMs Based on UAV Spatial Data
by Antonio Minervino Amodio, Gianluigi Di Paola and Carmen Maria Rosskopf
ISPRS Int. J. Geo-Inf. 2022, 11(3), 155; https://doi.org/10.3390/ijgi11030155 - 22 Feb 2022
Cited by 16 | Viewed by 3373
Abstract
The use of Unmanned Aerial Vehicles (UAVs) represents a rather innovative, quick, and low-cost methodological approach offering applications in several fields of investigation. The present study illustrates the developed method using Digital Elevation Models (DEMs) based on UAV-derived data for evaluating short-term morphological-topographic [...] Read more.
The use of Unmanned Aerial Vehicles (UAVs) represents a rather innovative, quick, and low-cost methodological approach offering applications in several fields of investigation. The present study illustrates the developed method using Digital Elevation Models (DEMs) based on UAV-derived data for evaluating short-term morphological-topographic changes of the beach system and related implications for coastal vulnerability assessment. UAV surveys were performed during the summers of 2019 and 2020 along a beach stretch affected by erosion, located along the central Adriatic coast. Acquired high-resolution aerial photos were used to generate large-scale DEMs as well as orthophotos of the beach using the Structure from Motion (SfM) image processing tool. Comparison of the generated 2019 and 2020 DEMs highlighted significant morphological changes and a sediment volume loss of about 780 m3 within a surface area of about 4400 m2. Based on 20 m spaced beach profiles derived from the DEMs, a coastal vulnerability assessment was performed using the CVA approach that highlighted some significant variations in the CVA index between 2019 and 2020. Results evidence that UAV surveys provide high-resolution topographic data, suitable for specific beach monitoring activities and the updating of some parameters that enter in the CVA model contributing to its correct application. Full article
(This article belongs to the Special Issue GIS-Supported DEM Analysis for Characterization of Short to Long Term Landscape Dynamics)
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17 pages, 9116 KiB  
Article
A Research on Landslides Automatic Extraction Model Based on the Improved Mask R-CNN
by Peng Liu, Yongming Wei, Qinjun Wang, Jingjing Xie, Yu Chen, Zhichao Li and Hongying Zhou
ISPRS Int. J. Geo-Inf. 2021, 10(3), 168; https://doi.org/10.3390/ijgi10030168 - 15 Mar 2021
Cited by 36 | Viewed by 3601
Abstract
Landslides are the most common and destructive secondary geological hazards caused by earthquakes. It is difficult to extract landslides automatically based on remote sensing data, which is import for the scenario of disaster emergency rescue. The literature review showed that the current landslides [...] Read more.
Landslides are the most common and destructive secondary geological hazards caused by earthquakes. It is difficult to extract landslides automatically based on remote sensing data, which is import for the scenario of disaster emergency rescue. The literature review showed that the current landslides extraction methods mostly depend on expert interpretation which was low automation and thus was unable to provide sufficient information for earthquake rescue in time. To solve the above problem, an end-to-end improved Mask R-CNN model was proposed. The main innovations of this paper were (1) replacing the feature extraction layer with an effective ResNeXt module to extract the landslides. (2) Increasing the bottom-up channel in the feature pyramid network to make full use of low-level positioning and high-level semantic information. (3) Adding edge losses to the loss function to improve the accuracy of the landslide boundary detection accuracy. At the end of this paper, Jiuzhaigou County, Sichuan Province, was used as the study area to evaluate the new model. Results showed that the new method had a precision of 95.8%, a recall of 93.1%, and an overall accuracy (OA) of 94.7%. Compared with the traditional Mask R-CNN model, they have been significantly improved by 13.9%, 13.4%, and 9.9%, respectively. It was proved that the new method was effective in the landslides automatic extraction. Full article
(This article belongs to the Special Issue GIS-Supported DEM Analysis for Characterization of Short to Long Term Landscape Dynamics)
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