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Quantifying Digital Geomorphology and Planetary Geomorphology Using Remote Sensing Techniques

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 3436

Special Issue Editors

Chinese Academy of Sciences, Beijing, China
Interests: GIS; digital topography and geomorphology analysis; information extraction; digital mapping; geomorphological application under eco-environmental change
Special Issues, Collections and Topics in MDPI journals
Department of Engineering, Università degli Studi di Palermo, 90100 Palermo, Italy
Interests: Galileo; GLONASS; GPS; GNSS; CORS; remote sensing; geomatics; dam displacements
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of globalization, researchers should develop geomorphological theories, techniques, and methodologies based on classical problems with the aid of techniques such as remote sensing, big data, and AI. Moreover, research on the global geomorphological application of these techniques should be conducted with a view to providing a scientific basis for the estimation of global change in ecology and the environment, the layout of the green silk road industrial park, and infrastructure site selection.

With the continuous promotion of remote sensing and sample studies on Mars, Venus, the moon, and other celestial bodies, on the basis of classical global geomorphology, understanding the mechanisms and effects of geomorphological formation and evolution, geology and geomorphology, environmental evolution, and the effects of geomorphology on the stars can better serve Earth science research.

This Special Issue welcomes research on quantitative and planetary geomorphology using remote sensing data acquired using different sensors and platforms, in the form of proper article formats such as research papers, review papers, and technical letters. Research topics may cover digital geomorphology on scales ranging from regional to global, and even planetary. Additionally, we welcome digital topographic analyses using DEM data from different sources and of varying resolutions.

This Special Issue focuses on quantitative geomorphology research using different data sources and techniques. The remote sensing method is undoubtedly one of the most important ways to acquire these data and techniques (for example, using remote sensing images in a long time-series, or DEM data acquired via stereo-image or InSAR methods). Hence, the subject matter of this Special Issue is closely related to the scope of Remote Sensing.

  • Articles may address, but are not limited to, the following topics:
  • Geomorphological classification and mapping;
  • Geomorphological information Tupu;
  • Geomorphological disasters;
  • Permafrost change monitoring;
  • Digital topographic analysis;
  • Ground subsidence monitoring;
  • Lunar carter extraction;
  • Water inrush disasters.

Prof. Dr. Weiming Cheng
Prof. Dr. Gino Dardanelli
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • quantitative geomorphology
  • planetary geomorphology
  • remote sensing
  • DEM
  • GIS
  • Impact geomorphology
  • ecological and environmental change
  • geology
  • geomorphological evolution
  • geomorphological classification and mapping

Published Papers (3 papers)

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Research

21 pages, 9091 KiB  
Article
Influential Topographic Factor Identification of Soil Heavy Metals Using GeoDetector: The Effects of DEM Resolution and Pollution Sources
by Yating Wu, Lingfeng Zhou, Yaobin Meng, Qigen Lin and Yang Fei
Remote Sens. 2023, 15(16), 4067; https://doi.org/10.3390/rs15164067 - 17 Aug 2023
Cited by 2 | Viewed by 1005
Abstract
Heavy metals (HMs) entering soils are redistributed by natural processes such as surface runoff and soil erosion. These natural processes are characterized by topographic factors (TFs, e.g., Topographic Wetness Index (TWI), Total Catchment Area (TCA), Slope, and Aspect), which are commonly quantified by [...] Read more.
Heavy metals (HMs) entering soils are redistributed by natural processes such as surface runoff and soil erosion. These natural processes are characterized by topographic factors (TFs, e.g., Topographic Wetness Index (TWI), Total Catchment Area (TCA), Slope, and Aspect), which are commonly quantified by a digital elevation model (DEM) of a certain spatial resolution. Nevertheless, few studies have examined how DEM resolutions affect the detection of influential TFs of soil HMs. In this study, we first applied the GeoDetector method to explore the coupling between the eight TFs and the concentrations of eight soil HMs under a gradient of DEM resolutions. We found that the important sorting of eight TFs on one HM at different resolutions is inconsistent. For example, for Hg, TWI emerged to be the dominant factor among the eight TFs at 90 m resolution, whereas TCA took the lead at 3000 m resolution. Moreover, the results strongly deny the existence of an optimal resolution (OR) among the HMs for any specific topographical factor. We further applied a source apportionment model (Positive Matrix Factorization—PMF) to explore the effects of five identified pollution sources and the underlying environmental processes on the inconsistent ORs. The main reason for such OR inconsistency is that each HM may be released from various sources and subsequently undergo environmental processes that are topographically modulated at different spatial scales. The main reason for such OR inconsistency is that each HM may have various sources and subsequent environmental processes that happen at different spatial scales. Moreover, each TF could simultaneously reflect different transport and transformation processes. Therefore, the apparent OR for one metal is jointly composed of the preferences of all the sources it contains; thus, it cannot be determined by the OR preferences of a single source alone. Based on the composition and intensity of pollution sources, we propose three possible strategies for a more robust GeoDetector analysis. The findings reported here provide new insights into the proper use of GeoDetector for selecting the appropriate DEM resolutions when identifying influential environmental factors. Full article
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25 pages, 14578 KiB  
Article
Boundary Delineator for Martian Crater Instances with Geographic Information and Deep Learning
by Danyang Liu, Weiming Cheng, Zhen Qian, Jiayin Deng, Jianzhong Liu and Xunming Wang
Remote Sens. 2023, 15(16), 4036; https://doi.org/10.3390/rs15164036 - 15 Aug 2023
Cited by 2 | Viewed by 918
Abstract
Detecting impact craters on the Martian surface is a critical component of studying Martian geomorphology and planetary evolution. Accurately determining impact crater boundaries, which are distinguishable geomorphic units, is important work in geological and geomorphological mapping. The Martian topography is more complex than [...] Read more.
Detecting impact craters on the Martian surface is a critical component of studying Martian geomorphology and planetary evolution. Accurately determining impact crater boundaries, which are distinguishable geomorphic units, is important work in geological and geomorphological mapping. The Martian topography is more complex than that of the Moon, making the accurate detection of impact crater boundaries challenging. Currently, most techniques concentrate on replacing impact craters with circles or points. Accurate boundaries are more challenging to identify than simple circles. Therefore, a boundary delineator for Martian crater instances (BDMCI) using fusion data is proposed. First, the optical image, digital elevation model (DEM), and slope of elevation difference after filling the DEM (called slope of EL_Diff to highlight the boundaries of craters) were used in combination. Second, a benchmark dataset with annotations for accurate impact crater boundaries was created, and sample regions were chosen using prior geospatial knowledge and an optimization strategy for the proposed BDMCI framework. Third, the multiple models were fused to train at various scales using deep learning. To repair patch junction fractures, several postprocessing methods were devised. The proposed BDMCI framework was also used to expand the catalog of Martian impact craters between 65°S and 65°N. This study provides a reference for identifying terrain features and demonstrates the potential of deep learning algorithms in planetary science research. Full article
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19 pages, 4797 KiB  
Article
Object-Oriented Remote Sensing Approaches for the Detection of Terrestrial Impact Craters as a Reconnaissance Survey
by Habimana Emmanuel, Jaehyung Yu, Lei Wang, Sung Hi Choi and Digne Edmond Rwabuhungu Rwatangabo
Remote Sens. 2023, 15(15), 3807; https://doi.org/10.3390/rs15153807 - 31 Jul 2023
Viewed by 958
Abstract
The purpose of this study is to employ a remote sensing reconnaissance survey based on optimal segmentation parameters and an object-oriented random forest approach to the identification of possible terrestrial impact craters from the global 30-m resolution SRTM DEM. A dataset consisting of [...] Read more.
The purpose of this study is to employ a remote sensing reconnaissance survey based on optimal segmentation parameters and an object-oriented random forest approach to the identification of possible terrestrial impact craters from the global 30-m resolution SRTM DEM. A dataset consisting of 94 confirmed and well-preserved terrestrial impact craters, 104 volcanic calderas, and 124 valleys were extracted from real-world surface features. For craters with different sizes, eight optimal scale parameters from 80 to 3000 have been identified using multi-resolution segmentation, where the scale parameters have a positive correlation (R2 = 0.78) with the diameters of craters. The object-oriented random forest approach classified the tested impact craters, volcanic calderas, and valleys with an overall accuracy of 88.4% and a Kappa coefficient of 0.8. The investigated terrestrial impact craters, in general, have relatively lower rim circularity, higher length-to-width ratio, and lower relief, slope, and elevation than volcanic calderas. The topographic characteristics can be explained by geological processes associated with the formation and post-deformation of impact craters. The excavation and ejection by initial impact and rebound of excavated materials contribute to low elevation. The post-impact deformation, including inward collapse and slump of unstable rims, weathering, erosion, and sediment deposition, further reduces elevation and relief and modifies shapes resulting in lower circularity and higher length-to-width ratio. Due to the resolution limitation of the source DEM data and the number of real-world samples, the model has only been validated for craters of 0.88 to 100 km in diameter, which can be generalized to explore undiscovered terrestrial impact craters using cloud computing with global datasets provided by platforms such as Google Earth Engine and Microsoft Planetary Computer. Full article
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