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Remote Sensing and GIS for Geomorphological Mapping

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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: closed (31 July 2022) | Viewed by 16972

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Special Issue Editors

Dr. Andy Gibson

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Guest Editor

Centre for Applied Geoscience, Global Earth Modelling Group, School of the Environment Geography and Geosciences, University of Portsmouth, Portsmouth PO1 2UP, UK
Interests: proximal remote sensing for the analyses of rocks; soils; dusts and contaminants; HSI for conservation; remote sensing of natural hazards

Dr. Mohammad Firuz Ramli

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Guest Editor

Faculty of Forestry and Environment, University Putra Malaysia, Seri Kembangan 43400, Malaysia
Interests: remote sensing; geographic information system; engineering geology and hydrogeology

Dr. Peter Redshaw

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Guest Editor

Ove Arup & Partners International Ltd., London W1T 4BJ, UK
Interests: using remote sensing and numerical modelling in natural hazard and risk assessments, particularly in data-poor regions; applications of remote sensing derived land classifications for carbon sequestration estimation

Special Issue Information

Dear Colleagues,

Geomorphological mapping is a key technique for the understanding of hazardous terrains, the quantification of risks to the built environment, and for a better understanding of geomorphological processes at different spatial and temporal scales. These are key activities that provide the evidence base for decision-makers concerned with environmental change, geopolitical uncertainty, and sustainable development.

Remote sensing and GIS technologies have long provided the ability to create robust geomorphological models and have been critical to how we understand and manage our environment.

Advances in satellite, drone, and proximal remote sensing mean that geomorphologists now have access to a huge variety of data, including multispectral, hyperspectral, radar, and laser-derived imagery, much of it with excellent ground calibration and archival libraries. Associated developments in GIS, such as online access and software integration, have further improved our understanding of, for instance, geomorphological processes such as landsliding, karstification, volcanic activity, or coastal change. Such tools have also changed the ways in which such advances are communicated with end-users.

This Special Issue invites papers describing new advances and case studies in the use of satellite, drone, or proximal remote sensing for geomorphological mapping.

Dr. Andy Gibson
Dr. Mohammad Firuz Ramli
Dr. Peter Redshaw
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

Geomorphological Mapping
Geomorphological Processes
GIS
Geological Hazards
Environmental Hazards
Risk Modelling
Evidence Base
Data Integration
Data Analytics

Published Papers (6 papers)

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Research

22 pages, 8319 KiB

Open AccessArticle

Route Plans for UAV Aerial Surveys according to Different DEMs in Complex Mountainous Surroundings: A Case Study in the Zheduoshan Mountains, China

by Qingsong Du, Guoyu Li, Yu Zhou, Dun Chen, Mingtang Chai, Shunshun Qi, Yapeng Cao, Liyun Tang and Hailiang Jia

Remote Sens. 2022, 14(20), 5215; https://doi.org/10.3390/rs14205215 - 18 Oct 2022

Cited by 2 | Viewed by 1742

Abstract

Accurate and error-free digital elevation model (DEM) data are a basic guarantee for the safe flight of unmanned aerial vehicles (UAVs) during surveys in the wild, especially in moun-tainous areas with large topographic undulations. Existing free and open-source DEM data gen-erally cover large areas, with relatively high spatial resolutions (~90, 30, and even 12.5 m), but they do not have the advantage of timeliness and cannot accurately reflect current and up-to-date topographical information in the survey area. UAV pre-scanning missions can provide highly accurate and recent terrain data as a reference for UAV route planning and ensure security for subsequent aerial survey missions; however, they are time consuming. In addition, being limited to the electric charge of the UAV, pre-scanning increases the human, financial, and time consumption of field missions, and it is not applicable for field aerial survey missions in reality, unless otherwise specified, especially in harsh environments. In this paper, we used interferometric synthetic aper-ture radar (InSAR) technology to process Sentinel-1a data to obtain the DEMs of the survey area, which were used for route planning, and other free and open-source DEMs were also used for flightline plans. The digital surface models (DSMs) were obtained from the structure of the UAV pre-scan mission images, applying structure for motion (SfM) technology as the elevation reference. Comparing the errors between the InSAR-derived DEMs and the four open-source DEMs based on the reference DSM to analyze the practicability of flight route planning, the results showed that among the four DEMs, the SRTM DEM with a spatial resolution of 30 m performed best, which was considered as the first reference for UAV route plans when the survey area in complex mountainous regions is covered with a poor or inoperative network. The InSAR-derived DEMs from the Sentinel-1 images have great potential value for UAV flight planning, with a large perpendicular baseline and short temporal baseline. This work quantitatively analyzed the errors among the different DEMs and provided a discussion regarding UAV flightline plans based on external DEMs. This can not only effectively reduce the manpower, materials, and time consumption of field operations, improving the efficiency of UAV survey tasks, but it also broadens the use of InSAR technology. Furthermore, with the launch of high-resolution SAR satellites, InSAR-derived DEMs with high spatial and temporal resolutions provide an optimistic and credible strategy for UAV route planning with small errors. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geomorphological Mapping)

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22 pages, 14461 KiB

Open AccessArticle

An Improved Method for the Evaluation and Local Multi-Scale Optimization of the Automatic Extraction of Slope Units in Complex Terrains

by Zhongkang Yang, Jinbing Wei, Jianhui Deng and Siyuan Zhao

Remote Sens. 2022, 14(14), 3444; https://doi.org/10.3390/rs14143444 - 18 Jul 2022

Cited by 2 | Viewed by 1522

Abstract

Slope units (SUs) are sub-watersheds bounded by ridge and valley lines. A slope unit reflects the physical relationship between landslides and geomorphological features and is especially useful for landslide sensitivity modeling. There have been significant algorithmic advances in the automatic delineation of SUs. But the intrinsic difficulties of determining input parameters and correcting for unreasonable SUs have hindered their wide application. An improved method of the evaluation and local multi-scale optimization for the automatic extraction of SUs is proposed. The Sus’ groups more consistent with the topographic features were achieved through a stepwise approach from a global optimum to a local refining. First, the preliminary subdivisions of multiple SUs were obtained based on the r.slopeunit software. The optimal subdivision scale was obtained by a collaborative evaluation approach capable of simultaneously measuring objective minimum discrepancies and seeking a global optimum. Second, under the selected optimal scale, unreasonable SUs such as over-subdivided slope units (OSSUs) and under-subdivided slope units (USSUs) were further distinguished. The local average similarity (LS) metric for each SU was designed based on calculating the SU’s area, common boundary and neighborhood variability. The inflection points of the cumulative frequency curve of LS were calculated as the distinguishing intervals for those unrealistic SUs by maximum interclass variance threshold. Third, a new effective optimization mechanism containing the re-subdivision of USSUs and merging of OSSUs was put into effect. We thus obtained SUs composed of terrain subdivisions with multiple scales, which is currently one of the few available methods for non-single scales. The statistical distributions of density, size and shapes demonstrate the excellent performance of the refined SUs in capturing the variability of complex terrains. Benefiting from the sufficient integrating approach of diverse features for each object, it is a significant advantage that the processing object can be transferred from general entirety to each precise individual. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geomorphological Mapping)

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34 pages, 13530 KiB

Open AccessArticle

Effects of Land Use/Cover on Regional Habitat Quality under Different Geomorphic Types Based on InVEST Model

by Baixue Wang and Weiming Cheng

Remote Sens. 2022, 14(5), 1279; https://doi.org/10.3390/rs14051279 - 05 Mar 2022

Cited by 43 | Viewed by 3969

Abstract

Research on habitat quality change is of great significance for regional ecological security. Analysis of spatiotemporal change of habitat quality based on different geomorphic types can restore the background of ecological environment in historical periods and provide scientific support for revealing the evolution law of regional ecological environment quality and ecological restoration. This study aimed to identify the change in habitat quality under different geomorphic types from 1995 to 2018. Based on DEM data, geomorphic types of different scales were divided. The InVEST habitat quality model was used to analyze the spatiotemporal change in habitat quality in individual land use types in the Altay region. The spatiotemporal changes and main influencing factors of habitat quality under the background of different geomorphic types were explored. Remote sensing data was used to analyze the land use/cover changes. Sixteen threat sources, their maximum distance of impact, mode of decay, and sensitivity to threats were also estimated for each land use type. The results showed that habitat quality decreased significantly in 2015, which was related to the rapid expansion of cultivated and construction land as threat sources, as well as the decrease of forestland and grassland as sensitive factors. However, habitat quality improved significantly in 2018, because of the implementation of ecological restoration policy in 2015. Affected by elevation and topographic relief, the geomorphic type with the best habitat quality index was the large undulating middle mountain (0.927) and the worst was the medium altitude platform (0.351). Woodland contributed the most to habitat quality in large undulating middle mountain (35.07), and bare rock gravel land contributed the most to medium altitude platform (127.68). Habitat quality of different geomorphic types showed obvious spatial aggregation, and from high altitude to low altitude showed a banded ladder-like distribution. Changes in habitat quality during the past three decades suggested that the conservation and restoration strategies applied in regional ecosystem were effective. On the basis of the analysis results, four types of zoning management schemes were divided, and the ecological management and conservation measures were put forward. Therefore, this study can help decision makers, especially regarding the lack of data on biodiversity. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geomorphological Mapping)

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22 pages, 5852 KiB

Open AccessArticle

Multi-Hypothesis Topological Isomorphism Matching Method for Synthetic Aperture Radar Images with Large Geometric Distortion

by Runzhi Jiao, Qingsong Wang, Tao Lai and Haifeng Huang

Remote Sens. 2021, 13(22), 4637; https://doi.org/10.3390/rs13224637 - 17 Nov 2021

Cited by 1 | Viewed by 1748

Abstract

The dramatic undulations of a mountainous terrain will introduce large geometric distortions in each Synthetic Aperture Radar (SAR) image with different look angles, resulting in a poor registration performance. To this end, this paper proposes a multi-hypothesis topological isomorphism matching method for SAR images with large geometric distortions. The method includes the Ridge-Line Keypoint Detection (RLKD) and Multi-Hypothesis Topological Isomorphism Matching (MHTIM). Firstly, based on the analysis of the ridge structure, a ridge keypoint detection module and a keypoint similarity description method are designed, which aim to quickly produce a small number of stable matching keypoint pairs under large look angle differences and large terrain undulations. The keypoint pairs are further fed into the MHTIM module. Subsequently, the MHTIM method is proposed, which uses the stability and isomorphism of the topological structure of the keypoint set under different perspectives to generate a variety of matching hypotheses, and iteratively achieves the keypoint matching. This method uses both local and global geometric relationships between two keypoints, hence it achieving better performance compared with traditional methods. We tested our approach on both simulated and real mountain SAR images with different look angles and different elevation ranges. The experimental results demonstrate the effectiveness and stable matching performance of our approach. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geomorphological Mapping)

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25 pages, 9559 KiB

Open AccessArticle

Automatic Landform Recognition from the Perspective of Watershed Spatial Structure Based on Digital Elevation Models

by Siwei Lin, Nan Chen and Zhuowen He

Remote Sens. 2021, 13(19), 3926; https://doi.org/10.3390/rs13193926 - 30 Sep 2021

Cited by 16 | Viewed by 2411

Abstract

Landform recognition is one of the most significant aspects of geomorphology research, which is the essential tool for landform classification and understanding geomorphological processes. Watershed object-based landform recognition is a new spot in the field of landform recognition. However, in the relevant studies, the quantitative description of the watershed generally focused on the overall terrain features of the watershed, which ignored the spatial structure and topological relationship, and internal mechanism of the watershed. For the first time, we proposed an effective landform recognition method from the perspective of the watershed spatial structure, which is separated from the previous studies that invariably used terrain indices or texture derivatives. The slope spectrum method was used herein to solve the uncertainty issue of the determination on the watershed area. Complex network and P–N terrain, which are two effective methodologies to describe the spatial structure and topological relationship of the watershed, were adopted to simulate the spatial structure of the watershed. Then, 13 quantitative indices were, respectively, derived from two kinds of watershed spatial structures. With an advanced machine learning algorithm (LightGBM), experiment results showed that the proposed method showed good comprehensive performances. The overall accuracy achieved 91.67% and the Kappa coefficient achieved 0.90. By comparing with the landform recognition using terrain indices or texture derivatives, it showed better performance and robustness. It was noted that, in terms of loess ridge and loess hill, the proposed method can achieve higher accuracy, which may indicate that the proposed method is more effective than the previous methods in alleviating the confusion of the landforms whose morphologies are complex and similar. In addition, the LightGBM is more suitable for the proposed method, since the comprehensive manifestation of their combination is better than other machine learning methods by contrast. Overall, the proposed method is out of the previous landform recognition method and provided new insights for the field of landform recognition; experiments show the new method is an effective and valuable landform recognition method with great potential as well as being more suitable for watershed object-based landform recognition. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geomorphological Mapping)

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30 pages, 14269 KiB

Open AccessArticle

A Novel GIS-Based Approach for Automated Detection of Nearshore Sandbar Morphological Characteristics in Optical Satellite Imagery

by Rasa Janušaitė, Laurynas Jukna, Darius Jarmalavičius, Donatas Pupienis and Gintautas Žilinskas

Remote Sens. 2021, 13(11), 2233; https://doi.org/10.3390/rs13112233 - 07 Jun 2021

Cited by 9 | Viewed by 3532

Abstract

Satellite remote sensing is a valuable tool for coastal management, enabling the possibility to repeatedly observe nearshore sandbars. However, a lack of methodological approaches for sandbar detection prevents the wider use of satellite data in sandbar studies. In this paper, a novel fully automated approach to extract nearshore sandbars in high–medium-resolution satellite imagery using a GIS-based algorithm is proposed. The method is composed of a multi-step workflow providing a wide range of data with morphological nearshore characteristics, which include nearshore local relief, extracted sandbars, their crests and shoreline. The proposed processing chain involves a combination of spectral indices, ISODATA unsupervised classification, multi-scale Relative Bathymetric Position Index (RBPI), criteria-based selection operations, spatial statistics and filtering. The algorithm has been tested with 145 dates of PlanetScope and RapidEye imagery using a case study of the complex multiple sandbar system on the Curonian Spit coast, Baltic Sea. The comparison of results against 4 years of in situ bathymetric surveys shows a strong agreement between measured and derived sandbar crest positions (R² = 0.999 and 0.997) with an average RMSE of 5.8 and 7 m for PlanetScope and RapidEye sensors, respectively. The accuracy of the proposed approach implies its feasibility to study inter-annual and seasonal sandbar behaviour and short-term changes related to high-impact events. Algorithm-provided outputs enable the possibility to evaluate a range of sandbar characteristics such as distance from shoreline, length, width, count or shape at a relevant spatiotemporal scale. The design of the method determines its compatibility with most sandbar morphologies and suitability to other sandy nearshores. Tests of the described technique with Sentinel-2 MSI and Landsat-8 OLI data show that it can be applied to publicly available medium resolution satellite imagery of other sensors. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geomorphological Mapping)

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