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Aerial and Drone LiDAR Data for Geomorphological Mapping, Landform Extraction and Landscape Evolution

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: 10 December 2024 | Viewed by 6349

Special Issue Editors

Dr. Dario Gioia

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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
Dr. Nicodemo Abate

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Guest Editor
Consiglio Nazionale delle Ricerche—Istituto di Scienze del Patrimonio Culturale (ISPC), Tito Scalo, Potenza, Italy
Interests: cultural heritage; museum studies; museum exhibition; cultural studies; arts and humanities; ancient history; art; visual culture; excavation
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Dr. Giuseppe Corrado

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Guest Editor
Dipartimento delle Culture Europee e del Mediterraneo (DiCEM), Università della Basilicata, Matera, Italy
Interests: geological mapping; tectonics; quaternary geology; sedimentology; coastal geomorphology; neotectonics; quaternary; coastal processes
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Dr. Antonio Minervino Amodio

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Guest Editor
Consiglio Nazionale delle Ricerche—Istituto di Scienze del Patrimonio Culturale (ISPC), Tito Scalo, Potenza, Italy
Interests: spatial analysis; satellite image analysis; mapping; environment; geoinformation; geomatics; geo-processing; land use modelling; topography; photogrammetry
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Prof. Marcello Schiattarella

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Dipartimento delle Culture Europee e del Mediterraneo (DiCEM), Università della Basilicata, Matera, Italy
Interests: tectonics; geology; geomorphology; tectonic geomorphology; quaternary geology; neotectonics; active tectonics; coastal geomorphology; physical geography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, the increased availability of ultra-high resolution LIDAR data has favored the spreading of different applications in the field of the quantitative landscape analyses. Such data strongly support traditional geomorphological methods of delineating geomorphological elements and types and rates of surface processes. The aim of this Special Issue is to collect multidisciplinary contributions on the use of airborne and drone LIDAR data to identify geomorphological features and processes, and solve issues of landscape evolution.

We encourage researchers to submit papers dealing with the multitemporal analysis of LIDAR DEMs aimed at the detailed reconstruction of short- and long-term topographic changes. Other relevant topics for this research proposal include the analysis of LIDAR-derived data for geomorphological mapping purposes, modeling of short- and long-term estimation of topographic changes and geomorphological processes in different climate contexts and at different spatial and temporal scales, and quantitative characterization of geomorphological processes and landform changes. Contributions on the high potential of LIDAR surveys for application in the field of landscape archaeology or the identification of small-scale landforms of archaeological significance are also welcomed.

Dr. Dario Gioia
Dr. Nicodemo Abate
Dr. Giuseppe Corrado
Dr. Antonio Minervino Amodio
Prof. Marcello Schiattarella
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

  • UAV LiDAR
  • geomatics
  • geomorphological mapping
  • object-based landform extraction
  • DEM of difference (Dod)
  • short-term geomorphological evolution
  • landscape evolution model (LEM)

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Published Papers (5 papers)

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Research

21 pages, 19359 KiB  
Article
Landslide Hazard Prediction Based on UAV Remote Sensing and Discrete Element Model Simulation—Case from the Zhuangguoyu Landslide in Northern China
by Guangming Li, Yu Zhang, Yuhua Zhang, Zizheng Guo, Yuanbo Liu, Xinyong Zhou, Zhanxu Guo, Wei Guo, Lihang Wan, Liang Duan, Hao Luo and Jun He
Remote Sens. 2024, 16(20), 3887; https://doi.org/10.3390/rs16203887 - 19 Oct 2024
Viewed by 682
Abstract
Rainfall-triggered landslides generally pose a high risk due to their sudden initiation, massive impact force, and energy. It is, therefore, necessary to perform accurate and timely hazard prediction for these landslides. Most studies have focused on the hazard assessment and verification of landslides [...] Read more.
Rainfall-triggered landslides generally pose a high risk due to their sudden initiation, massive impact force, and energy. It is, therefore, necessary to perform accurate and timely hazard prediction for these landslides. Most studies have focused on the hazard assessment and verification of landslides that have occurred, which were essentially back-analyses rather than predictions. To overcome this drawback, a framework aimed at forecasting landslide hazards by combining UAV remote sensing and numerical simulation was proposed in this study. A slow-moving landslide identified by SBAS-InSAR in Tianjin city of northern China was taken as a case study to clarify its application. A UAV with laser scanning techniques was utilized to obtain high-resolution topography data. Then, extreme rainfall with a given return period was determined based on the Gumbel distribution. The Particle Flow Code (PFC), a discrete element model, was also applied to simulate the runout process after slope failure under rainfall and earthquake scenarios. The results showed that the extreme rainfall for three continuous days in the study area was 151.5 mm (P = 5%), 184.6 mm (P = 2%), and 209.3 mm (P = 1%), respectively. Both extreme rainfall and earthquake scenarios could induce slope failure, and the failure probabilities revealed by a seepage–mechanic interaction simulation in Geostudio reached 82.9% (earthquake scenario) and 92.5% (extreme rainfall). The landslide hazard under a given scenario was assessed by kinetic indicators during the PFC simulation. The landslide runout analysis indicated that the landslide had a velocity of max 23.4 m/s under rainfall scenarios, whereas this reached 19.8 m/s under earthquake scenarios. In addition, a comparison regarding particle displacement also showed that the landslide hazard under rainfall scenarios was worse than that under earthquake scenarios. The modeling strategy incorporated spatial and temporal probabilities and runout hazard analyses, even though landslide hazard mapping was not actually achieved. The present framework can predict the areas threatened by landslides under specific scenarios, and holds substantial scientific reference value for effective landslide prevention and control strategies. Full article
(This article belongs to the Special Issue Aerial and Drone LiDAR Data for Geomorphological Mapping, Landform Extraction and Landscape Evolution)
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20 pages, 29877 KiB  
Article
A Comparison of Landforms and Processes Detection Using Multisource Remote Sensing Data: The Case Study of the Palinuro Pine Grove (Cilento, Vallo di Diano and Alburni National Park, Southern Italy)
by Mario Valiante, Alessandro Di Benedetto and Aniello Aloia
Remote Sens. 2024, 16(15), 2771; https://doi.org/10.3390/rs16152771 - 29 Jul 2024
Cited by 1 | Viewed by 734
Abstract
The automated recognition of landforms holds significant importance within the framework of digital geomorphological mapping, serving as a pivotal focal point for research and practical applications alike. Over the last decade, various methods have been developed to achieve this goal, ranging from grid-based [...] Read more.
The automated recognition of landforms holds significant importance within the framework of digital geomorphological mapping, serving as a pivotal focal point for research and practical applications alike. Over the last decade, various methods have been developed to achieve this goal, ranging from grid-based to object-based approaches, covering a range from supervised to completely unsupervised techniques. Furthermore, the vast majority of the methods mentioned depend on Digital Elevation Models (DEMs) as their primary input, highlighting the crucial significance of meticulous preparation and rigorous quality assessment of these datasets. In this study, we compare the outcomes of grid-based methods for landforms extraction and surficial process type assessment, leveraging various DEMs as input data. Initially, we employed a photogrammetric Digital Terrain Model (DTM) generated at a regional scale, along with two LiDAR datasets. The first dataset originates from an airborne survey conducted by the national government approximately a decade ago, while the second dataset was acquired by UAV as part of this study’s framework. The results highlight how the higher resolution and level of detail of the LiDAR datasets allow the recognition of a higher number of features at higher scales; but, in contrast, generally, a high level of detail corresponds with a higher risk of noise within the dataset, mostly due to unwanted natural features or anthropogenic disturbance. Utilizing these datasets for generating geomorphological maps harbors significant potential in the framework of natural hazard assessment, particularly concerning phenomena associated with geo-hydrological processes. Full article
(This article belongs to the Special Issue Aerial and Drone LiDAR Data for Geomorphological Mapping, Landform Extraction and Landscape Evolution)
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18 pages, 14154 KiB  
Article
Three-Dimensional Rockslide Analysis Using Unmanned Aerial Vehicle and LiDAR: The Castrocucco Case Study, Southern Italy
by Antonio Minervino Amodio, Giuseppe Corrado, Ilenia Graziamaria Gallo, Dario Gioia, Marcello Schiattarella, Valentino Vitale and Gaetano Robustelli
Remote Sens. 2024, 16(12), 2235; https://doi.org/10.3390/rs16122235 - 19 Jun 2024
Viewed by 796
Abstract
Rockslides are one of the most dangerous hazards in mountainous and hilly areas. In this study, a rockslide that occurred on 30 November 2022 in Castrocucco, a district located in the Italian municipality of Maratea (Potenza province) in the Basilicata region, was investigated [...] Read more.
Rockslides are one of the most dangerous hazards in mountainous and hilly areas. In this study, a rockslide that occurred on 30 November 2022 in Castrocucco, a district located in the Italian municipality of Maratea (Potenza province) in the Basilicata region, was investigated by using pre- and post-event high-resolution 3D models. The event caused a great social alarm as some infrastructures were affected. The main road to the tourist hub of Maratea was, in fact, destroyed and made inaccessible. Rock debris also affected a beach club and important boat storage for sea excursions to Maratea. This event was investigated by using multiscale and multisensor close-range remote sensing (LiDAR and SfM) to determine rockslide characteristics. The novelty of this work lies in how these data, although not originally acquired for rockslide analysis, have been integrated and utilized in an emergency at an almost inaccessible site. The event was analyzed both through classical geomorphological analysis and through a quantitative comparison of multi-temporal DEMs (DoD) in order to assess (i) all the morphological features involved, (ii) detached volume (approximately 8000 m3), and (iii) the process of redistributing and reworking the landslide deposit in the depositional area. Full article
(This article belongs to the Special Issue Aerial and Drone LiDAR Data for Geomorphological Mapping, Landform Extraction and Landscape Evolution)
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23 pages, 7834 KiB  
Article
A Multiscale Filtering Method for Airborne LiDAR Data Using Modified 3D Alpha Shape
by Di Cao, Cheng Wang, Meng Du and Xiaohuan Xi
Remote Sens. 2024, 16(8), 1443; https://doi.org/10.3390/rs16081443 - 18 Apr 2024
Viewed by 1269
Abstract
The complexity of terrain features poses a substantial challenge in the effective processing and application of airborne LiDAR data, particularly in regions characterized by steep slopes and diverse objects. In this paper, we propose a novel multiscale filtering method utilizing a modified 3D [...] Read more.
The complexity of terrain features poses a substantial challenge in the effective processing and application of airborne LiDAR data, particularly in regions characterized by steep slopes and diverse objects. In this paper, we propose a novel multiscale filtering method utilizing a modified 3D alpha shape algorithm to increase the ground point extraction accuracy in complex terrain. Our methodology comprises three pivotal stages: preprocessing for outlier removal and potential ground point extraction; the deployment of a modified 3D alpha shape to construct multiscale point cloud layers; and the use of a multiscale triangulated irregular network (TIN) densification process for precise ground point extraction. In each layer, the threshold is adaptively determined based on the corresponding α. Points closer to the TIN surface than the threshold are identified as ground points. The performance of the proposed method was validated using a classical benchmark dataset provided by the ISPRS and an ultra-large-scale ground filtering dataset called OpenGF. The experimental results demonstrate that this method is effective, with an average total error and a kappa coefficient on the ISPRS dataset of 3.27% and 88.97%, respectively. When tested in the large scenarios of the OpenGF dataset, the proposed method outperformed four classical filtering methods and achieved accuracy comparable to that of the best of learning-based methods. Full article
(This article belongs to the Special Issue Aerial and Drone LiDAR Data for Geomorphological Mapping, Landform Extraction and Landscape Evolution)
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28 pages, 51558 KiB  
Article
LiDAR-Based Morphometry of Dolines in Aggtelek Karst (Hungary) and Slovak Karst (Slovakia)
by Tamás Telbisz, László Mari and Balázs Székely
Remote Sens. 2024, 16(5), 737; https://doi.org/10.3390/rs16050737 - 20 Feb 2024
Cited by 1 | Viewed by 1773
Abstract
LiDAR-based digital terrain models (DTMs) represent an advance in the investigation of small-scale geomorphological features, including dolines of karst terrains. Important issues in doline morphometry are (i) which statistical distributions best model the size distribution of doline morphometric parameters and (ii) how to [...] Read more.
LiDAR-based digital terrain models (DTMs) represent an advance in the investigation of small-scale geomorphological features, including dolines of karst terrains. Important issues in doline morphometry are (i) which statistical distributions best model the size distribution of doline morphometric parameters and (ii) how to characterize the volume of dolines based on high-resolution DTMs. For backward compatibility, how previous datasets obtained predominantly from topographic maps relate to doline data derived from LiDAR is also examined. Our study area includes the karst plateaus of Aggtelek Karst and Slovak Karst national parks, whose caves are part of the UNESCO World Heritage. To characterize the study area, the relationships between doline parameters and topography were studied, as well as their geological characteristics. Our analysis revealed that the LiDAR-based doline density is 25% higher than the value calculated from topographic maps. Furthermore, LiDAR-based doline delineations are slightly larger and less rounded than in the case of topographic maps. The plateaus of the study area are characterized by low (5–10 km−2), moderate (10–30 km−2), and medium (30–35 km−2) doline densities. In terms of topography, the slope trend is decisive since the doline density is negligible in areas where the general slope is steeper than 12°. As for the lithology, 75% of the dolines can be linked to Wetterstein Limestone. The statistical distribution of the doline area can be well modeled by the lognormal distribution. To describe the DTM-based volume of dolines, a new parameter (k) is introduced to characterize their 3D shape: it is equal to the product of the area and the depth divided by the volume. This parameter indicates whether the idealized shape of the doline is closer to a cylinder, a bowl (calotte), a cone, or a funnel shape. The results show that most sinkholes in the study area have a transitional shape between a bowl (calotte) and a cone. Full article
(This article belongs to the Special Issue Aerial and Drone LiDAR Data for Geomorphological Mapping, Landform Extraction and Landscape Evolution)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: New paradigms for geomorphological mapping: a multi-source approach for landscape characterization.
Authors: Martina Cignetti; Danilo Godone; Daniele Ferrari Trecate; Marco Baldo
Affiliation: National Research Council of Italy, Research Institute for Geo-Hydrological Protection (CNR IRPI), Torino 10135, Italy
Abstract: The advent of geomatic techniques and novel sensors have opened the road to new approaches in mapping, including morphological one. The evolution of a land portion deserves the use of the state of the art of technologies and method to be correctly studied and described. Its graphical representation constitutes a fundamental for scientific and land planning purposes. In this context, new paradigms for geomorphological mapping, useful to modernize the creation of traditional geomorphological maps, become necessary for the creation of scalable digital representation of processes and landforms. A fully via-remote geomorphological mapping approach, based on multi-source and multi-sensor application was implemented to the recognition of landforms and processes. This methodology was applied to a study site located in central Italy, characterized by the presence of ‘calanchi’ (i.e. badlands). These landforms, jointly with several other erosion processes and mass movements, make the area particularly suitable for the study. Considering primarily the increasing availability of regional LiDAR products, an automated landform classification, i.e. Geomorphons, was adopted to map landforms at the slope scale. Jointly, by collecting and digitizing, a time-series of historical orthoimages a multi-temporal analysis was done. Finally, surveying the area with an Unmanned Aerial Vehicle, exploiting the high-resolution Digital Terrain Model and orthoimage, a local-scale geomorphological mapping was produced. This approach guarantees a multi-scale and multi-temporal cartography model to a full-coverage representation of landforms via scalable geomorphological maps, representing a useful tool for decision-maker in every action and decision on the territory.

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