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Aerial and Drone LiDAR Data for Geomorphological Mapping, Landform Extraction...
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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: closed (26 May 2024) | Viewed by 2069

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
Special Issues, Collections and Topics in MDPI journals
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)

Published Papers (2 papers)

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Research

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 590
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
Viewed by 840
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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