MDPI - Publisher of Open Access Journals

21 pages, 51554 KiB

Open AccessArticle

Airborne LiDAR Applications at the Medieval Site of Castel Fenuculus in the Lower Valley of the Calore River (Benevento, Southern Italy)

by Antonio Corbo

Land 2024, 13(12), 2255; https://doi.org/10.3390/land13122255 - 23 Dec 2024

Viewed by 1052

Abstract

This paper explores the application of Airborne Laser Scanning (ALS) technology in the investigation of the medieval Norman site of Castel Fenuculus, in the lower Calore Valley, Southern Italy. This research aims to assess the actual potential of the ALS dataset provided by the Italian Ministry of the Environment (MATTM) for the detection and visibility of archaeological features in a difficult environment characterised by dense vegetation and morphologically complex terrain. The study focuses on improving the detection and interpretation of archaeological features through a systematic approach that includes the acquisition of ALS point clouds, the implementation of classification algorithms, and the removal of vegetation layers to reveal the underlying terrain and ruined structures. Furthermore, the aim was to test different classification and filtering techniques to identify the best one to use in complex contexts, with the intention of providing a comprehensive and replicable methodological framework. Finally, the Digital Elevation Model (DTM), and various LiDAR-derived models (LDMs), were generated to visualise and highlight topographical features potentially related to archaeological remains. The results obtained demonstrate the significant potential of LiDAR in identifying and documenting archaeological features in densely vegetated and wooded landscapes. Full article

(This article belongs to the Section Landscape Archaeology)

► Show Figures

Figure 1

18 pages, 5987 KiB

Open AccessArticle

Research on Landslide Trace Recognition by Fusing UAV-Based LiDAR DEM Multi-Feature Information

by Lei Han, Ping Duan, Jiajia Liu and Jia Li

Remote Sens. 2023, 15(19), 4755; https://doi.org/10.3390/rs15194755 - 28 Sep 2023

Cited by 12 | Viewed by 2202

Abstract

Landslide traces are crucial geomorphological features of landslides. Through the recognition of landslide traces, a better grasp of the topographical features of landslides can be achieved, thereby aiding in the enhancement of capabilities for the prevention, response, and management of landslides. Aiming at the complex topographic features of landslide traces, only using a single DEM product could provide a complete and comprehensive recognition of landslide traces. A method of landslide tracing recognition based on the fusion of multi-feature information from the Unmanned Aerial Vehicle-based Light Detection and Ranging (UAV-based LiDAR) Digital Elevation Model (DEM) is proposed. First, a high-precision DEM is constructed by using the LiDAR point cloud data. Based on the DEM, four multi-feature images that can enhance the landslide geomorphology are generated: hillshading, slope, positive openness, and sky-view factor. Furtherore, the DEM multi-feature images were fused using the Visualization for Archaeological Topography (VAT) method to obtain the DEM Multi-Feature Fusion Image (DEM-DFFI). Finally, the landslide traces were extracted from the DEM-DFFI based on fractal theory. The method presented in this paper makes full use of DEM multi-feature images and fuses them, which can accurately and clearly show the topographic and geomorphological features of landslides. Based on this, it helps improve landslide trace recognition accuracy. Full article

(This article belongs to the Special Issue Lidar for Environmental Remote Sensing: Theory and Application)

► Show Figures

Figure 1

17 pages, 8721 KiB

Open AccessArticle

Mapping Relict Charcoal Hearths in New England Using Deep Convolutional Neural Networks and LiDAR Data

by Ji Won Suh, Eli Anderson, William Ouimet, Katharine M. Johnson and Chandi Witharana

Remote Sens. 2021, 13(22), 4630; https://doi.org/10.3390/rs13224630 - 17 Nov 2021

Cited by 19 | Viewed by 4664

Abstract

Advanced deep learning methods combined with regional, open access, airborne Light Detection and Ranging (LiDAR) data have great potential to study the spatial extent of historic land use features preserved under the forest canopy throughout New England, a region in the northeastern United States. Mapping anthropogenic features plays a key role in understanding historic land use dynamics during the 17th to early 20th centuries, however previous studies have primarily used manual or semi-automated digitization methods, which are time consuming for broad-scale mapping. This study applies fully-automated deep convolutional neural networks (i.e., U-Net) with LiDAR derivatives to identify relict charcoal hearths (RCHs), a type of historical land use feature. Results show that slope, hillshade, and Visualization for Archaeological Topography (VAT) rasters work well in six localized test regions (spatial scale: <1.5 km², best F1 score: 95.5%), but also at broader extents at the town level (spatial scale: 493 km², best F1 score: 86%). The model performed best in areas with deciduous forest and high slope terrain (e.g., >15 degrees) (F1 score: 86.8%) compared to coniferous forest and low slope terrain (e.g., <15 degrees) (F1 score: 70.1%). Overall, our results contribute to current methodological discussions regarding automated extraction of historical cultural features using deep learning and LiDAR. Full article

(This article belongs to the Special Issue Machine Learning Using Medium and High-Resolution Remote Sensing Datasets)

► Show Figures

Figure 1

14 pages, 6607 KiB

Open AccessArticle

VAT Method for Visualization of Mass Movement Features: An Alternative to Hillshaded DEM

by Timotej Verbovšek, Tomislav Popit and Žiga Kokalj

Remote Sens. 2019, 11(24), 2946; https://doi.org/10.3390/rs11242946 - 9 Dec 2019

Cited by 26 | Viewed by 6233

Abstract

Hillshaded digital elevation models are a well-known information layer used to determine the geomorphological properties of landslides. However, their use is limited because the results are dependent on a particular sun azimuth and elevation. Approaches proposed to overcome this bias include positive openness, sky-view factor, red relief image maps, and prismatic openness. We propose an upgrade to all these methods, a method named Visualization for Archaeological Topography (VAT). The method is based on a fusion of four information layers into a single image (hillshaded terrain, slope, positive openness, and sky-view factor). VAT can be used to enhance visibility of features of varied scale, height, orientation, and form that sit on terrain ranging from extremely flat to very steep. Besides this, the merits of VAT are that the results are comparable across diverse geographical areas. We have successfully tested the method for landslide recognition and analysis in five different areas in the Vipava Valley (SW Slovenia). Geomorphology of the area is very diverse and holds various types of mass movements. In contrast to classical hillshaded digital elevation models (DEMs), the geomorphological features of landslides obtained by the VAT method are very clearly seen in all studied mass movements. Full article

(This article belongs to the Special Issue Remote Sensing of Landslides II)

► Show Figures

Graphical abstract

Search Results (4)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI

Saved Queries

Search Filter Reset All

Years

Feature Papers

Subjects

Journals

Article Types

Countries / Regions

Search Results (4)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI