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Article

Comparing High Accuracy t-LiDAR and UAV-SfM Derived Point Clouds for Geomorphological Change Detection

1
Laboratory of Mineralogy-Geology, Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 75, Iera Odos str., 11855 Athens, Greece
2
Institute of Neotectonics and Natural Hazards, RWTH Aachen University, 52062 Aachen, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Wolfgang Kainz and Josef Strobl
ISPRS Int. J. Geo-Inf. 2021, 10(6), 367; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060367
Received: 18 March 2021 / Revised: 4 May 2021 / Accepted: 26 May 2021 / Published: 29 May 2021
(This article belongs to the Special Issue GIS and Remote Sensing Applications in Geomorphology)
Analysis of two small semi-mountainous catchments in central Evia island, Greece, highlights the advantages of Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanning (TLS) based change detection methods. We use point clouds derived by both methods in two sites (S1 & S2), to analyse the effects of a recent wildfire on soil erosion. Results indicate that topsoil’s movements in the order of a few centimetres, occurring within a few months, can be estimated. Erosion at S2 is precisely delineated by both methods, yielding a mean value of 1.5 cm within four months. At S1, UAV-derived point clouds’ comparison quantifies annual soil erosion more accurately, showing a maximum annual erosion rate of 48 cm. UAV-derived point clouds appear to be more accurate for channel erosion display and measurement, while the slope wash is more precisely estimated using TLS. Analysis of Point Cloud time series is a reliable and fast process for soil erosion assessment, especially in rapidly changing environments with difficult access for direct measurement methods. This study will contribute to proper georesource management by defining the best-suited methodology for soil erosion assessment after a wildfire in Mediterranean environments. View Full-Text
Keywords: Terrestrial Laser Scanning (TLS); Structure from Motion (SfM); drone; point cloud; soil erosion; wildfire; geoenvironment; remote sensing Terrestrial Laser Scanning (TLS); Structure from Motion (SfM); drone; point cloud; soil erosion; wildfire; geoenvironment; remote sensing
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MDPI and ACS Style

Alexiou, S.; Deligiannakis, G.; Pallikarakis, A.; Papanikolaou, I.; Psomiadis, E.; Reicherter, K. Comparing High Accuracy t-LiDAR and UAV-SfM Derived Point Clouds for Geomorphological Change Detection. ISPRS Int. J. Geo-Inf. 2021, 10, 367. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060367

AMA Style

Alexiou S, Deligiannakis G, Pallikarakis A, Papanikolaou I, Psomiadis E, Reicherter K. Comparing High Accuracy t-LiDAR and UAV-SfM Derived Point Clouds for Geomorphological Change Detection. ISPRS International Journal of Geo-Information. 2021; 10(6):367. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060367

Chicago/Turabian Style

Alexiou, Simoni, Georgios Deligiannakis, Aggelos Pallikarakis, Ioannis Papanikolaou, Emmanouil Psomiadis, and Klaus Reicherter. 2021. "Comparing High Accuracy t-LiDAR and UAV-SfM Derived Point Clouds for Geomorphological Change Detection" ISPRS International Journal of Geo-Information 10, no. 6: 367. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060367

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