Stott, D, Boyd, DS, Beck, A et al. (1 more author) (2015) Airborne LiDAR for the detection of archaeological vegetation marks using biomass as a proxy. Remote Sensing, 7 (2). 1594 - 1618. ISSN 2072-4292
Abstract
In arable landscapes, the airborne detection of archaeological features is often reliant on using the properties of the vegetation cover as a proxy for sub-surface features in the soil. Under the right conditions, the formation of vegetation marks allows archaeologists to identify and interpret archaeological features. Using airborne Laser Scanning, based on the principles of Light Detection and Ranging (LiDAR) to detect these marks is challenging, particularly given the difficulties of resolving subtle changes in a low and homogeneous crop with these sensors. In this paper, an experimental approach is adopted to explore how these marks could be detected as variations in canopy biomass using both range and full waveform LiDAR data. Although some detection was achieved using metrics of the full waveform data, it is the novel multi-temporal method of using discrete return data to detect and characterise archaeological vegetation marks that is offered for further consideration. This method was demonstrated to be applicable over a range of capture conditions, including soils deemed as difficult (i.e., clays and other heavy soils), and should increase the certainty of detection when employed in the increasingly multi-sensor approaches to heritage prospection and management.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | archaeology; Airborne LiDAR/Laser Scanning; vegetation marks; arable |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 20 Aug 2015 16:01 |
Last Modified: | 17 Jan 2018 04:17 |
Published Version: | http://dx.doi.org/10.3390/rs70201594 |
Status: | Published |
Publisher: | MDPI |
Identification Number: | 10.3390/rs70201594 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:85598 |