Next Article in Journal
A Novel Spectral Library Pruning Technique for Spectral Unmixing of Urban Land Cover
Previous Article in Journal
Assessment of Satellite-Derived Surface Reflectances by NASA’s CAR Airborne Radiometer over Railroad Valley Playa
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(6), 563; doi:10.3390/rs9060563

Archaeological Application of Airborne LiDAR with Object-Based Vegetation Classification and Visualization Techniques at the Lowland Maya Site of Ceibal, Guatemala

1
School of Anthropology, University of Arizona, Tucson, AZ 85721-0030, USA
2
Ceibal-Petexbatun Archaeological Project, Guatemala City 01005, Guatemala
3
Escuela de Historia, Universidad de San Carlos, Guatemala City 01012, Guatemala
4
Graduate School of Science Biology and Geosciences, Osaka City University, Osaka 558-8585, Japan
5
Faculty of Biosphere-Geosphere Science, Okayama University of Science, Okayama 700-0005, Japan
6
NSF National Center for Airborne Laser Mapping (NCALM), University of Houston, Houston, TX 77204-5059, USA
7
Faculty of Humanities, Ibaraki University, Mito 310-8512, Japan
8
Graduate School of Education, Naruto University of Education, Naruto 772-8502, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: Nicola Masini and Prasad S. Thenkabail
Received: 8 March 2017 / Revised: 16 May 2017 / Accepted: 31 May 2017 / Published: 5 June 2017
View Full-Text   |   Download PDF [27250 KB, uploaded 6 June 2017]   |  

Abstract

The successful analysis of LiDAR data for archaeological research requires an evaluation of effects of different vegetation types and the use of adequate visualization techniques for the identification of archaeological features. The Ceibal-Petexbatun Archaeological Project conducted a LiDAR survey of an area of 20 × 20 km around the Maya site of Ceibal, Guatemala, which comprises diverse vegetation classes, including rainforest, secondary vegetation, agricultural fields, and pastures. We developed a classification of vegetation through object-based image analysis (OBIA), primarily using LiDAR-derived datasets, and evaluated various visualization techniques of LiDAR data. We then compared probable archaeological features identified in the LiDAR data with the archaeological map produced by Harvard University in the 1960s and conducted ground-truthing in sample areas. This study demonstrates the effectiveness of the OBIA approach to vegetation classification in archaeological applications, and suggests that the Red Relief Image Map (RRIM) aids the efficient identification of subtle archaeological features. LiDAR functioned reasonably well for the thick rainforest in this high precipitation region, but the densest parts of foliage appear to create patches with no or few ground points, which make the identification of small structures problematic. View Full-Text
Keywords: LiDAR; archaeology; Maya; tropical lowlands; object-based image analysis (OBIA); vegetation classification; visualization techniques; Red Relief Image Map (RRIM) LiDAR; archaeology; Maya; tropical lowlands; object-based image analysis (OBIA); vegetation classification; visualization techniques; Red Relief Image Map (RRIM)
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Inomata, T.; Pinzón, F.; Ranchos, J.L.; Haraguchi, T.; Nasu, H.; Fernandez-Diaz, J.C.; Aoyama, K.; Yonenobu, H. Archaeological Application of Airborne LiDAR with Object-Based Vegetation Classification and Visualization Techniques at the Lowland Maya Site of Ceibal, Guatemala. Remote Sens. 2017, 9, 563.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top