Next Article in Journal
Exploring Subpixel Learning Algorithms for Estimating Global Land Cover Fractions from Satellite Data Using High Performance Computing
Next Article in Special Issue
Bayesian and Classical Machine Learning Methods: A Comparison for Tree Species Classification with LiDAR Waveform Signatures
Previous Article in Journal
A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering—Part 2: Application to XCO2 Retrievals from OCO-2
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(11), 1101; doi:10.3390/rs9111101

Assessment of Errors Caused by Forest Vegetation Structure in Airborne LiDAR-Derived DTMs

King’s College London, Department of Geography, London WC2R 2LS, UK
NERC National Centre for Earth Observation (NCEO), King’s College London, London WC2R 2LS, UK
Author to whom correspondence should be addressed.
Received: 22 September 2017 / Revised: 14 October 2017 / Accepted: 26 October 2017 / Published: 28 October 2017
(This article belongs to the Special Issue Lidar for Forest Science and Management)
View Full-Text   |   Download PDF [7504 KB, uploaded 28 October 2017]   |  


Airborne Light Detection and Ranging (LiDAR) is a survey tool with many applications in forestry and forest research. It can capture the 3D structure of vegetation and topography quickly and accurately over thousands of hectares of forest. However, very few studies have assessed how accurately LiDAR can measure surface topography under forest canopies, which may be important, for example, in relation to analysis of pre- and post-burn surface height maps used to quantify the combustion of organic soils. Here, we use ground survey equipment to assess digital terrain model (DTM) accuracy in a deciduous broadleaf forest, during both leaf-on and leaf-off conditions. Using the leaf-on LiDAR dataset we quantitatively assess vertical vegetation structure, and use this as a categorical explanatory variable for DTM accuracy. In the presence of leaf-on vegetation, DTM accuracy is severely reduced, with low-stature undergrowth vegetation (such as ferns) causing the greatest errors (RMSE > 1 m). Errors are lower under leaf-off conditions (RMSE = 0.22 m), but still of a magnitude similar to that reported for mean depths of burn in fires involving organic soils. We highlight the need for adequate ground control schemes to accompany any forest-based airborne LiDAR survey which require highly accurate DTMs. View Full-Text
Keywords: airborne LiDAR; DTM; accuracy assessment; vertical vegetation structure; ground control points airborne LiDAR; DTM; accuracy assessment; vertical vegetation structure; ground control points

Figure 1

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

Simpson, J.E.; Smith, T.E.L.; Wooster, M.J. Assessment of Errors Caused by Forest Vegetation Structure in Airborne LiDAR-Derived DTMs. Remote Sens. 2017, 9, 1101.

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



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