Next Article in Journal
Monitoring the Rapid-Moving Reactivation of Earth Flows by Means of GB-InSAR: The April 2013 Capriglio Landslide (Northern Appennines, Italy)
Previous Article in Journal
Algorithm Development for Land Surface Temperature Retrieval: Application to Chinese Gaofen-5 Data
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(2), 163; doi:10.3390/rs9020163

Quantifying the Effects of Normalisation of Airborne LiDAR Intensity on Coniferous Forest Leaf Area Index Estimations

1
Centre for Forest Operations and Environment, College of Engineering and Technology, Northeast Forestry University, Harbin 150040, China
2
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editors: Clement Atzberger and Prasad S. Thenkabail
Received: 9 December 2016 / Revised: 9 February 2017 / Accepted: 14 February 2017 / Published: 16 February 2017
View Full-Text   |   Download PDF [5567 KB, uploaded 16 February 2017]   |  

Abstract

The range between a sensor and the target, the incidence angle, and the target reflectance, are known factors that can influence the intensity values of LiDAR data and consequently, its use in many applications. However, very few studies have provided a quantitative analysis of the effects of normalisation of these three factors on forest leaf area index (LAI) estimations. In this paper, using two coniferous tree species (i.e., Scotch pine and Larch pine) as a case study, the effects of intensity normalisation on coniferous forest LAI estimations have, for the first time, been systematically examined and quantified. It was found that the intensity normalisation had a generally positive effect on the improvement of coniferous forest LAI estimations. However, the improvements were very minor. Specifically, the range normalisation did not improve the accuracy of the LAI estimation for either of the two coniferous tree species. The incidence angle and reflectance normalisation improved the accuracy of the LAI estimation for Scotch pine forests; however, they had no effect on the improvement of the LAI estimation for Larch pine forests. This experimental study suggests that range normalisation is not required for forest LAI estimations in areas with small elevation differences (i.e., less than 114 m). The incidence angle and target reflectance normalisation can marginally improve the accuracy of coniferous forest LAI estimations. However, the extent of this improvement varies among species, depending on the choice of incidence angle and reflectance coefficient. Overall, the effects of normalisation of airborne LiDAR intensity on coniferous forest LAI estimations are closely related to topographic conditions (i.e., elevation and slope), the tree species composition, and its associated structural attributes. Therefore, further research should explore the effects of LiDAR intensity normalisation on forest LAI estimations in regions with large elevation differences and diverse forest structures. View Full-Text
Keywords: range; incidence angle; target reflectance; laser penetration index; LAI range; incidence angle; target reflectance; laser penetration index; LAI
Figures

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

You, H.; Wang, T.; Skidmore, A.K.; Xing, Y. Quantifying the Effects of Normalisation of Airborne LiDAR Intensity on Coniferous Forest Leaf Area Index Estimations. Remote Sens. 2017, 9, 163.

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