Next Article in Journal
The Impact of Moss Species and Biomass on the Growth of Pinus sylvestris Tree Seedlings at Different Precipitation Frequencies
Next Article in Special Issue
Cross-Correlation of Diameter Measures for the Co-Registration of Forest Inventory Plots with Airborne Laser Scanning Data
Previous Article in Journal
Re-Greening Ethiopia: History, Challenges and Lessons
Previous Article in Special Issue
Estimation of the Timber Quality of Scots Pine with Terrestrial Laser Scanning
Article Menu

Export Article

Open AccessArticle
Forests 2014, 5(8), 1910-1930; doi:10.3390/f5081910

Correlating the Horizontal and Vertical Distribution of LiDAR Point Clouds with Components of Biomass in a Picea crassifolia Forest

1
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, CAS Olympic S&T Park No. 20 Da Tun Road P.O. Box 9718, China
2
University of Chinese Academy of Sciences, Beijing 100039, China
3
Fujian Agriculture and Forestry University, Fuzhou 350002, China
*
Author to whom correspondence should be addressed.
Received: 18 December 2013 / Revised: 24 May 2014 / Accepted: 24 July 2014 / Published: 5 August 2014
View Full-Text   |   Download PDF [30895 KB, uploaded 5 August 2014]   |  

Abstract

Light detection and ranging (LiDAR) has been widely used to estimate forest biomass. In this study, we aim to further explore this capability by correlating horizontal and vertical distribution of LiDAR data with components of biomass in a Picea crassifolia forest. Airborne small footprint full-waveform data were decomposed to acquire higher density point clouds. We calculated LiDAR metrics at the tree level and subplot level and correlated them to biomass components, including branch biomass (BB), leaf biomass (LB) and above-ground biomass (AGB), respectively. A new metric (Horizcv) describing the horizontal distribution of point clouds was proposed. This metric was found to be highly correlated with canopy biomass (BB and LB) at the tree level and subplot level. Correlation between AGB and Horizcv at the subplot level is much lower than that at tree level. AGB for subplot is highly correlated with the mean height metric (Hmean), canopy cover index (CCI) and the product of them. On the other hand, the relationship between the vertical distribution of LiDAR point and biomass was explored by developing two types of vertical profiles, including LiDAR distribution profiles and a biomass profile. Good relationships were found between these two types of vertical profiles and assessed by Pearson’s correlation coefficient (R) and the area of overlap index (AOI). These good correlations possess potential in predicting the vertical distribution of canopy biomass. Overall, it is concluded that not only the vertical, but also the horizontal distribution of LiDAR points should be taken into account in estimating components of biomass by LiDAR. View Full-Text
Keywords: horizontal and vertical distribution; LiDAR; biomass; Picea crassifolia horizontal and vertical distribution; LiDAR; biomass; Picea crassifolia
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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

Li, W.; Niu, Z.; Gao, S.; Huang, N.; Chen, H. Correlating the Horizontal and Vertical Distribution of LiDAR Point Clouds with Components of Biomass in a Picea crassifolia Forest. Forests 2014, 5, 1910-1930.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Forests EISSN 1999-4907 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top