Next Article in Journal
ALOS/PALSAR InSAR Time-Series Analysis for Detecting Very Slow-Moving Landslides in Southern Kyrgyzstan
Previous Article in Journal
Large-Area Landslides Monitoring Using Advanced Multi-Temporal InSAR Technique over the Giant Panda Habitat, Sichuan, China
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(7), 8950-8972; doi:10.3390/rs70708950

Moving Voxel Method for Estimating Canopy Base Height from Airborne Laser Scanner Data

1
Lappeenranta University of Technology, P. O. Box 20, Lappeenranta 53851, Finland
2
Faculty of Science and Technology, Mzumbe University, P. O. Box 1, Mzumbe, Tanzania
3
Arbonaut Ltd., Kaislakatu 2, Joensuu 80130, Finland
*
Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi and Prasad S. Thenkabail
Received: 9 March 2015 / Revised: 30 May 2015 / Accepted: 17 June 2015 / Published: 15 July 2015
View Full-Text   |   Download PDF [26469 KB, uploaded 20 July 2015]   |  

Abstract

Canopy base height (CBH) is a key parameter used in forest-fire modeling, particularly crown fires. However, estimating CBH is a challenging task, because normally, it is difficult to measure it in the field. This has led to the use of simple estimators (e.g., the average of individual trees in a plot) for modeling CBH. In this paper, we propose a method for estimating CBH from airborne light detection and ranging (LiDAR) data. We also compare the performance of several estimators (Lorey’s mean, the arithmetic mean and the 40th and 50th percentiles) used to estimate CBH at the plot level. The method we propose uses a moving voxel to estimate the height of the gaps (in the LiDAR point cloud) below tree crowns and uses this information for modeling CBH. The advantage of this approach is that it is more tolerant to variations in LiDAR data (e.g., due to season) and tree species, because it works directly with the height information in the data. Our approach gave better results when compared to standard percentile-based LiDAR metrics commonly used in modeling CBH. Using Lorey’s mean, the arithmetic mean and the 40th and 50th percentiles as CBH estimators at the plot level, the highest and lowest values for root mean square error (RMSE) and root mean square error for cross-validation (RMSEcv) and R2 for our method were 1.74/2.40, 2.69/3.90 and 0.46/0.71, respectively, while with traditional LiDAR-based metrics, the results were 1.92/2.48, 3.34/5.51 and 0.44/0.65. Moreover, the use of Lorey’s mean as a CBH estimator at the plot level resulted in models with better predictive value based on the leave-one-out cross-validation (LOOCV) results used to compute the RMSEcv values. View Full-Text
Keywords: canopy base height; CBH; forest fire; LiDAR; moving voxel canopy base height; CBH; forest fire; LiDAR; moving voxel
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

Maguya, A.S.; Tegel, K.; Junttila, V.; Kauranne, T.; Korhonen, M.; Burns, J.; Leppanen, V.; Sanz, B. Moving Voxel Method for Estimating Canopy Base Height from Airborne Laser Scanner Data. Remote Sens. 2015, 7, 8950-8972.

Show more citation formats Show less citations formats

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