A1 Journal article (refereed), original research

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

Open Access publication

LUT Authors / Editors

Publication Details
Authors: Maguya Almasi S., Tegel Katri, Junttila Virpi, Kauranne Tuomo, Korhonen Markus, Burns Janice, Leppänen Vesa, Sanz Blanca

Publication year: 2015
Language: English
Related Journal or Series Information: Remote Sensing
Volume number: 7
Start page: 8950
End page: 8972
JUFO-Level of this publication: 1
Open Access: Open Access publication

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 amethod for estimating CBH from airborne light detection and ranging (LiDAR) data. Wealso compare the performance of several estimators (Lorey’s mean, the arithmetic meanand the 40th and 50th percentiles) used to estimate CBH at the plot level. The methodwe propose uses a moving voxel to estimate the height of the gaps (in the LiDAR pointcloud) below tree crowns and uses this information for modeling CBH. The advantage ofthis 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. Ourapproach gave better results when compared to standard percentile-based LiDAR metricscommonly used in modeling CBH. Using Lorey’s mean, the arithmetic mean and the 40thand 50th percentiles as CBH estimators at the plot level, the highest and lowest values for rootmean 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 traditionalLiDAR-based metrics, the results were 1.92/2.48, 3.34/5.51 and 0.44/0.65. Moreover, the useof Lorey’s mean as a CBH estimator at the plot level resulted in models with better predictivevalue based on the leave-one-out cross-validation (LOOCV) results used to compute theRMSEcv values.

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Last updated on 2017-22-03 at 14:04