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Forests 2017, 8(7), 253; doi:10.3390/f8070253

Lidar and Multispectral Imagery Classifications of Balsam Fir Tree Status for Accurate Predictions of Merchantable Volume

1
Department of Wood and Forest Sciences, Université Laval, Quebec City, QC G1V 0A6, Canada
2
Department of Geography, Université du Québec à Montréal, Montréal, QC H3C 3P8, Canada
3
USDA Forest Service, PNW Research Station, Portland, OR 97205, USA
*
Author to whom correspondence should be addressed.
Received: 31 May 2017 / Revised: 9 July 2017 / Accepted: 11 July 2017 / Published: 15 July 2017
(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)
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Abstract

Recent increases in forest diseases have produced significant mortality in boreal forests. These disturbances influence merchantable volume predictions as they affect the distribution of live and dead trees. In this study, we assessed the use of lidar, alone or combined with multispectral imagery, to classify trees and predict the merchantable volumes of 61 balsam fir plots in a boreal forest in eastern Canada. We delineated single trees on a canopy height model. The number of detected trees represented 92% of field trees. Using lidar intensity and image pixel metrics, trees were classified as live or dead with an overall accuracy of 89% and a kappa coefficient of 0.78. Plots were classified according to their class of mortality (low/high) using a 10.5% threshold. Lidar returns associated with dead trees were clipped. Before clipping, the root mean square errors were of 22.7 m3 ha−1 in the low mortality plots and of 39 m3 ha−1 in the high mortality plots. After clipping, they decreased to 20.9 m3 ha−1 and 32.3 m3 ha−1 respectively. Our study suggests that lidar and multispectral imagery can be used to accurately filter dead balsam fir trees and decrease the merchantable volume prediction error by 17.2% in high mortality plots and by 7.9% in low mortality plots. View Full-Text
Keywords: forest disturbance; tree mortality; lidar intensity; multispectral imagery; classification; point cloud filtering; merchantable volume forest disturbance; tree mortality; lidar intensity; multispectral imagery; classification; point cloud filtering; merchantable volume
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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).

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MDPI and ACS Style

Yoga, S.; Bégin, J.; St-Onge, B.; Gatziolis, D. Lidar and Multispectral Imagery Classifications of Balsam Fir Tree Status for Accurate Predictions of Merchantable Volume. Forests 2017, 8, 253.

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