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Forests 2018, 9(2), 95; https://doi.org/10.3390/f9020095

Enhancing the Estimation of Stem-Size Distributions for Unimodal and Bimodal Stands in a Boreal Mixedwood Forest with Airborne Laser Scanning Data

1
Faculty of Forestry, University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada
2
Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Road, Victoria, BC V8Z 1M5, Canada
3
West Fraser—Slave Lake Pulp, P.O. Box 1790, Slave Lake, AB T0G 2A0, Canada
*
Author to whom correspondence should be addressed.
Received: 31 January 2018 / Revised: 16 February 2018 / Accepted: 17 February 2018 / Published: 18 February 2018
(This article belongs to the Section Forest Inventory, Quantitative Methods and Remote Sensing)
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Abstract

Stem size distribution (SSD), which describes tree frequencies in diameter classes within an area, has a variety of direct and indirect applications that are critical for forest management. In this study, we evaluated which structural characteristics derived from Airborne Laser Scanning (ALS) data were best able to differentiate between unimodal and bimodal stands in a managed boreal mixedwood forest in Alberta, Canada. We then used wall-to-wall ALS data to predict (for 20 m-by-20 m grid cells) the parameters of a Weibull SSD in unimodal cells, and a Finite Mixture Model (FMM) in bimodal cells. The resulting SSDs were evaluated for their fit to ground plot-measured SSDs using an Error Index (EI). We found that the variance of ALS return heights was the best metric for differentiating between unimodal and bimodal stands, with a classification accuracy of 77%. Parameters of both the Weibull and FMM distributions were accurately predicted (r2 ~ 0.5, Root Mean Square Error (RMSE) ~ 30%), and that differentiating for modality prior to estimating SSD improved the accuracy of estimates (EI of 49.13 with differentiation versus 51.31 without differentiation). Unique to our presented approach is the stratification by SSD modality prior to the modelling of distributions. To achieve this, we apply a threshold to an ALS metric that allows SSD modality to be distinguished for each cell at the landscape level, and this a priori information is then used to ensure that the appropriate distribution is modelled. Our approach is parsimonious and efficient, enabling improved accuracy in SSD estimation across diverse landscapes when ALS data is the sole data source. View Full-Text
Keywords: airborne laser scanning; diameter distributions; forest structure; mixture models; forest inventory; boreal airborne laser scanning; diameter distributions; forest structure; mixture models; forest inventory; boreal
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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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Mulverhill, C.; Coops, N.C.; White, J.C.; Tompalski, P.; Marshall, P.L.; Bailey, T. Enhancing the Estimation of Stem-Size Distributions for Unimodal and Bimodal Stands in a Boreal Mixedwood Forest with Airborne Laser Scanning Data. Forests 2018, 9, 95.

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