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Open AccessArticle

Estimating Stand Age from Airborne Laser Scanning Data to Improve Models of Black Spruce Wood Density in the Boreal Forest of Ontario

1
Department of Biology and Chemistry, Nipissing University, North Bay, ON P1B 8L7, Canada
2
Ontario Ministry of Natural Resources and Forestry, Forest Resource Inventory Unit, North Bay, ON P1A 4L7, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(17), 2022; https://doi.org/10.3390/rs11172022
Received: 29 May 2019 / Revised: 5 August 2019 / Accepted: 23 August 2019 / Published: 28 August 2019
(This article belongs to the Special Issue Remote Sensing of Boreal Forests)
Spatial models that provide estimates of wood quality enable value chain optimization approaches that consider the market potential of trees prior to harvest. Ecological land classification units (e.g., ecosite) and structural metrics derived from Airborne Laser Scanning (ALS) data have been shown to be useful predictors of wood quality attributes in black spruce stands of the boreal forest of Ontario, Canada. However, age drives much of the variation in wood quality among trees, and has not been included as a predictor in previous models because it is poorly represented in inventory systems. The objectives of this study were (i) to develop a predictive model of mean stem age of black spruce-dominated stands, and (ii) refine models of black spruce wood density by including age as a predictor variable. A non-parametric model of stand age that used a k nearest neighbor (kNN) classification based on a random forests (rf) distance metric performed well, producing a root mean square difference (RMSD) of 15 years and explaining 62% of the variance. The subsequent random forests model of black spruce wood density generated from age and ecosite predictors was useful, with a root mean square error (RMSE) of 59.1 kg·m−3. These models bring large-scale wood quality prediction closer to becoming operational by including age and site effects that can be derived from inventory data. View Full-Text
Keywords: black spruce; forest stand age; Airborne Laser Scanning (ALS); wood density modeling; boreal forest; predictive modeling; k-Nearest Neighbor; forest resource inventory; LiDAR black spruce; forest stand age; Airborne Laser Scanning (ALS); wood density modeling; boreal forest; predictive modeling; k-Nearest Neighbor; forest resource inventory; LiDAR
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MDPI and ACS Style

Wylie, R.R.M.; Woods, M.E; Dech, J.P. Estimating Stand Age from Airborne Laser Scanning Data to Improve Models of Black Spruce Wood Density in the Boreal Forest of Ontario. Remote Sens. 2019, 11, 2022.

AMA Style

Wylie RRM, Woods ME, Dech JP. Estimating Stand Age from Airborne Laser Scanning Data to Improve Models of Black Spruce Wood Density in the Boreal Forest of Ontario. Remote Sensing. 2019; 11(17):2022.

Chicago/Turabian Style

Wylie, Rebecca R.M.; Woods, Murray E; Dech, Jeffery P. 2019. "Estimating Stand Age from Airborne Laser Scanning Data to Improve Models of Black Spruce Wood Density in the Boreal Forest of Ontario" Remote Sens. 11, no. 17: 2022.

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