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

Rebecca R.M Wylie,Murray E Woods,Jeffery P Dech

doi:10.3390/rs11172022

Rebecca R.M Wylie, Murray E Woods + Show 1 more

Open Access

https://doi.org/10.3390/rs11172022

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Abstract

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.

Highlights

Airborne Laser Scanning (ALS) technology has become widely accepted as an important tool for enhancing forest resource inventory (FRI) systems by increasing the accuracy of vertical structural measurements [1]
We developed a regression tree of wood density from the list of predictors derived from the multiple linear regression (MLR), using the “rPart” Package [39] in R
The non-parametric rf-k nearest neighbor (kNN) model of forest stand age developed for black spruce stands in the boreal forest of Ontario from ALS structural information was useful for imputing accurate age estimates, which could be subsequently included as a derived variable in a given FRI system

Summary

Introduction

Airborne Laser Scanning (ALS) technology has become widely accepted as an important tool for enhancing forest resource inventory (FRI) systems by increasing the accuracy of vertical structural measurements [1]. Point clouds derived from ALS data have been shown to support a variety of ecological modeling initiatives that use implicit relationships to forest structures to make predictions of variables that are traditionally measured or estimated in the FRI such as stand age [2], as well as novel variables that could be used to optimize the value chain such as tree-level estimates of wood quality attributes [3,4]. Wood density is an important general indicator of wood quality, related to marketable values such as stiffness, strength and hardness, which are important characteristics of products that are produced from wood [8] Considering these possibilities has led to the development of models using site and ALS data to predict mean stand level wood density. In the boreal forest of Newfoundland, wood quality attributes were modeled using ALS structural, environmental and climate variables to predict wood density of black spruce with an RMSE of 20.9 kg·m−3 [3]

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