Predictive Modeling of Black Spruce (Picea mariana (Mill.) B.S.P.) Wood Density Using Stand Structure Variables Derived from Airborne LiDAR Data in Boreal Forests of Ontario

Abstract

Abstract:Our objective was to model the average wood density in black spruce trees in representative stands across a boreal forest landscape based on relationships with predictor variables extracted from airborne light detection and ranging (LiDAR) point cloud data. Increment core samples were collected from dominant or co-dominant black spruce trees in a network of 400 m 2 plots distributed among forest stands representing the full range of species composition and stand development across a 1,231,707 ha forest management unit in northeastern Ontario, Canada. Wood quality data were generated from optical microscopy, image analysis, X-ray densitometry and diffractometry as employed in SilviScan™. Each increment core was associated with a set of field measurements at the plot level as well as a suite of LiDAR-derived variables calculated on a 20 × 20 m raster from a wall-to-wall coverage at a resolution of~1 point m −2 . We used a multiple linear regression approach to identify important predictor variables and describe relationships between stand structure and wood density for average black spruce trees in the stands we observed. A hierarchical classification model was then fitted using random forests to make spatial predictions of mean wood density for average trees in black spruce stands. The model explained 39 percent of the variance in the response variable, with an estimated root mean square error of 38.8 (kg·m −3 ). Among the predictor variables, P20 (second decile LiDAR height in m) and quadratic mean diameter were most important. Other predictors describing canopy depth and cover were of secondary importance and differed according to the modeling approach. LiDAR-derived variables appear to capture differences in stand structure that reflect different constraints on growth rates, determining the proportion of thin-walled earlywood cells in black spruce stems, and ultimately influencing the pattern of variation in important wood quality attributes such as wood density. A spatial characterization of variation in a desirable wood quality attribute, such as density, enhances the possibility for value chain optimization, which could allow the forest industry to be more competitive through efficient planning for black spruce management by including an indication of suitability for specific products as a modeled variable derived from standard inventory data.

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

“…Large increment core samples (12 mm diameter) were collected from these plots over two field campaigns, conducted in 2011 and 2014. The approach taken with the wood density model was to predict an average tree level trait expected to occur within a given spatial area (in this case a 20 × 20 m raster pixel), which would allow users to identify portions of the landscape expected to contain trees with specific wood traits [4,9,34]. To facilitate this approach, it was important that the fitting dataset was composed of trees that were representative of the stands within which they occurred.…”

“…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 also 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]. Adding general indicators of wood quality to FRI polygons would supply basic information about where certain attributes are concentrated on the landscape.…”

“…From a modeling perspective, it is therefore important to understand the processes and mechanisms that drive spatial variation in the characteristics of woody tissues, and ultimately identify conditions that produce specific wood qualities (e.g., high density). There are tree and stand-level variables known to be useful indicators of wood quality, such as crown/canopy shape, stocking density, age, and site conditions, which can be derived from ALS point cloud data [2,4,7]. 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].…”

“…Pokharel et al [9] were able to use ecological land classification and mensuration variables described in a FRI to explain 44.1% of the variation in black spruce wood density in the first 50 years of growth, producing a model with a root mean square error (RMSE) of 40.4 kg•m −3 . Pokharel et al [4] predicted wood density using ALS structural metrics to explain 39% of the variation with a RMSE of 38.8 kg•m −3 . 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].…”

Modeling black spruce wood fiber attributes with terrestrial laser scanning

“…Airborne and terrestrial laser scanning (ALS and TLS) can provide a wide range of forest metrics for modeling purposes. The proof-of-concept was recently demonstrated for balsam fir and black spruce with the prediction of plot-level wood fiber attributes using only ALS data as covariates (Luther et al 2014;Pokharel et al 2016). Wood fiber attribute models were also successfully developed for balsam fir and black spruce in Newfoundland using a suite of local structural metrics derived from TLS data (Blanchette et al 2015).…”