Q4 – K. van Ewijk

What new LiDAR stand structure metrics can be developed and linked to forest inventory attributes such as lumber grade and log size class?

Karin van Ewijk

A literature review of LiDAR and optical remote sensing derived metrics used in Boreal forest studies worldwide for the prediction of forest stand stocking/growth and forest stand timber attributes will form the basis to answer this research question. Subsequently, we will analyze relationships between forest stand characteristics, such as vertical complexity and canopy layering resulting from different species composition, development stages, etc., as captured by LiDAR and optical remote sensing metrics, and identify subsets of important LiDAR and optical remote sensing metrics for the prediction of forest stand stocking/growth and timber attributes (see Fig. 1). Based on the literature review we will also identify “gaps” in existing LiDAR and optical remote sensing metrics to guide the development of new metrics.

Figure 1: Key forest stand and timber attributes to be estimated using remotely sensed metrics. Attributes of interest are in green and orange boxes. Attributes in light green boxes maybe required for stratification.

These literature review findings will be tested on Hearst Forest, located in northeastern Ontario (Fig. 2). Black spruce (Picea mariana Mill. B.S.P) is the major conifer species in this forest, dominating approximately two-thirds of the area. Other conifer species include white spruce (Picea glauca Moench Voss), balsam fir (Abies balsamea L. Mill.), tamarack (Larix laricana Du Roi K. Koch), cedar (Thuja occidentalis L.), and jack pine (Pinus banksiana Lamb.). Deciduous species include white birch (Betula papyrifera Marsh.), trembling aspen (Populus tremuloides Michx.) and balsam poplar (Populus balsamifera L.).

Figure 2: Location of Hearst forest in Ontario