Contribution of topographic features and categorization uncertainty for a tree species classification in the boreal biome of Northern Ontario

ABSTRACT

Variations within local topography can effectively impact the location of tree species within naturally forested areas. Furthermore, the uncertainty of prediction for classification can vastly differ amongst topography and the overlying tree species groupings. This study investigated the supplementation of a suite of topographic features corresponding to morphometry and hydrological considerations, in addition to multispectral imagery and other LiDAR-derived features, at fine (2 m) spatial resolution for a pixel-based tree species classification of a forested region of the boreal biome in northern Ontario, Canada. The study area conforms to the Abitibi River Forest (ARF) and consists of the tree species of black spruce (Picea mariana), balsam fir (Abies balsamea), trembling aspen (Populus tremuloides), balsam poplar (Populus balsamifera), tamarack (Larix laricina), white spruce (Picea glauca), and eastern white cedar (Thuja occidentalis). Random forest (RF) and support vector machines (SVMs) were implemented for the classification. Topographic features, specifically those conforming to channel base level, valley depth, and multi-resolution valley bottom flatness (MRVBF), were among the most important features for species predictors. The RF and SVM methods were trained on pixels of pure stands (composed of 70%+ of same tree species) for the tree species groupings, which were split by site level. Modelling accuracies for both the pixel and site level were reported, with the best model attaining an overall site level accuracy and corresponding Cohen’s kappa score of 0.79 and 0.69 for classification, respectively. Entropy maps were generated to characterize the uncertainty of prediction, and substantiate that the regions of lowest uncertainty correspond to wetlands, which are dominated by black spruce (Picea mariana). A modified entropy map was calculated from the normalized top two probabilities of tree species groupings predicted per pixel, so as to better highlight regions of prediction uncertainty. A prediction map for the second most-likely tree species groupings was also computed, which supports the presence of balsam fir (Abies balsamea) as a secondary tree species throughout the ARF region.

KEYWORDS:

• Tree species classification
• boreal forest
• topographic features
• entropy maps
• WorldView-2

Acknowledgments

LiDAR data were obtained from the Ontario Ministry of Natural Resources and Forestry (MNRF) via Land Information Ontario, and contain information licensed under the Open Government license – Ontario. Site data for tree species concentrations were obtained from the Ontario Forest Resources Inventory (FRI). WorldView-2 imagery was purchased from Maxar Technologies.

Disclosure statement

Both authors declare that they have no known competing financial or non-financial interests to report that could have appeared to influence the research presented in this paper.

Data availability statement

The LiDAR-derived feature data that support the findings of this study is openly available in figshare at https://dx.doi.org/10.6084/m9.figshare.21282675. The satellite imagery data are not publicly available.

Additional information

Funding

This research was funded by the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA) [Funding Number ND2017-3179] and the Natural Sciences and Engineering Research Council of Canada (NSERC) [Discovery Grant].