Mapping the presence and distribution of tree species in Canada's forested ecosystems

Knowledge of tree species is required to inform management, planning, and monitoring of forests as well as to characterize habitat and ecosystem function. Remotely sensed data and spatial modeling enable mapping of tree species presence and distribution. Following an assessment of tree species identified in the sample-based National Forest Inventory (NFI), we mapped 37 tree species over the 650-Mha, forest-dominated ecosystems of Canada representing 2019 conditions. Landsat imagery and related spectral indices, geographic and climate data, elevation derivatives, and remote sensing-derived phenology are used as predictor variables trained with calibration samples from Canada's NFI using the Random Forests machine learning algorithm. Based upon prior knowledge of tree species distributions, classification models were implemented on a regional basis, meaning only the tree species that are expected in a given mapping region were modeled using local calibration samples. Modeling resulted in class membership probabilities values for each regionally eligible tree species for all treed pixels as well as an indicator of attribution confidence derived from the distance in feature space between the two leading classes. Accuracy assessment was conducted using independent validation data also drawn from the NFI following the same selection rules and indicated an overall accuracy of 93.1% ± 0.1% (95%-confidence interval). Predictor variables informing on geographic, climatic and topographic conditions had the largest importance on the classification models. Nationally, the most common leading tree species were black spruce (Picea mariana; 203 Mha or 57.3% of the treed area), trembling aspen (Populus tremuloides; 34.7 Mha, 9.8%), and lodgepole pine (Pinus contorta; 21.1 Mha, 5.9%). Regionally, there was ecozone-level dominance of other tree species, including subalpine fir (Abies lasiocarpa; Montane Cordillera), western hemlock (Tsuga heterophylla; Pacific Maritime), and balsam fir (Abies balsamea; Atlantic Maritime). Based upon the per-pixel class membership probabilities, species assemblages akin to those in forest inventories can also be produced. Further, given the calibrated reflectance of Landsat imagery, the methods presented herein can be implemented over a time series of images. The approach uses open data as predictor variables, making the method portable to other areas given availability of tree species training data.