Extending Estimates of Tree and Tree Species Presence-Absence through Space and Time Using Landsat Composites

We developed a methodology for extending estimates of the presence-absence of trees and several tree species contained in the Canadian National Forest Inventory using nationally consistent Landsat data products. For a prototype boreal forest region of Newfoundland and Labrador, Canada, we modeled and assessed changes in the presence-absence of trees and tree species distributions over a 25-year period. Random Forest models of presence-absence of trees had an overall classification accuracy of 0.87 ± 0.019. For five tree species, overall classification accuracies were: 0.74 ± 0.017 for balsam fir; 0.75 ± 0.028 for black spruce; 0.64 ± 0.085 for trembling aspen; 0.64 ± 0.035 for tamarack; and 0.77 ± 0.041 for white birch. While the proportion of treed area increased by 8.5% over the 25-year period, the area occupied by black spruce declined by 13.5%. The area of balsam fir and white birch increased by 9.9% and 28.2%, respectively, while trembling aspen and tamarack changed by less than 5%. The map products developed and trends observed offer baseline information in support of long-term monitoring of treed area and tree species distributions. The demonstrated methods encourage development of spatially-explicit map products to complement spatially or temporally limited forest inventory datasets.