Canadian Forest Service Publications
Extending Estimates of Tree and Tree Species Presence-Absence through Space and Time Using Landsat Composites. 2020. Strickland, G.E.I.; Luther, J.E.; White, J.C.; Wulder, M.A. Canadian Journal of Remote Sensing 46 (5): 567-584.
Year: 2020
Issued by: Atlantic Forestry Centre
Catalog ID: 40539
Language: English
Availability: PDF (request by e-mail)
Available from the Journal's Web site. †
DOI: https://doi.org/10.1080/07038992.2020.1811083
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Abstract
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.
Plain Language Summary
Remotely sensed data and methods are increasingly used to support inventory of forest resources over large regions. A variety of approaches have been used to link multiple attributes measured in forest inventories to satellite data, thus enhancing the efficiency and cost-effectiveness in mapping large forest landscapes. 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. Models of tree presence-absence developed with multi-year National Forest Inventory (NFI) photo plot data predicted the presence of trees with high accuracy based on an assessment of withheld NFI sample plots. The individual tree species models had variable accuracies with the most commonly occurring species generally having higher overall accuracies. 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. Since the methodology was developed with nationally consistent data sets, it has the potential to be evaluated and applied at a national scale across Canada.