Canadian Forest Service Publications
A remote sensing approach to biodiversity assessment and regionalization of the Canadian boreal forest. 2013. Powers, R.P.; Coops, N.C.; Morgan, J.L.; Wulder, M.A.; Nelson, T.A.; Drever, C.R.; Cumming, S.G. Progress in Physical Geography 37(1): 36-62.
Issued by: Pacific Forestry Centre
Catalog ID: 34573
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Successful conservation planning for the Canadian boreal forest requires biodiversity data that are both accessible and reliable. Spatially exhaustive data is required to inform on conditions, trends, and context, with context enabling consideration of conservation opportunities and related trade-offs. However, conventional methods for measuring biodiversity, while useful, are spatially constrained, making it difficult to apply over wide geographic regions. Increasingly, remotely sensed imagery and methods are seen as a viable approach for acquiring explicit, repeatable and multi-scale biodiversity data over large areas. To identify relevant remotely derived environmental indicators specific to biodiversity within the Canadian boreal forest, we assessed indicators of the physical environment such as seasonal snow cover, topography, and vegetation production. Specifically, we determined if the indicators provided distinct information and whether they were useful predictors of species richness (tree, mammal, bird and butterfly species). Using cluster analysis, we also assessed the applicability of these indicators for broad ecosystem classification of the Canadian boreal forest and the subsequent attribution of these stratified regions (i.e., clusters). Our results reveal that the indicators used in the cluster creation provided unique information and explained much of the variance in tree (92.6 %), bird (84.6 %), butterfly (61.4 %) and mammal (22.6 %) species richness. Spring snow cover explained the most variance in species richness. Results further show that the fifteen clusters produced using cluster analysis were principally stratified along a latitudinal gradient and, while varied in size, captured a range of different environmental conditions across the Canadian boreal forest. The most important indicators for discriminating between the different cluster groups were seasonal greenness, a multipart measure of climate, topography and land use, and wetland cover, a measure of the percentage of wetland within a 1 km2 cell.
Plain Language Summary
This study uses remote sensing data to predict the species richness of birds, trees, mammals and butterflies across the Canadian boreal forest. Data are also used to classify the forest into 15 forest types. Since field evaluations of biodiversity are impractical for large isolated areas like Canada’s boreal region, the methods used in this study could be a practical alternative. Studies such as this could provide important information to forest managers and stakeholders who need to consider biodiversity when making conservation planning decisions.