TY - JOUR
T1 - A remote sensing approach to biodiversity assessment and regionalization of the Canadian boreal forest
AU - Powers, Ryan P.
AU - Coops, Nicholas C.
AU - Morgan, Jessica L.
AU - Wulder, Michael A.
AU - Nelson, Trisalyn A.
AU - Drever, Charles R.
AU - Cumming, Steven G.
N1 - Funding Information:
This research was undertaken as part of the ‘BioSpace: Biodiversity monitoring with Earth Observation data’ project jointly funded by the Ivey Foundation, the Nature Conservancy of Canada, Canadian Space Agency (CSA) Government Related Initiatives Program (GRIP), Canadian Forest Service (CFS) Pacific Forestry Centre (PFC) and the University of British Columbia (UBC).
PY - 2013
Y1 - 2013
N2 - 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 (84.07%), butterfly (61.4%) and mammal (22.6%) species richness. Spring snow cover explained the most variance in species richness. Results further show that the 15 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.
AB - 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 (84.07%), butterfly (61.4%) and mammal (22.6%) species richness. Spring snow cover explained the most variance in species richness. Results further show that the 15 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.
KW - MODIS
KW - biodiversity indicators
KW - boreal
KW - productivity
KW - topography
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U2 - 10.1177/0309133312457405
DO - 10.1177/0309133312457405
M3 - Article
AN - SCOPUS:84873438364
SN - 0309-1333
VL - 37
SP - 36
EP - 62
JO - Progress in Physical Geography
JF - Progress in Physical Geography
IS - 1
ER -