PowersCoopsMorganEtAl2013

Référence

Powers, R.P., Coops, N.C., Morgan, J.L., Wulder, M.A., Nelson, T.A., Drever, C.R. and Cumming, S.G. (2013) A remote sensing approach to biodiversity assessment and regionalization of the Canadian boreal forest. Progress in Physical Geography, 37(1):36-62. (Scopus )

Résumé

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. © The Author(s) 2012.

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@ARTICLE { PowersCoopsMorganEtAl2013,
    AUTHOR = { Powers, R.P. and Coops, N.C. and Morgan, J.L. and Wulder, M.A. and Nelson, T.A. and Drever, C.R. and Cumming, S.G. },
    TITLE = { A remote sensing approach to biodiversity assessment and regionalization of the Canadian boreal forest },
    JOURNAL = { Progress in Physical Geography },
    YEAR = { 2013 },
    VOLUME = { 37 },
    PAGES = { 36-62 },
    NUMBER = { 1 },
    ABSTRACT = { 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. © The Author(s) 2012. },
    COMMENT = { Export Date: 20 February 2013 Source: Scopus doi: 10.1177/0309133312457405 },
    ISSN = { 03091333 (ISSN) },
    KEYWORDS = { biodiversity indicators, boreal, MODIS, productivity, topography },
    OWNER = { Luc },
    TIMESTAMP = { 2013.02.20 },
    URL = { http://www.scopus.com/inward/record.url?eid=2-s2.0-84873438364&partnerID=40&md5=95f796807932a9d5e1bed2ee7ad956d6 },
}

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