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Comparison of forestry-based remote sensing methodologies to evaluate woodland caribou habitat in non-forested areas of Newfoundland
Author(s) -
Brian McLaren,
Shane P. Mahoney
Publication year - 2001
Publication title -
the forestry chronicle
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.335
H-Index - 49
eISSN - 1499-9315
pISSN - 0015-7546
DOI - 10.5558/tfc77866-5
Subject(s) - woodland caribou , habitat , vegetation (pathology) , forest inventory , woodland , geography , wildlife , vegetation classification , bog , forestry , remote sensing , wetland , ecology , environmental science , forest management , peat , archaeology , medicine , pathology , biology
Forest inventory maps and a manual interpretation of forestry-enhanced Landsat imagery are compared to the results of a detailed aerial photograph interpretation used to map habitat for caribou (Rangifer tarandus terra novae) in a relatively unforested region of Newfoundland. This comparison serves as an illustration of the pitfalls inherent in using readily available remote sensing technologies in applications for which they were not intended. The non-forest classes in the Newfoundland Forest Inventory are too broad to describe single vegetation communities, and only rarely are vegetation communities found entirely within a single forest inventory class. For example, "bog" is relatively well associated with wetland vegetation classes and "barren" with upland classes, but "scrub" is a misleading term used to describe both forest and non-forest communities. An earlier (global) forest classification for Newfoundland has a more reliable association of scrub with forest, but a less reliable identification of bog than later updates to the forest inventory in the study area. Landsat imagery applications for forest inventory updates do not appear useful in identifying non-forest vegetation communities. Caution should be taken in using forest inventory maps in wildlife habitat applications when the habitat includes important non-forest components. Key words: forest inventory, habitat classification, Landsat imagery, mapping, remote sensing

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