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Quantifying the precision of forest stand height and canopy cover estimates from air photo interpretation

by Marie-Claude Boileau | 14 November 2022

Published in Forestry: An International Journal of Forest Research, vol. 94(5): 611–629. Manual aerial photo interpretation is used as a cost-effective source of data for forest inventories; however, the process of photo interpretation is inherently subjective and is...

Application of shadow fraction models for estimating attributes of northern boreal forests

by Marie-Claude Boileau | 14 November 2022

Paru dans Canadian Journal of Forest Research, vol. 4, p. 1750-1757. A shadow fraction method was previously developed for mapping forest attributes of northern black spruce forests. This paper evaluates application of the method to (i) balsam fir stands, (ii) stands...

A new approach for mapping forest management areas in Canada

by Marie-Claude Boileau | 14 November 2022

Published in Forestry Chronicle, vol. 95(2), 101-112. Canada’s forests have frequently been characterized using binary classifications such as intact/non-intact or managed/unmanaged. A more nuanced classification approach is needed to better understand the geography...

Estimating stand attributes of boreal forest using digital aerial photography and a shadow fraction method

by Marie-Claude Boileau | 14 November 2022

Published in Canadian Journal of Remote Sensing., vol 39. p. 217-231. https://doi.org/10.5589/m13-030 The aim of this study was to develop an imputing method to map forest stand attributes (height and volume) using panchromatic aerial photographs with a spatial...

Forest attribute estimation of northeastern Canadian forests using Quickbird imagery and a shadow fraction method

by Marie-Claude Boileau | 14 November 2022

Published in Forest Ecology and Management, vol. 266, p. 66-74. This study demonstrates a method for mapping forest stand polygons based on four forest attributes (volume, basal area, height, and crown closure) using shadow fraction values that were estimated from...
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