LandryFournierAhernEtAl1997

Référence

Landry, R., Fournier, R.A., Ahern, F.J. and Lang, R.H. (1997) Tree vectorization: A methodology to characterize fine tree architecture in support of remote sensing models. Canadian Journal of Remote Sensing, 23(2):91-107. (Scopus )

Résumé

Plant canopy structure is important for the interpretation of optical and microwave remote sensing images of forests. The use of models by the scientific community to improve the understanding of the interaction of the incoming radiation (signal) with forest components is increasing. To support these models, an improved method to characterize tree architecture has been developed. This method is designed to reconstruct a statistical three-dimensional image of a complete tree from judicious subsampling of its components. Therefore, a sampling strategy is proposed to gather information (descriptive and spatial) on branches and their segments. A complete vectorization data set involves information on general aspects of the tree, the topology, a set of attributes and details on the foliage. A three-dimensional tree reconstruction procedure integrates the parameters of the vectorization data set for a final branch and tree representation. Spatial variation of foliage throughout the crown is assured by data collection at the segment level. This approach requires no a priori knowledge of a classification scheme for branches and provides local estimates of wood and foliage biomass. Information gathering on branch classes can be decided in the post-processing phase. Results for jack pine data sets are presented.

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@ARTICLE { LandryFournierAhernEtAl1997,
    AUTHOR = { Landry, R. and Fournier, R.A. and Ahern, F.J. and Lang, R.H. },
    TITLE = { Tree vectorization: A methodology to characterize fine tree architecture in support of remote sensing models },
    JOURNAL = { Canadian Journal of Remote Sensing },
    YEAR = { 1997 },
    VOLUME = { 23 },
    PAGES = { 91-107 },
    NUMBER = { 2 },
    ABSTRACT = { Plant canopy structure is important for the interpretation of optical and microwave remote sensing images of forests. The use of models by the scientific community to improve the understanding of the interaction of the incoming radiation (signal) with forest components is increasing. To support these models, an improved method to characterize tree architecture has been developed. This method is designed to reconstruct a statistical three-dimensional image of a complete tree from judicious subsampling of its components. Therefore, a sampling strategy is proposed to gather information (descriptive and spatial) on branches and their segments. A complete vectorization data set involves information on general aspects of the tree, the topology, a set of attributes and details on the foliage. A three-dimensional tree reconstruction procedure integrates the parameters of the vectorization data set for a final branch and tree representation. Spatial variation of foliage throughout the crown is assured by data collection at the segment level. This approach requires no a priori knowledge of a classification scheme for branches and provides local estimates of wood and foliage biomass. Information gathering on branch classes can be decided in the post-processing phase. Results for jack pine data sets are presented. },
    COMMENT = { Cited By (since 1996): 3 Export Date: 10 February 2010 Source: Scopus CODEN: CJRSD },
    ISSN = { 07038992 (ISSN) },
    KEYWORDS = { Mathematical models, Plants (botany), Statistical methods, Topology, Vectors, Remote sensing models, Tree vectorization, Remote sensing },
    OWNER = { Luc },
    TIMESTAMP = { 2010.02.10 },
    URL = { http://www.scopus.com/inward/record.url?eid=2-s2.0-0031167009&partnerID=40&md5=5566b5ab5a2a1204c94dd1d5252c40cd },
}

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