YogaBeginDaigleEtAl2018

Référence

Yoga, S., Bégin, J., Daigle, G., Riopel, M. and St-Onge, B. (2018) A generalized lidar-based model for predicting the merchantable volume of balsam fir of sites located along a bioclimatic gradient in Quebec, Canada. Forests, 9(4). (Scopus )

Résumé

Lidar-based models rely on an optimal relationship between the field and the lidar data for accurate predictions of forest attributes. This relationship may be altered by the variability in the stand growth conditions or by the temporal discrepancy between the field inventory and the lidar survey. In this study, we used lidar data to predict the timber merchantable volume (MV) of five sites located along a bioclimatic gradient of temperature and elevation. The temporal discrepancies were up to three years. We adjusted a random canopy height coefficient (accounting for the variability amongst sites), and a growth function (accounting for the growth during the temporal discrepancy), to the predictive model. The MV could be predicted with a pseudo-R2 of 0.86 and a residual standard deviation of 24.3 m3 ha-1. The average biases between the field-measured and the predicted MVs were small. The variability of MV predictions was related to the bioclimatic gradient. Fixed-effect models that included a bioclimatic variable provided similar prediction accuracies. This study suggests that the variability amongst sites, the occurrence of a bioclimatic gradient and temporal discrepancies are essential in building a generalized lidar-based model for timber volume. © 2018 by the authors.

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@ARTICLE { YogaBeginDaigleEtAl2018,
    AUTHOR = { Yoga, S. and Bégin, J. and Daigle, G. and Riopel, M. and St-Onge, B. },
    TITLE = { A generalized lidar-based model for predicting the merchantable volume of balsam fir of sites located along a bioclimatic gradient in Quebec, Canada },
    JOURNAL = { Forests },
    YEAR = { 2018 },
    VOLUME = { 9 },
    NUMBER = { 4 },
    NOTE = { cited By 0 },
    ABSTRACT = { Lidar-based models rely on an optimal relationship between the field and the lidar data for accurate predictions of forest attributes. This relationship may be altered by the variability in the stand growth conditions or by the temporal discrepancy between the field inventory and the lidar survey. In this study, we used lidar data to predict the timber merchantable volume (MV) of five sites located along a bioclimatic gradient of temperature and elevation. The temporal discrepancies were up to three years. We adjusted a random canopy height coefficient (accounting for the variability amongst sites), and a growth function (accounting for the growth during the temporal discrepancy), to the predictive model. The MV could be predicted with a pseudo-R2 of 0.86 and a residual standard deviation of 24.3 m3 ha-1. The average biases between the field-measured and the predicted MVs were small. The variability of MV predictions was related to the bioclimatic gradient. Fixed-effect models that included a bioclimatic variable provided similar prediction accuracies. This study suggests that the variability amongst sites, the occurrence of a bioclimatic gradient and temporal discrepancies are essential in building a generalized lidar-based model for timber volume. © 2018 by the authors. },
    AFFILIATION = { Department ofWood and Forest Sciences, Université Laval, Quebec City, QC, Canada; Department of Mathematics and Statistics, Université Laval, Quebec City, QC, Canada; Direction des Inventaires Forestiers, Ministère des Forêts, Faune et des Parcs du Québec, Quebec City, QC, Canada; Department of Geography, Université du Québec à Montréal, Montréal, QC, Canada },
    ART_NUMBER = { 166 },
    AUTHOR_KEYWORDS = { Balsam fir; Bioclimatic gradient; Canopy height adjustment; Generalized model; Growth adjustment function; Lidar; Temporal discrepancy; Timber merchantable volume },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.3390/f9040166 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044426841&doi=10.3390%2ff9040166&partnerID=40&md5=6b7998d8b55ec7a5fa66af84e6149bc3 },
}

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