Analysis and comparison of nonlinear tree height prediction strategies for Douglas-fir forests Public Deposited

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  • A` l’aide d’une banque de donne´es exhaustive sur le sapin Douglas du sud-ouest de l’Oregon, nous avons examine ´ (1) la performance et la pertinence des strate´gies de pre´diction se´lectionne´es, (2) la contribution de la position relative de l’arbre et de la densite´ du peuplement pour ame´liorer la pre´diction de la hauteur des arbres et (3) l’effet de diffe´rents dispositifs d’e´chantillonnage pour imputer la hauteur manquante dans un nouveau peuplement a` l’aide d’un mode`le non line´aire re´gional. Les mode`les non line´aires a` effets mixtes (MNLEM) ame´liorent substantiellement l’exactitude et la pre´cision des pre´dictions de la hauteur comparativement au mode`le non line´aire a` effets fixes conventionnel (MNLEF). Ce dernier suppose que les observations sont inde´pendantes, particulie`rement lorsque peu d’arbres sont e´chantillonne´s pour e´valuer la hauteur. La performance pre´dictive d’un facteur de correction pour le MNLEF base´ sur la mesure de la position relative de l’arbre et de la densite´ du peuplement est comparable a` celle du MNLEM lorsque quatre arbres ou plus sont e´chantillonne´s pour e´valuer la hauteur. Lorsque deux hauteurs ou plus sont e´chantillonne´es ale´atoirement, le MNLEM explique efficacement les diffe´rences dans la relation hauteur-diame`tre dues aux variations de la position relative des arbres et de la densite´ sans avoir a` les incorporer formellement dans le mode`le. Lorsqu’une seule hauteur est e´chantillonne´e, le choix du plus gros arbre dans le peuplement pourrait entraıˆner une erreur de pre´diction plus faible que lorsque la hauteur est se´lectionne´e au hasard, peu importe la forme du mode`le ou la strate´gie d’ajustement utilise´e.
  • Using an extensive Douglas-fir data set from southwest Oregon, we examined the (1) performance and suitability of selected prediction strategies, (2) contribution of relative position and stand-density measures in improving tree height (h) prediction values, and (3) effect of different subsampling designs to fill in missing h values in a new stand using a regional nonlinear model. Nonlinear mixed-effects models (NMEM) substantially improved the accuracy and precision of height prediction over the conventional nonlinear fixed-effects model (NFEM) that assumes the observations are independent, particularly when a few trees are subsampled for height. The predictive performance of a correction factor on a NFEM with relative position and stand-density measures was comparable to that of a NMEM when four or more trees were subsampled for height. When two or more heights were randomly subsampled, the NMEM efficiently explained the differences in the height–diameter relationship because of the variations in relative position of trees and stand density without having to incorporate them into the model. When only one height was subsampled, selecting the largest diameter tree in the stand would result in a lower predicted root mean square error (RMSE) than randomly selecting the height, regardless of the model form or fitting strategy used.
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  • Temesgen, H., V.J. Monleon, and D.W. Hann. 2008. Analysis and comparison of nonlinear tree height prediction strategies for Douglas-fir forests. Canadian Journal of Forest Research 38: 553-565, doi:10.1139/X07-104.
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  • description.provenance : Approved for entry into archive by Deborah Campbell(deborah.campbell@oregonstate.edu) on 2012-04-02T21:14:52Z (GMT) No. of bitstreams: 1 Analysis and comparison of nonlinear tree height prediction strategies for Douglas-fir forests.pdf: 450279 bytes, checksum: a758c2abcb693962cd1c7c5d7f92712d (MD5)
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