Article

Regional mixed-effects height–diameter models for loblolly pine (Pinus taeda L.) plantations

European Journal of Forest Research (Impact Factor: 1.68). 01/2007; 126(2):253-262. DOI: 10.1007/s10342-006-0141-7

ABSTRACT A height–diameter mixed-effects model was developed for loblolly pine (Pinus taeda L.) plantations in the southeastern US. Data were obtained from a region-wide thinning study established by the Loblolly
Pine Growth and Yield Research Cooperative at Virginia Tech. The height–diameter model was based on an allometric function,
which was linearized to include both fixed- and random-effects parameters. A test of regional-specific fixed-effects parameters
indicated that separate equations were needed to estimate total tree heights in the Piedmont and Coastal Plain physiographic
regions. The effect of sample size on the ability to estimate random-effects parameters in a new plot was analyzed. For both
regions, an increase in the number of sample trees decreased the bias when the equation was applied to independent data. This
investigation showed that the use of a calibrated response using one sample tree per plot makes the inclusion of additional
predictor variables (e.g., stand density) unnecessary. A numerical example demonstrates the methodology used to predict random
effects parameters, and thus, to estimate plot specific height–diameter relationships.

3 Followers
 · 
163 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Artificial neural network models offer a number of advantages including the ability to implicitly detect complex nonlinear relationships between input and output variables, which are very helpful in tree height modeling. Back-propagation artificial neural network models were produced for individual-tree height estimation and the results were compared with the most used tree height estimation methods. Height diameter (h-d) measurements of 1163 Crimean juniper trees in 63 sample plots from southwestern region of Turkey were used. A calibrated basic h-d mixed model, a generalized h-d model and back-propagation artificial neural network h-d models were constructed and compared. When the variability of the h-d relationship fronl. ss stand can be incorporated into the model, then both mixed-effects nonlinear regression and back propagation neural network modeling approaches can produce accurate results, reducing the root mean squared error by more than 20% as compared to a basic nonlinear regression model. The use of a generalized h-d model also showed reliable results (reduction of 13% in root mean squared error as compared to a nonlinear regression model). The back-propagation artificial neural network model seems a reliable alternative to the other methods examined possessing the best generalization ability. Further, from a practical point of view it has the advantage that no height measurements are needed for its implementation. On the contrary prior information is required for the mixed-effects model calibration which is a limiting factor according to its use.
    Forest Ecology and Management 10/2013; 306:52-60. DOI:10.1016/j.foreco.2013.06.009 · 2.67 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Una importante superficie de plantaciones de Eucalyptus tereticornis se ha establecido en la costa atlántica colombiana. Decisiones de planificación y estimaciones de disponibilidad futura de madera requieren contar con sistemas de proyección de crecimiento. Uno de los componentes de este sistema son modelos capaces de predecir la altura total de los árboles bajo diversas condiciones de edad, sitio y manejo. Esta investigación tuvo como objetivo construir modelos regionales de altura-diámetro para implementar un modelo de simulación de crecimiento. Se evaluaron diez modelos que se diferenciaron en el número y tipo de variables predictoras. La información requerida fue obtenida de una red de 63 parcelas permanentes establecidas en plantaciones entre 2-8 años de edad y entre 388-1.640 árboles por hectárea distribuidas en la costa atlántica colombiana. Medidas de sesgo, precisión y error probable fueron utilizadas para realizar la evaluación de los modelos. Durante el proceso de evaluación no se detectaron diferencias importantes entre los modelos. Sin embargo, los que presentaron como variables predictoras a nivel de rodal la altura y diámetro medio de los árboles dominantes mostraron el menor sesgo y error. El modelo regional propuesto por Krumland y Wensel fue seleccionado como el mejor, porque presentó los mejores indicadores de ajuste y predicción.
    Bosque 12/2012; 34(2):233-241. DOI:10.4067/S0717-92002013000200012 · 0.40 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Artificial neural network methods appear to be a reliable alternative to traditional methods of tree height prediction in even-aged stands. However, this has not been demonstrated for uneven-aged forests. Two back-propagation artificial neural networks were constructed, and their performance in estimating the height of pure uneven-aged stands of common beech (Fagus sylvatica L.) in northwestern Spain was compared with that of the models most commonly used to estimate tree height (nonlinear calibrated local and generalized mixed-effects models and generalized fixed-effects models). All approaches produced accurate results, reducing the root mean squared error by more than 22% relative to basic nonlinear regression. Nonetheless, considering practical use of the models, the traditional approaches require measurement of several trees for calculation of stand-specific variables (generalized models) and for model calibration (mixed-effects models). Back-propagation artificial neural networks require less sampling effort because no height measurements are required for their implementation. However, this technique was not the best height predictor, because of the high degree of variability in site quality between stands. In this case, the local mixed-effects models yielded the best results and provided the best balance between the accuracy of the model and sampling effort.
    Forest Ecology and Management 11/2013; 307:63-73. DOI:10.1016/j.foreco.2013.07.014 · 2.67 Impact Factor

Full-text

Download
31 Downloads
Available from
May 17, 2014