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  • 1
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    Coniferous Stands Characterized with The Weibull Distribution (1974)
    Canadian Science Publishing
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    Publication Date: 1974-12-01
    Description: The Weibull distribution, [Formula: see text], summarized diameter, basal area, surface area, biomass, and crown profile distribution data well for several different ages of white and loblolly pine plantations. The data for diameter, basal area, surface area, and biomass were easily summarized by this one distribution in a theoretically consistent fashion. This is not possible with the normal and the gamma distributions, and the lognormal gives less satisfactory results. The distribution function should prove useful in modeling tree stands since only the parameter values need to be changed over time for the above variables. The change in these parameters may be a good way to characterize and interpret changes in stands over time.
    Print ISSN: 0045-5067
    Electronic ISSN: 1208-6037
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Canadian Science Publishing
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  • 2
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    Accuracy and efficiency of area classifications based on tree tally (2001)
    Canadian Science Publishing
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    Publication Date: 2001-03-01
    Description: Inventory data are often used to estimate the area of the land base that is classified as a specific condition class. Examples include areas classified as old-growth forest, private ownership, or suitable habitat for a given species. Many inventory programs rely on classification algorithms of varying complexity to determine condition class. These algorithms can be simple decision trees applied in the field or computer calculations applied on a field data recorder or after the data are collected. The advantages to using these algorithms are consistent classification of the condition class, reduced crew training, and the ability to define new condition classes after the data are collected, which will be referred to as postclassification. We discuss three types of the errors that can occur when these types of algorithms are employed and quantify the potential for error with examples. The examples are substantial oversimplifications of the true problem, but they show how difficult it is to determine anything but the most general condition classes using plot data alone. A discussion of how condition class is scale dependent and some general guidelines and recommendations are given.
    Print ISSN: 0045-5067
    Electronic ISSN: 1208-6037
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Canadian Science Publishing
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  • 3
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    The SBBB distribution: a potentially useful trivariate distribution (1982)
    Canadian Science Publishing
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    Publication Date: 1982-09-01
    Description: The proposed SBBB distribution allows for a more complete summarization of stand structure data than has been possible in forestry until now. For two data sets constructed out of a large loblolly pine data set to resemble possible plantations, the univariate SB and the trivariate SBBB distributions fit the data reasonably well as measured by χ2. The median regression of volume in terms of diameter and height gave results almost as good as the standard combined variable model. Computer programs needed to implement the system are available.
    Print ISSN: 0045-5067
    Electronic ISSN: 1208-6037
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Canadian Science Publishing
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  • 4
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    Design-based estimation of forest volume within a model-based sample selection framework (1995)
    Canadian Science Publishing
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    Publication Date: 1995-01-01
    Description: Equations for predicting tree volume are often developed using data collected either by a model-based method such as purposive sampling or by stratified random sampling so that an "adequate" number of trees from each diameter class are sampled across the range of classes expected in populations of interest. Such equations are then used together with a design-based (probabilistic) sample such as variable radius plot sampling from a specific population to generate estimates of total volume. The probabilities of selection of the sample trees used in developing the volume equation are ignored, may not be known, or may not be appropriate for populations to which the equation are applied. Less biased and more efficient estimates of the population volume can be generated by using known frequencies or estimated frequencies of the diameter classes in the population from the probabilistic sample used for estimating total volume in the population. These frequencies are used as weighting factors in the construction of population-specific volume equations. We show a reduction in bias and increased efficiency in a simulation study for several forest populations with strong linear relationships between variables and reasonably well known error structure. A model-based sampling procedure called pscx sampling or a large-sample extension thereof is used to select sample trees for volume equations. Such bias reduction did not happen for other populations with weak linear relationships and unknown error structure.
    Print ISSN: 0045-5067
    Electronic ISSN: 1208-6037
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Canadian Science Publishing
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  • 5
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    Outlier-resistant estimators for Poisson sampling: a note (1998)
    Canadian Science Publishing
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    Publication Date: 1998-05-01
    Description: Poisson (3P) sampling is a commonly used method for generating estimates of timber volume. The usual estimator employed is the adjusted estimator, Y hata. The efficiency of this estimator can be greatly influenced by the presence of outliers. We formalize such a realistic situation for high-value timber estimation for which Y hata is inefficient. Here, yi approx beta xi for all but a few units in a population for which yi is large and xi very small. This situation can occur when estimating the net volume of high-value standing timber, such as that found in the Pacific Northwest region of the United States. A generalized regression estimator and an approximate Srivastava estimator are not affected by such data points. Simulations on a small population illustrate these ideas.
    Print ISSN: 0045-5067
    Electronic ISSN: 1208-6037
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Canadian Science Publishing
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