Calibrating Predicted Diameter Distribution with Additional Information

The diameter distribution of the growing stock is an essential starting point in many forest management planning problems. There are several methods for predicting the diameter distribution of a stand, varying from methods which utilize theoretical distribution functions to nonparametric methods. Usually the predicted diameter distribution is scaled so that the stem number corresponds to the measured value. However, if stem number and basal area are both known, it may be difficult to predict a distribution that gives correct estimates for both these characteristics. Such diameter distributions can be obtained using an approach adopted from sampling theory-calibration estimation. In this study, the diameter distributions of Scots pine were predicted with two different methods, the Weibull and percentile based methods, and then calibrated with additional information. The calibration reduced the RMSE of stand variables computed from the predicted distribution.

[1]  J. Gove,et al.  Modeling the Basal Area-size Distribution of Forest Stands: A Compatible Approach , 1998, Forest Science.

[2]  H. Burkhart,et al.  A segmented distribution approach for modeling diameter frequency data , 1984 .

[3]  John W. Moser,et al.  A Generalized Framework for Projecting Forest Yield and Stand Structure Using Diameter Distributions , 1983 .

[4]  R. Bailey,et al.  Percentile-Based Distributions Characterize Forest Stand Tables , 1987, Forest Science.

[5]  K. Rennolls,et al.  Timber Management-A Quantitative Approach. , 1984 .

[6]  Hans Gustav Gustavsen,et al.  Kivennäismaiden talousmetsien pysyvät (INKA ja TINKA) kokeet: suunnitelmat, mittausmenetelmät ja aineistojen rakenteet . , 1988 .

[7]  B. Borders,et al.  Projecting Stand Tables: A Comparison of the Weibull Diameter Distribution Method, a Percentile-Based Projection Method, and a Basal Area Growth Projection Method , 1990, Forest Science.

[8]  Harold E. Burkhart,et al.  A Growth and Yield Model for Thinned Stands of Yellow-Poplar , 1986 .

[9]  Matti Maltamo,et al.  Use of the Weibull function in estimating the basal area dbh-distribution. , 1989 .

[10]  M. Newby,et al.  The Properties of Moment Estimators for the Weibull Distribution Based on the Sample Coeffkient of Variation , 1980 .

[11]  Matti Maltamo,et al.  The K‐nearest‐neighbour method for estimating basal‐area diameter distribution , 1997 .

[12]  H. Schreuder,et al.  Statistical distributions for fitting diameter and height data in even-aged stands , 1977 .

[13]  Annika Kangas,et al.  Methods based on k-nearest neighbor regression in the prediction of basal area diameter distribution , 1998 .

[14]  T. Burk,et al.  Notes: A Simple Algorithm for Moment-Based Recovery of Weibull Distribution Parameters , 1984 .

[15]  C. Särndal,et al.  Calibration Estimators in Survey Sampling , 1992 .

[16]  M. Maltamo,et al.  Comparison of percentile based prediction methods and the Weibull distribution in describing the diameter distribution of heterogeneous Scots pine stands , 2000 .

[17]  T. R. Dell,et al.  Quantifying Diameter Distributions with the Weibull Function , 1973 .

[18]  Markku Siitonen Experiences in the use of forest management planning models. , 1993 .

[19]  Dean S. DeBell,et al.  Comparison of diameter-distribution-prediction, stand-table-projection, and individual-tree-growth modeling approaches for young red alder plantations , 1997 .