Calibrating Predicted Diameter Distribution with Additional Information

By Annika S. Kangas, Matti Maltamo. Forestry sciences, 2000

69 citations recorded by the source corpus.

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. FOR. SCI. 46(3): 390–396.

Subfields: Environmental Engineering, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/d3105e15690a2e6102f18a066535358be827073c

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In Environmental Engineering

In Nature and Landscape Conservation

Citations in the corpus

Parameter recovery vs. parameter prediction for the Weibull distribution validated for Scots pine stands in Finland (2013, methodology): “Strict compatibility, if required, may be difficult to achieve and the solution would require extra calibration functions, such as those shown by Kangas and Maltamo (2000a) and Palahi et al.”
Making forestry decisions with multiple criteria: A review and an assessment (2008, methodology): “Kangas and Maltamo (2000) also applied a GP model to calibrate a predicted diameter distribution of a forest growing stock with additional information.”
Performance of percentile based diameter distribution prediction and Weibull method in independent data sets (2000, methodology): “Kangas and Maltamo (2000a) used this approach to calibrate the predicted frequencies of a diameter distribution.”
Dynamic growth model for Scots pine (Pinus sylvestris L.) plantations in Galicia (north-western Spain) (2006, methodology): “Among these, the Weibull function has been the most frequently used in forest growth models because of its flexibility and simplicity ( Maltamo et al., 1995; R´ oo, 1999; Kangas and Maltamo, 2000; Torres-Rojo et al., 2000).”