Methods and applications for improving parameter prediction models for stand structures in Finland

Abstract

This thesis report attempts to improve the models for predicting forest stand structure for practical use, e.g. forest management planning (FMP) purposes in Finland. Comparisons were made between Weibull and Johnson's SB distribution and alternative regression estimation methods. Data used for preliminary studies was local but the final models were based on representative data. Models were validated mainly in terms of bias and RMSE in the main stand characteristics (e.g. volume) using independent data.The bivariate SBB distribution model was used to mimic realistic variations in tree dimensions by including within-diameter-class height variation. Using the traditional method, diameter distribution with the expected height resulted in reduced height variation, whereas the alternative bivariate method utilized the error-term of the height model. The lack of models for FMP was covered to some extent by the models for peatland and juvenile stands. The validation of these models showed that the more sophisticated regression estimation methods provided slightly improved accuracy.A flexible prediction and application for stand structure consisted of seemingly unrelated regression models for eight stand characteristics, the parameters of three optional distributions and Näslund's height curve. The cross-model covariance structure was used for linear prediction application, in which the expected values of the models were calibrated with the known stand characteristics. This provided a framework to validate the optional distributions and the optional set of stand characteristics. Height distribution is recommended for the earliest state of stands because of its continuous feature. From the mean height of about 4 m, Weibull dbh-frequency distribution is recommended in young stands if the input variables consist of arithmetic stand characteristics. In advanced stands, basal area-dbh distribution models are recommended. Näslund's height curve proved useful. Some efficient transformations of stand characteristics are introduced, e.g. the shape index, which combined the basal area, the stem number and the median diameter. Shape index enabled SB model for peatland stands to detect large variation in stand densities. This model also demonstrated reasonable behaviour for stands in mineral soils.Keywords: stand structure, size distribution, linear prediction, height-diameter relationship, stand characteristics, regression estimation 4 ACKNOWLEDGEMENTSThe basic work was done under a project called "Stand structure, competition dynamics and site productivity in growth models" and later in the project "Prediction and simulation of stand dynamics", led by Dr. Risto Ojansuu. Most of the models included in my thesis have been implemented in a MOTTI simulator in the project "Decision support system for stand management" led by Dr. Jari Hynynen. The work was carried out by the Finnish Forest Research Institute (Metla) in Vantaa.I wish to express my warmest thanks to my supervisors, Prof. Annika Kangas and Dr. Lauri Mehtätalo. Laur...

Reliability of the predicted stand structure for clear-cut stands using optional methods: airborne laser scanning-based methods, smartphone-based forest inventory application Trestima and pre-harvest measurement tool EMO

“…Stand-level information is converted into tree-level information through size distribution modelling, which means selecting the distribution function and the distribution modelling approach (Cao 2004;Siipilehto 2011). Diameter distributions are presented as either unweighted with respect to tree frequency (i.e., dbh-frequency distribution) or weighted with respect to tree basal area (i.e., basal area-dbh distribution) (Gove and Patil 1998).…”

Parameter recovery vs. parameter prediction for the Weibull distribution validated for Scots pine stands in Finland

“…Using the same data and the same validation criteria as Siipilehto (2011a) and Siipilehto (2011b) means that the results were fully comparable, not only between PPM and PRM methods for the Weibull in this paper but also among all of the models that were previously validated by Siipilehto (2011b). Note that the trees for young stands were sampled from the cumulative Weibull distribution, imitating fixed area sampling, whereas trees for the advanced stands were sampled from the weighted distribution, imitating relascope sampling.…”

“…Using these equations, four combinations of recovery equations are proposed. Two of these combinations are evaluated thoroughly and compared with the latest parameter prediction methods (Siipilehto 2011a;Siipilehto 2011b). The effect of the imputation of missing stand characteristics in PRM is also evaluated.…”

Calibration of the tree size distributions by combining the area-based approach and the individual tree detection using the airborne laser scanning

“…They concluded that the quantilebased method was able to describe more diverse forms of the diameter distributions, and predicted the stand characteristics at better accuracy than the Weibull distribution method. Siipilehto (2011), based on the field-measured data again, compared the Weibull distribution with the Johnson's SB distribution in predicting the forest stand structure variables. The RMSE% of the predicted total stem volume was about 2% for the advanced stands, but it varied from 10 to 24% for the young stands.…”