Digital Forestry: A white paper

Digital Forestry has been proposed as “the science, technology, and art of systematically acquiring, integrating, analyzing, and applying digital information to support sustainable forests.” Although rooted in traditional forestry disciplines, Digital Forestry draws from a host of other fields that, in the past few decades, have become important for implementing the concept of forest ecosystem management and the principle of sustainable forestry. Digital Forestry is a framework that links all facets of forestry information at local, national, and global levels through an organized digital network. It is anticipated that a new set of principles will be established when practicing Digital Forestry concept for the evolution of forestry education, research, and practices as the 21st century unfolds.

The Richit-Richards family of distributions and its use in forestry

Johnson's SB and the logit-logistic are four-parameter distribution models that may be obtained from the standard normal and logistic distributions by a four-parameter transformation. For relatively small data sets, such as diameter at breast height measurements obtained from typical sample plots, distribution models with four or less parameters have been found to be empirically adequate. However, in situations in which the distributions are complex, for example in mixed stands or when the stand has been thinned or when working with aggregated data, then distribution models with more shape parameters may prove to be necessary. By replacing the symmetric standard logistic distribution of the logit-logistic with a one-parameter “standard Richards” distribution and transforming by a five-parameter Richards function, we obtain a new six-parameter distribution model, the “Richit-Richards”. The Richit-Richards includes the “logit-Richards”, the “Richit-logistic”, and the logit-logistic as submodels. Maximum likelihood estimation is used to fit the model, and some problems in the maximum likelihood estimation of bounding parameters are discussed. An empirical case study of the Richit-Richards and its submodels is conducted on pooled diameter at breast height data from 107 sample plots of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.). It is found that the new models provide significantly better fits than the four-parameter logit-logistic for large data sets.