The Predictive Models and Procedures Used in the Forest Stand Generator (STAG)

Biging, G; Robards, T; Turnblom, E; Van Deusen, P

The Predictive Models and Procedures Used in the Forest Stand Generator (STAG)

Authors

Greg S. Biging
Timothy A. Robards
Eric C. Turnblom
Paul C. Van Deusen

Authors Affiliations

Greg Biging was Associate Professor, Department of Environmental Science, Policy and Management, University of California, Berkeley; Timothy A. Robards was Sustained Yield Forester with the California Department of Forestry and Fire Protection; Eric C. Turnblom was former Postgraduate Researcher, Department of Environmental Science, Policy and Management, University of California, Berkeley, and is now Assistant Professor at the University of Washington, Seattle; Paul C. Van Deusen was Research Scientist at the USDA Forest Service Southern Forest Experiment Station, New Orleans, Louisiana.

Publication Information

Hilgardia 61(1):1-36. DOI:10.3733/hilg.v61n01p037. November 1994.

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Abstract

The Forest Stand Generator, STAG, is a microcomputer-based program that uses statistical routines to produce a projection set composed of individual tree measurements of diameter at 4.5 feet above ground (called the diameter at breast height or DBH), total height, height-to-crown base, species, and tree expansion factor. When data sets are not complete, STAG can be used to produce a projection set for a wide class of inventory procedures. The authors will discuss the estimation procedures used by STAG to (1) fill in missing measurements of tree height, height-to-crown base, or both; (2) generate stands from summary statistics; and (3) convert stand table data—numbers of trees by DBH classes and species—to individual tree records, so that these projection sets, composed of complete individual tree records, can be analyzed by the California Conifer Timber Output Simulator (CACTOS) for simulation of tree growth and mortality, even though the initial data sets could not have been used with CACTOS. Also discussed are the predictive equations and analytic procedures used to produce a projection set for these three categories of data availability. Recommended uses of STAG and the type of data required for accurate development of projection sets are presented.

Research reported here was conducted under UC Agricultural Experiment Station projects 3679-ms and 3815-ms. The authors gratefully acknowledge important contributions to this work by Peter J. Daugherty and Vaughan Landrum. They also are grateful to Lee C. Wensel and the members of the Northern California Forest Yield Cooperative for their support.

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