A Technique for Implementing Group Selection Treatments with Multiple Objectives Using an Airborne Lidar-Derived Stem Map in a Heuristic Environment

Abstract

Group-selection silviculture has many beneficial attributes and has increased in application over the past 30 years. One difficulty with group-selection implementation is the designation of group openings within a stand to achieve a variety of complex management goals. This study presents a new method for utilizing geospatial census stem map data derived from airborne lidar in a heuristic environment to generate and select from treatment solutions that best meet management objectives in an efficient manner. The method successfully generated candidate treatment solutions over two entries that met a set of tree size, opening size and spacing constraints. The heuristic was implemented on two separate ponderosa pine stands with similar stand conditions using different group-selection opening sizes. Successful field implementations relied on a tree-marking technique developed in this study that relied on high-precision GPS receivers. The heuristic identified good solutions, but the quality is unknown as this is a large nonlinear optimization problem. Nonetheless, this study provides an innovative, efficient and mathematically defendable alternative for implementing group-selection treatments in stands where accurate geospatially-referenced census can be obtained.