Heuristic Optimization of Thinning Individual Douglas-Fir

Todd West,Bogdan M Strimbu,John Sessions

doi:10.3390/f12030280

Abstract

Research Highlights: (1) Optimizing mid-rotation thinning increased modeled land expectation values by as much as 5.1–10.1% over a representative reference prescription on plots planted at 2.7 and 3.7 m square spacings. (2) Eight heuristics, five of which were newly applied to selecting individual trees for thinning, produced thinning prescriptions of near identical quality. (3) Based on heuristic sampling properties, we introduced a variant of the hero heuristic with a 5.3–20% greater computational efficiency. Background and Objectives: Thinning, which is arguably the most subjective human intervention in the life of a stand, is commonly executed with limited decision support in tree selection. This study evaluated heuristics’ ability to support tree selection in a factorial experiment that considered the thinning method, tree density, thinning age, and rotation length. Materials and Methods: The Organon growth model was used for the financial optimization of even age Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) harvest rotations consisting of a single thinning followed by clearcutting on a high-productivity site. We evaluated two versions of the hero heuristic, four Monte Carlo heuristics (simulated annealing, record-to-record travel, threshold accepting, and great deluge), a genetic algorithm, and tabu search for their efficiency in maximizing land expectation value. Results: With 50–75 years rotations and a 4% discount rate, heuristic tree selection always increased land expectation values over other thinning methods. The two hero heuristics were the most computationally efficient methods. The four Monte Carlo heuristics required 2.8–3.4 times more computation than hero. The genetic algorithm and the tabu search required 4.2–8.4 and 21–52 times, respectively, more computation than hero. Conclusions: The accuracy of the resulting thinning prescriptions was limited by the quality of stand measurement, and the accuracy of the growth and yield models was linked to the heuristics rather than to the choice of heuristic. However, heuristic performance may be sensitive to the chosen models.

Highlights

While tree harvest scheduling has been studied since the late 1600s [1], it remains a topic of current interest due to its complexity
We evaluated two versions of the hero heuristic, four Monte Carlo heuristics, a genetic algorithm, and tabu search for their efficiency in maximizing land expectation value
The net present values (NPVs) associated with thinning prescription i are calculated from the real mean appreciated prices P() for the wood volume harvested, the volumes (Vj ) of individual trees, and the timber sale administration, harvest, and haul costs Cvariable and Cfixed

Summary

Introduction

While tree harvest scheduling has been studied since the late 1600s [1], it remains a topic of current interest due to its complexity. Previous studies have shown revenue varies with the species composition of the trees, their current density and sizes, the future growth rates anticipated on the site, the present and anticipated pricing of the wood products made from harvested trees, the number and frequency of harvests, and how many trees of which types are removed in each harvest (e.g., [3,4,5,6,7,8,9,10]) This broad range of parameters has motivated the ongoing demand for techniques that can identify the most desirable management choices, ideally at the level of individual trees. Recent works on thinning (Table 1) have included laser scanning to identify individual trees, the projection of individual trees’ growth, the estimation of timber assortments provided by harvest, and the use of 16 different optimization methods

Objectives

Methods

Results

Discussion

Conclusion