Heuristic Optimization of Thinning Individual Douglas-Fir
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 t...
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| Format: | Article |
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MDPI AG
2021-02-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/12/3/280 |
| _version_ | 1797416836702142464 |
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| author | Todd West John Sessions Bogdan M. Strimbu |
| author_facet | Todd West John Sessions Bogdan M. Strimbu |
| author_sort | Todd West |
| collection | DOAJ |
| description | 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 (<i>Pseudotsuga menziesii</i> (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. |
| first_indexed | 2024-03-09T06:09:56Z |
| format | Article |
| id | doaj.art-f85733dd82994fb985fc455422abfca9 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-09T06:09:56Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-f85733dd82994fb985fc455422abfca92023-12-03T12:00:31ZengMDPI AGForests1999-49072021-02-0112328010.3390/f12030280Heuristic Optimization of Thinning Individual Douglas-FirTodd West0John Sessions1Bogdan M. Strimbu2Department of Forest Engineering and Resource Management, Oregon State University, Corvallis, OR 97331, USADepartment of Forest Engineering and Resource Management, Oregon State University, Corvallis, OR 97331, USADepartment of Forest Engineering and Resource Management, Oregon State University, Corvallis, OR 97331, USAResearch 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 (<i>Pseudotsuga menziesii</i> (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.https://www.mdpi.com/1999-4907/12/3/280thinningDouglas-firindividual tree selectionherosimulated annealingthreshold accepting |
| spellingShingle | Todd West John Sessions Bogdan M. Strimbu Heuristic Optimization of Thinning Individual Douglas-Fir Forests thinning Douglas-fir individual tree selection hero simulated annealing threshold accepting |
| title | Heuristic Optimization of Thinning Individual Douglas-Fir |
| title_full | Heuristic Optimization of Thinning Individual Douglas-Fir |
| title_fullStr | Heuristic Optimization of Thinning Individual Douglas-Fir |
| title_full_unstemmed | Heuristic Optimization of Thinning Individual Douglas-Fir |
| title_short | Heuristic Optimization of Thinning Individual Douglas-Fir |
| title_sort | heuristic optimization of thinning individual douglas fir |
| topic | thinning Douglas-fir individual tree selection hero simulated annealing threshold accepting |
| url | https://www.mdpi.com/1999-4907/12/3/280 |
| work_keys_str_mv | AT toddwest heuristicoptimizationofthinningindividualdouglasfir AT johnsessions heuristicoptimizationofthinningindividualdouglasfir AT bogdanmstrimbu heuristicoptimizationofthinningindividualdouglasfir |