Selection of Trees for Thinning Using Machine Learning Algorithms and Competition Indices Article Swipe
Yong Kyu Lee
,
Jungsoo Lee
,
Sang-Kyun Han
,
Hyo-Vin Ji
,
Jin-Woo Park
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/f16010065
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/f16010065
In artificial forests, regular thinning is required to promote diameter growth for producing high-quality, large-diameter timber. However, selecting trees for thinning often relies on qualitative and subjective assessments by field workers. The purpose of this study was to develop a quantitative method for selecting trees for thinning by combining machine learning algorithms and competition indices. Our study site included the Pinus koraiensis area within a Kangwon National University research forest in the Republic of Korea. Data from a model development site were used for the basic crown classification model for Pinus koraiensis. The model was optimized by adjusting hyperparameters. Different algorithms, including Random Forest, XGBoost, and LightGBM (LGBM), were improved using Random Search. LGBM showed the highest accuracy of 71.6%. LGBM—in combination with the competition indices—was used to classify the crown class in the application site and select trees for thinning. Compared to the combination of Braathe and Martin-EK indices, the combination of LGBM and Hegyi index enabled the even distribution of the residual stand in the entire site after thinning. It lowered the distribution of hot spots, which represent competition. Thus, the combination of LGBM and Hegyi index was the most effective option to improve the spatial distribution of trees after thinning. Our findings can improve forest management by providing a quantitative and objective method for selecting trees for thinning.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/f16010065
- https://www.mdpi.com/1999-4907/16/1/65/pdf?version=1735806839
- OA Status
- gold
- Cited By
- 2
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405989484
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405989484Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/f16010065Digital Object Identifier
- Title
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Selection of Trees for Thinning Using Machine Learning Algorithms and Competition IndicesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-02Full publication date if available
- Authors
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Yong Kyu Lee, Jungsoo Lee, Sang-Kyun Han, Hyo-Vin Ji, Jin-Woo ParkList of authors in order
- Landing page
-
https://doi.org/10.3390/f16010065Publisher landing page
- PDF URL
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https://www.mdpi.com/1999-4907/16/1/65/pdf?version=1735806839Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1999-4907/16/1/65/pdf?version=1735806839Direct OA link when available
- Concepts
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Thinning, Pinus koraiensis, Random forest, Hyperparameter, Computer science, Artificial intelligence, Machine learning, Competition (biology), Forestry, Forest management, Algorithm, Ecology, Geography, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W6777895791, https://openalex.org/W3114925657, https://openalex.org/W6600774671, https://openalex.org/W2911906180, https://openalex.org/W2021208831, https://openalex.org/W6723485205, https://openalex.org/W2010975929, https://openalex.org/W6696194290, https://openalex.org/W2000468509, https://openalex.org/W2039931899, https://openalex.org/W4386832735, https://openalex.org/W2395582955, https://openalex.org/W2942137712, https://openalex.org/W2261059368, https://openalex.org/W2295598076, https://openalex.org/W2938102982, https://openalex.org/W6745609711, https://openalex.org/W3004933906, https://openalex.org/W2911964244, https://openalex.org/W3155356857, https://openalex.org/W3026063078, https://openalex.org/W6787657963, https://openalex.org/W2033600814, https://openalex.org/W2043725578, https://openalex.org/W2978631110, https://openalex.org/W2801244438, https://openalex.org/W1967137980, https://openalex.org/W6734116325, https://openalex.org/W2023930458, https://openalex.org/W4285186172, https://openalex.org/W2971616138, https://openalex.org/W4378085579, https://openalex.org/W2034829397, https://openalex.org/W3118636633, https://openalex.org/W2768348081, https://openalex.org/W18798787, https://openalex.org/W2590764202, https://openalex.org/W3027477918, https://openalex.org/W2289639857, https://openalex.org/W3101083045, https://openalex.org/W2492607155 |
| referenced_works_count | 41 |
| abstract_inverted_index.a | 39, 64, 77, 211 |
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| abstract_inverted_index.index | 156, 188 |
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| abstract_inverted_index.which | 178 |
| abstract_inverted_index.71.6%. | 119 |
| abstract_inverted_index.Korea. | 74 |
| abstract_inverted_index.Random | 102, 111 |
| abstract_inverted_index.entire | 167 |
| abstract_inverted_index.forest | 69, 207 |
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| abstract_inverted_index.spots, | 177 |
| abstract_inverted_index.within | 63 |
| abstract_inverted_index.(LGBM), | 107 |
| abstract_inverted_index.Braathe | 146 |
| abstract_inverted_index.Forest, | 103 |
| abstract_inverted_index.Kangwon | 65 |
| abstract_inverted_index.Search. | 112 |
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| abstract_inverted_index.Republic | 72 |
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| abstract_inverted_index.koraiensis. | 91 |
| abstract_inverted_index.qualitative | 24 |
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| corresponding_author_ids | https://openalex.org/A5034097374 |
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| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I165507594 |
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| sustainable_development_goals[0].display_name | Life in Land |
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