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林业研究(英文版) >

2024 , Vol. 35 >Issue 1: 106

DOI: https://doi.org/10.1007/s11676-024-01759-x

  • Yao Jiang 1, 2 ,
  • Zhou Wang 3 ,
  • Zhongrui Zhang 5 ,
  • Xiaogang Ding 5 ,
  • Shaowei Jiang 1, 2 ,
  • Jianguo Huang 4, f
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收稿日期: 2023-12-04

  录用日期: 2024-04-23

  网络出版日期: 2024-10-16

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Enhancing forest insect outbreak detection by integrating tree-ring and climate variables

  • Yao Jiang 1, 2 ,
  • Zhou Wang 3 ,
  • Zhongrui Zhang 5 ,
  • Xiaogang Ding 5 ,
  • Shaowei Jiang 1, 2 ,
  • Jianguo Huang 4, f
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  • 1 Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, 510650, Guangzhou, People’s Republic of China
  • 2 University of Chinese Academy of Sciences, 100049, Beijing, People’s Republic of China
  • 3 Ministry of Emergency Management of China, National Institute of Natural Hazards, 100085, Beijing, People’s Republic of China
  • 4 MOE Key Laboratory of Biosystems Homeostasis and Protection, College of Life Sciences, Zhejiang University, 310000, Hangzhou, People’s Republic of China
  • 5 Guangdong Academy of Forestry, 510520, Guangzhou, People’s Republic of China
f

Received date: 2023-12-04

  Accepted date: 2024-04-23

  Online published: 2024-10-16

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© Northeast Forestry University 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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本文引用格式

Yao Jiang , Zhou Wang , Zhongrui Zhang , Xiaogang Ding , Shaowei Jiang , Jianguo Huang . [J]. 林业研究(英文版), 2024 , 35(1) : 106 . DOI: 10.1007/s11676-024-01759-x

Abstract

Annual tree rings are widely recognized as valuable tools for quantifying and reconstructing historical forest disturbances. However, the influence of climate can complicate the detection of disturbance signals, leading to limited accuracy in existing methods. In this study, we propose a random under-sampling boosting (RUB) classifier that integrates both tree-ring and climate variables to enhance the detection of forest insect outbreaks. The study focused on 32 sites in Alberta, Canada, which documented insect outbreaks from 1939 to 2010. Through thorough feature engineering, model development, and tenfold cross-validation, multiple machine learning (ML) models were constructed. These models used ring width indices (RWIs) and climate variables within an 11-year window as input features, with outbreak and non-outbreak occurrences as the corresponding output variables. Our results reveal that the RUB model consistently demonstrated superior overall performance and stability, with an accuracy of 88.1%, which surpassed that of the other ML models. In addition, the relative importance of the feature variables followed the order RWIs > mean maximum temperature (T max) from May to July > mean total precipitation (P mean) in July > mean minimum temperature (T min) in October. More importantly, the dfoliatR (an R package for detecting insect defoliation) and curve intervention detection methods were inferior to the RUB model. Our findings underscore that integrating tree-ring width and climate variables as predictors in machine learning offers a promising avenue for enhancing the accuracy of detecting forest insect outbreaks.

Key words： Forest disturbance; Insect outbreaks; Machine learning; Tree-ring analysis

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