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

作　　者：Yao Jiang Zhou Wang Zhongrui Zhang Xiaogang Ding Shaowei Jiang Jianguo Huang 

机构地区：[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,Guangzhou 510650,People’s Republic of China [2]University of Chinese Academy of Sciences,Beijing 100049,People’s Republic of China [3]Ministry of Emergency Management of China,National Institute of Natural Hazards,Beijing 100085,People’s Republic of China [4]MOE Key Laboratory of Biosystems Homeostasis and Protection,College of Life Sciences,Zhejiang University,Hangzhou 310000,People’s Republic of China [5]Guangdong Academy of Forestry,Guangzhou 510520,People’s Republic of China

出　　处：《Journal of Forestry Research》2024年第6期279-293,共15页林业研究（英文版）

基　　金：supported by the Xinjiang Regional Collaborative Innovation Project(2022E01045);Zhejiang University(108000*1942222R1).

摘　　要：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(Tmax)from May to July>mean total precipita-tion(Pmean)in July>mean minimum temperature(Tmin)in October.More importantly,the dfoliatR(an R package for detecting insect defoliation)and curve intervention detec-tion methods were inferior to the RUB model.Our findings underscore that integrating tree-ring width and climate vari-ables as predictors in machine learning offers a promising avenue for enhancing the accuracy of detecting forest insect outbreaks.

关 键 词：Forest disturbance Insect outbreaks Machine learning Tree-ring analysis 

分 类 号：S718.5[农业科学—林学]