基于地理栅格变量与机器学习的松材线虫病扩散风险分析  

Prediction of spread risk of pine wilt disease based on geographic raster variables and machine learning models

作　　者：刘婷婷[1] 杨晋帆 周汝良[1] 刘琳 LIU Tingting;YANG Jinfan;ZHOU Ruliang;LIU Lin(College of Geography and Ecotourism,Southwest Forestry University,Kunming 650224,Yunnan,China;College of Science,Southwest Forestry University,Kunming 650224,Yunnan,China)

机构地区：[1]西南林业大学地理与生态旅游学院,云南昆明650224 [2]西南林业大学理学院,云南昆明650224

出　　处：《浙江农林大学学报》2023年第3期617-626,共10页Journal of Zhejiang A&F University

基　　金：国家自然科学基金资助项目(31760212);云南省教育厅基金项目(2021J0154);云南省科技厅重大科技专项(202002AA100007)。

摘　　要：【目的】松材线虫Bursaphelenchus xylophilus病是威胁中国森林生态系统安全最为严重的病害。本研究利用地理栅格模型及地图代数运算来模拟和表达驱动变量,构建松材线虫病测报系统,并以县域与地理栅格单元双尺度形成空间连续化测报。【方法】集成对松材线虫病扩散传播有影响的地形地貌、气象、寄主、人类活动和土地利用等地理栅格空间数据集,基于随机森林和支持向量机的机器学习方法构建模型,将测报得出的扩散风险概率与松科Pinaceae植物易感性叠加进行感染概率地图代数运算,基于地理栅格表达分析松材线虫病在中国范围内扩散的风险等级。【结果】①随机森林模型预测精度为83.95%,支持向量机模型预测精度为77.97%。②海拔、年均最低降水量、年均降水量、年均低温对模型构建的贡献率分别为0.151、0.303、0.258、0.194,是影响松材线虫病发生的主要因素;人类活动变量的贡献率为0.194,是影响扩散的决定性变量。③潜在扩散区位于人类活动密集的低海拔地区、道路通达的林区、城市城镇分布区和人工林分布区。其中,极高风险分布地区主要位于华东地区的浙江、江西、福建,华南地区的广西、广东,华中地区的湖南。【结论】利用空间模拟与机器学习方法构建了松材线虫病空间化测报模型,将松材线虫病的扩散风险测报到地理栅格单元,可为中国林草灾害精准监测提供方法借鉴,对中国松材线虫病的疫情防控攻坚行动具有重要指导意义。图2表7参36。[Objective]Considering the fact that pine wilt disease(PWD)has been the most serious disease threatening forest ecosystem in China.This study,with the simulation and expression of the driving variables by using geographic grid model and map algebra operation,is aimed to construct PWD measurement and forecasting system and form the spatial continuous measurement and prediction by using the dual scale of county area and geographic grid unit.[Method]First,datasets were formed by integrating geographic raster spatial data such as topography,meteorology,host,human activities and land use that affect PWD dispersal.Then,the model was constructed using the machine learning method of random forest and support vector machine before predicted results were superimposed with the susceptibility map of Pinaceae plants to perform the map algebraic operation of infection probability.Finally,an analysis was conducted of the risk level of PWD spread in the whole country based on geographic grid unit.[Result](1)The prediction accuracy was 83.95%for the random forest model and 77.97%for the support vector machine model;(2)Altitude,average annual minimum precipitation,average annual precipitation and average annual low temperature were the main factors affecting the occurrence of PWD,with their contribution rates to the model construction being 0.151,0.303,0.258 and 0.194 respectively whereas human activity variables were the decisive variable affecting the diffusion of PWD with their contribution rate to the model construction being 0.194;(3)The potential dispersal areas were located in low altitude areas with dense human activities,forest areas adjacent to roads,urban distribution areas and plantation distribution areas while the highest risk areas were mainly Zhejiang,Jiangxi and Fujian in East China,Guangxi and Guangdong in South China as well as Hunan in central China.[Conclusion]With the employment of spatial simulation and machine learning methods,a mapping model was constructed to predict the spatial transmission pattern of PW

关 键 词：松材线虫病 地理栅格 机器学习 风险分析 

分 类 号：S763.105[农业科学—森林保护学]