基于STWR模型的森林病虫影响因素研究  

Driving factors of forest diseases and insect pests based on STWR model

作　　者：苏少强 阙翔 严宣辉[4] 何中声[3] 刘金福[1,3] 李梦航 黄朝法 刘海 SU Shaoqiang;QUE Xiang;YAN Xuanhui;HE Zhongsheng;LIU Jinfu;LI Menghang;HUANG Chaofa;LIU Hai(College of Computer and Information Sciences,Fujian Agriculture and Forestry University,Fuzhou,Fujian 350002,China;Fujian Statistical Information Research Center,Fujian Agriculture and Forestry University,Fuzhou,Fujian 350002,China;Key Laboratory of Ecology and Resources Statistics of Universities,Fujian Agriculture and Forestry University,Fuzhou,Fujian 350002,China;Digital Fujian Environmental Monitoring Internet of Things Laboratory,Fujian Normal University,Fuzhou,Fujian 350117,China;Fujian Institute of Forest Inventory and Planning,Fuzhou,Fujian 350003,China)

机构地区：[1]福建农林大学计算机与信息学院,福建福州350002 [2]福建农林大学福建省统计信息研究中心,福建福州350002 [3]福建农林大学生态与资源统计福建省高校重点实验室,福建福州350002 [4]福建师范大学数字福建环境监测物联网实验室,福建福州350117 [5]福建省林业调查规划院,福建福州350003

出　　处：《西北农林科技大学学报（自然科学版）》2022年第11期81-92,共12页Journal of Northwest A&F University(Natural Science Edition)

基　　金：福建省自然科学基金(2021J05030);2021年福建省省级科技创新重点项目(2021G02007);中央引导地方科技发展专项(2021L3033,2020L3006);福建农林大学科技创新专项(CXZX2020149C,KCX21F33A)。

摘　　要：【目的】探究森林病虫害时空动态变化特征及关键因素驱动力的空间分布,为森林病虫害防控与治理提供参考。【方法】采用空间联系局部指标(LISA)和Mann-Kendall(M-K)趋势检验,分析2008-2018年福建省县域森林病虫害发生率时空变化特征,利用夏季均温、冬季均温、月均降水量、夜间灯光均值等变量构建时空加权回归(STWR)模型,分析各变量与森林病虫害发生率之间的时空异质性。【结果】①2008-2018年福建省有16个县域森林病虫害发生率呈下降趋势,空间分布以“低-低”聚集的空间聚集类型为主,空间分布范围先缩小后扩张。不同变量对森林病虫害发生率的影响程度从强到弱依次为夜间灯光均值、月均降水量、夏季均温、冬季均温。②4种不同变量的影响机制有明显的时空分异性,其中夜间灯光均值、夏季均温、冬季均温对森林病虫害发生有正向促进作用,月均降水量对森林病虫害发生有负向抑制作用。③在森林病虫害高发生率县域中,夜间灯光均值为最大的负向影响因素,月均降水量也通常为负向影响因素。【结论】STWR模型的拟合性能优于地理加权回归(GWR)模型与普通线性回归(OLS)模型,且STWR模型预测能力比GWR模型更佳,能更精细地分析4个变量对县域森林病虫害发生率影响的时空变化过程。【Objective】The temporal and spatial dynamic characteristics of forest diseases and pests and the spatial distribution of driving forces were investigated to provide references for forest pest control and management.【Method】The local indicators of spatial association(LISA)and Mann-Kendall(M-K)trend tests were used to analyze the spatiotemporal variation characteristics of forest diseases and pests incidence rates at county level in Fujian from 2008 to 2018.The spatiotemporal weighted regression(STWR)model was constructed by variables including summer average temperature,winter average temperature,monthly average precipitation,and average value of nighttime light.Then,it was used to analyze the temporal and spatial heterogeneity between variables and forest diseases and pests incidence rates.【Result】①From 2008 to 2018,forest diseases and pests incidence rates showed a downward trend in 16 counties in Fujian,mainly in the“low-low”aggregation type.The spatial distribution range first decreased and then expanded.The influences of factors were in the decreasing order of nighttime light,monthly average precipitation,summer average temperature,and winter average temperature.②Nighttime light,summer average temperature,and winter average temperature had positive effects on the occurrence of forest diseases and pests,while monthly average precipitation had negative effects.③Nighttime light was the largest negative influencing factor and monthly average precipitation was usually negative in counties with high incidences of forest diseases and insect pests.【Conclusion】The fitting performance of the STWR model was better than geographically weighted regression(GWR)and global least squares regression(OLS),and the predictive ability of the STWR model was better than that of the GWR model.Thus,the STWR model can be used for analyzing effects of different factors on spatiotemporal variations of incidence of forest diseases and pests.

关 键 词：时空异质性 森林病虫害 空间统计 时空加权回归(STWR)模型 福建省 

分 类 号：S763[农业科学—森林保护学]