首页|基于无人机高光谱的川南疫木林区早期监测研究

基于无人机高光谱的川南疫木林区早期监测研究

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为探明马尾松感染松材线虫病早期地理位置及发病率。2021年 7月上旬,利用无人机搭载高光谱成像仪采集遥感影像,选用支持向量机进行监督分类,在早期感病反演模型基础上,顺利提取了感病早期的马尾松地理位置及相关信息。结果表明:(1)利用 460 nm、525 nm和 635 nm的 3波段组合真彩色影像进行ROI勾绘,马尾松与其他地被物分离度较高;(2)基于支持向量机的监督分类,顺利获取 741株马尾松地理位置及高光谱反射率数据;(3)结合监测模型提取 64株疑似感病马尾松,通过随机采样及镜检,马尾松聚类范围感病植株提取准确率 86。67%,即马尾松林间发病率 7。49%。综上,初步揭示川南地区马尾松林自然状态下松材线虫发病率,有利于今后指导松材线虫病早期精准防治。
Research on early surveillance of pestilence forest area in southern Sichuan based on UAV hyperspectrum
To investigate the early geographical location and incidence rate of Masson pine infected with pine wood nematode disease,in early July 2021,remote sensing images were collected by a hyperspectral imager mounted on UAV,and support vector machines were selected for supervised classification.Based on the inversion model of early disease susceptibility,the geographical location and related information of M.pine in the early stage of disease susceptibility were successfully extracted.The results showed that:(1)Using three-band combined true color images of 460 nm,525 nm and 635 nm for ROI mapping,M.Pine has a high degree of separation from other ground cover.(2)Based on supervised classification of support vector machine,the geographic location and hyperspectral reflectance data of 741 Masson pines were obtained successfully.(3)Combined with the monitoring model,64 suspected infected M.pine were extracted.Through random sampling and microscopic examination,the accuracy rate of infected plants in the cluster range of M.pine was 86.67%,that is,the incidence rate of M.pine in the forest was 7.49%.In conclusion,the incidence of pine wood nematode in the natural state of M.pine forest in southern of Sichuan was preliminatively revealed,which is helpful to guide the early and accurate control of pine wood nematode disease in the future.

UAV hyperspectralpine wood nematode diseasesupport vector machine

曾全、蒲远凤、肖银波、杨双昱、杨远亮、王新、谢天资、满家银、贾玉珍

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四川省林业科学研究院,森林和湿地生态恢复与保育四川重点实验室,四川成都 610081

南充市林业技术推广站,四川南充 637000

无人机高光谱 松材线虫病 支持向量机

四川省科技厅重点研发项目森林和湿地生态恢复与保育四川重点实验室资助项目

2020YFN0040

2024

四川林业科技
四川省林学会 四川省林业科学研究院

四川林业科技

影响因子:0.452
ISSN:1003-5508
年,卷(期):2024.45(4)