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林业资源管理 ›› 2022, Vol. 0 ›› Issue (4): 126-133.doi: 10.13466/j.cnki.lyzygl.2022.04.016

• 技术应用 • 上一篇    下一篇

北京三号卫星数据在松材线虫病遥感监测中的应用评价

秦琳1(), 孟先进1, 张水花1, 薛亚东1, 刘新科1(), 邢鹏2   

  1. 1.广东省林业调查规划院,广州 510520
    2.二十一世纪(广州)空间技术应用有限公司,广州 510500
  • 收稿日期:2022-05-12 修回日期:2022-06-17 出版日期:2022-08-28 发布日期:2022-10-13
  • 通讯作者: 刘新科
  • 作者简介:秦琳(1979-),女,湖北武汉人,高工,主要从事林业遥感、地理信息和调查规划方面的研究。Email: luckykql@263.ent

Application Evaluation of BJ3 Satellite Data in Remote Sensing Monitoring of Pine Wilt Disease

QIN Lin1(), MENG Xianjin1, ZHANG Shuihua1, XUE Yadong1, LIU Xinke1(), Xing Peng2   

  1. 1. Guangdong Forestry Survey and Planning Institute,Guangzhou 510520,China
    2. Twenty First Century(Guangzhou)Aerospace Technology Co.Ltd.,Guangzhou 510500,China
  • Received:2022-05-12 Revised:2022-06-17 Online:2022-08-28 Published:2022-10-13
  • Contact: LIU Xinke

摘要:

北京三号国际合作星座(BJ3N)是拥有我国自主知识产权的国际领先甚高分辨率光学遥感卫星,首批两颗卫星于2021年成功发射,为了更好了解BJ3N的应用性能,利用BJ3N在广东省韶关市的相关影像数据开展松材线虫病遥感监测,并在影像融合、指数计算、信息提取等方面进行应用评价。结果表明,BJ3N数据通过PANSHARP融合法和NGRDI指数计算可大幅提升健康松树和变色松树间的差异化,有效增强变色松树的识别效果;通过深度学习智能提取变色松树精准率(查准率)95.8%,召回率(查全率)88.3%,满足松材线虫病遥感监测的工作需求,有利于松材线虫病的精准监测和防控。

关键词: 松材线虫病, BJ3N, 遥感, 评价

Abstract:

BJ3N is an international leading high-resolution optical remote sensing satellite with independent intellectual property rights of China.The first two satellites were successfully launched in 2021.In order to better understand the application performance of BJ3N,this paper used the relevant image data of BJ3N in Shaoguan City,Guangdong Province to carry out remote sensing monitoring of pine wilt disease,and the application evaluation in image fusion,index calculation,information extraction and other aspects.The results showed that BJ3N data could greatly improve the difference between healthy pines and discolored pines through PANSHARP fusion method and NGRDI index calculation,and effectively enhance the identification effect of discolored pine;the precision rate of intelligently extracting discolored pine through deep learning was 95.8%.,and the recall rate was 88.3%,which met the work needs of remote sensing monitoring of pine wilt disease,and was conducive to the accurate monitoring and prevention of pine wilt disease.

Key words: BJ3N, pine wilt disease, remote sensing, evaluation

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