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作物学报 ›› 2012, Vol. 38 ›› Issue (01): 129-139.doi: 10.3724/SP.J.1006.2012.00129

• 耕作栽培·生理生化 • 上一篇    下一篇

棉花黄萎病疑似病田的卫星遥感监测——以TM卫星影像为例

陈兵1,2,3，王克如1,2，李少昆1,2,*，肖春华1,2，苏毅1，唐强1，陈江鲁1，金秀良1，吕银亮1，刁万英1，王楷1   

  1. 1 新疆兵团绿洲生态农业重点开放实验室 / 石河子大学, 新疆石河子 832003; 2 中国农业科学院作物科学研究所 / 农业部作物生理生态与栽培重点开放实验室, 北京 100081; 3 新疆农垦科学院棉花研究所，新疆石河子 832000
  • 收稿日期:2011-05-25 修回日期:2011-09-13 出版日期:2012-01-12 网络出版日期:2011-11-07
  • 通讯作者: 李少昆, E-mail: lishk@mail.caas.net.cn, Tel: 010-68918891
  • 基金资助:

    本研究由国家自然科学基金项目(41161068, 30860139, 31071371)和新疆农垦科学院科技引导计划项目(YYD201102)资助。

Monitoring Cotton Field with Suspected Verticillium wilt Using Satellite Remote Sensing with TM Satellite Image as an Example

CHEN Bing1,2,3,WANG Ke-Ru1,2,LI Shao-Kun1,2,*,XIAO Chun-Hua1,2,SU Yi1,TANG Qiang1,CHEN Jiang-Lu1,JIN Xiu-Liang1,LÜ Yin-Liang1,DIAO Wan-Ying1,WANG Kai1   

  1. 1 Key Laboratory Of Oasis Ecology Agriculture Of Xinjiang Corps / Shihezi University, Shihezi 832003, China; 2 Institute Of Crop Sciences, Chinese Academy Of Agricultural Sciences / Key Laboratory Of Crop Physiology And Production Ministry Of Agriculture, Beijing 100081, China; 3 Institute Of Cotton, Xinjiang Academy Of Agricultural Reclamation Sciences, Shihezi 83200, China
  • Received:2011-05-25 Revised:2011-09-13 Published:2012-01-12 Published online:2011-11-07
  • Contact: 李少昆, E-mail: lishk@mail.caas.net.cn, Tel: 010-68918891

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被引次数

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摘要： 研究TM卫星影像最佳时相(单一时相)对黄萎病疑似病害棉田诊断和分类的技术与方法，为棉花生产提供具有针对性的管理方案，对促进棉田均衡增产、增效具有重要的意义。本研究通过分析试验区多时相卫星影像及准同步地面调查数据，从中优选病害棉田卫星影像诊断的最佳波段和时相，对黄萎病疑似病害棉田分类并地面验证。结果表明，棉花的关键生育期，健康与病害棉田在TM影像上明显不同，由此建立病害棉田解译标志是可行的，TM4波段可作为病害棉田卫星监测的最佳波段，棉花盛铃期(7月下旬至8月中旬)可作为黄萎病卫星监测的最佳时相。基于上述分析，在病害发生的最佳时相，利用平行六面体监督分类方法将示范区黄萎病疑似病害棉田划分为健康、轻病和重病棉田，其中2年病害棉田的面积分别占29%和23%。2年黄萎病疑似病害棉田分类结果的总体精度和Kappa系数均高于85%。进一步制作的棉花病田专题图也很好地反映了棉田内部的病害情况。因此,可利用多时相遥感数据进行棉花黄萎病疑似病田的诊断。

关键词: 棉花, 黄萎病, 多时相, TM卫星, 遥感监测

Abstract: The cotton fields of suspected disease were diagnosed and classed through analyzing and electing best period (single period) in multi-temporal information of TM image, which would provide the technology support to take the active measurements for cotton industry and increase the yield and efficiency of cotton production. Multi-temporal satellite images and data of ground surveying were taken in research areas. On the basis of them, the best wave band and the best period used to recognize cotton fields infected disease were selected and the cotton fields with suspected Verticillium wilt were classified and tested. The results showed that the health and disease cotton fields had obvious difference in the major cotton growth stages, it was feasible to diagnose cotton field with disease through the interpretation symbol of satellite image. In addition, TM4 band of satellite could be regarded as the best wave band to monitor cotton field with disease; the peak boll forming sage (from the last ten-day of July to mid Aug) could be regarded as the best period to monitor disease of cotton with TM image. On the basis of above analyses, using the supervised classification of parallelepiped, the cotton fields with suspected Verticillium wilt were divided into three classes: health, light disease and severe disease cotton fields. The proportions of cotton field infected disease were about 29% and 23% in two years, respectively. The tested results indicated the overall accuracy and kappa coefficient were both over 85% at the crucial growth periods in two years. The further analysis revealed the disease condition within cotton field infected Verticillium wiltcould be reflected by the thematic map of cotton disease. Thus, it is feasible to diagnose the cotton fields with suspected Verticillium wilt by multi-temporal satellite data.

Key words: Cotton, Verticillium wilt, Multi-temporal, TM satellite, Remote-sensing monitoring

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