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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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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

[1]Li G-Y(李国英). Study on strategy and technology of cotton primary diseases in Xinjiang. Xinjiang Farmland Sci & Technol (新疆农垦科技), 2000, (4): 23–25 (in Chinese with English abstract)
[2]Song Q-P(宋庆平), Chen Q(陈谦), Chen H(陈红), Gou C-H(苟春红). Prospect on strategy and technology of protection and control diseases and insects in Xinjiang cotton fields. China Cotton (中国棉花), 2002, 29(12): 7–9 (in Chinese with English abstract)
[3]Zhang H(张慧), Yang X-M(杨兴明), Ran W(冉炜), Xu Y-C(徐阳春), Shen Q-R(沈其荣). Screening of bacteria antagonistic against soil-borne cotton Verticillium wilt and their biological effects on the soil-cotton system. Acta Pedol Sin (土壤学报), 2008, 45(6): 1095–1101 (in Chinese with English abstract)
[4]Humid Muhammad H. Hyperspectral crop reflectance data for characteristic and estimating fungal disease severity in wheat. Biosyst Eng, 2005, 91: 9–20
[5]Adams M L, Norvel W A, Philpot W D, Peverly J H. Toward the discrimination of manganese, zinc, copper, and iron deficiency in ‘bragg’ soybean using spectral detection methods. Agron J, 2000, 92, 268–274
[6]Tilling A K, O’Leary G J, Ferwerda J G, Jones S D, Glenn J F, Rodriguez D, Belford R. Remote sensing of nitrogen and water stress in wheat. Field Crops Res, 2007, 104: 77–85
[7]Yang B-J(杨邦杰), Wang M-X(王茂新), Pei Z-Y(裴志远). Monitoring freeze injury to winter wheat using remote sensing. Trans CSAE (农业工程学报), 2002, 18(2): 136–140 (in Chinese with English abstract)
[8]Mirik M, Michels Jr G J, Kassymzhanova-Mirik S, Elliott N C. Reflectance characteristics of Russian wheat aphid (Hemiptera: Aphididae) stress and abundance in winter wheat. Comput Electron Agric, 2007, 57: 123–134
[9]Sun H(孙红), Li M-Z(李民赞), Zhou Z-Y(周志艳), Liu G(刘刚), Luo X-W(罗锡文). Monitoring of cnaphalocrocis medinalis guenee based on canopy reflectance. Spectroscopy Spectral Anal (光谱学与光谱分析), 2010, 30(4): 1080–1083 (in Chinese with English abstract)
[10]Johnson D A, Richard Alldredge J, Hamm P B, Frazier B E. Aerial photography used for spatial pattern analysis of late blight infection in irrigated potato circles. Phytopathology, 2003, 93: 805–812
[11]Huang W J, Lamb D W, Niu Z, Zhang Y J, Liu Y J, Wang J H. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precis Agric, 2007, 8: 187–197
[12]Pu R L, Kelly M, Anderson G L, Gong P. Using CASI hyperspectral imagery to detect mortality and vegetation stress associated with a new hardwood forest disease. Photogramm Eng Rem Sens, 2008, 74: 65–75
[13]Wu D(吴迪), Feng L(冯雷), Zhang C-Q(张传清), He Y(何勇). Early detection of gray mold (Cinerea) on eggplant leaves based on vis/near infrared spectra. J Infrared Mill Waves (红外与毫米波学报), 2007, 26(4): 269–273 (in Chinese with English abstract)
[14]Lathrop L D, Pennypacker S. Spectral classification of tomato disease severity levels. Photogramm Eng Rem Sens, 1980, 46: 1133–1138
[15]Malthus T J, Madeira A C. Height resolution spectradiometry: spectral reflectance of field bean leaves infected by Botrytis fabae. Remote Sens Environ, 1993, 45: 107–116
[16]Zhang H(张浩), Mao X-Q(毛雪琴), Zhang Z(张震), Zheng K-F(郑可锋), Du X-F(杜新法), Sun G-C(孙国昌). Hyperspectral remote sensing retriveral models of rice neck blasts severity. Res Agric Mod (农业现代化研究), 2009, 30(3): 369–372 (in Chinese with English abstract)
[17]Franke J, Menz G. Multi-temporal wheat disease detection by multi-spectral remote sensing. Precis Agric, 2007, 8: 161–172
[18]Liu L-Y(刘良云), Song X-Y(宋晓宇), Li C-J(李存军), Qi L(齐腊), Huang W-J(黄文江), Wang J-H(王纪华). Monitoring and evaluation of the diseases of and yield winter wheat from multi-temporal remotely-sensed data. Trans CSAE (农业工程学报), 2009, 25(1): 137–143 (in Chinese with English abstract)
[19]Zhang H-M(张宏名), Li Q-J(李庆基), Wang J-S(王家圣). The mathod for detecting withered and Verticillium wilt of cotton by remote sensing. Plant Protect (植物保护), 1991, 17(6): 6–8 (in Chinese)
[20]Jing X(竞霞), Huang W-J(黄文江), Ju C-Y(琚存勇), Xu X-G(徐新刚). Remote sensing monitoring severit level of cotton Verticillium wilt base on partial least squares regressive analysis. Trans CSAE (农业工程学报), 2010, 26(8): 229–235 (in Chinese with English abstract)
[21]Liu J, Pattey E, Miller J R, McNairn H, Smith A M, Hu B.Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model. Remote Sens Environ, 2010, 114: 1167–1177
[22]Chen B, Wang K R, Li S K, Xiao C H, Chen J L, Jin X L. Estimating severity level of cotton infected Verticillium wilt based on spectral indices of TM image. Sensor Lett, 2011, 9: 1157–1163
[23]Feng Z-C(冯志超). Effect of withered and Verticillium wilts of cotton in kuytun reclamation area on the yield and their control tactics. Xinjiang Agric Sci (新疆农业科学), 2004, 41(5): 367–369 (in Chinese with English abstract)
[24]Qin P(秦鹏), Chen J-F(陈健飞). Comparison between color normalized and HSV sharpen in methods in extracting urban vegetation information from ASTER image. J Geoinformation Sci (地球信息科学学报), 2009, 11(3): 400–404 (in Chinese with English abstract)
[25]Sivakumar M V K, Roy P S, Harmsen K, Saha S K. Satellite remote sensing and GIS applications in agricultural meteorology. World Meteorological Organization 7bis, Avenue de la Paix1211 Geneva 2, Switzerland.2004
[26]Chen B(陈兵), Li S-K(李少昆), Wang K-R(王克如), Bai J-H(柏军华), Sui X-Y(隋学艳), Bai C-Y(白彩云). Studies of remote sensing on monitoring crop diseases and pests. Cotton Sci (棉花学报), 2007, 19(1): 57–63 (in Chinese with English abstract)
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