刚竹毒蛾危害下的毛竹叶片光谱特征波长研究
|
摘要
旨在获取刚竹毒蛾危害下的毛竹叶片光谱特征波长,以助于该虫害的有效、准确识别。将于福建省顺昌县实测的105条高光谱数据随机划分为实验组(71条)和验证组(34条)。基于实验组数据,利用单因素方差分析获取健康、轻度危害、中度危害、重度危害等虫害等级间具有极显著差异的波长;结合常用遥感卫星的波段设置对上述波长进行筛选,采用欧式距离、相关系数及光谱角匹配等3种方法判定其虫害判别能力,获取特征波长,并引入验证组样本对其予以验证。结果表明:(1)受害叶片的光谱反射率明显低于健康叶片,虫害等级越高,其反射率越低;(2)受害叶片的光谱特征变化较大,随着虫害等级的上升,其光谱曲线中的"绿峰"及"红谷"趋于消失,"红边"斜率逐渐减小;(3)确定原始光谱703. 43～898. 56 nm及一阶微分光谱497. 68～540. 72,554. 53～585. 25和596. 24～618. 23 nm为刚竹毒蛾危害下的毛竹叶片光谱特征波长,其对该虫害具有较强的判别能力。该研究从叶片尺度剖析了寄主对刚竹毒蛾的响应机理,是"地-天"耦合的理论基础,可为虫害遥感监测技术体系的建立提供重要依据。
The paper aims to obtain the characteristic wavelengths of moso bamboo leaves damaged by Pantana phyllostachysae chao,with which the pest can be identified effectively and accurately. 105 hyperspectral data collected in Shunchang County,Fujian Province were randomly divided into two groups,i. e. the experimental group(71) and verificantion group(34). Selecting wavelengths which were highly significant differences between different pest levels group by One-way ANOVA. The wavelengths were screened by combining the wave band of the commonly used remote sensing satellite. The ability to discriminate between the pests of the selected wavelengths was analyzed by the Euclidean distance method,spectral angle mapping method and correlation coefficient method. According to the analysis results,the characteristic wavelengths were obtained and verified. The results showed that:(1) the spectral reflectance of moso bamboo leaves damaged by P. phyllostachysae were significantly lower than that of healthy leaves,and the higher the pest level is,the lower the reflectance will be;(2) with the increase in pest level,the spectral reflectance curves' "green peak"and"red balley"of Pinus massoniana gradually disappeared,and the red edge was leveled;(3) the characteristic wavelengths of 703. 43 ～ 898. 56 nm( original spectrum) and 497. 68 ～ 540. 72,554. 53 ～ 585. 25,596. 24 ～ 618. 23 nm( first derivative spectrum) were determined,which had good response ability at different pest levels. Our findings will not only provide the theoretical guidliances for"ground-space"coupling,but also provide important basis for establishing the system of pest remote sensing monitoring technology.
The paper aims to obtain the characteristic wavelengths of moso bamboo leaves damaged by Pantana phyllostachysae chao,with which the pest can be identified effectively and accurately. 105 hyperspectral data collected in Shunchang County,Fujian Province were randomly divided into two groups,i. e. the experimental group(71) and verificantion group(34). Selecting wavelengths which were highly significant differences between different pest levels group by One-way ANOVA. The wavelengths were screened by combining the wave band of the commonly used remote sensing satellite. The ability to discriminate between the pests of the selected wavelengths was analyzed by the Euclidean distance method,spectral angle mapping method and correlation coefficient method. According to the analysis results,the characteristic wavelengths were obtained and verified. The results showed that:(1) the spectral reflectance of moso bamboo leaves damaged by P. phyllostachysae were significantly lower than that of healthy leaves,and the higher the pest level is,the lower the reflectance will be;(2) with the increase in pest level,the spectral reflectance curves' "green peak"and"red balley"of Pinus massoniana gradually disappeared,and the red edge was leveled;(3) the characteristic wavelengths of 703. 43 ～ 898. 56 nm( original spectrum) and 497. 68 ～ 540. 72,554. 53 ～ 585. 25,596. 24 ～ 618. 23 nm( first derivative spectrum) were determined,which had good response ability at different pest levels. Our findings will not only provide the theoretical guidliances for"ground-space"coupling,but also provide important basis for establishing the system of pest remote sensing monitoring technology.
引文
[1] Radeloff V C,Mladenoff D J,Boyce M S. Remote Sensing of Envionment,1999,69(2):156.
[2] Latifi H,Schumann B,Kautz M,et al. Environmental Monitoring and Assessment,2014,186(1):441.
[3] ZHU Chen-hao,QU Shuai,ZHANG Xiao-li(朱程浩,瞿帅,张晓丽). Journal of Remote Sensing(遥感学报),2016,20(4):653.
[4] LIANG Guang-hong(梁光红). Journal of Fujian Forestry Science and Technology(福建林业科技),2003,30(1):34.
[5] CHEN De-liang,ZHAO Ren-you,QU Qiao-wen(陈德良,赵仁友,瞿巧文). Forest Pest and Disease(中国森林病虫),2006,25(6):14.
[6] WU Jian-qin(吴建勤). Plant Protection(植物保护),2010,36(3):172.
[7] LI Xing-tian(李兴天). Modern Horticulture(现代园艺),2013,(1):153.
[8] Niemann K O,Quinn G,Stephen R,et al. Canadian Journal of Remote Sensing,2015,41(3):191.
[9] Ju Y W,Pan J,Wang X T,et al. Nematology,2014,16:1197.
[10] LIU Fei,FENG Lei,CHAI Rong-yao,et al(刘飞,冯雷,柴荣耀,等). Acta Optica Sinica(光学学报),2010,30(2):585.
[11] Agrios G N. Plant Pathology(植物病理学). Beijing:China Agricultural Press(北京:中国农业大学出版社),2009.
[12] LI Min-zan(李民赞). Spectral Analysis Techniques and Applications(光谱分析技术及其应用). Beijing:Science Press(北京:科学出版社),2006.
[13] Jia J,Quan W. Ecological Informatics,2016,35:1.
[14] WU Wei-bin,ZHI Lei,HONG Tian-sheng,et al(吴伟斌,支磊,洪添胜,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2013,29(10):175.
[15] WANG Qiang,YI Qiu-xiang,BAO An-ming,et al(王强,易秋香,包安明,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2012,28(15):125.
[16] LI Ying,ZHANG Li-fu,YAN-Wei,et al(李颖,张立福,严薇,等). Journal of Remote Sensing(遥感学报),2013,17(4):855.
[17] Pu R L,Gong P,Biging G S,et al. IEEE Transactions on Geoscience and Remote Sensing,2003,341(4):916.
[18] Cho M A,Debba P,Mutanga O,et al. International Journal of Applied Earth Observation and Geoinformation,2012,16(16):85.
[19] GONG Zhao-ning,ZHAO Ya-li,ZHAO Wen-ji,et al(宫兆宁,赵雅莉,赵文吉,等). Acta Ecologica Sinica(生态学报),2014,34(20):5736.
[20] JI Rong-hua,ZHENG Li-hua,DENG Xiao-lei,et al(冀荣华,郑立华,邓小蕾,等). Transactions of The Chinese Society of Agricultural Machinery(农业机械学报),2014,45(8):269.
[21] LIU Xiao-jun,TIAN Yong-chao,YAO Xia,et al(刘小军,田永超,姚霞,等). Scientia Agricultura Sinica(中国农业科学),2012,45(3):435.
[2] Latifi H,Schumann B,Kautz M,et al. Environmental Monitoring and Assessment,2014,186(1):441.
[3] ZHU Chen-hao,QU Shuai,ZHANG Xiao-li(朱程浩,瞿帅,张晓丽). Journal of Remote Sensing(遥感学报),2016,20(4):653.
[4] LIANG Guang-hong(梁光红). Journal of Fujian Forestry Science and Technology(福建林业科技),2003,30(1):34.
[5] CHEN De-liang,ZHAO Ren-you,QU Qiao-wen(陈德良,赵仁友,瞿巧文). Forest Pest and Disease(中国森林病虫),2006,25(6):14.
[6] WU Jian-qin(吴建勤). Plant Protection(植物保护),2010,36(3):172.
[7] LI Xing-tian(李兴天). Modern Horticulture(现代园艺),2013,(1):153.
[8] Niemann K O,Quinn G,Stephen R,et al. Canadian Journal of Remote Sensing,2015,41(3):191.
[9] Ju Y W,Pan J,Wang X T,et al. Nematology,2014,16:1197.
[10] LIU Fei,FENG Lei,CHAI Rong-yao,et al(刘飞,冯雷,柴荣耀,等). Acta Optica Sinica(光学学报),2010,30(2):585.
[11] Agrios G N. Plant Pathology(植物病理学). Beijing:China Agricultural Press(北京:中国农业大学出版社),2009.
[12] LI Min-zan(李民赞). Spectral Analysis Techniques and Applications(光谱分析技术及其应用). Beijing:Science Press(北京:科学出版社),2006.
[13] Jia J,Quan W. Ecological Informatics,2016,35:1.
[14] WU Wei-bin,ZHI Lei,HONG Tian-sheng,et al(吴伟斌,支磊,洪添胜,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2013,29(10):175.
[15] WANG Qiang,YI Qiu-xiang,BAO An-ming,et al(王强,易秋香,包安明,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2012,28(15):125.
[16] LI Ying,ZHANG Li-fu,YAN-Wei,et al(李颖,张立福,严薇,等). Journal of Remote Sensing(遥感学报),2013,17(4):855.
[17] Pu R L,Gong P,Biging G S,et al. IEEE Transactions on Geoscience and Remote Sensing,2003,341(4):916.
[18] Cho M A,Debba P,Mutanga O,et al. International Journal of Applied Earth Observation and Geoinformation,2012,16(16):85.
[19] GONG Zhao-ning,ZHAO Ya-li,ZHAO Wen-ji,et al(宫兆宁,赵雅莉,赵文吉,等). Acta Ecologica Sinica(生态学报),2014,34(20):5736.
[20] JI Rong-hua,ZHENG Li-hua,DENG Xiao-lei,et al(冀荣华,郑立华,邓小蕾,等). Transactions of The Chinese Society of Agricultural Machinery(农业机械学报),2014,45(8):269.
[21] LIU Xiao-jun,TIAN Yong-chao,YAO Xia,et al(刘小军,田永超,姚霞,等). Scientia Agricultura Sinica(中国农业科学),2012,45(3):435.