马铃薯甲虫遥感监测技术研究
|
摘要
本文以马铃薯甲虫(Leptinotarsa decemlineata)为研究对象,采用遥感手段分析受害马铃薯冠层光谱特征;提取受害马铃薯的面积;反演马铃薯甲虫在我国其他地区的适宜温度和寄主分布范围。通过分析,得出如下结论:
1. 2008、2009年在新疆农科院植保所粮农厂基地天敌繁育中心(87°28′E,43°56′N),使用ASD光谱仪采集受害马铃薯试验地的冠层光谱数据,结果表明受害马铃薯冠层光谱反射率仍具有“绿峰”、“红谷”、“红外高台阶”特征;在750.0～935.0 nm范围内,受害后的马铃薯冠层反射率与健康马铃薯相比明显下降。随着危害加重,近红外波段的反射率下降;经光谱微分处理后,随危害加重,红边斜率下降,红边位置变化不大;此外选择了受害马铃薯的敏感波段为736.0～920.0 nm。
2.利用QuickBird卫星影像反演新疆伊犁受害马铃薯地(81°11′E,43°50′N)的危害情况,并同步测量地面光谱反射率。将不同受害程度的地面光谱数据进行比较分析,发现随受害程度加重,近红外光谱反射率明显下降;红边位置在720.0 nm处;采用“K-Means”方法分类,分为健康马铃薯地块、受害马铃薯地块和其他。结果显示受害马铃薯面积为1296.00 m~2,健康马铃薯面积为1474.56 m~2,其他地物面积为950.40 m~2,地面调查发现受害面积为939.00 m~2,健康面积为2246.00m~2,其他地物面积为565.00 m~2。经精度检验,总精度为76.27 %,kappa系数为0.5024。
3.利用MODIS温度和植被指数数据对马铃薯甲虫在全国范围内的适宜温度和寄主分布范围进行分析。认为我国华北、华中、华东大部分地区适合马铃薯甲虫的存活,新疆巴里坤、哈密地区具备马铃薯甲虫存活的温度和寄主条件,哈密以东至甘肃河西走廊一带植被较少,是马铃薯甲虫传入中国内陆最可能通道。
In the paper, the canopy spectral characteristics of potatoes damaged by Colorado potato beetle were analyzed; The area of damaged potatoes was extracted and the appropriate temperature and host of the potato beetle were retrieved around China. The important results are listed as follows:
1. The canopy spectrum of damaged potatoes in the field was collected using ASD FieldSpec HandHeld at the Natural Enemy Breeding Center of the Institute of Plant Protection in Xinjiang Academy of Agricultural Sciences in 2008 and 2009. The results showed that the canopy spectral reflectance of damaged potatoes was characterized by the“green peak”,“red valley”,“infrared high bench”, being significantly lower than that of healthy plants within the range of 750.0 to 935.0 nm. The infrared spectral reflectance decreased with the increase in damage. Using derivative spectrum technique, the result suggested red edge slopes decreased while the position remained unchanged. In addition, sensitive wave bands were found between 736.0 to 920.0 nm.
2. The information on the damage to the potatoes in Yili experimental field in Xinjiang(81°11′E, 43°50′N)was retrieved by QuickBird image, and the canopy spectral reflectance was also measured in the field. Compared with the potato canopy spectral reflectance of different degrees of damage, the infrared spectral reflectance decreased obviously as damage increased.The position of the red edge was 720.0 nm. By“K-means”method, the QuickBird image was classified into three kinds: the healthy potato field, the damaged field and others. The results showed that the area of damaged potato was 1296 m~2, the healthy area was 1474.56 m~2, and rest was 950.40 m~2. Investigation of the field revealed that the damaged area was 939.00 m~2, the healthy area was 2246.00 m~2 and rest was 565.00 m~2. By precision test, the total precision was 76.27 %, and kappa coefficient was 0.5024.
3. The appropriate temperature and host of the potato beetle were analyzed around China using MODIS temperature and Vegetations Index image. The results showed that Colorado potato beetle can survive in most areas of north, central and east China. Especially, it can live in Balikun and Hami in Xinjiang because of suitable temperature and host, as there is the most possible route between the east of Hami to Hexi Corridor of Gansu along which the beetle can invade the inland of China.
1. 2008、2009年在新疆农科院植保所粮农厂基地天敌繁育中心(87°28′E,43°56′N),使用ASD光谱仪采集受害马铃薯试验地的冠层光谱数据,结果表明受害马铃薯冠层光谱反射率仍具有“绿峰”、“红谷”、“红外高台阶”特征;在750.0～935.0 nm范围内,受害后的马铃薯冠层反射率与健康马铃薯相比明显下降。随着危害加重,近红外波段的反射率下降;经光谱微分处理后,随危害加重,红边斜率下降,红边位置变化不大;此外选择了受害马铃薯的敏感波段为736.0～920.0 nm。
2.利用QuickBird卫星影像反演新疆伊犁受害马铃薯地(81°11′E,43°50′N)的危害情况,并同步测量地面光谱反射率。将不同受害程度的地面光谱数据进行比较分析,发现随受害程度加重,近红外光谱反射率明显下降;红边位置在720.0 nm处;采用“K-Means”方法分类,分为健康马铃薯地块、受害马铃薯地块和其他。结果显示受害马铃薯面积为1296.00 m~2,健康马铃薯面积为1474.56 m~2,其他地物面积为950.40 m~2,地面调查发现受害面积为939.00 m~2,健康面积为2246.00m~2,其他地物面积为565.00 m~2。经精度检验,总精度为76.27 %,kappa系数为0.5024。
3.利用MODIS温度和植被指数数据对马铃薯甲虫在全国范围内的适宜温度和寄主分布范围进行分析。认为我国华北、华中、华东大部分地区适合马铃薯甲虫的存活,新疆巴里坤、哈密地区具备马铃薯甲虫存活的温度和寄主条件,哈密以东至甘肃河西走廊一带植被较少,是马铃薯甲虫传入中国内陆最可能通道。
In the paper, the canopy spectral characteristics of potatoes damaged by Colorado potato beetle were analyzed; The area of damaged potatoes was extracted and the appropriate temperature and host of the potato beetle were retrieved around China. The important results are listed as follows:
1. The canopy spectrum of damaged potatoes in the field was collected using ASD FieldSpec HandHeld at the Natural Enemy Breeding Center of the Institute of Plant Protection in Xinjiang Academy of Agricultural Sciences in 2008 and 2009. The results showed that the canopy spectral reflectance of damaged potatoes was characterized by the“green peak”,“red valley”,“infrared high bench”, being significantly lower than that of healthy plants within the range of 750.0 to 935.0 nm. The infrared spectral reflectance decreased with the increase in damage. Using derivative spectrum technique, the result suggested red edge slopes decreased while the position remained unchanged. In addition, sensitive wave bands were found between 736.0 to 920.0 nm.
2. The information on the damage to the potatoes in Yili experimental field in Xinjiang(81°11′E, 43°50′N)was retrieved by QuickBird image, and the canopy spectral reflectance was also measured in the field. Compared with the potato canopy spectral reflectance of different degrees of damage, the infrared spectral reflectance decreased obviously as damage increased.The position of the red edge was 720.0 nm. By“K-means”method, the QuickBird image was classified into three kinds: the healthy potato field, the damaged field and others. The results showed that the area of damaged potato was 1296 m~2, the healthy area was 1474.56 m~2, and rest was 950.40 m~2. Investigation of the field revealed that the damaged area was 939.00 m~2, the healthy area was 2246.00 m~2 and rest was 565.00 m~2. By precision test, the total precision was 76.27 %, and kappa coefficient was 0.5024.
3. The appropriate temperature and host of the potato beetle were analyzed around China using MODIS temperature and Vegetations Index image. The results showed that Colorado potato beetle can survive in most areas of north, central and east China. Especially, it can live in Balikun and Hami in Xinjiang because of suitable temperature and host, as there is the most possible route between the east of Hami to Hexi Corridor of Gansu along which the beetle can invade the inland of China.
引文
1.程义美,崔良刚,陈月颖,史殿军,王威.马铃薯甲虫在吉林省发生的可能性分析.吉林农业科学,2005,30(3):33～35.
2.董瑶海. FY-1C/D极轨气象卫星及其在轨运行.上海航天,2004,(4):1～11.
3.郭洁滨,黄冲,王海光,马占鸿.基于SPOT5影像的小麦条锈病遥感监测初探.植物保护学报,2009,36(5):473～474.
4.何国金,胡德永,金小华,杨建国,李国扬.北京麦蚜虫害的光谱测量与分析.遥感技术与应用,2002,17(3):119～124.
5.洪波.基于GIS的有害生物空间分布预测系统研究. [硕士学位论文].西北农林科技大学,2009.
6.黄幼玲.马铃薯甲虫的入侵及其防控对策.中国农业小康科技,2007,11:58～59.
7.季荣,张霞,谢宝瑜,李哲,刘团结,刘闯.用MODIS遥感数据监测东亚飞蝗灾害—以河北省南大港为例.昆虫学报,2003,46(6):713～719.
8.金哲雄,娄巍,席家文.加强中俄边界马铃薯甲虫调查及监测.植物检疫,1995,9(1):51～52.
9.李红,秦晓辉,赛丽蔓.博州地区马铃薯甲虫发生特点与综合防治技术.中国蔬菜,2007,7:55～56.
10.李开丽,倪绍祥.东亚飞蝗生境的遥感分类-以河北省黄骅地区为例.地理研究, 2006,25(4):579～585.
11.李学湛,吕典秋.东北地区脱毒马铃薯种薯质量安全现状及建议.农业质量标准,2004(4):36～37.
12.梁忆冰,林伟,王跃进,徐亮,翟图娜,陆平.生态因子在马铃薯甲虫地理分布中的作用.植物检疫,1999,13(5):3～8.
13.刘良云,黄木易,黄文江,王纪华,赵春江,郑兰芬.利用多时相的高光谱航空图像监测冬小麦条锈病.遥感学报,2004,8(3):275～281.
14.刘玉洁,杨忠东. MODIS遥感信息处理原理与算法.北京:科学出版社,2001:1～3.
15.刘振波,倪绍祥,查勇,葛云健.河北省黄骅市三个重点蝗区两个时段土壤湿度的遥感提取.动物学研究,2006,27(3):281～285.
16.倪绍祥,蒋建军,王杰臣.遥感与GIS在蝗虫灾害防治研究中的应用进展.地球科学进展, 2000,15(1):97～100.
17.浦瑞良,宫鹏.高光谱遥感及其应用.北京:高等教育出版社,2000,3～5,89～93.
18.齐静,程登发,乔红波,张云慧,陈林,孙京瑞,洪波.马铃薯受马铃薯甲虫危害后冠层光谱特征的初步研究.植物保护,2010,36(1):69～72.
19.齐静,程登发,张云慧,孙京瑞.马铃薯甲虫危害后马铃薯冠层光谱数据分析.见:成卓敏(编).粮食安全与植保科技创新.北京:中国农业科学技术出版社,2009,308～311.
20.钱天荣,曲能治,陈宏,周永淑.马铃薯甲虫在我国及周边地区适生地的初步预测.植物检疫,1995,9(2):95～97.
21.乔红波,程登发,孙京瑞,田喆，陈林，林芙蓉.麦蚜对小麦冠层光谱特性的影响研究.植物保护,2005,31(2):21～26.
22.沙尼彦,玛依拉.云菊乳油防治马铃薯甲虫试验示范.农业信息与技术,2008,17:38.
23.石瑞香,刘闯,李典谟,谢宝瑜.蝗虫生境监测方法研究进展.生态学报,2003,23(11):2476～2482.
24.隋广义,焦晓丹,袁来喜.马铃薯甲虫入侵黑龙江省的风险植物检疫,2008,22(6):369～371.
25.吐尔逊.马铃薯甲虫发生规律检疫和防治技术研究[硕士学位论文].乌鲁木齐:新疆农业大学,1995.
26.王俊,王登元,侯洪.新疆马铃薯甲虫的发生与防治现状.新疆农业科技,2008,3:60.
27.王正军,张爱兵,李典谟.遥感技术在昆虫生态学中的应用途径与进展.昆虫知识,2003,40(2):97～100.
28.吴继友,倪建.松毛虫危害的光谱特征与虫害早期探测模式.环境遥感,1995,10(1):250～251.
29.吴曙雯,王人潮,陈晓斌,沈掌泉,史舟.稻叶瘟对水稻光谱特性的影响研究.上海交通大学学报:农业科学版,2002,20(1):73～84.
30.杨海霞,陈红印,李强.浅谈马铃薯甲虫的生物防治.植物检疫,2007,21(6):368～372.
31.杨青生,刘闯. MODI S数据陆面温度反演研究.遥感技术与应用,2004,19(2):90～93.
32.姚春生,张增祥,汪潇.使用温度植被干旱指数法(TVDI)反演新疆土壤湿度.遥感技术与应用,2004,19(6):473～478.
33.曾庆财,于丽萍,吕兴盛.在中俄边境地区建立马铃薯甲虫疫情监测网势在必行.黑龙江对外经贸,2002,5:60.
34.张永强,文丽萍,石洁,王振营,何康来,程登发.亚洲玉米螟为害后玉米冠层光谱变化和产量损失研究.植物保护,2007,33(5):54～57.
35.张玉萍,郭洁滨,王爽,王海光,马占鸿.小麦条锈病卫星与近地光谱反射率的比较.植物保护学报,2009,36(2):119～122.
36.赵博光.龙葵对马铃薯甲虫产卵的引诱作用.植物检疫,1996,10(3):137～138.
37.赵红山,秦晓辉,马德成,杨栋,夏正汉,付文君. 7种药剂对马铃薯甲虫的防治效果评价.植物检疫,2007,21(4):217～219.
38.赵建周.国外马铃薯甲虫发生危害与防治概况.植物保护,1995,21(4):35～36.
39.赵英时.遥感应用分析原理与方法.北京:科学出版社,2003.
40.……. 2008-06-29取自http://www.sfhciq.com/old/common/ywgz/2005/20051102-2.htm
41.……. 2008-09-20取自http://www.nfiieos.cn/
42.……. 2009-02-05取自http://www.rsgs.ac.cn/2_quickbird.html
43. Ashouri, A., Michaud, D., & Cloutier, C. Recombinant and classically selected factors of potato plant resistance to the Colorado potato beetle, Leptinotarsa decemlineata, variously affect the potato aphid parasitoid Aphidius nigripes. BioControl, 2001, 46(4): 401～418.
44. Baret, F., Guyot, G., & Major, D. TSAVI: a vegetation index which minimizes soil brightness effects on LAI or APAR estimation. 12th Canadian Symposium on Remote Sensing and IGARSS, Vancouver, Canada, 1990, 7: 10～14.
45. Broge, N. H., & Mortensen, J. V. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral re?ectance data. Remote Sensing of Environment, 2002, 81: 45～57.
46. Carter, G. A. Responses of leaf spectral re?ectance to plant stress. American Journal of Botany, 1993, 80: 239～243.
47. Casagrande, R. A. The“Iowa”potato beetle, its discovery and spreads to potatoes. Bulletin of the Entomological Society of America, 1985, 31(2): 27～29.
48. Casagrande, R. A. The potato beetle: 125 years of mismanagement.Bull. Entomol Soc Am, 1987, 33(2): 142～150.
49. Clevers, J. G. P. W. The derivation of a simplified reflectance model for the estimation of leaf area index. Remote Sensing of Environment, 1988, 35: 53～70.
50. Coops, N. C. Johnson, M., Wulder, M. A., & White, J. C. Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation. Remote Sensing of Environment, 2006, 103: 67～80.
51. Crippen, R.E. Calculating the vegetation index faster. Remote Sensing of Environment, 1990, 34: 71～73.
52. Deering, D.W., Rouse, J.W., Haas, R.H., & Schell, J.A. Measuring forage production of grazing units from Landsat MSS data. Proceedings of Tenth International Symposium on Remote Sensing of Environment Ann Arbor ERIM, 1975, 2: 1169～1178.
53. Duchesne, R. M., & Parent, J. G.. The flowering tobacco as a new alternative host for the Colorado potato beetle. American Journal of Potato Research, 1991, 68(11): 743～750.
54. Fernandez, S., Vidal, D., Simon, E., & Sole-sugarnes, L. Radiometric characteristic of Triticum aestivum cv. Astral under water and nitrogen stress. International Journal of Remote Sensing, 1994, 15(9): 1867～1884.
55. Ferro, D. N., Logan, J. A., Voss. R. H., & Elkinton, J. S. Colorado potato beetle temperature development growth and feeding rates. Environmental Entomology, 1985, 14: 343～348.
56. Franke, J., & Menz, G. Multi-temporal wheat disease detection by multi-spectral remote sensing. Precision Agriculture, 2007, 8: 161～172.
57. Gamon, J. A., & Surfus, J. S. Assessing leaf pigment content andactivity with a reflectometer. New Phytologist, 1999, 143: 105～117.
58. Habutová, O., Weismann, L'., Harangozó, M., & Bumbálová, A. Influence of Copper from the Kuprikol 50 Fungicide on Colorado Potato Beetle Adults Studied by Radionuclide X—Ray Fluorescence Analysis. Journal of Radioanalytical and Nuclear Chemistry, 2000, 243(3): 825～826.
59. Hare, J. D. Ecology and management of the Colorado potato beetle. Ann Rev Entomol, 1990, 35: 81～100.
60. Hatfield, J. L., & Pinter, P. J. Remote sensing for crop protection. Crop Protection, 1993, 12(6): 403～413.
61. Horler, D. N. H., Dockray, M., & Barber, J. The red edge of plant leaf reflectance. International Journal of Remote Sensing, 1983, 4: 273～288.
62. Huete, A.R. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 1988, 25: 295～309.
63. Hurst, G. W. Meteorology and the Colorado potato beetle. World Meteorological Organization Technical Notes. 1975, 137: 1～51.
64. Jacques, Jr H. L. The Potato Beetles, The genus Leptinotarsa in North America (Coleoptera: Chrysomelidae) Flora & Fauna Handbook, 1988, 3: 1～143.
65. Jordan, C. F. Derivation of leaf area index from quality of light on the forest floor. Ecology, 1969, 50(4): 663～666.
66. Jordan, C.F. Derivation of leaf area index from quality of light on the forest floor. Ecology, 1969, 50: 663～666.
67. Kaitazov, A. Bio ecological studies on the Colorado potato beetle in Bulgaria. Rast Vud Nauki, 1964, 1(2): 123～132.
68. Kaufman, Y. J., & Tanré, D. Atmospherically resistant vegetation index-ARVI for EOS/MODIS. IEEE Transactions on Geosciences and Remote Sensing, 1992, 30(2): 261～270.
69. Kauth R. J., & Thomas G.S. The tasseled cap-A graphic description of the spectral temporal development of agricultural crops as seen by Landsat. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data. Purdue University, West Lafayette, Indiana. 1976: 41～51.
70. Lillesand, T. M., & Kiefer, R.W. Remote sensing and image interpretation, 2nd edition, John Wiley and Sons, New York, Chichester, Brisbane, Toronto, Singapore. 1987: 721.
71. Ma, J. W., Han, X. Z., Gan, H. S. B., Wang, C. L., Zhang, Y. J., &Tang. J. Y. Monitoring East Asian migratory locust plagues using remote sensing data and field investigations. International Journal of Remote Sensing, 2005, 26 (3): 629～634.
72. Malthus, T. J., & Madeira, A. C. High resolution spectroradiometry: spectral re?ectance of field beans leaves infected by Botrytisfabae. Remote Sensing of Environment, 1993, 45: 107～116.
73. Minder, I. F., & Chesnek, S. I. The dependence of cold resistance in the Colorado potato beetle on the time of the onset of diapause. Zoologicheskii Zhurnal, 1970, 49(6): 855～861.
74. Mirik, M., Michels, G. J. Jr., Kassymzhanova-Mirik, S., & Elliott, N. C. Reflectance characteristics of Russian wheat aphid (Hemiptera: Aphididae) stress and abundance in winter wheat. Computers and Electronics in Agriculture, 2007, 57(2): 123～134.
75. Moran, M. S., Inoue, Y., & Barnes, E. M. Opportunities and limitations for image-based remote sensing in Precision Crop Management. Remote Sensing of Environment, 1997, 61: 319～346.
76. Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, L., & McCartney, A. Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps. Real-Time Imaging, 2005, 11: 75～83.
77. Penuelas, J., & Filella, I. Visible and near-infrared re?ectance techniques for diagnosing plant physiological status. Trends in Plant Science, 1998, 3: 151～156.
78. Qi, J., Chehbouni, A., Huete, A. R., & Ker, Y. H. Modified Soil Adjusted Vegetation Index (MSAVI). Remote Sensing of Environment, 1994a, 48: 119～126.
79. Qi, J., Kerr, Y., & Chehbouni, A. External factor consideration in vegetation index development. Proc. of Physical Measurements and Signatures in Remote Sensing, ISPRS, 1994b: 723～730.
80. Richardson, A. J., & Wiegand, C. L. (1977). Distinguishing vegetation from soil background information. Photogramm Eng Rem, S43, 1541～1552.
81. Riley, J. R. Remote sensing in entomology. Annual Review of Entomology, 1989, 34: 247～271.
82. Rouse, J.W., Haas, R.H., Schell, J.A., & Deering, D.W. Monitoring vegetation systems in the great plains with ERTS. Third ERTS Symposium, NASA SP-351. 1973, 1: 309～317.
83. Shibayama, M., Takahashi, W., Morinaga, S., & Akiyama, T. Canopy water deficit detection in paddy rice using a high resolution field spectroradiometer. Remote Sensing of Environment, 1993, 45: 117～126.
84. Sims, D. A., & Gamon, J. A. Relationships between leaf pigments content and spectral re?ectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 2002, 81: 337～354.
85. Stuben, M. On the influence of light on Leptinotarsa. decemlineata. I. The influence of light intensity on the oviposition. Z PflK rankh PflSchutz, 1964, 71: 533～538.
86. Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 1979, 8: 127～150.
87. Ustin, S. L., Zomer, R. J., Garcia, M., Roberts, D. A., & Green, R. O. Remote sensing methods monitor natural resources. Photonics Spectra, 1999, 33(10): 108～113.
88. Voss, R. H., & Ferro, D. N. Ecology of migrating Colorado potato beetles (Coleoptera: Chrysomelidae) in western Massachusetts. Environmental entomology, 1990, 19: 123～129.
89. Whalen, M. D. Speciation in Solanum, Section Androceras. In: J. G. Hawkes, R.N. Lester. & A. D. Skelding (Eds.), The biology and taxonomy of the Solanaceae. London: Acadmic press, 1979: 581～596.
90. Worner, S. P. Establishment of exotic pests. Journal of Economic Entomology, 1988, 81(40): 973～983
91. Yang, Z., Rao, M. N., Elliott, N. C., Kindler, S. D., & Popham, T. W. Using ground-based multispectral radiometry to detect stress in wheat caused by greenbug (Homoptera: Aphididae) infestation. Computers and Electronics in Agriculture, 2005, 47 (2): 121～135.
92. Yang, Z., Rao, M. N., Elliott, N. C., Kindler, S. D., & Popham, T. W. Differentiating stress induced by greenbugs and Russian wheat aphids in wheat using remote sensing. Computers and Electronics in Agriculture, 2009, 67(1-2): 64～70.
2.董瑶海. FY-1C/D极轨气象卫星及其在轨运行.上海航天,2004,(4):1～11.
3.郭洁滨,黄冲,王海光,马占鸿.基于SPOT5影像的小麦条锈病遥感监测初探.植物保护学报,2009,36(5):473～474.
4.何国金,胡德永,金小华,杨建国,李国扬.北京麦蚜虫害的光谱测量与分析.遥感技术与应用,2002,17(3):119～124.
5.洪波.基于GIS的有害生物空间分布预测系统研究. [硕士学位论文].西北农林科技大学,2009.
6.黄幼玲.马铃薯甲虫的入侵及其防控对策.中国农业小康科技,2007,11:58～59.
7.季荣,张霞,谢宝瑜,李哲,刘团结,刘闯.用MODIS遥感数据监测东亚飞蝗灾害—以河北省南大港为例.昆虫学报,2003,46(6):713～719.
8.金哲雄,娄巍,席家文.加强中俄边界马铃薯甲虫调查及监测.植物检疫,1995,9(1):51～52.
9.李红,秦晓辉,赛丽蔓.博州地区马铃薯甲虫发生特点与综合防治技术.中国蔬菜,2007,7:55～56.
10.李开丽,倪绍祥.东亚飞蝗生境的遥感分类-以河北省黄骅地区为例.地理研究, 2006,25(4):579～585.
11.李学湛,吕典秋.东北地区脱毒马铃薯种薯质量安全现状及建议.农业质量标准,2004(4):36～37.
12.梁忆冰,林伟,王跃进,徐亮,翟图娜,陆平.生态因子在马铃薯甲虫地理分布中的作用.植物检疫,1999,13(5):3～8.
13.刘良云,黄木易,黄文江,王纪华,赵春江,郑兰芬.利用多时相的高光谱航空图像监测冬小麦条锈病.遥感学报,2004,8(3):275～281.
14.刘玉洁,杨忠东. MODIS遥感信息处理原理与算法.北京:科学出版社,2001:1～3.
15.刘振波,倪绍祥,查勇,葛云健.河北省黄骅市三个重点蝗区两个时段土壤湿度的遥感提取.动物学研究,2006,27(3):281～285.
16.倪绍祥,蒋建军,王杰臣.遥感与GIS在蝗虫灾害防治研究中的应用进展.地球科学进展, 2000,15(1):97～100.
17.浦瑞良,宫鹏.高光谱遥感及其应用.北京:高等教育出版社,2000,3～5,89～93.
18.齐静,程登发,乔红波,张云慧,陈林,孙京瑞,洪波.马铃薯受马铃薯甲虫危害后冠层光谱特征的初步研究.植物保护,2010,36(1):69～72.
19.齐静,程登发,张云慧,孙京瑞.马铃薯甲虫危害后马铃薯冠层光谱数据分析.见:成卓敏(编).粮食安全与植保科技创新.北京:中国农业科学技术出版社,2009,308～311.
20.钱天荣,曲能治,陈宏,周永淑.马铃薯甲虫在我国及周边地区适生地的初步预测.植物检疫,1995,9(2):95～97.
21.乔红波,程登发,孙京瑞,田喆，陈林，林芙蓉.麦蚜对小麦冠层光谱特性的影响研究.植物保护,2005,31(2):21～26.
22.沙尼彦,玛依拉.云菊乳油防治马铃薯甲虫试验示范.农业信息与技术,2008,17:38.
23.石瑞香,刘闯,李典谟,谢宝瑜.蝗虫生境监测方法研究进展.生态学报,2003,23(11):2476～2482.
24.隋广义,焦晓丹,袁来喜.马铃薯甲虫入侵黑龙江省的风险植物检疫,2008,22(6):369～371.
25.吐尔逊.马铃薯甲虫发生规律检疫和防治技术研究[硕士学位论文].乌鲁木齐:新疆农业大学,1995.
26.王俊,王登元,侯洪.新疆马铃薯甲虫的发生与防治现状.新疆农业科技,2008,3:60.
27.王正军,张爱兵,李典谟.遥感技术在昆虫生态学中的应用途径与进展.昆虫知识,2003,40(2):97～100.
28.吴继友,倪建.松毛虫危害的光谱特征与虫害早期探测模式.环境遥感,1995,10(1):250～251.
29.吴曙雯,王人潮,陈晓斌,沈掌泉,史舟.稻叶瘟对水稻光谱特性的影响研究.上海交通大学学报:农业科学版,2002,20(1):73～84.
30.杨海霞,陈红印,李强.浅谈马铃薯甲虫的生物防治.植物检疫,2007,21(6):368～372.
31.杨青生,刘闯. MODI S数据陆面温度反演研究.遥感技术与应用,2004,19(2):90～93.
32.姚春生,张增祥,汪潇.使用温度植被干旱指数法(TVDI)反演新疆土壤湿度.遥感技术与应用,2004,19(6):473～478.
33.曾庆财,于丽萍,吕兴盛.在中俄边境地区建立马铃薯甲虫疫情监测网势在必行.黑龙江对外经贸,2002,5:60.
34.张永强,文丽萍,石洁,王振营,何康来,程登发.亚洲玉米螟为害后玉米冠层光谱变化和产量损失研究.植物保护,2007,33(5):54～57.
35.张玉萍,郭洁滨,王爽,王海光,马占鸿.小麦条锈病卫星与近地光谱反射率的比较.植物保护学报,2009,36(2):119～122.
36.赵博光.龙葵对马铃薯甲虫产卵的引诱作用.植物检疫,1996,10(3):137～138.
37.赵红山,秦晓辉,马德成,杨栋,夏正汉,付文君. 7种药剂对马铃薯甲虫的防治效果评价.植物检疫,2007,21(4):217～219.
38.赵建周.国外马铃薯甲虫发生危害与防治概况.植物保护,1995,21(4):35～36.
39.赵英时.遥感应用分析原理与方法.北京:科学出版社,2003.
40.……. 2008-06-29取自http://www.sfhciq.com/old/common/ywgz/2005/20051102-2.htm
41.……. 2008-09-20取自http://www.nfiieos.cn/
42.……. 2009-02-05取自http://www.rsgs.ac.cn/2_quickbird.html
43. Ashouri, A., Michaud, D., & Cloutier, C. Recombinant and classically selected factors of potato plant resistance to the Colorado potato beetle, Leptinotarsa decemlineata, variously affect the potato aphid parasitoid Aphidius nigripes. BioControl, 2001, 46(4): 401～418.
44. Baret, F., Guyot, G., & Major, D. TSAVI: a vegetation index which minimizes soil brightness effects on LAI or APAR estimation. 12th Canadian Symposium on Remote Sensing and IGARSS, Vancouver, Canada, 1990, 7: 10～14.
45. Broge, N. H., & Mortensen, J. V. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral re?ectance data. Remote Sensing of Environment, 2002, 81: 45～57.
46. Carter, G. A. Responses of leaf spectral re?ectance to plant stress. American Journal of Botany, 1993, 80: 239～243.
47. Casagrande, R. A. The“Iowa”potato beetle, its discovery and spreads to potatoes. Bulletin of the Entomological Society of America, 1985, 31(2): 27～29.
48. Casagrande, R. A. The potato beetle: 125 years of mismanagement.Bull. Entomol Soc Am, 1987, 33(2): 142～150.
49. Clevers, J. G. P. W. The derivation of a simplified reflectance model for the estimation of leaf area index. Remote Sensing of Environment, 1988, 35: 53～70.
50. Coops, N. C. Johnson, M., Wulder, M. A., & White, J. C. Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation. Remote Sensing of Environment, 2006, 103: 67～80.
51. Crippen, R.E. Calculating the vegetation index faster. Remote Sensing of Environment, 1990, 34: 71～73.
52. Deering, D.W., Rouse, J.W., Haas, R.H., & Schell, J.A. Measuring forage production of grazing units from Landsat MSS data. Proceedings of Tenth International Symposium on Remote Sensing of Environment Ann Arbor ERIM, 1975, 2: 1169～1178.
53. Duchesne, R. M., & Parent, J. G.. The flowering tobacco as a new alternative host for the Colorado potato beetle. American Journal of Potato Research, 1991, 68(11): 743～750.
54. Fernandez, S., Vidal, D., Simon, E., & Sole-sugarnes, L. Radiometric characteristic of Triticum aestivum cv. Astral under water and nitrogen stress. International Journal of Remote Sensing, 1994, 15(9): 1867～1884.
55. Ferro, D. N., Logan, J. A., Voss. R. H., & Elkinton, J. S. Colorado potato beetle temperature development growth and feeding rates. Environmental Entomology, 1985, 14: 343～348.
56. Franke, J., & Menz, G. Multi-temporal wheat disease detection by multi-spectral remote sensing. Precision Agriculture, 2007, 8: 161～172.
57. Gamon, J. A., & Surfus, J. S. Assessing leaf pigment content andactivity with a reflectometer. New Phytologist, 1999, 143: 105～117.
58. Habutová, O., Weismann, L'., Harangozó, M., & Bumbálová, A. Influence of Copper from the Kuprikol 50 Fungicide on Colorado Potato Beetle Adults Studied by Radionuclide X—Ray Fluorescence Analysis. Journal of Radioanalytical and Nuclear Chemistry, 2000, 243(3): 825～826.
59. Hare, J. D. Ecology and management of the Colorado potato beetle. Ann Rev Entomol, 1990, 35: 81～100.
60. Hatfield, J. L., & Pinter, P. J. Remote sensing for crop protection. Crop Protection, 1993, 12(6): 403～413.
61. Horler, D. N. H., Dockray, M., & Barber, J. The red edge of plant leaf reflectance. International Journal of Remote Sensing, 1983, 4: 273～288.
62. Huete, A.R. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 1988, 25: 295～309.
63. Hurst, G. W. Meteorology and the Colorado potato beetle. World Meteorological Organization Technical Notes. 1975, 137: 1～51.
64. Jacques, Jr H. L. The Potato Beetles, The genus Leptinotarsa in North America (Coleoptera: Chrysomelidae) Flora & Fauna Handbook, 1988, 3: 1～143.
65. Jordan, C. F. Derivation of leaf area index from quality of light on the forest floor. Ecology, 1969, 50(4): 663～666.
66. Jordan, C.F. Derivation of leaf area index from quality of light on the forest floor. Ecology, 1969, 50: 663～666.
67. Kaitazov, A. Bio ecological studies on the Colorado potato beetle in Bulgaria. Rast Vud Nauki, 1964, 1(2): 123～132.
68. Kaufman, Y. J., & Tanré, D. Atmospherically resistant vegetation index-ARVI for EOS/MODIS. IEEE Transactions on Geosciences and Remote Sensing, 1992, 30(2): 261～270.
69. Kauth R. J., & Thomas G.S. The tasseled cap-A graphic description of the spectral temporal development of agricultural crops as seen by Landsat. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data. Purdue University, West Lafayette, Indiana. 1976: 41～51.
70. Lillesand, T. M., & Kiefer, R.W. Remote sensing and image interpretation, 2nd edition, John Wiley and Sons, New York, Chichester, Brisbane, Toronto, Singapore. 1987: 721.
71. Ma, J. W., Han, X. Z., Gan, H. S. B., Wang, C. L., Zhang, Y. J., &Tang. J. Y. Monitoring East Asian migratory locust plagues using remote sensing data and field investigations. International Journal of Remote Sensing, 2005, 26 (3): 629～634.
72. Malthus, T. J., & Madeira, A. C. High resolution spectroradiometry: spectral re?ectance of field beans leaves infected by Botrytisfabae. Remote Sensing of Environment, 1993, 45: 107～116.
73. Minder, I. F., & Chesnek, S. I. The dependence of cold resistance in the Colorado potato beetle on the time of the onset of diapause. Zoologicheskii Zhurnal, 1970, 49(6): 855～861.
74. Mirik, M., Michels, G. J. Jr., Kassymzhanova-Mirik, S., & Elliott, N. C. Reflectance characteristics of Russian wheat aphid (Hemiptera: Aphididae) stress and abundance in winter wheat. Computers and Electronics in Agriculture, 2007, 57(2): 123～134.
75. Moran, M. S., Inoue, Y., & Barnes, E. M. Opportunities and limitations for image-based remote sensing in Precision Crop Management. Remote Sensing of Environment, 1997, 61: 319～346.
76. Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, L., & McCartney, A. Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps. Real-Time Imaging, 2005, 11: 75～83.
77. Penuelas, J., & Filella, I. Visible and near-infrared re?ectance techniques for diagnosing plant physiological status. Trends in Plant Science, 1998, 3: 151～156.
78. Qi, J., Chehbouni, A., Huete, A. R., & Ker, Y. H. Modified Soil Adjusted Vegetation Index (MSAVI). Remote Sensing of Environment, 1994a, 48: 119～126.
79. Qi, J., Kerr, Y., & Chehbouni, A. External factor consideration in vegetation index development. Proc. of Physical Measurements and Signatures in Remote Sensing, ISPRS, 1994b: 723～730.
80. Richardson, A. J., & Wiegand, C. L. (1977). Distinguishing vegetation from soil background information. Photogramm Eng Rem, S43, 1541～1552.
81. Riley, J. R. Remote sensing in entomology. Annual Review of Entomology, 1989, 34: 247～271.
82. Rouse, J.W., Haas, R.H., Schell, J.A., & Deering, D.W. Monitoring vegetation systems in the great plains with ERTS. Third ERTS Symposium, NASA SP-351. 1973, 1: 309～317.
83. Shibayama, M., Takahashi, W., Morinaga, S., & Akiyama, T. Canopy water deficit detection in paddy rice using a high resolution field spectroradiometer. Remote Sensing of Environment, 1993, 45: 117～126.
84. Sims, D. A., & Gamon, J. A. Relationships between leaf pigments content and spectral re?ectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 2002, 81: 337～354.
85. Stuben, M. On the influence of light on Leptinotarsa. decemlineata. I. The influence of light intensity on the oviposition. Z PflK rankh PflSchutz, 1964, 71: 533～538.
86. Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 1979, 8: 127～150.
87. Ustin, S. L., Zomer, R. J., Garcia, M., Roberts, D. A., & Green, R. O. Remote sensing methods monitor natural resources. Photonics Spectra, 1999, 33(10): 108～113.
88. Voss, R. H., & Ferro, D. N. Ecology of migrating Colorado potato beetles (Coleoptera: Chrysomelidae) in western Massachusetts. Environmental entomology, 1990, 19: 123～129.
89. Whalen, M. D. Speciation in Solanum, Section Androceras. In: J. G. Hawkes, R.N. Lester. & A. D. Skelding (Eds.), The biology and taxonomy of the Solanaceae. London: Acadmic press, 1979: 581～596.
90. Worner, S. P. Establishment of exotic pests. Journal of Economic Entomology, 1988, 81(40): 973～983
91. Yang, Z., Rao, M. N., Elliott, N. C., Kindler, S. D., & Popham, T. W. Using ground-based multispectral radiometry to detect stress in wheat caused by greenbug (Homoptera: Aphididae) infestation. Computers and Electronics in Agriculture, 2005, 47 (2): 121～135.
92. Yang, Z., Rao, M. N., Elliott, N. C., Kindler, S. D., & Popham, T. W. Differentiating stress induced by greenbugs and Russian wheat aphids in wheat using remote sensing. Computers and Electronics in Agriculture, 2009, 67(1-2): 64～70.