تشخیص و بررسی استرس گندم با استفاده از تصاویر ماهواره‌ای (مطالعه موردی : دشت مغان )

محورهای موضوعی : کاربرد GIS&RS در برنامه ریزی

عادل مردانه ¹ (کارشناس ارشد سنجش از دور و سیستم اطلاعات جغرافیایی، دانشگاه تهران، ایران)
فرشاد امیر اصلانی ² (استاد گروه سنجش از دور و سیستم اطلااعات جغرافیایی ، دانشگاه تهران)
سید کاظم علوی پناه ³ (گروه سنجش از دور و سیستم اطلاعات جغرافیایی، دانشگاه تهران)

تاریخ دریافت : 1399/03/28 تاریخ پذیرش : 1399/05/03 تاریخ انتشار : 1399/06/01

کلید واژه: گندم, شاخص‌های سبزینگی گیاهی, چندطیفی, استرس گیاهی,

چکیده مقاله :

در این پژوهش هدف بررسی توانمندی و قابلیت سنجش‌ازدور و تصاویر ماهواره‌ای در بررسی تغییرات طیفی گیاه گندم و تشخیص امراض و استرس آن در منطقه دشت مغان در شهرستان پارس‌آباد هست. دستیابی به چنین قابلیتی می‌تواند در پیش‌بینی به‌موقع امراض و آفت‌ها و هم‌چنین جلوگیری از گسترش آن و استفاده به‌موقع از قارچ‌کش‌ها و سموم دفع آفات نباتی مفید واقع شود. در راستای نیل به این هدف خصوصیات طیفی گندم و گیاهان دیگر موجود با استفاده از ماهواره‌های IRS و لندست 8 موردبحث و بررسی قرار می‌گیرد. گیاه گندم دارای گونه‌های مختلفی هست که در این منطقه گندم آتیلا و گندم کوهی بیشتر کشت می‌شود که در این تحقیق رفتارهای طیفی آن‌ها مورد ملاحظه قرار می‌گیرد. در این پژوهش از 146 نقطه کنترل زمینی استفاده شد که بیشترین درصد منطقه ازلحاظ درجه استرس به طبقه 2 و کمترین آن به درجه صفر یا طبقه سالم تعلق گرفت. پس از اعمال تصحیحات اتمسفری و هندسی بر روی تصاویر ماهواره‌ای موجود ؛ نسبت‌های باندی مختلفی به‌منظور شناسایی بهترین ترکیب باندی و تفکیک‌پذیری کلاس‌های سالم و درجه‌های استرس یک ، دو و سه مدنظر قرار گرفت. جهت نیل به این هدف از شاخص‌های استرس و سبزینگی پوشش گیاهی مختلفی استفاده شد. از بین شاخص‌ها، شاخص‌ GNDVI بیشترین کارایی را داشت و توانست 81% وضعیت مناطق را درست برآورد کند. شاخص GVI دارای بیشترین مقدار ضریب کاپا و صحت کلی به ترتیب با 94/0 و 3/95 است که نشان از کاربرد بالای این شاخص در درجه بندی استرس گیاه گندم می باشد. هم چنین این شاخص بیشترین مساحت را به درجه استرس یک اختصاص داد.

چکیده انگلیسی:

In this study, the aim is to investigate the ability of remote sensing and satellite images to study the spectral changes of wheat and diagnose diseases and its stress in the Moghan plain area in Parsabad city. Achieving such a capability can be useful in prediction of diseases and pests, as well as preventing its spread and timely use of fungicides and pesticides. In order to achieve this goal, the spectral properties of wheat and other plants are studied using IRS and Landsat 8 satellites. Wheat plant has different species in which Attila and Koohi wheat are mostly cultivated in this region, and in this study, their spectral behaviors are considered. In this study, 146 ground control points were used, the highest percentage of the region in terms of the degree of stress to grade 2 and the lowest to the healthy class. After applying atmospheric and geometric corrections on satellite images; Bond ratios were used to identify the best band composition and separability of healthy classes and stress levels one, two, and three. To achieve this goal, different vegetation indices were used. Among the indicators, the GNDVI index was the most efficient and was able to accurately estimate 81% of the areas. Also, GVI index has the highest value of kappa coefficient and overall accuracy with 0.94 and 95.3, respectively, which indicates the high use of this index in grading wheat stress. This index also gave the most area to the degree of stress one.

منابع و مأخذ:

Adler-Golden, S. M., M. W. Matthew, L. S. Bernstein, R. Y. Levine, A. Berk, S. C. Richtsmeier, P. K. Acharya, G. P. Anderson, G. Felde, J. Gardner, M. Hoke, L. S. Jeong, B. Pukall, A. Ratkowski, and Burke, H. (0111). "Atmospheric Correction for Short-wave Spectral Imagery Based on MODTRAN4". SPIE Proceedings on Imaging Spectrometry.3253: 60-61
Allison, E.W. (0191).Monitoring drought affected vegetation with AVHRR Digest-International Geoscience and Remote Sensing Symposium, 460165-0162.

Berk, A., Bernstein, L. S., Anderson, G. P., P. K., Acharya, Robertson, D. C., Chetwynd, J.H., and Adler-Golden, S. M. (0119)." MODTRAN Cloud and Multiple Scattering Upgrades with Application to AVIRIS". Remote Sensing of the Environment.65: 362-325.
Bernstein, L. S., Adler-Golden, S. M., Sundberg, R. L., et al. (7115). "Validation of the QUick Atmospheric Correction (QUAC) algorithm for VNIR-SWIR multi- and hyperspectral imagery". SPIE Proceedings, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI.5916: 669-
629.
Carter G A (0114). Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing, 05, 612-213
Ceccato, P., et al. "Detecting Vegetation Leaf Water Content Using Reflectance in the Optical Domain." Remote Sensing of Environment 22 (7110): 77-33.
Champagne, C., et al. "Mapping Crop Water Status: Issues of Scale in the Detection of Crop Water Stress Using Hyperspectral Indices." Proceedings of the 9th International Symposium on Physical Measurements and Signatures in Remote Sensing, Aussois, France (7110):
21-94.
Chavez, P S. "Image – based atmospheric corrections – Revisied and improved," Photogrammetric Engineering & Remote Sensing, vol. 0175-0136, 0116.
Crippen, R. "Calculating the Vegetation Index Faster." Remote Sensing of Environment 34 (0111): 20-23.
Franke, J. S. & Menz, G. R. (7112)."Multi-temporal wheat disease detection by multi-spectral remote sensing". Precision Agric (7112) , 96060-027. dio:01.0112/s00001-112-1136-y.
Gao, B. "Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space." Proceedings of SPIE 7491 (0115): 775-736.
Gitelson, A., and M. Merzlya. "Remote Sensing of Chlorophyll Concentration in Higher Plant Leaves." Advances in Space Research 77 (0119): 691-617.
Gitelson, A., et al. "Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy." Photochemistry and Photobiology 25 (7117): 727-790.
Gitelson, A., M. Merzlyak, and O. Chivkunova. "Optical Properties and Nondestructive Estimation of Anthocyanin Content in Plant Leaves." Photochemistry and Photobiology 20 (7110): 39-45.
Gitelson, A., Y. Kaufman, and M. Merzylak. "Use of a Green Channel in Remote Sensing of Global Vegetation from EOS-MODIS." Remote Sensing of Environment 59(0116): 791-719.
Goetz A F H., Gao B C., Wessman C A., Bowman W D (0111). Estimation of biochemical constituents from fresh green leaves by spectrum matching techniques. In: Proceedings 01th International Geoscience & Remote Sensing Symposiom (IGARSS'11), vol. 7, pp. 120-124
Gumma, M.K., Thenkabail, P.S., Teluguntla, P., Rao, M.N., Mohammed, I.A. and Whitbread, A.M., 7106, Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 751 m time-series data, International Journal of Digital Earth, 1(01): 190-0113.
Hamid Muhammed H., Larsolle A (7113). Feature – vector based analysis of hyperspectral crop reflectance data for discrimination quantification of fungal disease severity in wheat. Biosystems Engineering, 96(7) , 075-034, dio:01.0106/S0532-5001(13)11111-4.
Hardisky, M., V. Klemas, and R. Smart. "The Influences of Soil Salinity, Growth Form, and Leaf Moisture on the Spectral Reflectance of Spartina Alterniflora Canopies." Photogrammetric Engineering and Remote Sensing 41(0193): 22-93.
Hunt Jr., E., and B. Rock. "Detection of Changes in Leaf Water Content Using Near- And Middle-Infrared Reflectances." Remote Sensing of Environment 31(0191): 43-54.
Jackson, T., et al. "Vegetation Water Content Mapping Using Landsat Data Derived Normalized Difference Water Index for Corn and Soybeans." Remote Sensing of Environment 17 (7114): 425-497.
Kassa, A.(0111). Drought risk monitoring for Sudan using NDVI, 0197-0113. A Dissertation submitted to the University College London.
Kauth, R., and G. Thomas. "The Tasselled Cap-A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen By Landsat" In Proceedings of the LARS 0126 Symposium of Machine Processing of Remotely-Sensed Data, West Lafayette, IN: Purdue University, pp. 4B40-4B50.
King, L., Adusei, B., Stehman, S.V., Potapov, P.V., Song, X.P., Krylov, A., Bella, C.D., Loveland, T.R., Johnson, D.M. and Hansen, M.C., 7102, A multi-resolution approach to national-scale cultivated area estimation of soybean, Remote Sensing of Environment, 015: 03-71.
Koch B., Ammer U., Schneider T., Wittweier H (0111). Spectroradiometer measurements in the laboratory and in the field to analyse the influence of different damage symptoms on the reflection spectra of forest trees.International Journal of Remote Sensing, 00(2) , 0045-0063.
Lelong C C D., Pinet P C.,Poilve H (0119). Hyperspectral Imaging and stress mapping in agriculture: a case study on wheat in Beauce (France). Remote Sensing of Environment, 66, 021-090.
Lillesand, T. M. and Kiefer, R. W. (7111). "Remote sensing and image interpretation". 4th Ed. New York: John Wiley and Sons.
Michael P., Richard M., Pual V (7101). "Detecting Sugarcane leaf virus infection in asympomatic leaves with hyperspectral remote sensing and associated leaf pigment changes". Journal of Virological Methods 062:041-045.
Mobasheri, M.R., Rezaei, Y. and Valadan Zoej, M.J..(7112). "A Method in extracting Vegetation Quality Parameters Using Hyperion Images with Application to Precision Farming". World Applied Scences Journal 7(5): 426-493.
Murray, G. M., & Brown, J. F. (0192). The incidence and relative importance of wheat diseases in Australia. Australasian Plant Pathology, 06(7), 34–32.