تفکیک طیفی گونه های مهم باغی با استفاده از شاخص های ابرطیفی و رویکردهای هوش مصنوعی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری محیط‌زیست، پژوهشکده انگور و کشمش، دانشگاه ملایر

2 استایار گروه علوم و مهندسی جنگل، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد

3 استاد گروه مهندسی آب، دانشکده کشاورزی، دانشگاه بوعلی سینا، همدان

4 استادیار گروه محیط‌زیست، دانشکده منابع طبیعی و محیط‌زیست، دانشگاه ملایر

5 استادیار گروه فضای سبز، دانشکده کشاورزی، دانشگاه ملایر

چکیده

مطالعه انعکاس طیفی پدیده‌ها از طریق شاخص­ های طیفی امکان استفاده بهینه از دامنه وسیع طول‌موج‌های طیفی را در داده‌های ابرطیفی فراهم می‌کند. هدف از تحقیق، معرفی و ارزیابی عملکرد شاخص ­های طیفی در تفکیک گونه ­های غالب باغی در استان چهارمحال و بختیاری است. در این تحقیق 150 نمونه طیفی در محدوده 350 الی 2500 نانومتر، از گونه ­های انگور، گردو و بادام در انواعی از شرایط برداشت شد و پس از تصحیح اولیه، 30 عدد از مهم‌ترین شاخص­ های طیفی موجود در این زمینه استخراج شدند. آزمون واریانس و مقایسه میانگین ­ها جهت شناسایی شاخص­ های بهینه در تفکیک گونه ­ها، در سطح 99 درصد اطمینان اجرا شد. سپس از دو رویکرد شبکه عصبی مصنوعی و ماشین بردار پشتیبان جهت ارزیابی عملکرد شاخص­ ها در تفکیک گونه­ ها استفاده شد. نتایج آزمون واریانس نشان داد که شاخص­ های تنش رطوبت، نسبت باند در 1200 نانومتر، شاخص نرمال شده فئوئوفیتین و شاخص جذب سلولز جهت تفکیک گونه­ های موردمطالعه بهینه هستند. نتایج ارزیابی عملکرد شاخص­ های معرفی‌شده نتیجه 100 درصد تفکیک گونه‌ها را در دو رویکرد شبکه عصبی مصنوعی و ماشین بردار پشتیبان، در هر دو مرحله آموزش و آزمون نشان داده است. این نتایج لزوم انجام مطالعات طیف‌سنجی را برای تفکیک گونه ­های باغی پیش از تحلیل داده‌های تصویری ابرطیفی به دلیل حجم وسیع و هزینه بیشتر تهیه و تحلیل آن‌ها نشان می ­دهد.

کلیدواژه‌ها


عنوان مقاله [English]

Spectral discrimination of important orchard species using hyperspectral indices and artificial intelligence approaches

نویسندگان [English]

  • Mohsen Mirzaie 1
  • Mozhgan Abbasi 2
  • Safar Marofi 3
  • Eisa Solgi 4
  • Roohollah Karimi 5
1 Ph.D Student of Environment, Research Institute for Grapes and Raisin (RIGR), Malayer University
2 Assis. Prof. College of Forest Science and Engieering, Department of Natural Resources and Earth Sciences, Shahrekord University
3 Prof. College of Water Engineering, Department of Agriculture, Bu-Ali Sina University, Hamedan
4 Assis. Prof. College of Environment, Department of Natural Resources and Environments, Malayer University
5 Assis. Prof. College of Green Space Design, Department of Agriculture, Malayer University
چکیده [English]

Study spectral reflectance through spectral indices allows the optimal use of the wide range of spectral wavelengths in hyperspectral data. The purpose of this study was to introduce and evaluate the performance of spectral indices to discriminate dominant orchard species in Chaharmahal Bakhtiari province. In this study, 150 spectral curves were measured in the range of 350 to 2500 mm, from grapes, walnuts and almond trees. After the initial correction, 30 of the most important spectral indices were extracted. Analysis of variance and comparisons of meanings was applied to identify the optimal indices for species discrimination at a 99% confidence level. Then, an artificial neural network (ANN) and support vector machine (SVM) approaches were used to evaluate the performance of indices in species discrimination. ANOVA results indicated that the Moisture Stress Index (MSI), Band ratio at 1,200 nm, normalized phaepophytiniz index (NPQI) and cellulose absorption index (CAI) indices are optimal for discrimination of the studied species. The performance evaluation of the introduced indicators in some of the ANN and SVM enhancement structures has been associated with 100% accuracy in both education and testing, which shows the effectiveness of these studies in distinguishing orchard species. The performance evaluation of the introduced indicators has been validated at 100% in both training and testing stages. This result emphasizes the necessity of performing spectroscopic studies to separate the orchard species before analyzing the hyperspectral images due to their large data volume, high cost and huge data analysis.

کلیدواژه‌ها [English]

  • Field spectroscopy
  • Plant species discrimination
  • Spectral indices
  • Artificial Neural Network (ANN)
  • Support vector machine (SVM)

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پیشنماز احمدی، م.، م. ح. رضائی مقدم و ب. فیضی زاده. 1396. بررسی شاخص‌ها و تهیۀ نقشه شوری خاک با استفاده از داده‌های سنجش‌ازدور (مطالعۀ موردی: دلتای آجی‌چای). سنجش‌ازدور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 8(1): 85-96.

درویش‌صفت، ع. ا.، م. عباسی و م. مروی مهاجر. 1388. امکان تهیه نقشه تیپ راش به کمک داده­های سنجنده ETM+. مجله جنگل ایران، 1(2): 105-113.

رحیم‌زادگان، م. و م. پورغلام. 1395. تعیین سطح زیر کشت گیاه زعفران با استفاده از تصاویر لندست (مطالعۀ موردی: شهرستان تربت‌ حیدریه). سنجش‌ازدور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 7(4): 97-115.

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Adam E, Mutanga O. 2009. Spectral discrimination of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands using field spectrometry. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6): 612-620.

Adam E, Mutanga O, Rugege D. 2010. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecology and Management. 18(3): 281-296.

Aneece I, Epstein H. 2017. Identifying invasive plant species using field spectroscopy in the VNIR region in successional systems of north-central Virginia. International Journal of Remote Sensing, 38(1): 100-122.

ASD, Analytical Spectral Devices, Inc. 2005. Handheld Spectroradiometer: User’s Guide, Version 4.05. Boulder, USA.

Atherton D, Choudhary R, Watson D.  2017. Hyperspectral Remote Sensing for Advanced Detection of Early Blight (Alternaria solani) Disease in Potato (Solanum tuberosum) Plants Prior to Visual Disease Symptoms. In: 2017 ASABE Annual International Meeting, American Society of Agricultural and Biological Engineers, 10 pp.

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Beeri O, Phillips R, Hendrickson J, Frank AB, Kronberg S. 2007. Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie. Remote Sensing of Environment, 110(2): 216-225.

Belluco E, Camuffo M, Ferrari S, Modenese L, Silvestri S, Marani A, Marani M. 2006. Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing. Remote Sensing of Environment, 105(1): 54–67.

Bratsch SN, Epstein HE, Buchhorn M, Walker DA. 2016. Differentiating among four Arctic tundra plant communities at Ivotuk, Alaska using field spectroscopy, Remote Sensing, 8(1): 45-51.

Carter GA. 1994. Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. Remote sensing, 15(3): 697-703.

Cho MA, Sobhan I, Skidmore AK, De Leeuw J. 2008. Discriminating species using hyperspectral indices at leaf and canopy scales. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. 369-376.

Clark ML, Roberts DA. 2012. Species-level differences in hyperspectral metrics among tropical rainforest trees as determined by a tree-based classifier. Remote Sensing4(6): 1820-1855.

Clevers JG, Kooistra L, Schaepman ME. 2010. Estimating canopy water content using hyperspectral remote sensing data. International Journal of Applied Earth Observation and Geoinformation12(2): 119-125.

Datt B. 1999. Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves. International Journal of Remote Sensing, 20(14): 2741-2759.

Galvão LS, Formaggio AR, Tisot DA. 2005. Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data. Remote sensing of Environment94(4): 523-534.

Gamon JA, Field CB, Goulden ML, Griffin KL, Hartley AE, Joel G, Valentini R. 1995. Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecological Applications5(1): 28-41.

Gao BC. 1996. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote sensing of environment58(3): 257-266.

Gitelson AA, Merzlyak MN. 1997. Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing18(12): 2691-2697.

Gong P, Pu R, Heald RC. 2002. Analysis of in situ hyperspectral data for nutrient estimation of giant sequoia. International Journal of Remote Sensing23(9): 1827-1850.

Kalacska M, Bohlman S, Sanchez-Azofeifa GA, Castro-Esau K, Caelli T. 2007. Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels. Remote Sensing of Environment109(4): 406-415.

Kent M. 2011. Vegetation description and data analysis: a practical approach. John Wiley & Sons. 432 pp.

Koch B. 2010. Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS Journal of Photogrammetry and Remote Sensing65(6): 581-590.

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