Taze Fasulyede Su Stresinin Belirlenmesinde Termal Görüntülerin ve Spektral Verilerin Kullanımı

Year 2013, Volume: 1 Issue: 1, 15 - 27, 01.06.2013

Gökhan Çamoğlu Levent Genç

Abstract

Bitkilerin fizyolojik aktivitelerindeki değişim kalıcı hale gelmeden önce su stresini belirlemek önemlidir. Bunun sonucunda ciddi oranda verim kaybı yaşanabilir. Birçok araştırmacı, bitki taç ve yapraklarından toplanan termal ve spektral verilerin bitki su stresinin belirlenmesinde kullanılabilecek potansiyel araçlar olduğunu vurgulamışlardır. Çalışmanın amacı, taze fasulye (Phaseolus vulgaris L. cv. Gina) bitkisinde, spektral yansıma verilerini ve termal görüntüleme tekniğini kullanarak su stresinin belirlenmesidir. Bu amaçla, dört farklı sulama konusu (tüketilen suyun %100’ü (kontrol), %75’i, %50’si ve %25’i) oluşturulmuştur.Araştırmada, termal görüntüler kızılötesi termal kamerayla, spektral veriler ise spektroradyometreyle elde edilmiştir.Çalışma sonucunda, konulara uygulanan toplam sulama suyu miktarları 232–681 mm ve elde edilen mevsimlik bitki su tüketimi değerleri 379–804 mm arasında bulunmuştur. Sınıflandırma ve regresyon ağacı analizlerine göre, su stresinin özellikle I–100 seviyesinde termal indeksler ile daha iyi açıklanabileceği sonucuna ulaşılmıştır. Çalışmada, spektral indekslerden Yapısal Bağımsız Pigment İndeksi (SIPI) ve Normalize Edilmiş Vejetatif Değişim İndeksinin (NDVI), termal indekslerden de amprik esasa dayalı hesaplanan bitki su stresi indeksi (CWSIe) ve yapay referans yüzeylere göre hesaplanan bitki su stresi indeksinin (CWSIa) taze fasulyede su stresinin belirlenmesinde kullanılmaları önerilebilir

Keywords

Su stresi, Taze fasulye, Bitki su stresi indeksi, Spektral yansıma

Use of thermal imaging and spectral data to detect water stress in green bean

Abstract

It is important to detect water stress before physiological activities of plants permanently change. Otherwise production might decrease dramatically. Many researches showed that thermal imaging or spectral data, collected from plant leaves or canopy, have potential to determine plant water stress. In this study, our objective was to determine water stress on green beans (Phaseolus vulgaris L. cv. Gina) using both thermal imaging technique and spectral reflectance data. In order to determine water stress of green bean, experiment were formed with four irrigation levels (100% of the water consumed (I–100), 75% (I–75), 50% (I–50), and 25% (I–25). Thermal images and spectral data were collect using infrared thermal camera and spectro–radiometer respectively. It was found that the total amount of irrigation and seasonal evapotranspiration were found between 232–681 mm and 379–804 mm, respectively. According to the classification and regression tree analysis, it was found that water stress could be better explained by thermal indices especially in I-100 level. In this study, It was concluded that there are several indices calculated both from thermal images (crop water stress indices calculated empirically (CWSIe) and based on reference surface (CWSIa)) and spectral data (Structural Independent Pigment Index (SIPI) and Normalized Difference Vegetation Index (NDVI)), could be used to determine the green beans water stress levels

Keywords

Water stress, Green bean, Crop water stress index, Spectral reflectance

References

• Akkuzu, E., Camoglu, G., Kaya, U., 2010. Diurnal Variation of canopy temperature differences and leaf water potential of field-grown olive trees (Olea europaea l. Cv. Memecik). Philippine Agricultural Scientist, 93 (4): 399–405.
• Baluja, J., Diago, M.P., Balda, P., Zorer, R., Meggio, F., Morales, F., Tardaguila, J., 2012. Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). Irrigation Science, DOI 10.1007/s00271–012–0382–9.
• Ben-Gal, A., Agam, N., Alchanatis, V., Cohen, Y., Yermiyahu, U., Zipori, I., Presnov, E., Sprintsin, M., Dag, A., 2009. Evaluating water stress in irrigated olives: correlation of soil water status, tree water status, and thermal imagery. Irrigation Science, 27: 367–376.
• Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., 1984. Classification and regression trees. Monterey, Calif., U.S.A. Wadsworth, Inc.
• Cohen, Y., Alchanatis, V., Meron, M., Saranga, S., Tsipris, J., 2005. Estimation of leaf water potential by thermal imagery and spatial analysis. Journal of Experimental Botany, 56: 1843–1852.
• Çamoğlu, G., Aşık, Ş., Genç, L., 2010. Mısır bitkisinin su stresine karşı spektral tepkileri. Tarım Bilimleri
• Araştırma Dergisi, 3 (1): 37–43.
• Camoglu, G., 2013. The Effects of water stress on evapotranspiration and leaf temperatures.
• Žemdirbystė=Agriculture, 100 (1): 91–98.
• Çamoğlu, G., Kaya U., Akkuzu, E., Genc, L., Gurbuz, M., Pamuk Mengu, G., Kızıl, U., 2013. Prediction of leaf
• water status using spectral indices at young olive trees. Fresenius Environmental Bulletin, 22 (8) (in press).
• Diaz-Espejo, A., Nicolas, E., Fernandez, J.E., 2007. Seasonal evolution of diffusional limitations and photosynthetic capacity in olive under drought. Plant Cell Environment, 30 (8): 922–933.
• Fuentes, S., De Bei R., Pech, J., Tyerman, S., 2012. Computational water stress indices obtained from thermal image analysis of grapevine canopies. Irrigation Science, DOI 10.1007/s00271–012–0375–8.
• Fujiwara, H., Endo, T., Yasuoka, Y., 2004. Evaluation of water stress on a crop using the portable hyper spectral imager. The 25th Asian Conference & 1th Asain Space Conference on Remote Sensing. 22–26 November, Thailand.
• Gamon, J.A., Penuelas J., Field, C.B., 1992. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41: 35–44.
• Genc, L., Demirel, K., Camoglu, G., Asık, S., Smith, S., 2011. Determination of plant water stress using spectral
• reflectance measurements in watermelon. American-Eurasian J. Agric. & Environ. Sci., 11 (2): 296– 304.
• Genc, L., Inalpulat, M., Kızıl, U., Mirik, M., Smith, S.E., Mendes, M., 2013. Determination of water stress with
• spectral reflectance on sweet corn (Zea mays L.) using classification tree (CT) analysis. Zemdirbyste
• Agriculture, 100 (1): 81–90.
• Gençoğlan, C., Yazar, A., 1999. Çukurova koşullarında yetiştirilen I. ürün mısır bitkisinde infrared termometre
• değerlerinde yararlanılarak bitki su stresi indeksi (CWSI) ve sulama zamanının belirlenmesi. Tr. J. of
• Agriculture and Forestry, 23: 87–95.
• Grant, O.M., Tronina, L., Jones, H.G., Chaves, M.M., 2007. Exploring thermal imaging variables for the detection of stress responses in grapevine under different irrigation regimes. Journal of Experimental Botany, 58 (4): 815–825.
• Heute, A.R., 1988. A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing Environment, 25: 295–309.
• Hsiao, T.C., 1973. Plant responses to water stress. Annual Review of Plant Physiology, 24: 519–570.
• Idso, S.B., Jackson, R.D., Reginato, R.J., 1978. Remote sensing for agricultural water management and crop yield prediction. Agriculture Water Management, 1: 299–310.
• Idso, S.B., Jackson, R.D., Pinter, P.J., Reginato, R.J., Hatfield, J.L., 1981. Normalizing the stress–degree–day parameter for environmental variability. Agricultural Meteorology, 24: 45–55.
• Jackson, R.D., Pinter, Jr., P.J., Reginato, R.J., Idso, S.B., 1980. Hand-held radiometry. A Set of Notes Developed for Use at the Workshop on Hand-Held Radiometry, February 25–26, Phoenix, Arizona.
• Jackson, R.D., 1982. Canopy temperature and crop water stress. Advances in Irrigation Research, 1: 43–85.
• James, L.G., 1988. Principles of Farm Irrigation Systems Design. John Wiley and Sons, New York.
• Jimenez-Bello, M.A., Ballester, C., Castel, J.R., Intrigliolo, D.S., 2011. Development and validation of an automatic thermal imaging process for assessing plant water status. Agricultural Water Management, 98: 1497–504.
• Jones, H.G., Aikman, D.A., McBurney, T., 1997. Improvements to infrared thermometry for irrigation scheduling. Acta Horticulturae, 449: 259–266.
• Jones, H.G., 1999a. Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling. Agric. Forest Meterol., 95: 139–149.
• Jones, H.G., 1999b. Use of thermography for quantitative studies of spatial and temporal variation of stomatal conductance over leaf surfaces. Plant, Cell Environ., 22: 1043–1055.
• Jones, H.G., Stoll, M., Santos T., de Saousa, C., Chaves, M.M., Grant, O., 2002. Use of infrared thermography for monitoring stomatal closure in the Şeld: application to grapevine. Journal of Experimental Botany, 53: 2249–2260.
• Jones, H.G., Leinonen, I., 2003. Thermal imaging for the study of plant water relations. J. Agric. Meteorol., 59: 205–217.
• Jones, C.L., Schofield, P., 2008. Thermal and other remote sensing of plant stress. Gen. Appl. Plant, 34 (1–2): 19–32.
• Köksal, E.S., İlbeyi, A., Üstün, H., Özcan, H., 2007. Yeşil fasulye sulama suyu yönetiminde örtü sıcaklığı ve spektral yansıma oranı değerlerinin kullanım olanakları. Toprak, Gübre ve Su Kaynakları Araştırma Enstitüsü Yayınları, 91s.
• Köksal, E.S., Kara, T., Apan, M., Üstün, H., İlbeyi, A., 2008. Estimation of green bean yield, water deficiency and productivity using spectral indexes during the growing season. Irrig. Drainage Syst., 22: 209–223.
• Köksal, E.S., Üstün, H., İlbeyi, A., 2010. Bodur yeşil fasulyenin sulama zamanı göstergesi olarak yaprak su potansiyeli ve bitki su stres indeksi sinir değerleri. U.Ü. Ziraat Fakültesi Dergisi, 24 (1): 25–36.
• Leinonen, I., Jones, H., 2004. Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress. Journal of Experimental Botany, 55 (401): 1423–1431.
• Lopez, A., Molina-Aiz, F.D., Valera, D.L., Pena, A., 2012. Determining the emissivity of the leaves of nine horticultural crops by means of infrared thermography. Scientia Horticulturae, 137: 49–58.
• Meyer, W.S., Reicosky, D.C., Schaefer, N.L., 1985. Errors in field measurement of leaf diffusive conductance associated with leaf temperature. Agricultural and Forest Meteorology, 36 (1): 55–64.
• Monteith, J.L., Unsworth, M.L., 1990. Principles of Environmental Physics. 2nd ed. Edward Arnold, London, United Kingdom, p. 414.
• Möller, M., Alchanatis, V., Cohen, Y., Meron, M., Tsipris, J., Naor, A., Ostrovsky, V., Sprintsin, M., Cohen, S., 2007. Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. Journal of Experimental Botany. 58: 827–838.
• O’Shaughnessy, S.A., Evett, S.R., Colaizzi, P.D., Howell, T.A., 2011. Using radiation thermography and thermometry to evaluate crop water stress in soybean and cotton. Agricultural Water Management, 98: 1523–1535.
• Padhi, J., Misra, R.K., Payero, J.O., 2012. Estimation of soil water deficit in an irrigated cotton field with infrared thermography. Field Crops Research, 126: 45–55.
• Penuelas, J., Filella, I., Biel, C., Serrano, L., Save, R., 1993. The reflectance at the 950–970 nm region as an indicator of plant water status. Int. J. of Remote Sensing, 14: 1887–1905.
• Penuelas, J., Gamon, J.A., Fredeen, A.L., Merino, J., Field, C.B., 1994. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sensing of Environment, 48: 135–146.
• Penuelas, J., Baret, F., Filella, I., 1995. Semi-empirical indices to assess carotenoids/chlorophyll-a ratio from leaf spectral reflectance. Photosynthetica, 31: 221–230.
• Penuelas, J., Pinol, J., Ogaya, R., Fiella, I., 1997. Estimation of plant water concentration by the reflectance water index WI (R900/R970). Int. J. of Remote Sensing. 18: 2869–2875.
• R Development Core Team, 2012. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3–900051–07–0, URL http://www.R-project.org/.
• Shibayama, M., Takahashi, W., Morinaga, S., Akiyama, T., 1993. Canopy water deficit detection in paddy rice using a high resolution field spectroradiometer. Remote Sensing of Environment, 45 (2): 117–126.
• Stoll, M., Jones, H., 2007. Thermal imaging as a viable tool for monitoring plant stress. International Journal of Vine and Wine Sciences, 41 (2): 77–84.
• Strachan, I.B., Pattey E., Boisvert, J.B., 2002. Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance. Remote Sensing of Environment, 80 (2): 213–224.
• Üstün, H., Aran, A., Yıldırım, O., 1997. Ankara koşullarında damla sulama yöntemi ile sulanan taze fasulyenin sulama suyu ihtiyacı. Köy Hizmetleri Ankara Araştırma Enstitüsü Müdürlüğü Yayınları, 207, Rapor Seri No: R–113, 56 p, Ankara.