Determination of The Relationship Between The Spectral Reflections of The Sugar Beet and The Heavy Metal Contents in The Soils

Year 2018, Volume: 13 Issue: 2, 36 - 45, 31.12.2018

Mert Dedeoğlu , Levent Başayiğit

Abstract

When plant production is carried out in soil contaminated with heavy metals, this is included in the food chain and adversely affects all living systems, especially human and animal health. For this reason, the contamination of heavy metals which can be added to the soil environment naturally and/or artificially is one of the obligatory subjects to be controlled and followed especially in agricultural areas. Today, spectral techniques which are practical, fast and environmentally friendly methods are used intensively to identify heavy metal pollution or high pollution potential areas in different studies.  In this study, it was aimed to determine of Ni and Cr accumulation from geogenesis origin to spectral signatures of sugar beet leaves by using Landsat – 7 ETM+ multispectral image in TURKEY - Konya region. The mean values of the spectral reflections of sugar beet belonging to normal parcels and contaminating parcels were statistically compared with the ANOVA test (p< 0.05). The study results showed the change in ~ 560 – 630 – 780  nm  wavelengths with graphical comparison of the mean reflectance, but, we determined that this change was not due to the heavy metal content of the soil and there was no statistically significant difference in the leaf spectral reflectances compared to the non-contaminated zones. Thus, it is necessary to examine the content of other nutrient elements affecting the mean reflection, and, depending on the risk of continuation of heavy metal accumulation, it is recommended to analyze the soil of the region periodically.

Keywords

ANOVA, Heavy metal, Landsat – 7, Spectral reflectance, Sugar beet

Şeker Pancarı Yaprak Spektral Yansımalarında Meydana Gelen Değişimin Topraklardaki Farklı Seviyede Ağır Metal İçeriklerine Bağlılığının Belirlenmesi

Abstract

Ağır
metaller ile kirlenmiş topraklarda gerçekleştirilen bitkisel üretimler gıda
zincirine dahil olmakta ve tüm yaşam sistemlerini olumsuz etkilemektedir. Bu
nedenle, toprakta doğal ve / veya yapay yollarla  meydana gelebilen ağır metal  kirliliği, özellikle tarım alanlarında
kontrol edilmesi ve izlenmesi gereken zorunlu konulardan biridir. Günümüzde,
pratik, hızlı ve çevre dostu yöntemler olan spektral teknikler, farklı
çalışmalarda ağır metal kirliliği veya kirlilik potansiyeli olan alanları
belirlemek için kullanılmaktadır. Bu çalışmada Konya – Çumra bölgesinde
jeogenesis kaynaklı Ni ve Cr ağır metal birikiminin, bölgede geniş bir alanda
yetiştirilen şeker pancarı bitkisi yapraklarının spektral imzasına etkilerinin
Landsat-7 ETM+ çok bantlı uydu görüntüsü ile belirlenmesi amaçlanmıştır. Bu
amaçla ağır metal birikimi belirlenen parseller ile kirlilik olmayan parsellere
ait şeker pancarı yaprak spektral yansımalarının ortalama değerleri ANOVA (p< 0.05) testi ile istatistiksel
olarak karşılaştırılmıştır. Araştırma ile ortalam spktral imza eğrilerinde
grafiksel olarak ~ 560 – 630 – 780  nm
dalgaboylarında yansıma farklılıları oluştuğu, ancak Ni ve Cr ile kontamine
olmuş topraklardan elde edilen yaprak spektral yansımalarında kontaminasyon
olmayan bölgelere göre istatistiksel olarak anlamlı bir fark olmadığı
belirlenmiştir. Çalışma sonucu, yansımaları etkileyen diğer besin elementi
içeriklerinin grafiksel değişimin nedeni olarak ayrıca incelenmesi gerektiği ve
ağır metal birikiminin devam etmesi riskine bağlı olarak bölge topraklarının
belli aralıklar ile analiz edilmesi önerilmektedir.

Keywords

Ağır Metal, ANOVA, Landsat – 7, Spektral yansıma, Şeker pancarı

References

• Anonim 2012. Bitkisel Üretim Çiftçi Rehberi, konyaseker.com.tr/Upload/Files/seker-pancari.pdf.
• And, T.K.; Sommer, S. 2012. Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environ. Sci. Technol. 2002, 36, 2742.
• Arellano, P., Tansey, K., Balzter, H., & Boyd, D. S. 2015. Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images. Environmental Pollution, 205, 225-239.
• Chen, B.C.; Lai, H.Y.; Juang, K.W. 2012. Model evaluation of plant metal content and biomass yield for the phytoextraction of heavy metals by switchgrass. Ecotoxicol. Environ. Saf. 2012, 80, 393–400.
• De Meester, T. 1970. Soil Map of the Great Konya Basin, Turkey. Agricultural University, Wageningen
• Demirtaş, E. I., Nuri, A. R. I., ÖZKAN, C. F., & ASRİ, F. Ö. 2016. Domates yetiştiriciliğinde kentsel katı atık kompost kullanımının verim kalite ve ağır metal kirliliği üzerine etkileri. Derim, 33(1), 144-158.
• Demotes-Mainard, S., Boumaza, R., Meyer, S., & Cerovic, Z. G. 2008. Indicators of nitrogen status for ornamental woody plants based on optical measurements of leaf epidermal polyphenol and chlorophyll contents. Scientia Horticulturae, 115(4), 377-385.
• ERDAS, 2009. User's guide, http://www.erdas.com.
• ESRI, 2004. ArcGIS Desktop Developer Guid: ArcGIS 9.1. ESRI, Redlands, CA, 335pp.
• Faberio, C., Martin de Santa Olalla, F., Lopez, R., Dominguez, A. 2003. Production and Quality of the Sugar Beet Cultivated Under Contrelled Deficit Irrigation Conditions in a Semi-Arid Climate. Agric. Water Manage., 62: 215-227.
• Ferreira, G., Cayford, E. L., Feng, L., Shao, Y., & Casares, M. I. 2018. Use of satellite remote-sensing techniques to predict the variation of the nutritional composition of corn (Zea mays L) for silage. Maydica, 61(1), 6.
• Filchev, L., & Roumenina, E. 2012. Detection and assessment of abiotic stress of coniferous landscapes caused by uranium mining (using multitemporal high resolution Landsat data). Geography, Environment, Sustainability, 5(1), 52-66.
• Gür, N., Topdemir, A., Munzuroğlu, Ö., & Çobanoğlu, D. 2004. Ağır Metal İyonlarının (Cu+ 2, Pb+ 2, Hg+ 2, Cd+ 2) Clivia sp. Bitkisi Polenlerinin Çimlenmesi ve Tüp Büyümesi Üzerine Etkileri. FÜ Fen ve Matematik Bilimleri Dergisi, 16(2), 177-182.
• Jackson R.D. 1986. Remote sensing of biotic and abiotic plant stress. Annual Review of Phytopathology 24, 265–286.
• Jia L., Yu Z., Li F., Gnyp M., Koppe W., Bareth G., Miao Y., Chen X., Zhang F. 2011. Nitrogen status estimation of winter wheat by using an Ikonos satellite image in the north china plain. Computer and computing technologis in agriculture V. 5 th IFIP TC5/SIG 5,1 Conference, CCTA 2011 Beijing, Cina, October 2011 Proceedings, Part II.
• Jin, M., Liu, X., & Zhang, B. 2017. Evaluating heavy-metal stress levels in rice using a theoretical model of canopy-air temperature and leaf area index based on remote sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(7), 3232-3242.
• Kennedy, C. D., & Gonsalves, F. A. N. 1987. The action of divalent zinc, cadmium, mercury, copper and lead on the trans-root potential and H+, efflux of excised roots. Journal of Experimental Botany, 38(5), 800-817.
• Kokaly, R.F.; Clark, R.N. 1999. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sens. Environ. 1999, 67, 267–287.
• Krupa, Z., Siedlecka, A., Skorzynska-Polit, E., & Maksymiec, W. 2002. Heavy metal interactions with plant nutrients. In Physiology and biochemistry of metal toxicity and tolerance in plants (pp. 287-301). Springer, Dordrecht.
• Li, X., Liu, X., Liu, M., Wang, C., & Xia, X. 2015. A hyperspectral index sensitive to subtle changes in the canopy chlorophyll content under arsenic stress. International Journal of Applied Earth Observation and Geoinformation, 36, 41-53.
• Maimaitiyiming, M., Ghulam, A., Bozzolo, A., Wilkins, J. L., & Kwasniewski, M. T. 2017. Early Detection of Plant Physiological Responses to Different Levels of Water Stress Using Reflectance Spectroscopy. Remote Sensing, 9(7), 745.
• MGM, 2015. Orman ve Su İşleri Bakanlığı, Meteoroloji Genel Müdürlüğü, Ankara.
• Minitab, I. 2014. MINITAB release 17: statistical software for windows. Minitab Inc, USA.
• Morisette, J.T., Jarnevich,C.S., Ullah, A., Cai, W., Pedelty, J.A., Gentle, J.E., Stohlgren, T.J., Schnase, J.L. 2006. A tamarisk habitat suitability map for the continental United States Front. Ecol. Environ., 4 (2006), pp. 11-17.
• Nobi, E.P.; Dilipan, E.; Thangaradjou, T.; Sivakumar, K.; Kannan, L. 2010. Geochemical and geo-statistical assessment of heavy metal concentration in the sediments of different coastal ecosystems of Andaman Islands, India. Estuar. Coast. Shelf Sci. 2010, 87, 253–264.
• Özdeniz, E., Özbey, B. G., Kurt, L., & Bölükbaşı, A. 2017. Serpantin ekolojisi ve Türkiye serpantin florası’na katkılar. Toprak Bilimi ve Bitki Besleme Dergisi, 5(1), 22-33.
• Peng, J. F., Song, Y. H., Yuan, P., Cui, X. Y., & Qiu, G. L. 2009. The remediation of heavy metals contaminated sediment. Journal of hazardous materials, 161(2-3), 633-640.
• Peñuelas, J.; Gamon, J.A.; Fredeen, A.L.; Merino, J.; Field, C.B. 1999. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sens. Environ. 1994, 48, 135–146.
• Seven, T., Can, B., Darende, B. N., & Ocak, S. 2018. Hava ve Toprakta Ağır Metal Kirliliği. Ulusal Çevre Bilimleri Araştırma Dergisi, 1(2), 91-103.
• Shin, J. H., Yu, J., Kim, S., Shin, H., & Koh, S. M. 2017. Relationship between spectral reflectance and metal content of Korean pine needles as a metal contamination indicator. In Geoscience and Remote Sensing Symposium (IGARSS), 2017 IEEE International (pp. 6166-6169). IEEE.
• Shou, L.N., Jia, L.L., Cui, Z.L., Chen, X.P., Zhang, F.S. 2007. Using high-resolution satellite image to evaluate nitrogen status of winter wheat in the North China Plain. Journal of Plant Nutrition 30(10), 1669–1680.
• Takeda, A., Kimura, K., & Yamasaki, S. I. 2004. Analysis of 57 elements in Japanese soils, with special reference to soil group and agricultural use. Geoderma, 119(3-4), 291-307.
• Tapur, T. 1998. Eski Konya gölü ve çevresinin fiziki coğrafya özellikleri (Doktora Tezi, Selçuk Üniversitesi Sosyal Bilimler Enstitüsü).
• USGS, 2018. LANDSAT 7 (L7) DATA USERS HANDBOOK, U.S. Geological Survey (USGS) Landsat Project Science Office at the Earth Resources Observation and Science (EROS) Center in South Dakota.
• Wójtowicz, M., Wójtowicz, A., & Piekarczyk, J. 2016. Application of remote sensing methods in agriculture. Communications in Biometry and Crop Science, 11, 31-50.
• Zhao, H., Xia, B., Fan, C., Zhao, P., & Shen, S. 2012. Human health risk from soil heavy metal contamination under different land uses near Dabaoshan Mine, Southern China. Science of the Total Environment, 417, 45-54.
• Zhou, C., Chen, S., Zhang, Y., Zhao, J., Song, D., & Liu, D. 2018. Evaluating Metal Effects on the Reflectance Spectra of Plant Leaves during Different Seasons in Post-Mining Areas, China. Remote Sensing, 10(8), 1211.
• Zuzana, L., Lukáš, B., Lucie, K., Veronika, K., Markéta, P., Jan, M., ... & Jana, A. 2013. Detection of multiple stresses in Scots pine growing at post-mining sites using visible to near-infrared spectroscopy. Environmental Science: Processes & Impacts, 15(11), 2004-2015.