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The Effect of Restricted Irrigation Applications on Vegetation Index Based on UAV Multispectral Sensing

Year 2021, Volume 31, Issue 3, 629 - 643, 15.09.2021
https://doi.org/10.29133/yyutbd.910909

Abstract

Increasing water demands in agricultural cultivation have made it necessary to develop better irrigation management strategies within today's development and technologies. Information production based on imaging technologies is also included in these uses. In this study, the usability of UAV-based multispectral images in the evaluation of subsurface and surface drip irrigation applications was investigated. For this purpose, a silage maize trial programmed to be multiples of ET0 (0.00, 0.25, 0.50, 0.75, 1.00, 1.25, 1.5) was imaged using an unmanned aerial vehicle during the growing season. 9 different vegetation indexes were created in the images taken and their usability in monitoring the applications was compared with each other. It was determined that LCI and TGI indexes in subsurface drip irrigation method and VARI index in surface drip irrigation method can be used at the level of irrigation programs (p<0.05). When the temporal data were examined during the development period, it was determined that there were differences between the 9 plant index results (p<0.05). When the surface and subsurface drip irrigation methods derived from the analysis of multispectral images were compared with the Vegetation Indexes (VI), it was observed that there was a statistically significant difference between the treatments. When irrigation rates were compared, similar differences were determined in vegetation index values. The obtained results demonstrated the feasibility of UAV-integrated multispectral images to characterize the responses of plants to different irrigation applications. It is thought that Smart Agriculture, Precision Agriculture, Organic Agriculture, and Good Agricultural Practices made with UAVs will have high utilization potential at the farm level.

References

  • Al Sayah, M. J., Abdallah, C., Khouri, M., Nedjai, R., & Darwich, T. (2021). A framework for climate change assessment in Mediterranean data-sparse watersheds using remote sensing ve ARIMA modeling. Theoretical ve Applied Climatology, 143(1), 639-658.
  • Alaboz, P., Demir, S., & Dengiz, O. (2020). Farklı Enterpolasyon Yöntemleri Kullanılarak Toprakların Nem Sabitelerine Ait Konumsal Dağılımların Belirlenmesi, Isparta Atabey Ovası Örneği. Tekirdağ Ziraat Fakültesi Dergisi, 17(3), 432-444.
  • Ali, W., Nadeem, M., Ashiq, W., Zaeem, M., Thomas, R., Kavanagh, V., & Cheema, M. (2019). Forage yield ve quality indices of silage-corn following organic ve inorganic phosphorus amendments in podzol soil under boreal climate. Agronomy, 9(9), 489.
  • Alvino, F. C., Aleman, C. C., Filgueiras, R., Althoff, D., & da Cunha, F. F. (2020). Vegetatıon Indıces For Irrıgated Corn Monıtorıng. Engenharia Agrícola, 40(3), 322-333.
  • Ballesteros, R., Ortega, J. F., Hernandez, D., Del Campo, A., & Moreno, M. A. (2018). Combined use of agro-climatic ve very high-resolution remote sensing information for crop monitoring. International Journal of Applied Earth Observation Ve Geoinformation, 72, 66-75.
  • Bausch, W. C. (1993). Soil background effects on reflectance-based crop coefficients for corn. Remote Sensing of Environment, 46(2), 213-222.
  • Becker, T., Nelsen, T. S., Leinfelder-Miles, M., & Lundy, M. E. (2020). Differentiating between Nitrogen ve Water Deficiency in Irrigated Maize Using a UAV-Based Multi-Spectral Camera. Agronomy, 10(11), 1671.
  • Boon, M. A., Greenfield, R., & Tesfamichael, S. (2016). Wetland assessment using unmanned aerial vehicle (UAV) photogrammetry.
  • Calera, A., Campos, I., Osann, A., D’Urso, G., & Menenti, M. (2017). Remote sensing for crop water management: from ET modelling to services for the end users. Sensors, 17(5), 1104.
  • Cleverly, J., Eamus, D., Coupe, N. R., Chen, C., Maes, W., Li, L., ... & Huete, A. (2016). Soil moisture controls on phenology ve productivity in a semi-arid critical zone. Science of the Total Environment, 568, 1227-1237.
  • Coors, J. G., Carter, P. R., & Hunter, R. B. (1994). Silage corn. Specialty corns.CRC PRess Inc.Boca Raton, USA.
  • Costa, J. M., Ortuño, M. F., Lopes, C. M., & Chaves, M. M. (2012). Grapevine varieties exhibiting differences in stomatal response to water deficit. Functional Plant Biology, 39(3), 179-189.
  • Çakmak, B., Kendirli, B., & Uçar, Y. (2007). Evaluation of Agricultural water use: A Case study for Kizilirmak. Journal of Tekirdag Agricultural Faculty, 4(2), 175-185.
  • Çakmakci, T., & Şahın, Ü. (2020). Aritilmis Atik Suyun Farkli Sulama Yöntemleriyle Uygulanmasinin Silajlik Misirda Makro-Mikro Element ve Agir Metal Birikimine Etkisi. Journal of Tekirdag Agricultural Faculty, 17(1), 12-23.
  • Danandeh Mehr, A., Sorman, A. U., Kahya, E., & Hesami Afshar, M. (2020). Climate change impacts on meteorological drought using SPI ve SPEI: case study of Ankara, Turkey. Hydrological Sciences Journal, 65(2), 254-268.
  • Datt, B., McVicar, T. R., Van Niel, T. G., Jupp, D. L., & Pearlman, J. S. (2003). Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes. IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1246-1259.
  • Daughtry, C. S. T., Walthall, C. L., Kim, M. S., De Colstoun, E. B., & McMurtrey Iii, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote sensing of Environment, 74(2), 229-239.
  • Demir, S., & Başayiğit, L. Sorunlu Gelişim Gösteren Bitkilerin İnsansız Hava Araçları (İHA) ile Belirlenmesi. Türk Bilim ve Mühendislik Dergisi, 2(1), 12-22.
  • DJI, 2021. DJI drone üreticisi (Phantom Serisi), Hong Kong. https://www.dji.com/support/product/phantom-4-pro (Erişim tarihi: 02 Şubat 2021) ERDAS (1999). ERDAS IMAGINE 8.2. field guide. Erdas INC. Atlanta, Georgia.
  • Fernández García, I., Lecina, S., Ruiz-Sánchez, M. C., Vera, J., Conejero, W., Conesa, M. R., ... & Montesinos, P. (2020). Trends ve challenges in irrigation scheduling in the semi-arid area of Spain. Water, 12(3), 785.
  • Folberth, C., Khabarov, N., Balkovič, J., Skalský, R., Visconti, P., Ciais, P., ... & Obersteiner, M. (2020). The global cropland-sparing potential of high-yield farming. Nature Sustainability, 3(4), 281-289.
  • Gaitán, E., Monjo, R., Pórtoles, J., & Pino-Otín, M. R. (2020). Impact of climate change on drought in Aragon (NE Spain). Science of The Total Environment, 740, 140094.
  • Gezan, S. A., & Carvalho, M. (2018). Analysis of repeated measures for the biological ve agricultural sciences. Applied Statistics İn Agricultural, Biological, and Environmental Sciences, 279-297.
  • Giordano, M., Scheierling, S. M., Tréguer, D. O., Turral, H., & McCornick, P. G. (2021). Moving beyond ‘more crop per drop’: insights from two decades of research on agricultural water productivity. International Journal of Water Resources Development, 37(1), 137-161.
  • Gitelson, A. A., Stark, R., Grits, U., Rundquist, D., Kaufman, Y., & Derry, D. (2002). Vegetation and soil lines in visible spectral space: a concept and technique for remote estimation of vegetation fraction. International Journal of Remote Sensing, 23(13), 2537-2562.
  • Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289-298.
  • Gitelson, A., & Merzlyak, M. N. (1994). Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobiology B: Biology, 22(3), 247-252.
  • Gómez-Candón, D., De Castro, A. I., & López-Granados, F. (2014). Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision Agriculture, 15(1), 44-56.
  • Han, L., Yang, G., Yang, H., Xu, B., Li, Z., & Yang, X. (2018). Clustering field-based maize phenotyping of plant-height growth and canopy spectral dynamics using a UAV remote-sensing approach. Frontiers in plant science, 9, 1638.
  • Heber, U. (1969). Conformational changes of chloroplasts induced by illumination of leaves in vivo. Biochimica et Biophysica Acta (BBA)-Bioenergetics, 180(2), 302-319.
  • Huang, Y., Reddy, K. N., Fletcher, R. S., & Pennington, D. (2018). UAV low-altitude remote sensing for precision weed management. Weed Technology, 32(1), 2-6.
  • Huete, A., Justice, C., & Van Leeuwen, W. (1999). MODIS vegetation index (MOD13). Algorithm theoretical basis document, 3, 213.
  • Hunt Jr, E. R., Doraiswamy, P. C., McMurtrey, J. E., Daughtry, C. S., Perry, E. M., & Akhmedov, B. (2013). A visible band index for remote sensing leaf chlorophyll content at the canopy scale. International Journal of Applied Earth Observation and Geoinformation, 21, 103-112.
  • Jeyaseelan, A. T. (2003). Droughts & floods assessment ve monitoring using remote sensing and GIS. Satellite Remote Sensing and GIS Applications in Agricultural Meteorology, 291.
  • Jin, X., Liu, S., Baret, F., Hemerlé, M., & Comar, A. (2017). Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment, 198, 105-114.
  • Kallapur, A. G., & Anavatti, S. G. (2006, November). UAV linear and nonlinear estimation using extended Kalman filter. In 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06) (pp. 250-250). IEEE.
  • Lelong, C., Burger, P., Jubelin, G., Roux, B., Labbé, S., & Baret, F. (2008). Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors, 8(5), 3557-3585.
  • Ma, S., Zhou, Y., Gowda, P. H., Dong, J., Zhang, G., Kakani, V. G., ... & Jiang, W. (2019). Application of the water-related spectral reflectance indices: A review. Ecological Indicators, 98, 68-79.
  • Marino, G., Pallozzi, E., Cocozza, C., Tognetti, R., Giovannelli, A., Cantini, C., & Centritto, M. (2014). Assessing gas exchange, sap flow ve water relations using tree canopy spectral reflectance indices in irrigated ve rainfed Olea europaea L. Environmental and Experimental Botany, 99, 43-52.
  • Matese, A., Baraldi, R., Berton, A., Cesaraccio, C., Di Gennaro, S. F., Duce, P., ... & Zaldei, A. (2018). Estimation of water stress in grapevines using proximal ve remote sensing methods. Remote Sensing, 10(1), 114.
  • McKee, T. B., Doesken, N. J., & Kleist, J. (1993, January). The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology (Vol. 17, No. 22, pp. 179-183).
  • MGM, 2021. Türkiye İklim İstatistikleri. Meteoroloji Genel Müdürlüğü, Ankara. https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?m=ISPARTA (Erişim tarihi:02.02.2021)
  • Neitzel, F., & Klonowski, J. (2011). Mobile 3D mapping with a low-cost UAV system. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, 38, 1-6.
  • Osakabe, Y.; Osakabe, K.; Shinozaki, K.; Tran, L.-S.P. Response of plants to water stress. Front. Plant Sci. 2014, 5.
  • Peppa, M. V., Hall, J., Goodyear, J., & Mills, J. P. (2019). Photogrammetric assessment and comparison of DJI Phantom 4 pro and phantom 4 RTK small unmanned aircraft systems. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, XLII-2, 503-509.
  • Pokhrel, Y., Felfelani, F., Satoh, Y., Boulange, J., Burek, P., Gädeke, A., ... & Wada, Y. (2021). Global terrestrial water storage ve drought severity under climate change. Nature Climate Change, 1-8.
  • Raeva, P. L., Šedina, J., & Dlesk, A. (2019). Monitoring of crop fields using multispectral and thermal imagery from UAV. European Journal of Remote Sensing, 52(sup1), 192-201.
  • Rhew, I. C., Vander Stoep, A., Kearney, A., Smith, N. L., & Dunbar, M. D. (2011). Validation of the normalized difference vegetation index as a measure of neighborhood greenness. Annals of Epidemiology, 21(12), 946-952.
  • Rock, G., Ries, J. B., & Udelhoven, T. (2011, January). Sensitivity analysis of UAV-photogrammetry for creating digital elevation models (DEM). In Proceedings of Conference on Unmanned Aerial Vehicle in Geomatics. Switzerland: Zurich.
  • Rouse Jr, J., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS.
  • Sentera, 2021. Sentera sensör üreticisi (Double 4K Multispektral Tarım Sensör), ABD. https://sentera.com/introducing-multispectral-double-4k-sensor/ (Erişim tarihi: 02 Şubat 2021)
  • Taghvaeian, S., Chávez, J. L., & Hansen, N. C. (2012). Infrared thermometry to estimate crop water stress index ve water use of irrigated maize in Northeastern Colorado. Remote Sensing, 4(11), 3619-3637.
  • Tiryaki, T. (2018). Su Stresinin Yağ Gülü (Rosa Damascena Mill.) Fidanlarında Morfolojik Ve Biyokimyasal Özellikler Üzerine Etkisi. (Yüksek Lisans Tezi) Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü, Isparta
  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150.
  • Uçar, Y., Kazaz, S., İnal, F. E., & Baydar, H. (2017). Empirical Models Likely to Be Used to Estimate the Evapotranspiration of Oil Rose (Rosa damascena Mill.). Ziraat Fakültesi Dergisi, 12(1), 1-10.
  • Uçar, Y. (2011). Performance assessment irrigation schemes according to comparative indicators: A case study of Isparta, Turkey. European Journal of Scientific Research, 52(1), 82-90.
  • Wahab, I., Hall, O., & Jirström, M. (2018). Remote sensing of yields: Application of uav imagery-derived ndvi for estimating maize vigor ve yields in complex farming systems in sub-saharan africa. Drones, 2(3), 28.
  • Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments ve applications. Journal of Sensors, 2017.
  • Zhang, L., Zhang, H., Niu, Y., & Han, W. (2019). Mapping maize water stress based on UAV multispectral remote sensing. Remote Sensing, 11(6), 605.
  • Zhao, C. (2014). Advances of research and application in remote sensing for agriculture. Nongye Jixie Xuebao= Transactions of the Chinese Society for Agricultural Machinery, 45(12), 277-293.

Kısıtlı Sulama Uygulamalarının İHA Multispektral Algılamaya Dayalı Vejetasyon İndekslerine Etkisi

Year 2021, Volume 31, Issue 3, 629 - 643, 15.09.2021
https://doi.org/10.29133/yyutbd.910909

Abstract

Tarımsal yetiştiricilikte artan su talepleri, günümüz gelişen ve teknolojilerini daha iyi sulama yönetimi stratejilerini geliştirmeyi zorunlu hale getirmiştir. Görüntüleme teknolojilerine dayalı bilgi üretimi de bu amaçlı kullanımlar içerisinde yer almaktadır. Bu çalışmada, İHA tabanlı multispektral görüntülerin yüzey altı ve yüzey üstü damla sulama uygulamalarının değerlendirilmesinde kullanılabilirliği araştırılmıştır. Bu amaçla ET0 (0.00, 0.25, 0.50, 0.75, 1.00, 1.25, 1.5) katları olacak şekilde programlanan bir slajlık mısır denemesinin büyüme sezonu boyunca insansız hava aracı kullanılarak görüntülenmiştir. Alınan görüntülerde 9 farklı vejetasyon indeksi oluşturularak uygulamaların izlenmesinde kullanılabilirlikleri birbirleri ile karşılaştırılmıştır. Yüzey altı damlama sulama yönteminde LCI ve TGI indeksleri, yüzey üstü damlama sulama yönteminde VARI indeksinin sulama programları düzeyinde kullanılabileceği belirlenmiştir (p<0.05). Gelişme dönemi boyunca temporal veriler incelendiğinde 9 bitki indeksi sonuçları arasında farklılıklar olduğu tespit edilmiştir (p<0.05). Multispektral görüntülerin analizinden türetilen yüzey ve yüzey altı damla sulama yöntemleri Vejetasyon İndeksleri (VI) ile karşılaştırıldığında işlemler arasında istatistiksel olarak anlamlı farklılık olduğu gözlemlenmiştir. Sulama oranları karşılaştırıldığında, bitki örtüsü indeksi değerlerinde de benzer farklılıklar belirlenmiştir. Elde edilen sonuçlar, farklı sulama uygulamalarına bitkilerin tepkilerini karakterize etmek için İHA entegrasyonlu multispektral görüntülerin uygulanabilirliğini göstermiştir. İHA’lar ile yapılan Akıllı Tarım, Hassas Tarım, Organik Tarım ve İyi Tarım Uygulamalarının çiftlik düzeyinde yüksek kullanım potansiyeline sahip olacağı düşünülmektedir.

References

  • Al Sayah, M. J., Abdallah, C., Khouri, M., Nedjai, R., & Darwich, T. (2021). A framework for climate change assessment in Mediterranean data-sparse watersheds using remote sensing ve ARIMA modeling. Theoretical ve Applied Climatology, 143(1), 639-658.
  • Alaboz, P., Demir, S., & Dengiz, O. (2020). Farklı Enterpolasyon Yöntemleri Kullanılarak Toprakların Nem Sabitelerine Ait Konumsal Dağılımların Belirlenmesi, Isparta Atabey Ovası Örneği. Tekirdağ Ziraat Fakültesi Dergisi, 17(3), 432-444.
  • Ali, W., Nadeem, M., Ashiq, W., Zaeem, M., Thomas, R., Kavanagh, V., & Cheema, M. (2019). Forage yield ve quality indices of silage-corn following organic ve inorganic phosphorus amendments in podzol soil under boreal climate. Agronomy, 9(9), 489.
  • Alvino, F. C., Aleman, C. C., Filgueiras, R., Althoff, D., & da Cunha, F. F. (2020). Vegetatıon Indıces For Irrıgated Corn Monıtorıng. Engenharia Agrícola, 40(3), 322-333.
  • Ballesteros, R., Ortega, J. F., Hernandez, D., Del Campo, A., & Moreno, M. A. (2018). Combined use of agro-climatic ve very high-resolution remote sensing information for crop monitoring. International Journal of Applied Earth Observation Ve Geoinformation, 72, 66-75.
  • Bausch, W. C. (1993). Soil background effects on reflectance-based crop coefficients for corn. Remote Sensing of Environment, 46(2), 213-222.
  • Becker, T., Nelsen, T. S., Leinfelder-Miles, M., & Lundy, M. E. (2020). Differentiating between Nitrogen ve Water Deficiency in Irrigated Maize Using a UAV-Based Multi-Spectral Camera. Agronomy, 10(11), 1671.
  • Boon, M. A., Greenfield, R., & Tesfamichael, S. (2016). Wetland assessment using unmanned aerial vehicle (UAV) photogrammetry.
  • Calera, A., Campos, I., Osann, A., D’Urso, G., & Menenti, M. (2017). Remote sensing for crop water management: from ET modelling to services for the end users. Sensors, 17(5), 1104.
  • Cleverly, J., Eamus, D., Coupe, N. R., Chen, C., Maes, W., Li, L., ... & Huete, A. (2016). Soil moisture controls on phenology ve productivity in a semi-arid critical zone. Science of the Total Environment, 568, 1227-1237.
  • Coors, J. G., Carter, P. R., & Hunter, R. B. (1994). Silage corn. Specialty corns.CRC PRess Inc.Boca Raton, USA.
  • Costa, J. M., Ortuño, M. F., Lopes, C. M., & Chaves, M. M. (2012). Grapevine varieties exhibiting differences in stomatal response to water deficit. Functional Plant Biology, 39(3), 179-189.
  • Çakmak, B., Kendirli, B., & Uçar, Y. (2007). Evaluation of Agricultural water use: A Case study for Kizilirmak. Journal of Tekirdag Agricultural Faculty, 4(2), 175-185.
  • Çakmakci, T., & Şahın, Ü. (2020). Aritilmis Atik Suyun Farkli Sulama Yöntemleriyle Uygulanmasinin Silajlik Misirda Makro-Mikro Element ve Agir Metal Birikimine Etkisi. Journal of Tekirdag Agricultural Faculty, 17(1), 12-23.
  • Danandeh Mehr, A., Sorman, A. U., Kahya, E., & Hesami Afshar, M. (2020). Climate change impacts on meteorological drought using SPI ve SPEI: case study of Ankara, Turkey. Hydrological Sciences Journal, 65(2), 254-268.
  • Datt, B., McVicar, T. R., Van Niel, T. G., Jupp, D. L., & Pearlman, J. S. (2003). Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes. IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1246-1259.
  • Daughtry, C. S. T., Walthall, C. L., Kim, M. S., De Colstoun, E. B., & McMurtrey Iii, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote sensing of Environment, 74(2), 229-239.
  • Demir, S., & Başayiğit, L. Sorunlu Gelişim Gösteren Bitkilerin İnsansız Hava Araçları (İHA) ile Belirlenmesi. Türk Bilim ve Mühendislik Dergisi, 2(1), 12-22.
  • DJI, 2021. DJI drone üreticisi (Phantom Serisi), Hong Kong. https://www.dji.com/support/product/phantom-4-pro (Erişim tarihi: 02 Şubat 2021) ERDAS (1999). ERDAS IMAGINE 8.2. field guide. Erdas INC. Atlanta, Georgia.
  • Fernández García, I., Lecina, S., Ruiz-Sánchez, M. C., Vera, J., Conejero, W., Conesa, M. R., ... & Montesinos, P. (2020). Trends ve challenges in irrigation scheduling in the semi-arid area of Spain. Water, 12(3), 785.
  • Folberth, C., Khabarov, N., Balkovič, J., Skalský, R., Visconti, P., Ciais, P., ... & Obersteiner, M. (2020). The global cropland-sparing potential of high-yield farming. Nature Sustainability, 3(4), 281-289.
  • Gaitán, E., Monjo, R., Pórtoles, J., & Pino-Otín, M. R. (2020). Impact of climate change on drought in Aragon (NE Spain). Science of The Total Environment, 740, 140094.
  • Gezan, S. A., & Carvalho, M. (2018). Analysis of repeated measures for the biological ve agricultural sciences. Applied Statistics İn Agricultural, Biological, and Environmental Sciences, 279-297.
  • Giordano, M., Scheierling, S. M., Tréguer, D. O., Turral, H., & McCornick, P. G. (2021). Moving beyond ‘more crop per drop’: insights from two decades of research on agricultural water productivity. International Journal of Water Resources Development, 37(1), 137-161.
  • Gitelson, A. A., Stark, R., Grits, U., Rundquist, D., Kaufman, Y., & Derry, D. (2002). Vegetation and soil lines in visible spectral space: a concept and technique for remote estimation of vegetation fraction. International Journal of Remote Sensing, 23(13), 2537-2562.
  • Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289-298.
  • Gitelson, A., & Merzlyak, M. N. (1994). Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobiology B: Biology, 22(3), 247-252.
  • Gómez-Candón, D., De Castro, A. I., & López-Granados, F. (2014). Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision Agriculture, 15(1), 44-56.
  • Han, L., Yang, G., Yang, H., Xu, B., Li, Z., & Yang, X. (2018). Clustering field-based maize phenotyping of plant-height growth and canopy spectral dynamics using a UAV remote-sensing approach. Frontiers in plant science, 9, 1638.
  • Heber, U. (1969). Conformational changes of chloroplasts induced by illumination of leaves in vivo. Biochimica et Biophysica Acta (BBA)-Bioenergetics, 180(2), 302-319.
  • Huang, Y., Reddy, K. N., Fletcher, R. S., & Pennington, D. (2018). UAV low-altitude remote sensing for precision weed management. Weed Technology, 32(1), 2-6.
  • Huete, A., Justice, C., & Van Leeuwen, W. (1999). MODIS vegetation index (MOD13). Algorithm theoretical basis document, 3, 213.
  • Hunt Jr, E. R., Doraiswamy, P. C., McMurtrey, J. E., Daughtry, C. S., Perry, E. M., & Akhmedov, B. (2013). A visible band index for remote sensing leaf chlorophyll content at the canopy scale. International Journal of Applied Earth Observation and Geoinformation, 21, 103-112.
  • Jeyaseelan, A. T. (2003). Droughts & floods assessment ve monitoring using remote sensing and GIS. Satellite Remote Sensing and GIS Applications in Agricultural Meteorology, 291.
  • Jin, X., Liu, S., Baret, F., Hemerlé, M., & Comar, A. (2017). Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment, 198, 105-114.
  • Kallapur, A. G., & Anavatti, S. G. (2006, November). UAV linear and nonlinear estimation using extended Kalman filter. In 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06) (pp. 250-250). IEEE.
  • Lelong, C., Burger, P., Jubelin, G., Roux, B., Labbé, S., & Baret, F. (2008). Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors, 8(5), 3557-3585.
  • Ma, S., Zhou, Y., Gowda, P. H., Dong, J., Zhang, G., Kakani, V. G., ... & Jiang, W. (2019). Application of the water-related spectral reflectance indices: A review. Ecological Indicators, 98, 68-79.
  • Marino, G., Pallozzi, E., Cocozza, C., Tognetti, R., Giovannelli, A., Cantini, C., & Centritto, M. (2014). Assessing gas exchange, sap flow ve water relations using tree canopy spectral reflectance indices in irrigated ve rainfed Olea europaea L. Environmental and Experimental Botany, 99, 43-52.
  • Matese, A., Baraldi, R., Berton, A., Cesaraccio, C., Di Gennaro, S. F., Duce, P., ... & Zaldei, A. (2018). Estimation of water stress in grapevines using proximal ve remote sensing methods. Remote Sensing, 10(1), 114.
  • McKee, T. B., Doesken, N. J., & Kleist, J. (1993, January). The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology (Vol. 17, No. 22, pp. 179-183).
  • MGM, 2021. Türkiye İklim İstatistikleri. Meteoroloji Genel Müdürlüğü, Ankara. https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?m=ISPARTA (Erişim tarihi:02.02.2021)
  • Neitzel, F., & Klonowski, J. (2011). Mobile 3D mapping with a low-cost UAV system. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, 38, 1-6.
  • Osakabe, Y.; Osakabe, K.; Shinozaki, K.; Tran, L.-S.P. Response of plants to water stress. Front. Plant Sci. 2014, 5.
  • Peppa, M. V., Hall, J., Goodyear, J., & Mills, J. P. (2019). Photogrammetric assessment and comparison of DJI Phantom 4 pro and phantom 4 RTK small unmanned aircraft systems. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, XLII-2, 503-509.
  • Pokhrel, Y., Felfelani, F., Satoh, Y., Boulange, J., Burek, P., Gädeke, A., ... & Wada, Y. (2021). Global terrestrial water storage ve drought severity under climate change. Nature Climate Change, 1-8.
  • Raeva, P. L., Šedina, J., & Dlesk, A. (2019). Monitoring of crop fields using multispectral and thermal imagery from UAV. European Journal of Remote Sensing, 52(sup1), 192-201.
  • Rhew, I. C., Vander Stoep, A., Kearney, A., Smith, N. L., & Dunbar, M. D. (2011). Validation of the normalized difference vegetation index as a measure of neighborhood greenness. Annals of Epidemiology, 21(12), 946-952.
  • Rock, G., Ries, J. B., & Udelhoven, T. (2011, January). Sensitivity analysis of UAV-photogrammetry for creating digital elevation models (DEM). In Proceedings of Conference on Unmanned Aerial Vehicle in Geomatics. Switzerland: Zurich.
  • Rouse Jr, J., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS.
  • Sentera, 2021. Sentera sensör üreticisi (Double 4K Multispektral Tarım Sensör), ABD. https://sentera.com/introducing-multispectral-double-4k-sensor/ (Erişim tarihi: 02 Şubat 2021)
  • Taghvaeian, S., Chávez, J. L., & Hansen, N. C. (2012). Infrared thermometry to estimate crop water stress index ve water use of irrigated maize in Northeastern Colorado. Remote Sensing, 4(11), 3619-3637.
  • Tiryaki, T. (2018). Su Stresinin Yağ Gülü (Rosa Damascena Mill.) Fidanlarında Morfolojik Ve Biyokimyasal Özellikler Üzerine Etkisi. (Yüksek Lisans Tezi) Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü, Isparta
  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150.
  • Uçar, Y., Kazaz, S., İnal, F. E., & Baydar, H. (2017). Empirical Models Likely to Be Used to Estimate the Evapotranspiration of Oil Rose (Rosa damascena Mill.). Ziraat Fakültesi Dergisi, 12(1), 1-10.
  • Uçar, Y. (2011). Performance assessment irrigation schemes according to comparative indicators: A case study of Isparta, Turkey. European Journal of Scientific Research, 52(1), 82-90.
  • Wahab, I., Hall, O., & Jirström, M. (2018). Remote sensing of yields: Application of uav imagery-derived ndvi for estimating maize vigor ve yields in complex farming systems in sub-saharan africa. Drones, 2(3), 28.
  • Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments ve applications. Journal of Sensors, 2017.
  • Zhang, L., Zhang, H., Niu, Y., & Han, W. (2019). Mapping maize water stress based on UAV multispectral remote sensing. Remote Sensing, 11(6), 605.
  • Zhao, C. (2014). Advances of research and application in remote sensing for agriculture. Nongye Jixie Xuebao= Transactions of the Chinese Society for Agricultural Machinery, 45(12), 277-293.

Details

Primary Language Turkish
Subjects Agriculture
Published Date Eylül 2021
Journal Section Articles
Authors

Sinan DEMİR> (Primary Author)
ısparta uygulamalı bilimler üniversitesi
0000-0002-1119-1186
Türkiye


Levent BAŞAYİĞİT>
ISPARTA UYGULAMALI BİLİMLER ÜNİVERSİTESİ
0000-0003-2431-5763
Türkiye

Thanks Bu çalışmada, arazi verilerinin elde edilmesinde Prof. Dr. Yusuf UÇAR’a, Öğr. Gör. Mehmet ALAGÖZ’e ve Arş. Gör. Emre TOPÇU’ya yapmış oldukları desteklerinden dolayı teşekkür ederiz. Çalışma süresince desteğini esirgemeyen Ziraat Yük. Müh. Tuğba TİRYAKİ’ye teşekkür ederiz.
Publication Date September 15, 2021
Published in Issue Year 2021, Volume 31, Issue 3

Cite

APA Demir, S. & Başayiğit, L. (2021). Kısıtlı Sulama Uygulamalarının İHA Multispektral Algılamaya Dayalı Vejetasyon İndekslerine Etkisi . Yuzuncu Yıl University Journal of Agricultural Sciences , 31 (3) , 629-643 . DOI: 10.29133/yyutbd.910909
Creative Commons License
Yuzuncu Yil University Journal of Agricultural Sciences by Van Yuzuncu Yil University Faculty of Agriculture is licensed under a Creative Commons Attribution 4.0 International License.