The Efficiency of Vegetation Spectral Indices Using Remote Sensing Drone Images

Document Type : Research Paper

Authors

1 Department of Water Engineering, Faculty of Agriculture and Natural Resources, Urmia University, Urmia, Iran

2 Faculty of Planning and Environmental Sciences, Department of Remote Sensing and Geographical Information System (GIS), Tabriz University, Tabriz, Iran

Abstract

In recent years, due to the widespread use of remote sensing drones, the qualitative and quantitative monitoring of agricultural farms using this technology has also increased significantly. In this regard, many vegetation indices were introduced to study the plants specifications. It is understood that each method has different strength and capabilities which should be taken into account of consideration when pressing the drone images. In this research, the efficiency of four high frequently vegetation indices were evaluated using the drone spectral data for monitoring the corn field. Field experiments were carried out in the research farm of Urmia University in 2018. The research methodology was developed by evaluating the effect of different levels of irrigation and fertilization on the crop biomass and four spectral indices such as NDVI, GNDVI, SAVI and NDRE. The experimental design was considered in the form of complete randomized blocks with three levels of irrigation and fertilization application, including 100, 80 and 60% of irrigation water requirements and fertilizer requirements within the four evolution step. The imaging operation was designed and performed using an ebee+ fixed wing drone equipped with the Sequoia remote sensing sensor. After performing the required photogrammetric and preprocessing operations by Pix4Dmapper software, the images were used to calculate vegetation index layers. Finally, the effect of different irrigation and fertilization application levels on crop biomass and vegetation indices were evaluated using statistical analysis of variance in the SPSS software. The results indicated that the crop biomass was significantly affected by different levels of water and fertilizer usage, and no significant effect observed on NDVI and SAVI indices in response to water and fertilizer levels. In contrast, The SAVI index was significant to irrigation levels and the NDRE index was significant to irrigation and fertilizer levels.

Keywords


Afshar, M. H. and Yilmaz, M. T. (2017). The added utility of nonlinear methods compared to linear methods in rescaling soil moisture products. Remote Sensing of Environment, 196, 224-237
Afshar, M. H., Yilmaz, M. T. and Crow, W. T. (2019). Impact of rescaling approaches in simple fusion of soil moisture products. Water Resources Research, 55(9), 7804-7825
Alizadeh, H. A., Liaghat, A. and Abbasi, F. (2009). Effect of furrow fertigation on fertilizer and water use efficiency, productivity and yield components of corn (Zea mays L.). Journal of Water and Soil, 23(2009), 137-147.(In Farsi)
Baio, F. H. R., Neves, D. C., Campos, C. N. S. and Teodoro, P. E. (2018). Relationship between cotton productivity and variability of NDVI obtained by landsat images. Bioscience Journal, (34), 197–205.
Bibe, S. M., Jadhav, K. T. and Kalasare, R. S. (2018). Studies on Fertigation Management in Post Kharif Maize. International Journal of Current Microbiology and Applied Sciences. Special Issue (6), 1343-1347.
Broge, N. H. and Mortensen, J. V. (2002). Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote sensing of environment, (81), 45– 57.
Carneiro, F. M., Furlani, C. E. A., Zerbato, C., de Menezes, P. C., da Silva Gírio, L. A. and de Oliveira, M. F. (2019). Comparison between vegetation indices for detecting spatial and temporal variabilities in soybean crop using canopy sensors. Precision Agriculture, 1-29.
Dehkordi, P. A., Nehbandani, A., Hassanpour-bourkheili, S. and Kamkar, B. (2020). Yield gap analysis using remote sensing and modelling approaches: Wheat in the northwest of Iran. International Journal of Plant Production, 14(3), 443-452.
Gitelson, A. A. and 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.
Gitelson, A. A., Kaufman, Y. J. and 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.
Huete, A. (1988). Huete, AR A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment. Remote sensing of environment, (25), 295–309.
Jamshidi, S., Zand-Parsa, S., and Niyogi, D. (2021). Assessing Crop Water Stress Index of Citrus Using In-Situ Measurements, Landsat, and Sentinel-2 Data. International Journal of Remote Sensing, 42(5), 1893-1916.
Jorge, J., Vallbé, M. and Soler, J.A. (2019). Detection of irrigation in-homogeneities in an olive grove using the NDRE vegetation index obtained from UAV images. European Journal of Remote Sensing, 52(1), 169-177.
Kross, A., McNairn, H., Lapen, D., Sunohara, M. and Champagne, C. (2015). Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. International Journal of Applied Earth Observation and Geoinformation, (34), 235-248.
Li, B., Xu, X., Zhang, L., Han, J., Bian, C., Li, G., Liu, J. and Jin, L. (2020). Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyper-spectral imaging. ISPRS Journal of Photogrammetry and Remote Sensing, (162), 161-172
Mohammadi Ahmad Mahmoudi, E., kamkar, B. and Abdi, O. (2015). Comparison of geostatistical- and remote sensing data-based methods in wheat yield predication in some of growing stages (A case study: Nemooneh filed, Golestan province). Journal of Crop Production, 8(2), 51- 76. (In Farsi)
Poorazar, H., Samadzadegan, F., Dadras Javan, F. and asadi, A. (2017). Multi spectral aerial imagery for peach health assessment. International Conference on agricultural, Natural resources and sustainable resource, 8-8 Oct., Shiraz, Iran. (In Farsi)
Rouse Jr, J. W., Haas, R. H., Schell, J. A. and Deering, D. W. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS. NASA special publication, (351), 309.
Taghizadeh, R. and Seyed Sharifi, R. (2011). Effect of nitrogen on yield attributes and nitrogen use efficiency in corn cultivars. Journal of Water and Soil Science, 15(57), 209-217. (In Farsi)
Yeom, J., Jung, J., Chang, A., Ashapure, A., Maeda, M., Maeda, A. and Landivar, J. (2019). Comparison of vegetation indices derived from UAV data for differentiation of tillage effects in agriculture. Remote Sensing, 11(13), 1548.
Zhao, Y., Potgieter, A. B., Zhang, M., Wu, B., Hammer, G. L. (2020). Predicting wheat yield at the field scale by combining high-resolution sentinel-2 satellite imagery and crop modeling. Remote Sensing, 12(6), 1024.