The Efficiency of Vegetation Spectral Indices Using Remote Sensing Drone Images

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.