01_COMPREHENSIVE ANALYSIS OF VEGETATION INDICES USING MULTITEMPORAL DRONE IMAGES
COMPREHENSIVE ANALYSIS OF VEGETATION INDICES
USING MULTITEMPORAL DRONE IMAGES
Marcel Berzéki*, Veronika Kozma-Bognár
Drone Technology and Image Processing Scientific Lab, Dennis Gabor University, Hungary
∗ Correspondence: mberzeki@gmail.com
Abstract
The use of drones (UAVs) has been showing an increasing trend in recent years. The available aerial vehicles and the cameras or camera systems mounted on them significantly influence—and in some cases may even limit—their effective and reliable application in surveying agricultural areas, while also meeting data security requirements. The extractable information content of the produced visual and non-visual data is not only determined by the data itself but is also greatly affected by the methods used for processing and analysis. This paper focused on the analysis and comparison of vegetation indices derived from multispectral drone imagery, specifically for monitoring corn growth and health. The introduction outlines the significance of using these indices in agriculture and environmental protection, aiming to enhance sustainable and cost-effective farming practices. The study emphasizes the potential benefits of the precise and timely monitoring of vegetation indices contributes to improved crop yields, optimized nutrient and water usage, and minimized environmental impacts. The research involves capturing images at different times and altitudes using both RGB and multispectral drones to gather reliable data about plant health during various growth stages.
The methodology section describes the study area, the drone and imaging systems used, and the software applied for data processing. It details the specific vegetation indices calculated, both discrete and non-discrete, and the statistical methods employed to analyse their correlations. The results section discusses findings related to the discrete and non-discrete indices, how they reflect the phenological phases of vegetation throughout the growing season and might replace to complement.
Keywords: vegetation index, UAV, multitemporal, drone images