Evaluating unmanned aerial vehicle based crop indexing techniques
Solecki, C. F. (2017). Evaluating unmanned aerial vehicle based crop indexing techniques: Modified consumer grade RGB vs. multispectral. Retrieved from Thompson Rivers University
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The use of unmanned aerial vehicles (UAVs) in agriculture is a relatively new and rapidly expanding concept. By using UAVs equipped with multispectral near infrared sensors, farmers and land managers can detect intra-field crop variability which enables adjustments to be made to crop applications and other management decisions. This type of management has been termed precision agriculture and employs the use of various crop indices such as the normalized difference vegetation index (NDVI). The NDVI is one of the most common crop indices; and is used to measure relative chlorophyll content in green vegetation. In this study, I investigated the sensing abilities of two aerial cameras to determine whether filter modified consumer cameras can produce NDVI maps equivalent to those produced by a multispectral camera. I compared a MicaSense RedEdge® multispectral camera to a modified DJI Zenmuse X3 camera, mounted simultaneously onboard a DJI Inspire 1 UAV, with respect to their ability to generate reliable NDVI maps using Pix4D photogrammetry software. Through evaluation of the index maps produced by the two cameras, the MicaSense RedEdge® was found to produce index values that were more representative of the study site than the modified DJI Zenmuse X3. Spatial vegetation patterns observed by the two sensors were also determined to be significantly different. This study revealed that the two sensors did not produce equivalent NDVI results, and multispectral cameras appear to be a more accurate tool for examining crop productivity and variability.