This paper describes the results of the work, which was divided into three parts: 1) the proof-of-concept of lightweight low cost UAV-borne multispectral NIR (shortwave infrared) sensor, 2) the evaluation of performances of airborne tailored NIR sensor vs. consumer-grade RGB digital camera, and 3) the feasibility study and the comparison of NIR multispectral data vs. Sentinel-2A high-resolution satellite multispectral imagery. The moderated cost-efficient UAV-borne imaging remote sensing solution was requested by the agricultural sector in Ukraine. The existing solutions did not meet the requirements of the end user neither suitable high-resolution satellite multispectral imagery was available too. The designed system integrated consumer-grade RGB digital camera and NIR sensor. Multi-temporary data was collected eight times during one growth season. Eight aerial missions were conducted in midsummer 2015 over the same area, which consisted of 50 plots with various cultivars of wheat, barley and rye. The vegetation indices were calculated for both datasets. Vegetation indices calculated from the NIR sensor highly correlated with plant chlorophyll and plant LAI, and revealed satisfactory correlation with plant fresh mass. Vegetation indices derived from onboard RGB camera did not reveal the significant correlation with any of plant growth parameter. The NDVI values calculated from NIR data demonstrated high correlation with the ones, which were derived from the Sentinel-2 high-resolution satellite multispectral images. The values of UAV-derived NDVI were lower than Sentinel-2-derived NDVI, and the regression slope of this relationship varied in different plant species. Reasons of such variation are discussed in the paper. After the numerous field-tests the customer accepted the developed tailored COTS UAV-borne multispectral solution cost-efficient and sufficient.
Taras Kazantsev, Viktor Shevchenko, Oksana Bondarenko, Mykhailo Furier, Andre Samberg, Fevzi Ametov, and Valerii Iakovenko, "COTS UAV-borne multispectral system for vegetation monitoring," Proc. SPIE 10783, Remote Sensing for Agriculture, Ecosystems, and Hydrology XX, 107830A (Presented at SPIE Remote Sensing: September 11, 2018; Published: 18 October 2018); https://doi.org/10.1117/12.2501859.
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