Hybrid Approach Utilizing Unmanned Aerial Vehicle and Unmanned Surface Vehicle to Assess the Map of Chlorophyll-a in a Stream

Eunju Kim; Sook-Hyun Nam; Jae-Wuk Koo; Tae-Mun Hwang

Working Paper Article Version 1 This version is not peer-reviewed

Version 1 : Received: 18 January 2021 / Approved: 19 January 2021 / Online: 19 January 2021 (09:14:00 CET)

The current study investigated the use of two-dimensional spatial distribution mapping representing the chlorophyll-a level in a river generated via an unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV). A domestically developed UAV (Remo-M, Uconsystem Inc., Korea) and a USV developed by our research team were used to collect data from the Nae Seong stream in Korea. An adaptation of the “Data Cleaner” tool was developed and used for USV data processing and analysis. The operation of the autonomous USV was successful. Four previously described indices for quantifying algal blooms in rivers were utilized to create chlorophyll-a images, the normalized difference vegetation index (NDVI), the normalized green red difference index, the green normalized difference vegetation index (GNDVI), and the normalized difference red edge index. The suitability of the linear regression analysis of the correlation between the spectral indices obtained using the UAV and the in situ chlorophyll-a data obtained using the USV was evaluated with the coefficient of determination (R2) at a significance level of p < 0.001. In field application and correlational analysis the NDVI was strongly correlated with chlorophyll-a (R2 = 0.88, p < 0.001), and the GNDVI was moderately correlated with chlorophyll-a (R2 = 0.74, p < 0.001). The map of chlorophyll-a was successfully quantified using the UAV and USV hybrid platforms.

aerial vehicle; algal bloom index; autonomous; chlorophyll-a mapping; GNDVI; NDVI; surface vehicle; unmanned

ENGINEERING, Automotive Engineering

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Hybrid Approach Utilizing Unmanned Aerial Vehicle and Unmanned Surface Vehicle to Assess the Map of Chlorophyll-a in a Stream

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