Hybrid Approach of Unmanned Aerial Vehicle and Unmanned Surface Vehicle for Assessment of Chlorophyll-a Imagery Using Spectral Indices in Stream, South Korea

Eun-Ju Kim,Jae-Wuk Koo,Sook-Hyun Nam,Tae-Mun Hwang

doi:10.3390/w13141930

Abstract

The purpose of this study is to compare the spectral indices for a two-dimensional river algae map using an unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV) hybrid system. The UAV and USV hybrid systems can overcome the limitation of not being able to effectively compare images of the same region obtained at different times and under different seasonal conditions, when using a method of comparing and analyzing with absolute values in remote sensing. Radiometric correction was performed to minimize the interference that could distort the analysis results of the UAV imagery, and the images were taken under weather conditions that would minimally affect them. Three spectral indices, namely, normalized difference vegetation index (NDVI), normalized green–red difference index (NGRDI), green normalized difference vegetation index (GNDVI), and normalized difference red edge index (NDRE) were compared for the chlorophyll-a images. 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). As a result of comparing the chlorophyll-a concentration with the in-situ chlorophyll-a imagery by UAV, we obtained the RMSE of NDVI at 2.25, and the RMSE of GNDVI at 3.41.

Highlights

IntroductionAlgal blooms are natural phenomena that occur in water-based ecosystems in response to environmental factors, such as nutrition, light, water temperature, and wind speed [1]
AsData seenAnalysis in the histogram in Figure 7a, the chlorophyll-a data obtained by the unmanned surface vehicle (USV) have right-skewed distribution
The normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) were reported to be effective indicesinina previous study, which was conducted on the detection of chlorophyll-a concentration using a previous study, which was conducted on the detection of chlorophyll-a concentration vegetation indices and images obtained from a multispectral sensor-integrated unmanned aerial vehicle (UAV) [33]

Summary

Introduction

Algal blooms are natural phenomena that occur in water-based ecosystems in response to environmental factors, such as nutrition, light, water temperature, and wind speed [1]. Harmful algal blooms can cause substantial water quality problems that persist in rivers, lakes, and reservoirs [2,3,4]. Monitoring the algae in rivers, lakes, and other freshwater bodies is proving to be an increasingly concerning issue. Data pertaining to green algae hot-spots need to be collected quickly and frequently, because green algae exhibits repeated cycles of growth and death depending on environmental conditions such as light (solar radiation), water temperature, nutritional salts (nitrogen, phosphorus), and their duration of residence [1]

Objectives

Results

Conclusion