A study of a matching pixel by pixel (MPP) algorithm to establish an empirical model of water quality mapping,as based on unmanned aerial vehicle (UAV) images |
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| Institution: | 1. Christian-Albrechts-Universität zu Kiel, Department of Geography, Earth Observation and Modelling, Ludewig-Meyn-Str. 14, 24098 Kiel, Germany;2. EOMAP GmbH & Co.KG, Castle Seefeld, Schlosshof 4a, 82229 Seefeld, Germany;1. Marine and Freshwater Research Centre, Galway-Mayo Institute of Technology (GMIT), Dublin Road, Galway City, Ireland;2. Department of Marine and Coastal Sciences, Rutgers University, 71 Dudley Road, New Brunswick, NJ, United States of America;1. Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu 151-744 Seoul, South Korea;2. Department of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology, Gyeonggi-Do, South Korea;3. IIHR—Hydroscience and Engineering, University of Iowa, Iowa City, IA 52242, USA;1. Mario Gulich Institute, CONAE-UNC, Córdoba, Argentina;2. National Council of Scientific Research and Technology (CONICET), Buenos Aires, Argentina;3. Geography Department, National University of Cordoba, Argentina |
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| Abstract: | Linear regression models are a popular choice for the relationships between water quality parameters and bands (or band ratios) of remote sensing data. However, this research regards the phenomena of mixed pixels, specular reflection, and water fluidity as the challenges to establish a robust regression model. Based on the data of measurements in situ and remote sensing data, this study presents an enumeration-based algorithm, called matching pixel by pixel (MPP), and tests its performance in an empirical model of water quality mapping. Four small reservoirs, which cover a mere several hundred-thousand m2, in Kinmen, Taiwan, are selected as the study sites. The multispectral sensors, carried on an unmanned aerial vehicle (UAV), are adopted to acquire remote sensing data regarding water quality parameters, including chlorophyll-a (Chl-a), Secchi disk depth (SDD), and turbidity in the reservoirs. The experimental results indicate that, while MPP can reduce the influence of specular reflection on regression model establishment, specular reflection does hamper the correction of thematic map production. Due to water fluidity, sampling in situ should be followed by UAV imaging as soon as possible. Excluding turbidity, the obtained estimation accuracy can satisfy the national standard. |
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| Keywords: | Unmanned aerial vehicle (UAV) Enumeration-based algorithm Small reservoir Water quality mapping Specular reflection Water fluidity |
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