Browse > Article
http://dx.doi.org/10.7780/kjrs.2016.32.3.2

Estimation of Water Quality Index for Coastal Areas in Korea Using GOCI Satellite Data Based on Machine Learning Approaches  

Jang, Eunna (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Im, Jungho (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Ha, Sunghyun (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Lee, Sanggyun (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Park, Young-Gyu (Korea Institute of Ocean Science and Technology)
Publication Information
Korean Journal of Remote Sensing / v.32, no.3, 2016 , pp. 221-234 More about this Journal
Abstract
In Korea, most industrial parks and major cities are located in coastal areas, which results in serious environmental problems in both coastal land and ocean. In order to effectively manage such problems especially in coastal ocean, water quality should be monitored. As there are many factors that influence water quality, the Korean Government proposed an integrated Water Quality Index (WQI) based on in situmeasurements of ocean parameters(bottom dissolved oxygen, chlorophyll-a concentration, secchi disk depth, dissolved inorganic nitrogen, and dissolved inorganic phosphorus) by ocean division identified based on their ecological characteristics. Field-measured WQI, however, does not provide spatial continuity over vast areas. Satellite remote sensing can be an alternative for identifying WQI for surface water. In this study, two schemes were examined to estimate coastal WQI around Korea peninsula using in situ measurements data and Geostationary Ocean Color Imager (GOCI) satellite imagery from 2011 to 2013 based on machine learning approaches. Scheme 1 calculates WQI using estimated water quality-related factors using GOCI reflectance data, and scheme 2 estimates WQI using GOCI band reflectance data and basic products(chlorophyll-a, suspended sediment, colored dissolved organic matter). Three machine learning approaches including Random Forest (RF), Support Vector Regression (SVR), and a modified regression tree(Cubist) were used. Results show that estimation of secchi disk depth produced the highest accuracy among the ocean parameters, and RF performed best regardless of water quality-related factors. However, the accuracy of WQI from scheme 1 was lower than that from scheme 2 due to the estimation errors inherent from water quality-related factors and the uncertainty of bottom dissolved oxygen. In overall, scheme 2 appears more appropriate for estimating WQI for surface water in coastal areas and chlorophyll-a concentration was identified the most contributing factor to the estimation of WQI.
Keywords
Water Quality Index; GOCI; machine learning;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Breiman, L., 2001. Random forests. Machine learning, 45(1): 5-32.   DOI
2 Harvey, E.T., S. Kratzer, and P. Philipson, 2015. Satellite-based water quality monitoring for improved spatial and temporal retrieval of chlorophyll-a in coastal waters. Remote Sensing of Environment, 158: 417-430.   DOI
3 Hunter, P.D., A.N. Tyler, L. Carvalho, G.A. Codd, and S.C. Maberly, 2010. Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes. Remote Sensing of Environment, 114(11): 2705-2718.   DOI
4 Johnson, R., P.G. Strutton, S.W. Wright, A. McMinn, and K.M. Meiners, 2013. Three improved satellite chlorophyll algorithms for the Southern Ocean. Journal of Geophysical Research: Oceans, 118(7): 3694-3703.   DOI
5 Kim, Y.H., J. Im, H.K. Ha, J.K. Choi, and S. Ha, 2014. Machine learning approaches to coastal water quality monitoring using GOCI satellite data. GIScience & Remote Sensing, 51(2): 158-174.   DOI
6 Kim, Y.J., H.C. Kim, Y.B. Son, M.O. Park, W.C. Shin, S.W. Kang, and T.K. Rho, 2012. Verification of CDOM algorithms based on ocean color remote sensing data in the East Sea. Korean Journal of Remote Sensing, 28(4): 421-434. (in Korean with English abstract)   DOI
7 Le, C., C. Hu, D. English, J. Cannizzaro, and C. Kovach, 2013. Climate-driven chlorophyll-a changes in a turbid estuary: Observations from satellites and implications for management. Remote Sensing of Environment, 130: 11-24.   DOI
8 Lee, K.H., and S.H. Lee, 2012. Monitoring of Floating Green Algae Using Ocean Color Satellite Remote Sensing. Journal of the Korean Association of Geographic Information Studies, 15(3): 137-147 (in Korean with English abstract).   DOI
9 Lumb, A., T.C. Sharma, and J.F. Bibeault, 2011. A review of genesis and evolution of water quality index(WQI) and some future directions. Water Quality, Exposure and Health, 3(1): 11-24.   DOI
10 Min, J.E., J.H. Ryu, S. Lee, and S. Son, 2012. Monitoring of suspended sediment variation using Landsat and MODIS in the Saemangeum coastal area of Korea. Marine Pollution Bulletin, 64(2): 382-390.   DOI
11 Ministry of Land, Transport and Maritime Affairs, 2011. The marine environmental standards on the Marine Environment Management Act, Ministry of Land, Transport and Maritime Affairs, South Korea.
12 Mountrakis, G., J. Im, and C. Ogole, 2011. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3): 247-259.   DOI
13 Novoa, S., G. Chust, J.M. Froidefond, C. Petus, J. Franco, E. Orive, S. Soane, and A. Borja, 2012. Water quality monitoring in Basque coastal areas using local chlorophyll-a algorithm and MERIS images. Journal of Applied Remote Sensing, 6(1): 063519-1.   DOI
14 Park, S., and S.R. Lee, 2013. Marine disasters prediction system model using marine environment monitoring. The Journal of Korean Institute of Communications and Information Sciences, 38(3): 263-270.
15 RuleQuest Research., 2012. RuleQuest Research data mining tools. from http://www.rulequest.com/.