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http://dx.doi.org/10.15681/KSWE.2014.30.3.329

Estimating Chlorophyll-a Concentration using Spectral Mixture Analysis from RapidEye Imagery in Nak-dong River Basin  

Lee, Hyuk (Water Quality Assessment Research Division, National Institute of Environmental Research)
Nam, Gibeom (Water Quality Assessment Research Division, National Institute of Environmental Research)
Kang, Taegu (Water Quality Assessment Research Division, National Institute of Environmental Research)
Yoon, Seungjoon (Korean Environmental Industry and Technology Institute)
Publication Information
Journal of Korean Society on Water Environment / v.30, no.3, 2014 , pp. 329-339 More about this Journal
Abstract
This study aims to estimate chlorophyll-a concentration in rivers using multi-spectral RapidEye imagery and Spectral Mixture Analysis (SMA) and assess the applicability of SMA for multi-temporal imagery analysis. Comparison between images (acquired on Oct. and Nov., 2013) predicted and ground reference chlorophyll-a concentration showed significant performance statistically with determination coefficients of 0.49 and 0.51, respectively. Two band (Red-RE) model for the October and November 2013 RapidEye images showed low performance with coefficient of determinations ($R^2$) of 0.26 and 0.16, respectively. Also Three band (Red-RE-NIR) model showed different performance with $R^2$ of 0.016 and 0.304, respectively. SMA derived Chlorophyll-a concentrations showed relatively higher accuracy than band ratio models based values. SMA was the most appropriate method to calculate Chlorophyll-a concentration using images which were acquired on period of low Chlorophyll-a concentrations. The results of SMA for multi-temporal imagery showed low performance because of the spatio-temporal variation of each end members. This approach provides the potential of providing a cost effective method of monitoring river water quality and management using multi-spectral imagery. In addition, the calculated Chlorophyll-a concentrations using multi-spectral RapidEye imagery can be applied to water quality modeling, enhancing the predicting accuracy.
Keywords
Chlorophyll-a concentration; Minimum noise fraction (MNF); Pixel purity index (PPI); RapidEye imagery; Spectral mixture analysis (SMA);
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1 Boardman, J. W., Kruse, F. A., and Green, R. O. (1995). Mapping Target Signatures Via Partial Unmixing of AVRIS Data: in Summaries, Fifth JPL Airborne Earth Science Workshop, JPL Publication, 1, pp. 23-26.
2 Chen, J., Lin, H., and Pei, Z. (2007). Application of ENVISAT ASAR Data in Mapping Rice Crop Growth in Southern China, IEEE Geoscience Remote Sensing Letters, 4(3), pp. 431-435.   DOI   ScienceOn
3 Cho, M. A., Mathieu, R., Asner, G. P., Naidoo, L., Aardt, J. V., Ramoelo, A., Debba, P., Wessels, K., Main, R., Smit, I. P. J., and Erasmus, B. (2012). Mapping Tree Species Composition in South African Savannas using an Integrated Airborne Spectral and LiDAR System, Remote Sensing of Environment, 125, pp. 214-226.   DOI   ScienceOn
4 Choi, E. Y., Lee, J. W., and Lee, J. K. (2011). Estimation of Chlorophyll-a Concentrations in the Nakdong River Using High-Resolution Satellite Image, Korean Journal of Remote Sensing, 27(5), pp. 613-623. [Korean Literature]   과학기술학회마을   DOI   ScienceOn
5 Gitelson, A. A., Dall'Olmo, G., Moses, W., Rundquist, D. C., Barrow, T., Fisher, T. R., Gurlin, D., and Holz, J. (2008). A Simple Semi-analytical Model for Remote Estimation of Chlorophyll-a in Turbid Water: Validation, Remote Sensing of Environment, 112(9), pp. 3582-3593.   DOI   ScienceOn
6 Green, A. A., Berman, M., Switzer, P., and Craig, M. D. (1988). A Transform for Ordering Multispectral Data in terms of Image Quality with Implications for Noise Removal, IEEE Transactions on Geoscience and Remote Sensing, 26(1), pp. 65-74.   DOI   ScienceOn
7 Guan, L. and Kawamura, H. (2004). Merging Satellite Infra-red and Microwave SSTs: Methodology and Evaluation of the New SST, Journal of Oceanography, 60(5), pp. 905-912.   DOI   ScienceOn
8 Han, Y. K., Kim, Y. I., Han, D. Y., and Choi, J. W. (2013). Mosaic Image Generation of AISA Eagle Hyperspectral Sensor using SIFT Method, Korean Journal of Geomatics, 31(2), pp. 165-172. [Korean Literature]   과학기술학회마을   DOI   ScienceOn
9 Jenson, J. R. (2006). Remote Sensing of the Environment, Sigmapress, pp. 411-415. [Korean Literature]
10 Johnson, R. W. and Tothill, J. C. (1985). In Ecology and Management of the World's Savannas, Tothill, J. C. and Mott, J. J., Australlian Academia of Science.
11 Kang, J. M., Zhang, C., Park, J. K., and Kim, M. G. (2010). Forest Fire Damage Analysis using Satellite Images, Korean Journal of Geomatics, 28(1), pp. 21-28. [Korean Literature]   과학기술학회마을
12 Kim, Y. H., Hong, S. Y., Lee, K. D., Jang, S. Y., Lee, H. Y. and Oh, Y. S. (2013). Monitoring Wheat Growth by COSMO-SkyMed SAR Images, Korean Journal of Remote Sensing, 29(1), pp. 35-43. [Korean Literature]   과학기술학회마을   DOI   ScienceOn
13 Loboda, T. V., French, N. H. F., Hight-Harf, C., Jenkins, L., and Miller, M. E. (2013). Mapping Fire Extent and Burn Severity in Alaskan Tussock Tundra: An Analysis of the Spectral Response of Tundra Vegetation to Wildland Fire, Remote Sensing of Environment, 134, pp. 194-209.   DOI   ScienceOn
14 Oyama, Y., Matsushita, B., Fukushima, T., Matsushige, K., and Imai, A. (2009). Application of Spectral Decomposition Algorithm for Mapping Water Quality in a Turbid Lake (Lake Kasumigaura, Japan) from Landsat TM Data, ISPRS Journal of Photogrammetry and Remote Sensing, 64(1), pp. 73-85.   DOI   ScienceOn
15 Svab, E., Tyler, A. N., Preston, T., Presing, M., and Balogh, K. V. (2005). Characterizing the Spectral Reflectance of Algae in Lake Waters with High Suspended Sediment Concentrations, International Journal of Remote Sensing, 26(5), pp. 919-928.   DOI   ScienceOn
16 Park, K. A. and Kim, Y. H. (2009). A Methodology for 3-D Optimally-Interpolated Satellite Sea Surface Temperature Field and Limitation, Journal of the Korean Earth Science Society, 30(2), pp. 223-233. [Korean Literature]   과학기술학회마을   DOI   ScienceOn
17 RapidEye AG. (2007). RapidEyeTM Image Product Specifications, RapidEye AG.
18 Saatchi, S., Buermann, W., Steege, H., Mori, S., and Smith, T. B. (2008). Modeling Distribution of Amazonian Tree Species and Diversity using Remote Sensing Measurements, Remote Sensing of Environment, 112(3), pp. 2000-2017.   DOI   ScienceOn
19 Tyler, A. N., Svab, E., Preston, T., Presing, M., and Kovacs, W. A. (2006). Remote Sensing of the Water Quality of Shallow Lakes: A Mixture Modelling Approach to Quantifying Phytoplankton in Water Characterized by High-suspended Sediment, International Journal of Remote Sensing, 27(1), pp. 1521-1537.   DOI   ScienceOn
20 Veraverbeke, S., Lhermitte, S., Verstraeten, W. W., and Goossens, R. (2011). A Time-integrated MODIS Burn Severity Assessment using the Multi-temporal Differenced Normalized Burn Ratio(dNBRMT), International Journal of Applied Earth Observation and Geoinformation, 13(1), pp. 52-58.   DOI   ScienceOn
21 Ryu, J. H., Han, K. S., Pi, K. J., and Lee, M. J. (2013). Analysis of Land Cover Change Around Desert Areas of East Asia, Korean Journal of Remote Sensing, 29(1), pp. 105-114. [Korean Literature]   과학기술학회마을   DOI   ScienceOn