The contemporary scientific literature that deals with the dynamics of marine chlorophyll-a concentration is already customarily employing data mining techniques in small geographic areas or regional samples. However, there is little focus on the issue of missing data related to chlorophyll-a concentration estimated by remote sensors. Intending to provide greater scope to the identification of the spatiotemporal distribution patterns of marine chlorophyll-a concentrations, and to improve the reliability of results, this study presents a data mining approach to cluster similar chlorophyll-a concentration behaviors while implementing an iterative spatiotemporal interpolation technique for missing data inference. Although some dynamic behaviors of said concentrations in specific areas are already known by specialists, systematic studies in large geographical areas are still scarce due to the computational complexity involved. For this reason, this study analyzed 18 years of NASA satellite observations in one portion of the Western Atlantic Ocean, totaling more than 60 million records. Additionally, performance tests were carried out in low-cost computer systems to check the accessibility of the proposal implemented for use in computational structures of different sizes. The approach was able to identify patterns with high spatial resolution, accuracy and reliability, rendered in low-cost computers even with large volumes of data, generating new and consistent patterns of spatiotemporal variability. Thus, it opens up new possibilities for data mining research on a global scale in this field of application.
References
[1]
Field, C.B., Behrenfeld, M.J., Randerson, J.T. and Falkowski, P. (1998) Primary Production of the Biosphere: Integrating Terrestrial and Oceanic Components. Science, 281, 237-240. https://doi.org/10.1126/science.281.5374.237
[2]
Kirk, J.T.O. (2011) Light and Photosynthesis in Aquatic Ecosystems. 3rd Edition, Cambridge University Press, Cambridge, England.
[3]
Reygondeau, G., Longhurst, A., Martinez, E., Beaugrand, G., Antoine, D. and Maury, O. (2013) Dynamic Biogeochemical Provinces in the Global Ocean. Global Biogeochemical Cycles, 27, 1046-1058.
https://doi.org/10.1002/gbc.20089
[4]
Devred, E., Sathyendranath, S. and Platt, T. (2007) Delineation of Ecological Provinces Using Ocean Colour Radiometry. Marine Ecology Progress Series, 346, 1-13.
https://doi.org/10.3354/meps07149
[5]
Longhurst, A. (2007) Toward an Ecological Geography of the Sea. Academic Press, London. https://doi.org/10.1016/B978-012455521-1/50002-4
[6]
Longhurst, A. (1995) Seasonal Cycles of Pelagic Production and Consumption. Progress in Oceanography, 36, 77-167.
https://doi.org/10.1016/0079-6611(95)00015-1
[7]
O’Reilly, J.E., et al. (2000) Ocean Color Chlorophyll a Algorithms for SeaWiFS, OC2, and OC4: Version 4. In: Hooker, S.B. and Firestone, E.R., Eds., SeaWiFS Postlaunch Technical Report Series, SeaWiFS Postlaunch Calibration and Validation Analyses: Part 3, NASA Goddard Space Flight Center, Greenbelt, MD, 9-23.
[8]
Zhang, Y.-L., et al. (2009) Modeling Remote-Sensing Reflectance and Retrieving Chlorophyll-a Concentration in Extremely Turbid Case-2 Waters (Lake Taihu, China). IEEE Transactions on Geoscience and Remote Sensing, 47, 1937-1948.
https://doi.org/10.1109/TGRS.2008.2011892
[9]
Vilas, L.G., Spyrakos, E. and Palenzuela. J.M.T. (2011) Neural Network Estimation of Chlorophyll a from MERIS Full Resolution Data for the Coastal Waters of Galician Rias (NW Spain). Remote Sensing of Environment, 115, 524-535.
https://doi.org/10.1016/j.rse.2010.09.021
[10]
Mcginty, N., Power, A.M. and Johnson, M.P. (2011) Variation among Northeast Atlantic Regions in the Responses of Zooplankton to Climate Change: Not All Areas Follow the Same Path. Journal of Experimental Marine Biology and Ecology, 400, 120-131. https://doi.org/10.1016/j.jembe.2011.02.013
[11]
Spyrakos, E., Vilas, L.G., Palenzuela, J.M.T. and Barton, E.D. (2011) Remote Sensing Chlorophyll a of Optically Complex Waters (Rias Baixas, NW Spain): Application of a Regionally Specific Chlorophyll a Algorithm for MERIS Full Resolution Data during an Upwelling Cycle. Remote Sensing of Environment, 115, 2471-2485.
https://doi.org/10.1016/j.rse.2011.05.008
[12]
Alameddine, I., Cha, Y.-K. and Reckhow, K.H. (2011) An Evaluation of Automated Structure Learning with Bayesian Networks: An Application to Estuarine Chlorophyll Dynamics. Environmental Modelling & Software, 26, 163-172.
https://doi.org/10.1016/j.envsoft.2010.08.007
[13]
Gaetan, C., Girardi, P. and Pastres, R. (2014) The Role of Spatial Dependence on the Functional Clustering Based on the Smoothing Splines Regression. Proceedings of the METMA VII and GRASPA14 Conference, Torino, 10-12 September 2014, 1-5.
[14]
Mateu, J. and Romano, E. (2017) Advances in Spatial Functional Statistics. Stochastic Environmental Research and Risk Assessment, 31, 1-6.
https://doi.org/10.1007/s00477-016-1346-z
[15]
Nazeer, M. and Nichol, J.E. (2015) Modeling of Chlorophyll-a Concentration for the Coastal Waters of Hong Kong. 2015 Joint Urban Remote Sensing Event, Lausanne, 30 March-1 April 2015, 1-4. https://doi.org/10.1109/JURSE.2015.7120460
[16]
Zheng, G. and Di Giacomo P.M. (2017) Remote Sensing of Chlorophyll-a in Coastal Waters Based on the Light Absorption Coefficient of Phytoplankton. Remote Sensing of Environment, 201, 331-341. https://doi.org/10.1016/j.rse.2017.09.008
[17]
Huang, Z. and Wang, X.-H. (2019) Mapping the Spatial and Temporal Variability of the Upwelling Systems of the Australian South-Eastern Coast Using 14-Year of MODIS Data. Remote Sensing of Environment, 227, 90-109.
https://doi.org/10.1016/j.rse.2019.04.002
[18]
Djavidnia, S., M’Elin, F. and Hoepffner, N. (2010) Comparison of Global Ocean Colour Data Records. Ocean Science, 6, 61-76.
https://doi.org/10.5194/os-6-61-2010
[19]
Giovanni (2019) The Bridge Between Data and Science, Version 4.30, USA.
https://giovanni.gsfc.nasa.gov/giovanni/
[20]
Feldman, G.C. (2014) Chlorophyll a (chlor_a). NASA, Ocean Color Web.
https://oceancolor.gsfc.nasa.gov/atbd/chlor_a/
[21]
Haidvogel, D.B., et al. (2008) Ocean Forecasting in Terrain-Following Coordinates: Formulation and Skill Assessment of the Regional Ocean Modeling System. Journal of Computational Physics, 227, 3595-3624.
https://doi.org/10.1016/j.jcp.2007.06.016
[22]
Macqueen, J. (1967) Some Methods for Classification and Analysis of Multivariate Observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1: Statistics, University of California Press, Berkeley, CA, 281-297.
[23]
Jain, A.K., Murty, M.N. and Flynn, P. (1999) Data Clustering: A Review. ACM Computing Surveys, 31, 264-323. https://doi.org/10.1145/331499.331504
[24]
Xu, R. and Wunsch II, D. (2005) Survey of Clustering Algorithms. IEEE Transaction on Neural Networks, 16, 645-678. https://doi.org/10.1109/TNN.2005.845141
MySQL, Version 8.0.16. (2019) Oracle Corporation, USA.
[27]
Irwin, A.J. and Finkel, Z.V. (2008) Mining a Sea of Data: Deducing the Environmental Controls of Ocean Chlorophyll. PLoS ONE, 3, e3836.
https://doi.org/10.1371/journal.pone.0003836
[28]
Moore, T.S. and Campbell, J.W. (2009) A Class Based Approach for Characterizing the Uncertainty of the MODIS Chlorophyll Product. Remote Sensing of Environment, 113, 2424-2430. https://doi.org/10.1016/j.rse.2009.07.016
[29]
Savtchenko, A., Ouzounov, D., Ahmad, S., Acker, J., Leptoukh, G., Koziana, J. and Nickless, D. (2004) Terra and Aqua MODIS Products Available from NASA GES DAAC. Advances in Space Research, 34, 710-714.
https://doi.org/10.1016/j.asr.2004.03.012
