Heavy Metals Estimation in Coastal Areas Using Remote Sensing, Field Sampling and Classical and Robust Statistic

Authors: Elena Castillo-López, Raúl Pereda, Julio Manuel de Luis, Rubén Pérez, Felipe Piña

Abstract:

Sediments are an important source of accumulation of toxic contaminants within the aquatic environment. Bioassays are a powerful tool for the study of sediments in relation to their toxicity, but they can be expensive. This article presents a methodology to estimate the main physical property of intertidal sediments in coastal zones: heavy metals concentration. This study, which was developed in the Bay of Santander (Spain), applies classical and robust statistic to CASI-2 hyperspectral images to estimate heavy metals presence and ecotoxicity (TOC). Simultaneous fieldwork (radiometric and chemical sampling) allowed an appropriate atmospheric correction to CASI-2 images.

Keywords: Remote sensing, intertidal sediment, airborne sensors, heavy metals, ecotoxicity, robust statistic, estimation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132270

References:

[1] Castillo, E., Pereda, R., de Luis, J.M., Medina, R. and Viguri, J. Sediment grain size estimation using airborne remote sensing, field sampling and robust statistic. Environmental Monitoring and Assessment, 181: 431-444, 2011.
[2] Salomons, W., and Brils, J. Contaminated sediments in European River Basins. European Sediment.2004.
[3] Alvarez-Guerra, M., San-Martín, D., and Viguri, J. Predicting toxicity from sediment chemistry using artificial neural networks: Screening tools for sustainable sediment management. In J.R. 11th Mediterranean Congress of Chemical Engineering, 2008.
[4] Del Valls, T. A., Andres, A., Belzunce, M. J., Buceta, J. L., Casado-Martínez, M. C., Castro, R., Riba, I., Viguri, J.R. and Blasco, J. Chemical and Ecotoxicological Guidelines for Managing Disposal of Dredged Material, 23, 819-828, 2004.
[5] Alvarez-Guerra, M., Viguri, J. R., Casado-Martínez, Carmen, M., & DelValls, A. T. Sediment quality assessment and dredged material management in Spain: Part II, analysis of action levels for dredged material management and application to the Bay of Cádiz. Integrated Environmental Assessment and Management, 3(4), 539–551, 2007.
[6] Alvarez-Guerra, M., Viguri, J. R., Casado-Martínez, M. C., & DelValls, T. A. Sediment quality assessment and dredged material management in Spain: Part I, application of sediment quality guidelines in the Bay of Santander. Integrated Environmental Assessment and Management, 3, 529–538, 2007.
[7] Campbell, N. A. The decorrelation stretch transformation. International Journal Remote Sensing, 10, 1939–1949. Canadian Council of Ministers of the Environment (2001).
[8] Van der Wal, D., & Herman, P. M. J. Regression based on synergy of optical, shortwave infrared and microwave remote sensing for monitoring the grain-size of intertidal sediments. Remote Sensing of Environment, 111, 89–106, 2007.
[9] Folving, S. The Danish Wadden Sea: Thematic mapping by means of remote sensing. Folia Geographica Danica, 15(2), 1–56, 1984.
[10] Bartholdy, J., & Folving, S. Sediment classification and surface type mapping in the Danish Wadden Sea by remote sensing. Netherlands Journal of Sea Research, 20, 337–345, 1986.
[11] Yachts, M. G., Jones, A. R.,
[12] McGrorty, S., & Goss-Custard, J. D. The uses of satellite imagery to determine the distribution of intertidal surface sediments of The Wash, England. Marine. Coastal and Shelf Science, 36, 333–344, 1993.
[13] Populus, J., Moreau, F., Coquelet, D., & Xavier, J. P. An assessment of environmental sensitivity to marine pollutions: Solutions with remote sensing and geographical information systems. International Journal of Remote Sensing, 16(1), 3–15., 1995.
[14] Populus, J., et al. Remote sensing as a tool for diagnostic of water quality in Indonesian seas. Ocean and Coastal Management, 27(3), 197–215, 1995.
[15] Ortega, J. Formación y Desarrollo de una Economía Moderna. Santander, España, 1986.
[16] ICANE, Cantabria. https://www.icane.es/.