Next Article in Journal
Quantifying Escherichia coli and Suspended Particulate Matter Concentrations in a Mixed-Land Use Appalachian Watershed
Next Article in Special Issue
Improving the Accuracy of Hydrodynamic Model Predictions Using Lagrangian Calibration
Previous Article in Journal
Mathematical Model of Small-Volume Air Vessel Based on Real Gas Equation
Previous Article in Special Issue
Wave Forecasting in Shallow Water: A New Set of Growth Curves Depending on Bed Roughness
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Bathymetry Time Series Using High Spatial Resolution Satellite Images

by
Manuel Erena
1,*,
José A. Domínguez
1,
Joaquín F. Atenza
1,
Sandra García-Galiano
2,
Juan Soria
3 and
Ángel Pérez-Ruzafa
4
1
GIS and Remote Sensing, Murcia Institute of Agri-Food Research and Development, C/Mayor s/n, La Alberca, 30150 Murcia, Spain
2
Department of Mining and Civil Engineering, Universidad Politécnica de Cartagena, 30203 Cartagena Spain
3
Instituto Cavanilles de Biodiversidad y Biología Evolutiva, Universidad de Valencia, 46980 Paterna, Spain
4
Department of Ecology and Hydrology, University of Murcia, 30100 Murcia, Spain
*
Author to whom correspondence should be addressed.
Water 2020, 12(2), 531; https://doi.org/10.3390/w12020531
Submission received: 20 December 2019 / Revised: 3 February 2020 / Accepted: 4 February 2020 / Published: 14 February 2020

Abstract

The use of the new generation of remote sensors, such as echo sounders and Global Navigation Satellite System (GNSS) receivers with differential correction installed in a drone, allows the acquisition of high-precision data in areas of shallow water, as in the case of the channel of the Encañizadas in the Mar Menor lagoon. This high precision information is the first step to develop the methodology to monitor the bathymetry of the Mar Menor channels. The use of high spatial resolution satellite images is the solution for monitoring many hydrological changes and it is the basis of the three-dimensional (3D) numerical models used to study transport over time, environmental variability, and water ecosystem complexity.
Keywords: Mar Menor; spatio-temporal variability; Pleiades-1 Mar Menor; spatio-temporal variability; Pleiades-1

Share and Cite

MDPI and ACS Style

Erena, M.; Domínguez, J.A.; Atenza, J.F.; García-Galiano, S.; Soria, J.; Pérez-Ruzafa, Á. Bathymetry Time Series Using High Spatial Resolution Satellite Images. Water 2020, 12, 531. https://doi.org/10.3390/w12020531

AMA Style

Erena M, Domínguez JA, Atenza JF, García-Galiano S, Soria J, Pérez-Ruzafa Á. Bathymetry Time Series Using High Spatial Resolution Satellite Images. Water. 2020; 12(2):531. https://doi.org/10.3390/w12020531

Chicago/Turabian Style

Erena, Manuel, José A. Domínguez, Joaquín F. Atenza, Sandra García-Galiano, Juan Soria, and Ángel Pérez-Ruzafa. 2020. "Bathymetry Time Series Using High Spatial Resolution Satellite Images" Water 12, no. 2: 531. https://doi.org/10.3390/w12020531

APA Style

Erena, M., Domínguez, J. A., Atenza, J. F., García-Galiano, S., Soria, J., & Pérez-Ruzafa, Á. (2020). Bathymetry Time Series Using High Spatial Resolution Satellite Images. Water, 12(2), 531. https://doi.org/10.3390/w12020531

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop