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Benthic Habitat Mapping Using Multispectral High-Resolution Imagery: Evaluation of Shallow Water Atmospheric Correction Techniques

Overview
Journal Sensors (Basel)
Publisher MDPI
Specialty Biotechnology
Date 2017 Nov 17
PMID 29144444
Citations 2
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Abstract

Remote multispectral data can provide valuable information for monitoring coastal water ecosystems. Specifically, high-resolution satellite-based imaging systems, as WorldView-2 (WV-2), can generate information at spatial scales needed to implement conservation actions for protected littoral zones. However, coastal water-leaving radiance arriving at the space-based sensor is often small as compared to reflected radiance. In this work, complex approaches, which usually use an accurate radiative transfer code to correct the atmospheric effects, such as FLAASH, ATCOR and 6S, have been implemented for high-resolution imagery. They have been assessed in real scenarios using field spectroradiometer data. In this context, the three approaches have achieved excellent results and a slightly superior performance of 6S model-based algorithm has been observed. Finally, for the mapping of benthic habitats in shallow-waters marine protected environments, a relevant application of the proposed atmospheric correction combined with an automatic deglinting procedure is presented. This approach is based on the integration of a linear mixing model of benthic classes within the radiative transfer model of the water. The complete methodology has been applied to selected ecosystems in the Canary Islands (Spain) but the obtained results allow the robust mapping of the spatial distribution and density of seagrass in coastal waters and the analysis of multitemporal variations related to the human activity and climate change in littoral zones.

Citing Articles

Impact of Atmospheric Correction on Classification and Quantification of Seagrass Density from WorldView-2 Imagery.

Hill V, Zimmerman R, Bissett P, Kohler D, Schaeffer B, Coffer M Remote Sens (Basel). 2024; 15(19):1-25.

PMID: 38362160 PMC: 10866308. DOI: 10.3390/rs15194715.


Preliminary Classification of Selected Farmland Habitats in Ireland Using Deep Neural Networks.

Abraham L, Davy S, Zawish M, Mhapsekar R, Finn J, Moran P Sensors (Basel). 2022; 22(6).

PMID: 35336361 PMC: 8955725. DOI: 10.3390/s22062190.

References
1.
Albert A, Gege P . Inversion of irradiance and remote sensing reflectance in shallow water between 400 and 800 nm for calculations of water and bottom properties. Appl Opt. 2006; 45(10):2331-43. DOI: 10.1364/ao.45.002331. View

2.
Kotchenova S, Vermote E, Matarrese R, Klemm Jr F . Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: path radiance. Appl Opt. 2006; 45(26):6762-74. DOI: 10.1364/ao.45.006762. View

3.
Marcello J, Eugenio F, Estrada-Allis S, Sangra P . Segmentation and tracking of anticyclonic eddies during a submarine volcanic eruption using ocean colour imagery. Sensors (Basel). 2015; 15(4):8732-48. PMC: 4431296. DOI: 10.3390/s150408732. View

4.
Lee Z, Carder K, Mobley C, Steward R, Patch J . Hyperspectral remote sensing for shallow waters. I. A semianalytical model. Appl Opt. 2008; 37(27):6329-38. DOI: 10.1364/ao.37.006329. View

5.
El Hajj M, Begue A, Lafrance B, Hagolle O, Dedieu G, Rumeau M . Relative Radiometric Normalization and Atmospheric Correction of a SPOT 5 Time Series. Sensors (Basel). 2016; 8(4):2774-2791. PMC: 3673445. DOI: 10.3390/s8042774. View