Type Article
Date 2018
Language English
Author(s) Barde J.1, Bonhommeau SylvainORCID2, Chassot E.3, Motah B.4
Affiliation(s) 1 : MARBEC (IRD/Ifremer/UM2/CNRS) Bd. Jean Monnet 34200 Ste, France
2 : Ifremer, DOI rue Jean Bertho 97822 Le Port Cedex, Reunion Island
3 : Chassot Consulting Inc., Mah, Seychelles
4 : Mauritius Oceanographic Institute, Albion, Mauritius
Meeting FOSS4G 2018 – Academic Track, 29–31 August 2018, Dar es Salaam, Tanzania
Source ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2194-9034) (Copernicus GmbH), 2018 , Vol. XLII-4/W8 , P. 3-10
DOI 10.5194/isprs-archives-XLII-4-W8-3-2018
Keyword(s) Citizen science, ocean and coastal observing systems, surfing, action cameras, coral reef mapping, photogrammetry, deep learning, R

ollecting data on aquatic biodiversity is very challenging because of the difficulty to access underwater ecosystems. Over the years, field surveys have become easier and cheaper with the development of low cost electronics. Commercial and recreational vessels, including sailboats, can now substantially complement expensive scientific surveys and arrays of observation buoys deployed across the world oceans (Pesant et al., 2015, Karsenti et al., 2011). Meanwhile, a large variety of marine animals such as birds, mammals, and fish have become data collection platforms for both biological and environmental parameters through the advent of archival tags. It becomes obvious that data collection in coastal and high seas will become more popular and that citizen will play a growing role in acquiring information on ocean dynamics (physical, chemical and biological parameters). However, currently, very few attempts have been made to use Human beings as observation platforms. In this paper we describe large datasets (more than 200,000 pictures) that have been recently collected along the coast of Mauritius by using popular and cheap platforms such as kite surf and Stand Up Paddle. We describe the characteristics of the data collected and showcase how they can be geolocated and used to complement remote sensing and mapping in order to drastically extend the current scope of “old school” fieldwork. We point out some of the main limitations encountered which need to be addressed to foster this citizen science approach such as data storage and transmission, deep learning to automate image recognition. The methods are all based on open source softwares.

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