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RSS FeedsRemote Sensing, Vol. 8, Pages 879: Citizen Bio-Optical Observations from Coast- and Ocean and Their Compatibility with Ocean Colour Satellite Measurements (Remote Sensing)

 
 

25 october 2016 12:31:27

 
Remote Sensing, Vol. 8, Pages 879: Citizen Bio-Optical Observations from Coast- and Ocean and Their Compatibility with Ocean Colour Satellite Measurements (Remote Sensing)
 


Marine processes are observed with sensors from both the ground and space over large spatio-temporal scales. Citizen-based contributions can fill observational gaps and increase environmental stewardship amongst the public. For this purpose, tools and methods for citizen science need to (1) complement existing datasets; and (2) be affordable, while appealing to different user and developer groups. In this article, tools and methods developed in the 7th Framework Programme of European Union (EU FP 7) funded project Citclops (citizens` observatories for coast and ocean optical monitoring) are reviewed. Tools range from a stand-alone smartphone app to devices with Arduino and 3-D printing, and hence are attractive to a diversity of users; from the general public to more specified maker- and open labware movements. Standardization to common water quality parameters and methods allows long-term storage in regular marine data repositories, such as SeaDataNet and EMODnet, thereby providing open data access. Due to the given intercomparability to existing remote sensing datasets, these tools are ready to complement the marine datapool. In the future, such combined satellite and citizen observations may set measurements by the engaged public in a larger context and hence increase their individual meaning. In a wider sense, a synoptic use can support research, management authorities, and societies at large.


 
116 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 8, Pages 881: Vocalization Source Level Distributions and Pulse Compression Gains of Diverse Baleen Whale Species in the Gulf of Maine (Remote Sensing)
Remote Sensing, Vol. 8, Pages 882: Mapping Distinct Forest Types Improves Overall Forest Identification Based on Multi-Spectral Landsat Imagery for Myanmar`s Tanintharyi Region (Remote Sensing)
 
 
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