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The sudden algal bloom in shallow water may be a serious problem for sea coastal economy based on clams farming because it leads quickly to anoxia conditions with the consequent death of the molluscs. In order to detect the rise of algae, normally satellite remote sensing is used, exploiting the higher response in the near infrared wavelengths. A recent progress in monitoring this phenomenon derives from the availability of unmanned aerial vehicles (UAVs) equipped with lightweight multispectral cameras. Such technique makes it possible to acquire detailed spectral information with narrow bands attaining an assessment of the algal bloom at both high geometric and radiometric resolutions. In this work, we tested the MicaSense RedEdge-M multispectral camera mounted on a DJI Phantom 3 Professional aircraft to map submerged seaweeds and assess their evolution with particular regard to the importance of the radiometric calibration of raw imageries using a Downwelling Light Sensor (DLS) and a known reflectance panel. The case study is the lagoon of Goro (Northern Adriatic Sea, Italy), a crucial environment for the clams farming in the Emilia-Romagna region. Digital images acquired in two subsequent flights were processed with either Agisoft PhotoScan Professional and Pix4D Mapper Pro varying the calibration strategies. After a pre-analysis, we applied two different approaches for the seaweed detection: NDVI and maximum likelihood classification. All the tests performed in this study confirm that the monitoring over time with a multispectral lightweight camera mounted on a UAV is possible, but also that by applying proper radiometric corrections, most accurate and reliable results can be achieved.
Taddia et al. (Wed,) studied this question.