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Articles | Volume XLIII-B3-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1011–1017, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1011-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1011–1017, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1011-2022
 
30 May 2022
30 May 2022

SEAWEED PRESENCE DETECTION USING MACHINE LEARNING AND REMOTE SENSING

F. Tonion1,2 and F. Pirotti2 F. Tonion and F. Pirotti
  • 1T.E.R.R.A. S.r.l. Galleria Progresso 5, 30027 San Donà di Piave (VE), Italy
  • 2Department of Land, Environment, Agriculture and Forestry (TESAF), University of Padova. Via dell’Università 16, 35020 Legnaro (PD), Italy

Keywords: Machine Learning, Supervised Classification, Seaweed’s Detection, Random Forest, Ecosystems Monitoring

Abstract. The human pressure over coastal areas is becoming increasingly relevant, due to the combinations of resource depletion, climate change effects and ocean eutrophication. Coastal ecosystems are so exposed to a huge number of stress factors that endanger their ecosystem services, like carbon uptake and biodiversity maintenance, that can be crucial in facing the effects of climate changes. With a particular focus on seaweeds, these ecosystems are becoming rapidly relevant both for carbon sinks and as a source of high value products, for example thanks to cosmetic and food industries that produce high added values products.

In this contest the capability of conducting efficient monitoring is crucial to monitor environmental dynamics and resources trends. Traditionally seaweed monitoring was carried out with on field surveys that could be based on botanic analysis combined with genetic study, depending on the aims. Recently Remote Sensing techniques, combined with Artificial Intelligence ones, gave a new perspective to seaweed monitoring, introducing tools that are always more efficient.

In this contest the present work aims to test the potentiality of remote sensing and artificial intelligence techniques for seaweed monitoring along the Irish west coast, building the basis for a fully automated tool for monitoring. The results showed that, with a supervised classification approach, it is possible to train Random Forest (RF) to perform very precise classification over the entire West Coast of Ireland. In particular, with all the RF configurations tested the Overall Accuracy (OA) was greater than 98.61, with the best performance obtained with the configuration Ntree = 600 and mtry = 2 that produced an OA = 98.87.