Preprint Article Version 1 This version is not peer-reviewed

Precision Mapping of Seagrass Habitats Using Multispectral Drones and Machine Learning

Version 1 : Received: 4 November 2024 / Approved: 5 November 2024 / Online: 6 November 2024 (07:26:17 CET)

How to cite: Nogueira, P.; Silva, M.; Quelhas, M.; Adão, H. Precision Mapping of Seagrass Habitats Using Multispectral Drones and Machine Learning. Preprints 2024, 2024110297. https://doi.org/10.20944/preprints202411.0297.v1 Nogueira, P.; Silva, M.; Quelhas, M.; Adão, H. Precision Mapping of Seagrass Habitats Using Multispectral Drones and Machine Learning. Preprints 2024, 2024110297. https://doi.org/10.20944/preprints202411.0297.v1

Abstract

Seagrass beds and salt marshes play a crucial role in preserving and restoring estuarine ecosystems, providing essential services such as carbon sequestration, water filtration, and habitat for diverse marine life. Mapping these areas, particularly in remote locations, is challenging but essential for effective conservation efforts. This study explores the use of drones equipped with multispectral cameras to map seagrass meadows and salt marshes in estuarine environments. The methodology involved composing images into a multilayer multidimensional dataset, performing exploratory data analysis and principal component analysis, and utilizing Random Forest supervised classification to generate detailed maps. Applied to a case study of the Mira River in southern Portugal, this approach achieved mapping accuracies as high as 94% with kappa values of 0.91. These results demonstrate the efficacy of drone-based multispectral imaging and advanced analytical techniques in accurately mapping and monitoring critical estuarine habitats, offering a valuable tool for conservation and restoration initiatives.

Keywords

Estuarine Ecosystems; Machine Learning Classification; Multispectral images; Unmanned Aerial Vehicle; Seagrass beds

Subject

Environmental and Earth Sciences, Remote Sensing

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