Machine learning classification of intertidal macroalgae using UAV imagery and topographical indexes

Abstract

Intertidal macroalgae play a vital role in marine ecosystems, necessitating effective monitoring of their coverage and diversity. Traditional monitoring methods are labour-intensive and costly, prompting exploration of the use of unmanned aerial vehicles (UAVs) to characterize intertidal ecosystems. We propose an alternative process integrating UAV red-green-blue (RGB) imagery and topographic indexes to classify complex intertidal macroalgae assemblages automatically. We studied two intertidal areas capturing eight flights between May and September 2023. Orthoimages and Digital Elevation Models (DEMs) were generated. Manual segmentations for 24 classes were cropped into images of individual labels. Additional channels with five topographic indices were added to the RGB images. The resulting dataset of 6412 images was then used to train a Convolutional Neural Network (CNN). We tested the benefit of the additional topographic indices by training the CNN with and without the topographic channels. The best results were given by the inclusion of the Analytical hillshade to the RGB images, showing a relative 11.3% increase in classification accuracy. This indicates that 3D data can enhance the performance of macroalgae classification models. However, there was no significant improvement when using more than one topographic index to train the CNN. Our workflow offers a cost-effective and robust solution for intertidal macroalgae monitoring, contributing to ecological conservation efforts.