Semi-Supervised Segmentation for Coastal Monitoring Seagrass Using RPA Imagery

Abstract

Intertidal seagrass plays a vital role in estimating the overall health and dynamics of coastal environments due to its interaction with tidal changes. However, most seagrass habitats around the globe have been in steady decline due to human impacts, disturbing the already delicate balance in the environmental conditions that sustain seagrass. Miniaturization of multi-spectral sensors has facilitated very high resolution mapping of seagrass meadows, which significantly improves the potential for ecologists to monitor changes. In this study, two analytical approaches used for classifying intertidal seagrass habitats are compared—Object-based Image Analysis (OBIA) and Fully Convolutional Neural Networks (FCNNs). Both methods produce pixel-wise classifications in order to create segmented maps. FCNNs are an emerging set of algorithms within Deep Learning. Conversely, OBIA has been a prominent solution within this field, with many studies leveraging in-situ data and multiresolution segmentation to create habitat maps. This work demonstrates the utility of FCNNs in a semi-supervised setting to map seagrass and other coastal features from an optical drone survey conducted at Budle Bay, Northumberland, England. Semi-supervision is also an emerging field within Deep Learning that has practical benefits of achieving state of the art results using only subsets of labelled data. This is especially beneficial for remote sensing applications where in-situ data is an expensive commodity. For our results, we show that FCNNs have comparable performance with the standard OBIA method used by ecologists.

Biological traits approaches in benthic marine ecology: Dead ends and new paths

“…New data can be collected via innovative technology and methods development; for example, benthic sampling with vehicles such as AUVs/ROVs could record in situ morphological traits, size, position, body form, etc. ; passive and active acoustic monitoring and sensors could be used to record movement rates; remotely piloted aircraft (i.e., drones) have potential for obtaining high‐resolution images of intertidal benthos (e.g., Chand et al, 2020 ; Hobley et al, 2021 ). Some of these technologies are still expensive, but low‐cost technologies are emerging and gaining traction.…”

Seaweed Habitats on the Shore: Characterization through Hyperspectral UAV Imagery and Field Sampling

“…So, the use of common algorithms could be perfectible, but it does answer the problem of the study by classifying correctly macroalgal communities. Other algorithms such as random forests or support vector machines might be considered to estimate entire shores, as for coastal/terrestrial objects [93][94][95][96][97].…”

Species level mapping of a seagrass bed using an unmanned aerial vehicle and deep learning technique

“…It is also possible to adjust the survey time and day, which is impossible with satellites in a fixed orbit. In seagrass research, UAVs have been used for detailed bed mapping ( Duffy et al, 2018 ; Nahirnick et al, 2019 ; Hobley et al, 2021 ). Nonetheless, most of these studies mapped seagrass beds consisting of only a single species or conducted mapping without species discrimination.…”

“…A deep neural network (DNN) can automatically extract these various features using a convolutional neural network (CNN), the basic network used for DNN image processing ( Traore, Kamsu-Foguem & Tangara, 2018 ). Seagrass mappings using UAV images and DNN have been conducted in recent years, but they are limited to single species or discriminating seagrass from macroalgae ( Hobley et al, 2021 ; Jeon et al, 2021 ). There has been a report of successful species classification when used with underwater images ( Noman et al, 2021 ), but no reports with aerial images.…”