Application of Deep Learning for Classification of Intertidal Eelgrass from Drone-Acquired Imagery

Abstract

Shallow estuarine habitats are globally undergoing rapid changes due to climate change and anthropogenic influences, resulting in spatiotemporal shifts in distribution and habitat extent. Yet, scientists and managers do not always have rapidly available data to track habitat changes in real-time. In this study, we apply a novel and a state-of-the-art image segmentation machine learning technique (DeepLab) to two years of high-resolution drone-based imagery of a marine flowering plant species (eelgrass, a temperate seagrass). We apply the model to eelgrass (Zostera marina) meadows in the Morro Bay estuary, California, an estuary that has undergone large eelgrass declines and the subsequent recovery of seagrass meadows in the last decade. The model accurately classified eelgrass across a range of conditions and sizes from meadow-scale to small-scale patches that are less than a meter in size. The model recall, precision, and F1 scores were 0.954, 0.723, and 0.809, respectively, when using human-annotated training data and random assessment points. All our accuracy values were comparable to or demonstrated greater accuracy than other models for similar seagrass systems. This study demonstrates the potential for advanced image segmentation machine learning methods to accurately support the active monitoring and analysis of seagrass dynamics from drone-based images, a framework likely applicable to similar marine ecosystems globally, and one that can provide quantitative and accurate data for long-term management strategies that seek to protect these vital ecosystems.

Comparative Assessment of Five Machine Learning Algorithms for Supervised Object-Based Classification of Submerged Seagrass Beds Using High-Resolution UAS Imagery

“…CNN and RNN architecture is a hot topic in the deep learning community and a number of studies have already demonstrated the potential of such methods. Tallam et al [69], for example, applied a deep learning CNN for the image segmentation and classification of UAS-derived imagery of intertidal seagrasses, while Csillik et al [70] used a simple CNN to map the complex agricultural environment of a citrus tree plantation from UAS-derived imagery. Other studies have shown that the combination of OBIA and CNN resulted in less misclassification of fragmented coastal areas [71] and wetlands [38] than with SVM and RF or have shown the general benefits of using the OBIA framework for CNN applications [72][73][74].…”

TENet: A Texture-Enhanced Network for Intertidal Sediment and Habitat Classification in Multiband PolSAR Images

“…In recent years, deep learning frameworks have shown great potential in the field of remote sensing applications, exhibiting impressive capabilities and performance [20][21][22][23][24][25][26]. There exist already some data-driven models based on optical images for sediment and habitat classification [27,28]. However, there is still very little research reported on datadriven approaches for classifying intertidal sediments and habitats using SAR images.…”

Algal Bed Region Segmentation Based on a ViT Adapter Using Aerial Images for Estimating CO2 Absorption Capacity

“…This relationship between the size of algal beds and their CO 2 absorption ability shows the importance of accurate area measurements in evaluating their role in blue carbon strategies [9]. Several studies have demonstrated the feasibility of identifying algal beds by analyzing aerial or unmanned aerial vehicle images, even though their primary focus has been on the detection of seaweed presence rather than blue carbon assessment [10,11].…”

“…IoU = P overlap P pred + P gt − P overlap , (11) where P overlap represents the number of overlapping pixels between the predicted algal bed area and ground truth. • Precision: A metric that indicates the proportion of correctly predicted algal bed areas.…”