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

Abstract

Knowledge about the spatial distribution of seagrasses is essential for coastal conservation efforts. Imagery obtained from unoccupied aerial systems (UAS) has the potential to provide such knowledge. Classifier choice and hyperparameter settings are, however, often based on time-consuming trial-and-error procedures. The presented study has therefore investigated the performance of five machine learning algorithms, i.e., Bayes, Decision Trees (DT), Random Trees (RT), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM) when used for the object-based classification of submerged seagrasses from UAS-derived imagery. The influence of hyperparameter tuning and training sample size on the classification accuracy was tested on images obtained from different altitudes during different environmental conditions. The Bayes classifier performed well (94% OA) on images obtained during favorable environmental conditions. The DT and RT classifier performed better on low-altitude images (93% and 94% OA, respectively). The kNN classifier was outperformed on all occasions, while still producing OA between 89% and 95% in five out of eight scenarios. The SVM classifier was most sensitive to hyperparameter tuning with OAs ranging between 18% and 97%; however, it achieved the highest OAs most often. The findings of this study will help to choose the appropriate classifier and optimize related hyperparameter settings.

UAV-Based Subsurface Data Collection Using a Low-Tech Ground-Truthing Payload System Enhances Shallow-Water Monitoring

“…The depth measurements collected by the payload system (Figure 8) could be interpolated to a detailed bathymetry map of the study area using the spline interpolation method (Figure 11a). Shallow-water bathymetry is an important factor that greatly influences environmental processes in the coastal zone and is therefore a crucial input parameter for many benthic habitat assessments [47] and ecological models [41], which in turn are used by coastal managers for policy making [42]. Nevertheless, detailed bathymetry maps are often not Shallow-water bathymetry is an important factor that greatly influences environmental processes in the coastal zone and is therefore a crucial input parameter for many benthic habitat assessments [47] and ecological models [41], which in turn are used by coastal managers for policy making [42].…”

“…Shallow-water bathymetry is an important factor that greatly influences environmental processes in the coastal zone and is therefore a crucial input parameter for many benthic habitat assessments [47] and ecological models [41], which in turn are used by coastal managers for policy making [42]. Nevertheless, detailed bathymetry maps are often not Shallow-water bathymetry is an important factor that greatly influences environmental processes in the coastal zone and is therefore a crucial input parameter for many benthic habitat assessments [47] and ecological models [41], which in turn are used by coastal managers for policy making [42]. Nevertheless, detailed bathymetry maps are often not available due to the ineffectiveness and limitation of traditional techniques, such as boat surveys, in shallow waters.…”

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].…”