Integrating Angular Backscatter Response Analysis Derivatives Into a Hierarchical Classification for Habitat Mapping

Abstract

Accurate maps of biological communities are essential for monitoring and managing marine protected areas but more information on the most effective methods for developing these maps is needed. In this study, we use Wilsons Promontory Marine National Park in southeast Australia as a case study to determine the best combination of variables and scales for producing accurate habitat maps across the site. Wilsons Promontory has full multibeam echosounder (MBES) coverage coupled with towed video, remotely operated underwater vehicle (ROV) and drop video observations. Our study used an image segmentation approach incorporating MBES backscatter angular response curve and bathymetry derivatives to identify benthic community types using a hierarchical habitat classification scheme. The angular response curve data were extracted from MBES data using two different methods: 1) angular range analysis (ARA) and 2) backscatter angular response (AR). Habitat distributions were predicted using a supervised Random Forest approach combining bathymetry, ARA, and AR derivatives. Variable importance metrics indicated that ARA derivatives, such as grain size, impedance and volume heterogeneity were more important to model performance than AR derivatives mean, skewness, and kurtosis. Additionally, this study investigated the impact of segmentation software settings when creating segmented surfaces and their impact on overall model accuracy. We found using fine scale segmentation resulted in the best model performance. These results indicate the importance of incorporating backscatter derivatives into biological habitat maps and the need to consider scale to increase the accuracy of the outputs to help improve the spatial management of marine environments.

Benthic habitat mapping: A review of three decades of mapping biological patterns on the seafloor

“…Trzcinska et al (2020), additionally introduce a range of "spectral" backscatter features that may be calculated to characterize the seabed. It is also possible to retain the angular backscatter response prior to compensation and raster mosaicking to calculate statistics and features that provide a richer acoustic characterization of the substrate (e.g., Fonseca & Mayer, 2007;Parnum, 2007;Che Hasan et al, 2012Porskamp et al, 2022) -though, this could arguably be considered "primary" rather than "derived" geospatial data. A range of secondary features may also be calculated from spectral remote sensing data acquired using airor satellite-borne optical sensors.…”