Habitat Classification of Temperate Marine Macroalgal Communities Using Bathymetric LiDAR

Abstract

Abstract:Here, we evaluated the potential of using bathymetric Light Detection and Ranging (LiDAR) to characterise shallow water (<30 m) benthic habitats of high energy subtidal coastal environments. Habitat classification, quantifying benthic substrata and macroalgal communities, was achieved in this study with the application of LiDAR and underwater video groundtruth data using automated classification techniques. Bathymetry and reflectance datasets were used to produce secondary terrain derivative surfaces (e.g., rugosity, aspect) that were assumed to influence benthic patterns observed. An automated decision tree classification approach using the Quick Unbiased Efficient Statistical Tree (QUEST) was applied to produce substrata, biological and canopy structure habitat maps of the study area. Error assessment indicated that habitat maps produced were primarily accurate (>70%), with varying results for the classification of individual habitat classes; for instance, producer accuracy for mixed brown algae and sediment substrata, was 74% and 93%, respectively. LiDAR was also successful for differentiating canopy structure of macroalgae communities (i.e., canopy structure classification), such as canopy forming kelp versus erect fine branching algae. In conclusion, habitat characterisation using bathymetric LiDAR provides a unique potential to collect baseline information about biological assemblages and, hence, potential reef connectivity over large areas beyond the range of direct observation. This research contributes a new perspective for assessing the OPEN ACCESS Remote Sens. 2014, 6 2155 structure of subtidal coastal ecosystems, providing a novel tool for the research and management of such highly dynamic marine environments.

Data-Driven Approach to Benthic Cover Type Classification Using Bathymetric LiDAR Waveform Analysis

“…The methodology is useful for mapping large coastal areas to detect the structure and variation in the benthic environment. However, even if interpreted as a classification attempt, the results are comparable to those presented, for example, in [2].…”

“…They found that combined with multispectral (three visible bands plus NIR) data, the LiDAR-derived DEM gave good classification accuracy when used for discrimination between 22 supralittoral, intertidal and subtidal habitats. Zavalas et al [2] used six features derived from the DEM (obtained using bathymetric LiDAR) combined with three reflectance-based features to produce substrata, biological and canopy habitat maps of a coastal area between Warrnambool and Port Fairy, Western Victoria, Australia. The overall classification accuracy was 75% for substrata, 72% for biological and 72% for the canopy habitat map.…”

“…In Europe, the Marine Strategy Framework Directive obliges the member states to develop monitoring programs for marine waters, including the seabed [1]. At open sea, the seabed can be scanned efficiently from vessels using sonar techniques, however, the coastal areas are more difficult to access for mapping due to potential hazards [2]. These areas are the most critical when considering the effect of human action on the marine ecosystem.…”

A review of marine geomorphometry, the quantitative study of the seafloor

“…Current geomorphometric applications on bathymetric lidar data are mainly related to the exploration of coastal ecosystems (e.g. Wedding et al, 2008;Zavalas et al, 2014) and geomorphology (e.g. Arifin and Kennedy, 2011;Kennedy et al, 2014), but are likely to extend to other applications such as marine archaeology and natural hazards assessment (e.g.…”

Detection of Seabed Rock Using Airborne Bathymetric Lidar and Hyperspectral Data in the East Sea Coastal Area

“…In a hydrographic survey, researchers are able to measure water depth, coast line, and seabed topography using airborne bathymetric Lidar (Seo and Kim, 2008;Zavalas et al, 2014).…”