A Combination of Remote Sensing Datasets for Coastal Marine Habitat Mapping Using Random Forest Algorithm in Pistolet Bay, Canada

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 14; p. 2654
• Main Authors: Mahdavi, Sahel, Amani, Meisam, Parsian, Saeid, MacDonald, Candace, Teasdale, Michael, So, Justin, Zhang, Fan, Gullage, Mardi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-07-2024
• Subjects: Airborne lasers; Algorithms; Analysis; Aquatic habitats; Aquatic plants; aquatic vegetation; Area; Artificial intelligence; Biodiversity; Canada; Climate change; Coastal ecology; Coastal zone; Color imagery; Datasets; drone; Ecosystems; Flora; Geospatial data; Global positioning systems; GPS; Habitats; image classification; Image processing; Image resolution; LiDAR; Mapping; Marine animals; Marine ecosystems; Marine technology; Natural resources; Newfoundland and Labrador; Optical radar; Remote sensing; Remotely operated vehicles; ROV; Satellites; Sensors; Shoreline protection; Topography; Vegetation; Canada; Newfoundland and Labrador
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16142654

Marine ecosystems serve as vital indicators of biodiversity, providing habitats for diverse flora and fauna. Canada’s extensive coastal regions encompass a rich range of marine habitats, necessitating accurate mapping techniques utilizing advanced technologies, such as remote sensing (RS). This study focused on a study area in Pistolet Bay in Newfoundland and Labrador (NL), Canada, with an area of approximately 170 km2 and depths varying between 0 and −28 m. Considering the relatively large coverage and shallow depths of water of the study area, it was decided to use airborne bathymetric Light Detection and Ranging (LiDAR) data, which used green laser pulses, to map the marine habitats in this region. Along with this LiDAR data, Remotely Operated Vehicle (ROV) footage, high-resolution multispectral drone imagery, true color Google Earth (GE) imagery, and shoreline survey data were also collected. These datasets were preprocessed and categorized into five classes of Eelgrass, Rockweed, Kelp, Other vegetation, and Non-Vegetation. A marine habitat map of the study area was generated using the features extracted from LiDAR data, such as intensity, depth, slope, and canopy height, using an object-based Random Forest (RF) algorithm. Despite multiple challenges, the resulting habitat map exhibited a commendable classification accuracy of 89%. This underscores the efficacy of the developed Artificial Intelligence (AI) model for future marine habitat mapping endeavors across the country.