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

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 stud...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: Sahel Mahdavi, Meisam Amani, Saeid Parsian, Candace MacDonald, Michael Teasdale, Justin So, Fan Zhang, Mardi Gullage
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2024
Subjects:
aquatic vegetation
LiDAR
drone
ROV
image classification
Science
Q
Newfoundland
Online Access:https://doi.org/10.3390/rs16142654
https://doaj.org/article/bde6f8beafe0408f86f266ec3a53b474
Description
Summary: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 km 2 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.

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