Geomatics and Environmental Engineering, vol. 17, no. 2

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Vol. 17 No. 2 (2023): Geomatics and Environmental Engineering

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Submitted
Feb 14, 2022
Accepted
Nov 29, 2022
Published
Jan 30, 2023
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Assessing the Shallow Water Habitat Mapping Extracted from High-Resolution Satellite Image with Multi Classification Algorithms

https://doi.org/10.7494/geom.2023.17.2.69
Muhammad Rizki Nandika
National Research and Innovation Agency, Research Center for Oceanography, Jakarta, Indonesia
https://orcid.org/0000-0003-2514-4927
Azura Ulfa
National Research and Innovation Agency, Research Center for Remote Sensing, Jakarta, Indonesia
https://orcid.org/0000-0002-2229-9045
Andi Ibrahim
National Research and Innovation Agency, Research Center for Remote Sensing, Jakarta, Indonesia; University of Twente, Faculty of Geo-Information Science and Earth Observation, Enschede, Netherlands
https://orcid.org/0000-0002-6561-2978
Anang Dwi Purwanto
National Research and Innovation Agency, Research Center for Remote Sensing, Jakarta, Indonesia; Bandung Institute of Technology, Geodesy and Geomatics Engineering Department, Bandung, Indonesia
https://orcid.org/0000-0003-1055-9302

Corresponding Author(s) : Muhammad Rizki Nandika

rizki.nandika@gmail.com

Geomatics and Environmental Engineering, Vol. 17 No. 2 (2023): Geomatics and Environmental Engineering

Abstract

Remote sensing technology is reliable in identifying the distribution of seabed cover yet there are still challenges in retrieving the data collection of shallow water habitats than with other objects on land. Classification algorithms based on remote sensing technology have been developed for application to map benthic habitats, such as Maximum Likelihood, Minimum Distance, and Support Vector Machine. This study focuses on examining those three classification algorithms to retrieve information on the benthic habitat in Pari Island, Jakarta using visual interpretation data for classification, and data field measurements for accuracy testing. This study used five classes of benthic objects, namely sand, sand-seagrass, rubble, seagrass, and coral. The results show how the proposed approach in this study provides an overall good classification of marine habitat with an accuracy produced 63.89–81.95%. The Support Vector Machine algorithm produced the highest accuracy rate of about 81.95%. The Support Vector Machine algorithm at a very high spatial resolution is considered to be capable of identifying, monitoring, and performing the rapid assessment of benthic habitat objects.

Keywords

accuracy coral seagrass Maximum Likelihood Minimum Distance Support Vector Machine remote sensing
Nandika, M. R., Ulfa, A., Ibrahim, A., & Purwanto, A. D. (2023). Assessing the Shallow Water Habitat Mapping Extracted from High-Resolution Satellite Image with Multi Classification Algorithms. Geomatics and Environmental Engineering, 17(2), 69–87. https://doi.org/10.7494/geom.2023.17.2.69
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