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

• Autorzy: Nandika Muhammad Rizki , Ulfa Azura , Ibrahim Andi , Purwanto Anang Dwi

Identyfikatory

DOI

10.7494/geom.2023.17.2.69

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

accuracy; coral; seagrass; Maximum Likelihood; Minimum Distance; Support Vector Machine; remote sensing

• Wydawca: AGH University of Science and Technology Press
• Czasopismo: Geomatics and Environmental Engineering
• Rocznik: 2023
• Tom: Vol. 17, no. 2
• Strony: 69--87
• Opis fizyczny: Bibliogr. 46 poz., rys., tab., wykr.

Twórcy

autor

Nandika Muhammad Rizki

• National Research and Innovation Agency, Research Center for Oceanography, Jakarta, Indonesia

• https://orcid.org/0000-0003-2514-4927

autor

Ulfa Azura

• National Research and Innovation Agency, Research Center for Remote Sensing, Jakarta, Indonesia

• https://orcid.org/0000-0002-2229-9045

autor

Ibrahim Andi

• 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

autor

Purwanto Anang Dwi

• 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

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