Geomorphological classification of benthic structures of Kaledupa Atoll Wakatobi National Park, Indonesia

Setiawan, Alim; Siregar, Vincentius P.; Susilo, Setyo Budi; Mardiastuti, Ani; Agus, Syamsul Bahri

doi:10.13057/biodiv/d230755

Cited by 1 publication

(1 citation statement)

References 24 publications

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“…Several studies have demonstrated that integrating satellite remote sensing data with ground-truth data yields the most accurate results when mapping benthic habitats, particularly through the use of high-resolution satellite imagery (Mohamed et al 2018;Wilson et al 2019;Vahtmäe et al 2021). Furthermore, these maps can effectively describe the characteristics of shallow water benthic habitats across various geomorphological zones (Roelfsema et al 2018;Akhlaq and Winarso 2019;Setiawan et al 2022a). Remote sensing technology is considered more efficient and effective compared to conventional methods when mapping benthic habitats on a large spatial scale.…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution of benthic habitats in Kapota Atoll (Wakatobi National Park, Indonesia) using remote sensing imagery

HAMUNA,

PUJIYATI,

GAOL

et al. 2023

Biodiversitas

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Abstract. Hamuna B, Pujiyati S, Gaol JL, Hestirianoto T. 2023. Spatial distribution of benthic habitats in Kapota Atoll (Wakatobi National Park, Indonesia) using remote sensing imagery. Biodiversitas 24: 3700-3707. This study aims to classify and map benthic habitats in Kapota Atoll, Wakatobi National Park, using Sentinel-2A satellite imagery, that has undergone both atmospheric and geometric corrections. Sentinel-2A satellite imagery was processed using pixel-based classification method. This study used two machine learning algorithms: support vector machine (SVM) and random forest (RF). This study used nine classified benthic habitat classes, namely five homogeneous benthic classes (sand, live coral, dead coral, rubble, and dense seagrass) and four mixed benthic classes (mixed rubble and sand, mixed sand and rare seagrass, mixed medium seagrass and sand, and a mixture of rubble, dead coral, and seagrass). The RF algorithm produced benthic habitat maps with a higher overall accuracy than the SVM algorithm, which was 65.31% and 63.51%, respectively. These accuracy values have met the standards of accuracy for mapping benthic habitats that apply in Indonesia. The SVM algorithm classification results showed that the benthic habitat of the mixed class of medium seagrass and sand was more dominant than other benthic habitat classes. In contrast, the RF algorithm showed the class dense seagrass was more dominant. The selection of the appropriate algorithm for processing satellite imagery was found to be a significant factor in producing an accurate benthic habitat map.

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