Benthic habitat mapping on different coral reef types using random forest and support vector machine algorithm

Zhafirah Zhafarina; Pramaditya Wicaksono

doi:10.1117/12.2540727

24 December 2019 Benthic habitat mapping on different coral reef types using random forest and support vector machine algorithm

Zhafirah Zhafarina, Pramaditya Wicaksono

Author Affiliations +

Proceedings Volume 11372, Sixth International Symposium on LAPAN-IPB Satellite; 113721M (2019) https://doi.org/10.1117/12.2540727
Event: Sixth International Symposium on LAPAN-IPB Satellite, 2019, Bogor, Indonesia

Abstract

Machine learning classification in remote sensing imagery is considered capable of producing classification results with high accuracy in short processing times. This research was conducted with the aim of mapping the spatial distribution of benthic habitat on different types of coral reefs in the waters of Flores Island, NTT using PlanetScope image using Random Forest (RF) and Support Vector Machine (SVM) classification algorithm. Benthic habitat information from field surveys were used to train the RF and SVM algorithm and validate the classification results. The classification results indicated that Mesa Island, the Northern and the Western side of Labuan Bajo are dominated by seagrass beds, and on Bangkau Island is dominated by coral reefs and bare substratum. The highest overall accuracy of the RF classification results is 71.88% from West Labuan Bajo (fringing reef) result. Meanwhile, the highest overall accuracy of the SVM classification is 76.74% from Bangkau Island (patch reef) result.

Citation Download Citation

Zhafirah Zhafarina and Pramaditya Wicaksono "Benthic habitat mapping on different coral reef types using random forest and support vector machine algorithm", Proc. SPIE 11372, Sixth International Symposium on LAPAN-IPB Satellite, 113721M (24 December 2019); https://doi.org/10.1117/12.2540727

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KEYWORDS

Image classification

Machine learning

Data modeling

Accuracy assessment

Water

Animal model studies

Associative arrays

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