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Article

Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms

1
Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
2
Faculty of Engineering, Universitas Esa Unggul, Jakarta 11510, Indonesia
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1279; https://doi.org/10.3390/rs11111279
Received: 15 April 2019 / Revised: 21 May 2019 / Accepted: 25 May 2019 / Published: 29 May 2019
(This article belongs to the Section Coral Reefs Remote Sensing)
This research was aimed at developing the mapping model of benthic habitat mapping using machine-learning classification algorithms and tested the applicability of the model in different areas. We integrated in situ benthic habitat data and image processing of WorldView-2 (WV2) image to parameterise the machine-learning algorithm, namely: Random Forest (RF), Classification Tree Analysis (CTA), and Support Vector Machine (SVM). The classification inputs are sunglint-free bands, water column corrected bands, Principle Component (PC) bands, bathymetry, and the slope of underwater topography. Kemujan Island was used in developing the model, while Karimunjawa, Menjangan Besar, and Menjangan Kecil Islands served as test areas. The results obtained indicated that RF was more accurate than any other classification algorithm based on the statistics and benthic habitats spatial distribution. The maximum accuracy of RF was 94.17% (4 classes) and 88.54% (14 classes). The accuracies from RF, CTA, and SVM were consistent across different input bands for each classification scheme. The application of RF model in the classification of benthic habitat in other areas revealed that it is recommended to make use of the more general classification scheme in order to avoid several issues regarding benthic habitat variations. The result also established the possibility of mapping a benthic habitat without the use of training areas. View Full-Text
Keywords: classification; benthic habitat; machine-learning; mapping; random forest; support vector machine; classification tree analysis; accuracy assessment classification; benthic habitat; machine-learning; mapping; random forest; support vector machine; classification tree analysis; accuracy assessment
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MDPI and ACS Style

Wicaksono, P.; Aryaguna, P.A.; Lazuardi, W. Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms. Remote Sens. 2019, 11, 1279. https://doi.org/10.3390/rs11111279

AMA Style

Wicaksono P, Aryaguna PA, Lazuardi W. Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms. Remote Sensing. 2019; 11(11):1279. https://doi.org/10.3390/rs11111279

Chicago/Turabian Style

Wicaksono, Pramaditya, Prama A. Aryaguna, and Wahyu Lazuardi. 2019. "Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms" Remote Sensing 11, no. 11: 1279. https://doi.org/10.3390/rs11111279

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