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Proceedings Paper

Support vector machine for seagrass percent cover mapping using PlanetScope image in Labuan Bajo, East Nusa Tenggara
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Paper Abstract

Seagrass meadows have many ecosystem services to coastal areas and adjacent ecosystems, these services include nursery area for marine organisms, sea turtle feeding ground, and blue carbon sequestration. Therefore, it is important to protect seagrass in order to preserve their functions. Seagrass percent cover is one of the parameters to asses seagrass condition. Several approaches have been developed to map seagrass in optically shallow waters and one of them is by using remote sensing. This approach is more effective and efficient compared to field survey alone. The aim of this study is to produce seagrass spatial distribution and percent cover map using high resolution image. In this research, Support Vector Machine (SVM) classification and regression, one of the machine learning algorithms, was used to classify PlanetScope image using field data as training area to map seagrass spatial distribution and percent cover. The result show that SVM produced 73.98% overall accuracy for benthic mapping, with seagrass class producer’s accuracy and user’s accuracy is 93.71% and 85.35% respectively. Meanwhile, for seagrass percent cover, the SVM algorithm produced map with 26.48% standard error.

Paper Details

Date Published: 24 December 2019
PDF: 6 pages
Proc. SPIE 11372, Sixth International Symposium on LAPAN-IPB Satellite, 113721R (24 December 2019); doi: 10.1117/12.2541849
Show Author Affiliations
Miftakhul Munir, Univ. Gadjah Mada (Indonesia)
Pramaditya Wicaksono, Univ. Gadjah Mada (Indonesia)


Published in SPIE Proceedings Vol. 11372:
Sixth International Symposium on LAPAN-IPB Satellite
Yudi Setiawan; Lilik Budi Prasetyo; Tien Dat Pham; Kasturi Devi Kanniah; Yuji Murayama; Kohei Arai; Gay Jane P. Perez, Editor(s)

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