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

Miftakhul Munir; Pramaditya Wicaksono

doi:10.1117/12.2541849

24 December 2019 Support vector machine for seagrass percent cover mapping using PlanetScope image in Labuan Bajo, East Nusa Tenggara

Miftakhul Munir, Pramaditya Wicaksono

Author Affiliations +

Proceedings Volume 11372, Sixth International Symposium on LAPAN-IPB Satellite; 113721R (2019) https://doi.org/10.1117/12.2541849
Event: Sixth International Symposium on LAPAN-IPB Satellite, 2019, Bogor, Indonesia

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.

Citation Download Citation

Miftakhul Munir and Pramaditya Wicaksono "Support vector machine for seagrass percent cover mapping using PlanetScope image in Labuan Bajo, East Nusa Tenggara", Proc. SPIE 11372, Sixth International Symposium on LAPAN-IPB Satellite, 113721R (24 December 2019); https://doi.org/10.1117/12.2541849

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available