文章基本信息

标题：Classification of shallow water seabed profile based on Landsat 8 imagery and in-situ data. Case study in Gili Matra Island Lombok, Indonesia
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作者：Ratih Ayustina ; Zahra Aulia ; Haji Mustakin 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2018
卷号：47
页码：1-12
DOI：10.1051/e3sconf/20184704002
出版社：EDP Sciences
摘要：Shallow water seabed profile has considerable potential resources so the availability of information which very important for coastal resources. The use of remote sensing techniques is considered to provide coastal information effective and efficient. This research aimed to determine the shallow water seabed profile based on Landsat 8 Imagery and its accuracy related to the in situ data. Methods of this research are satellite mage pre-processing, image classification, field survey, image classification, and accuracy assesment . Therefore, 6 classification of shallow water seabed profile, there are rubble (R), seagrass mixed sand (MIX -SG/SD), coral reefs mixed rubble (MIX-C/RB), rubble mixed dead coral (MIX-RB/DC), sand mixed rubble (MIX-SD/RB), and sand mixed seagrass (MIX-SD/SG), respectevely. The result of this classification has an accuracy value 80%.
其他摘要：Shallow water seabed profile has considerable potential resources so the availability of information which very important for coastal resources. The use of remote sensing techniques is considered to provide coastal information effective and efficient. This research aimed to determine the shallow water seabed profile based on Landsat 8 Imagery and its accuracy related to the in situ data. Methods of this research are satellite mage pre-processing, image classification, field survey, image classification, and accuracy assesment . Therefore, 6 classification of shallow water seabed profile, there are rubble (R), seagrass mixed sand (MIX -SG/SD), coral reefs mixed rubble (MIX-C/RB), rubble mixed dead coral (MIX-RB/DC), sand mixed rubble (MIX-SD/RB), and sand mixed seagrass (MIX-SD/SG), respectevely. The result of this classification has an accuracy value 80%.