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Google Earth Engine Algorithm for Evaluating the Performance of Landsat OLI-8 and Sentinel-2 in Mangrove Monitoring

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dc.contributor.author Sitthi A.
dc.contributor.other Srinakharinwirot University
dc.date.accessioned 2023-11-15T02:08:46Z
dc.date.available 2023-11-15T02:08:46Z
dc.date.issued 2023
dc.identifier.uri https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144026033&doi=10.1007%2f978-3-031-16217-6_15&partnerID=40&md5=e3f8dbd57f781700423edd1f60210a18
dc.identifier.uri https://ir.swu.ac.th/jspui/handle/123456789/29508
dc.description.abstract Mapping mangrove forest extents is important to assess the general health of coastal ecosystems. However, as mangrove forests are dense by nature and tend to grow in mudflats, this chapter introduces physical challenges that often hinder remote scientists from easily accessing the mangrove forestry in field surveys. The publicly available imagery produced by the commonly used Landsat OLI-8 satellite imagery and the newly released Sentinel-2 satellites may be used to map mangrove forest extents remotely. This chapter uses the Google Earth Engine (GEE) tool to conduct a comparative evaluation of the performance of Landsat OLI-8 and Sentinel-2 satellite imagery to map mangrove forest extents on the coast of the Trat province of Thailand. The results indicated that Sentinel-2 is visually and quantitatively preferable compared to Landsat OLI-8 when mapping mangrove forest extents with 88 percentage of accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
dc.publisher Springer Science and Business Media Deutschland GmbH
dc.subject Artificial intelligence
dc.subject Machine learning
dc.subject Mangrove
dc.subject Remote sensing
dc.title Google Earth Engine Algorithm for Evaluating the Performance of Landsat OLI-8 and Sentinel-2 in Mangrove Monitoring
dc.type Book chapter
dc.rights.holder Scopus
dc.identifier.bibliograpycitation Springer Geography. Vol , No. (2023), p.195-205
dc.identifier.doi 10.1007/978-3-031-16217-6_15


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