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 |
|