Improving the accuracy of mangrove forest mapping using sentinel image and developed classification algorithm based on machine learning

Publons ID(not set)
Wos IDWOS:001198490200001
Doi10.1080/14498596.2024.2337747
TitleImproving the accuracy of mangrove forest mapping using sentinel image and developed classification algorithm based on machine learning
First Author
Last Author
AuthorsPurwanto, AD; Wikantika, K; Deliar, A; Darmawan, S; Harto, AB; Khomarudin, MR; Ardli, ER;
Publish DateJUL 2 2024
Journal NameJOURNAL OF SPATIAL SCIENCE
Citation
AbstractThe national mangrove rehabilitation required mangrove spatial information with very high accuracy. This study successfully developed the classification algorithm measurable for mapping mangrove forest change based on machine learning. We also modified the recursive feature elimination (RFE) method to obtain the most optimal feature importance (FI). The results showed that the developed classification algorithm was built based on the five most important features including digital elevation model (DEM), near-infrared (NIR), normalized difference moisture index (NDMI), normalized difference water index (NDWI), and distances from brackish water river (DBWR). The developed algorithm increased overall and kappa accuracy by 0.48% and 0.01, respectively.
Publish TypeJournal
Publish Year2024
Page Begin1019
Page End1045
Issn1449-8596
Eissn1836-5655
Urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001198490200001
AuthorDr.rer.nat. ERWIN RIYANTO ARDLI, M.Sc.
File153937.pdf