Comparison of vegetation and mangrove indices in mangrove mapping on Sentinel-2 imagery based on Google Earth Engine | ||
| تحقیقات جنگل و صنوبر ایران | ||
| Article 3, Volume 30, Issue 3 - Serial Number 89, December 2022, Pages 224-240 PDF (548.88 K) | ||
| Document Type: Research article | ||
| DOI: 10.22092/ijfpr.2022.359648.2063 | ||
| Authors | ||
| Y. Erfanifard* 1; M. Lotfi Nasirabad2 | ||
| 1Corresponding author, Associate Prof., Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran | ||
| 2M.Sc., Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran | ||
| Abstract | ||
| Effective mangrove mapping needs reliable indices that can characterize mangroves from other land cover types on remote sensing data. Currently, a variety of vegetation- and mangrove indices are available, while a comparative assessment of their efficiency seems essential. The aim of this study was to evaluate six vegetation indices and seven mangrove indices developed for Sentinel-2 imagery to obtain a robust approach in mangrove mapping within Google Earth Engine (GEE) cloud computing platform. The rasterized indices were classified by support vector machine. The final maps were evaluated by area under curve (AUC) of receiver operating characteristic (ROC) in addition to common accuracy assessment criteria. Results showed that mangrove indices were more reliable than vegetation indices. Amongst the vegetation indices, Modified Chlorophyll Absorption in Reflectance Index (MCARI) (AUCmangrove from 0.91 to 0.92) achieved the highest AUC values, while MFI (Mangrove Forest Index) returned the highest values amongst the mangrove indices (AUCmangrove from 0.93 to 0.95). All in all, results revealed that MFI on Sentinel-2 imagery in GEE was efficient in mangrove mapping within the study sites. | ||
| Keywords | ||
| Avicennia marina; Google Earth; mangrove index; receiver operating characteristic; support vector machine | ||
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