The principal objective of this study was to determine the accuracy of an object-based image analysis (OBIA) approach in classifying mangroves from spaceborne synthetic aperture radar (SAR) data, specifically Advanced Land Observation Satellite (ALOS), phased array L-band synthetic
aperture radar (PALSAR), and single-polarized (HH) and dual-polarized (HH + HV) L-bands. The accuracy of the object parameters was examined to determine the optimal colour and shape ratios for the hierarchical classification. At the first level of classification (mangroves from non-mangroves),
the results indicate that it is possible to accurately separate mangrove areas from saltpan and water/shallow zones using both sets of SAR images for the Mabala and Yélitono islands of southern Guinea. The final accuracies, based on the most optimal object parameters, were 91.1% and
92.3% for the single- and dual-polarized data, respectively. At the second level of classification, separation among the three mangrove classes identified was most accurate when using the dual-polarized data, at an overall accuracy of only 63.4%. The three mangrove classes identified included
tall red mangrove (Rhizophora racemosa), dwarf red mangrove (R. mangle and R. harisonii), and black mangrove (Avicennia germinans). Using the optimal combination of parameters, the extent to which a filter could be used to improve the accuracy was examined. At this level,
it was determined that the dual-polarized data, filtered with a 3 × 3 Lee speckle filter and a segmentation scale of 5, resulted in an overall accuracy of 64.9%. Consequently, it is recommended that for persistently cloud-covered regions, such as Guinea, ALOS PALSAR data using
an OBIA could be useful as a quick method for mapping and monitoring mangroves.
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Document Type: Research Article
Department of Geography,The University of Western Ontario, London,ON, CanadaN6A 5C2,
Department of Geography,Nipissing University, North Bay,ON, CanadaP1B 8L7,
Environnement Illimité Inc., Montreal,QC, CanadaH2L 3N7,
Publication date: 2013-01-20
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