Remote Sens. 2011, 3(10), 2222-2242; doi:10.3390/rs3102222
Article

Hyperspectral Data for Mangrove Species Mapping: A Comparison of Pixel-Based and Object-Based Approach

1 Biophysical Remote Sensing Group, Centre for Spatial and Environmental Research, School of Geography, Planning and Environmental Management, The University of Queensland, Brisbane, QLD 4072, Australia 2 Cartography and Remote Sensing Section, Faculty of Geography, Gadjah Mada University, Bulaksumur, Yogyakarta 55281, Indonesia
* Author to whom correspondence should be addressed.
Received: 12 August 2011; in revised form: 4 October 2011 / Accepted: 11 October 2011 / Published: 20 October 2011
(This article belongs to the Special Issue Hyperspectral Remote Sensing)
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Abstract: Visual image interpretation and digital image classification have been used to map and monitor mangrove extent and composition for decades. The presence of a high-spatial resolution hyperspectral sensor can potentially improve our ability to differentiate mangrove species. However, little research has explored the use of pixel-based and object-based approaches on high-spatial hyperspectral datasets for this purpose. This study assessed the ability of CASI-2 data for mangrove species mapping using pixel-based and object-based approaches at the mouth of the Brisbane River area, southeast Queensland, Australia. Three mapping techniques used in this study: spectral angle mapper (SAM) and linear spectral unmixing (LSU) for the pixel-based approaches, and multi-scale segmentation for the object-based image analysis (OBIA). The endmembers for the pixel-based approach were collected based on existing vegetation community map. Nine targeted classes were mapped in the study area from each approach, including three mangrove species: Avicennia marina, Rhizophora stylosa, and Ceriops australis. The mapping results showed that SAM produced accurate class polygons with only few unclassified pixels (overall accuracy 69%, Kappa 0.57), the LSU resulted in a patchy polygon pattern with many unclassified pixels (overall accuracy 56%, Kappa 0.41), and the object-based mapping produced the most accurate results (overall accuracy 76%, Kappa 0.67). Our results demonstrated that the object-based approach, which combined a rule-based and nearest-neighbor classification method, was the best classifier to map mangrove species and its adjacent environments.
Keywords: mangrove; hyperspectral; spectral angle mapper (SAM); linear spectral unmixing (LSU); object-based image analysis (OBIA); CASI-2

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MDPI and ACS Style

Kamal, M.; Phinn, S. Hyperspectral Data for Mangrove Species Mapping: A Comparison of Pixel-Based and Object-Based Approach. Remote Sens. 2011, 3, 2222-2242.

AMA Style

Kamal M, Phinn S. Hyperspectral Data for Mangrove Species Mapping: A Comparison of Pixel-Based and Object-Based Approach. Remote Sensing. 2011; 3(10):2222-2242.

Chicago/Turabian Style

Kamal, Muhammad; Phinn, Stuart. 2011. "Hyperspectral Data for Mangrove Species Mapping: A Comparison of Pixel-Based and Object-Based Approach." Remote Sens. 3, no. 10: 2222-2242.

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