Hyperspectral data for mangrove species mapping: a comparison of pixel-based and object-based approach

Kamal, Muhammad and Phinn, Stuart (2011) Hyperspectral data for mangrove species mapping: a comparison of pixel-based and object-based approach. Remote Sensing, 3 10: 2222-2242. doi:10.3390/rs3102222

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Author Kamal, Muhammad
Phinn, Stuart
Title Hyperspectral data for mangrove species mapping: a comparison of pixel-based and object-based approach
Journal name Remote Sensing   Check publisher's open access policy
ISSN 2072-4292
Publication date 2011-10-01
Year available 2011
Sub-type Article (original research)
DOI 10.3390/rs3102222
Open Access Status DOI
Volume 3
Issue 10
Start page 2222
End page 2242
Total pages 21
Place of publication Basel, Switzerland
Publisher M D P I AG
Language eng
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.
Formatted 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.
Keyword Mangrove
Hyperspectral
Spectral angle mapper (SAM)
Linear spectral unmixing (LSU)
Object-based image analysis (OBIA)
CASI-2
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Special Issue: "Hyperspectral Remote Sensing"

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Geography, Planning and Environmental Management Publications
Official 2012 Collection
 
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Created: Tue, 28 Feb 2012, 01:28:33 EST by Alexandra Simmonds on behalf of School of Geography, Planning & Env Management