Next Article in Journal
Monitoring Urban Tree Cover Using Object-Based Image Analysis and Public Domain Remotely Sensed Data
Next Article in Special Issue
Use of Variogram Parameters in Analysis of Hyperspectral Imaging Data Acquired from Dual-Stressed Crop Leaves
Previous Article in Journal
Analysis of Incidence Angle and Distance Effects on Terrestrial Laser Scanner Intensity: Search for Correction Methods
Previous Article in Special Issue
Monitoring the Extent of Contamination from Acid Mine Drainage in the Iberian Pyrite Belt (SW Spain) Using Hyperspectral Imagery
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

1,2,*  and 1
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 / Revised: 4 October 2011 / Accepted: 11 October 2011 / Published: 20 October 2011
(This article belongs to the Special Issue Hyperspectral Remote Sensing)
Download PDF [1441 KB, 19 June 2014; original version 19 June 2014]

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 mangrove; hyperspectral; spectral angle mapper (SAM); linear spectral unmixing (LSU); object-based image analysis (OBIA); CASI-2
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote
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.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here

Comments

Cited By

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert