Hyperspectral Data for Mangrove Species Mapping: A Comparison of Pixel-Based and Object-Based ApproachReportar como inadecuado


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


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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.



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. View Full-Text

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





Autor: Muhammad Kamal 1,2,* and Stuart Phinn 1

Fuente: http://mdpi.com/



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