| Abstrak/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. |
