Mangrove species identification: comparing WorldView-2 with aerial photographs

Heenkenda, Muditha K., Joyce, Karen E., Maier, Stefan W., and Bartolo, Renee (2014) Mangrove species identification: comparing WorldView-2 with aerial photographs. Remote Sensing, 6. pp. 6064-6088.

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Abstract

Remote sensing plays a critical role in mapping and monitoring mangroves. Aerial photographs and visual image interpretation techniques have historically been known to be the most common approach for mapping mangroves and species discrimination. However, with the availability of increased spectral resolution satellite imagery, and advances in digital image classification algorithms, there is now a potential to digitally classify mangroves to the species level. This study compares the accuracy of mangrove species maps derived from two different layer combinations of WorldView-2 images with those generated using high resolution aerial photographs captured by an UltraCamD camera over Rapid Creek coastal mangrove forest, Darwin, Australia. Mangrove and non-mangrove areas were discriminated using object-based image classification. Mangrove areas were then further classified into species using a support vector machine algorithm with best-fit parameters. Overall classification accuracy for the WorldView-2 data within the visible range was 89%. Kappa statistics provided a strong correlation between the classification and validation data. In contrast to this accuracy, the error matrix for the automated classification of aerial photographs indicated less promising results. In summary, it can be concluded that mangrove species mapping using a support vector machine algorithm is more successful with WorldView-2 data than with aerial photographs.

Item ID: 40839
Item Type: Article (Refereed Research - C1)
Additional Information:

© The authors. 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.

ISSN: 2072-4292
Date Deposited: 14 Oct 2015 01:57
FoR Codes: 09 ENGINEERING > 0909 Geomatic Engineering > 090905 Photogrammetry and Remote Sensing @ 80%
05 ENVIRONMENTAL SCIENCES > 0502 Environmental Science and Management > 050206 Environmental Monitoring @ 20%
SEO Codes: 96 ENVIRONMENT > 9605 Ecosystem Assessment and Management > 960503 Ecosystem Assessment and Management of Coastal and Estuarine Environments @ 100%
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