Details

Autor(en) / Beteiligte

• Held, Alex
• Ticehurst, Catherine
• Lymburner, Leo
• Williams, Nicole

Titel

High resolution mapping of tropical mangrove ecosystems using hyperspectral and radar remote sensing

Ist Teil von

• International journal of remote sensing, 2003-07, Vol.24 (13), p.2739-2759

Ort / Verlag

Taylor & Francis Group

Erscheinungsjahr

2003

Quelle

Taylor & Francis

Beschreibungen/Notizen

• Mangrove ecosystems are in serious decline around the world and various initiatives are underway to assess their current coverage and loss in cover. These ecosystems occur as thin strips along coastlines or rivers and, due to the strong environmental gradients present, mangroves show high spatial variability along short transects. Remote sensing tools that offer high spatial resolution mapping and high information content are needed to provide good differentiation of the various mangrove zones and types. The added complexities of tropical atmospheric conditions provide further challenges in terms of the selection of sensors and image analysis methodologies. This paper explores the possibility of combining a high spatial/spectral resolution scanner, 'Compact Airborne Spectrographic Imager' (CASI), with the airborne National Aeronautics @ Space Administration's polarimetric radar, 'AIRSAR', for mapping and monitoring of mangrove estuaries. The Daintree River estuary in far North Queensland, Australia was chosen for this study due to its diversity of mangrove species. Imagery acquired by both the CASI airborne scanner (14 bands, 2.5 m pixel) and the AIRSAR (L- and P-band polarimetry, C-band interferometry, 10 m pixel) has been used to produce detailed maps of the mangrove zones in the estuary. The advantages and difficulties associated with multi-source data integration are investigated in this paper. While radar provides general structural information in relation to mangrove zonation, high-resolution hyperspectral scanners allow for finer-detailed analysis and green-biomass information. Classifications (maximum likelihood) of both the individual and integrated datasets are performed, with the latter producing more accurate results. Application of a hierarchical neural network classification is also explored, where the more general mangrove zones are separated first based on structural information, then species or specie-complexes are extracted in subsequent levels using spectral differences.