Mangroves, important components of the world’s coastal ecosystems, are
threatened by the expansion of human settlements, the boom in commercial aquaculture,
the impact of tidal waves and storm surges, etc. Such threats are leading to the increasing
demand for detailed mangrove maps for the purpose of measuring the extent of the
decline of mangrove ecosystems. Detailed mangrove maps at the community or species
level are, however, not easy to produce, mainly because mangrove forests are very
difficult to access. Without doubt, remote sensing is a serious alternative to traditional
field-based methods for mangrove mapping, as it allows information to be gathered from
the forbidding environment of mangrove forests, which otherwise, logistically and
practically speaking, would be extremely difficult to survey. Remote sensing applications
for mangrove mapping at the fundamental level are already well established but,
surprisingly, a number of advanced remote sensing applications have remained
unexplored for the purpose of mangrove mapping at a finer level. Consequently, the aim
of this thesis is to unveil the potential of some of the unexplored remote sensing
techniques for mangrove studies. Specifically, this thesis focuses on improving class
separability between mangrove species or community types. It is based on two important
ingredients: (i) the use of narrow-band hyperspectral data, and (ii) the integration of
ecological knowledge of mangrove-environment relationships into the mapping process