Wetland Habitat Studies using various Classification Techniques on Multi-Spectral Landsat Imagery: Case study: Tram chim National Park, Dong Thap Vietnam
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesWetland is one of the most valuable ecological systems in nature. Wetland habitat is
a set of comprehensive information of wetland distribution, wetland habitat types are
essential to wetland management programs. Maps of wetland should provide
sufficient detail, retain an appropriate scale and be useful for further mapping and
inventory work (Queensland wetland framework).
Remotely sensed image classification techniques are useful to detect vegetation
patterns and species combination in the inaccessible regions. Automated
classification procedures are conducted to save the time of the research.
The purpose of the research was to develop a hierarchical classification approach
that effectively integrate ancillary information into the classification process and
combines ISODATA (iterative self-organizing data analysis techniques algorithm)
clustering, Maximum likelihood and rule-based classifier. The main goal was to find
out the best possible combination or sequence of classifiers for typically classifying
wetland habitat types yields higher accuracy than the existing classified wetland
map from Landsat ETM data. Three classification schemes were introduced to
delineate the wetland habitat types in the idea of comparison among the methods.
The results showed the low accuracy of different classification schemes revealing
the fact that image classification is still on the way toward a fine proper procedure to
get high accuracy result with limited effort to make the investigation on sites. Even
though the motivation of the research was to apply an appropriate procedure with
acceptable accuracy of classified map image, the results did not achieve a higher
accuracy on knowledge-based classification method as it was expected. The
possible reasons are the limitation of the image resolution, the ground truth data
requirements, and the difficulties of building the rules based on the spectral
characteristics of the objects which contain high mix of spectral similarities