NWI maps (onlv wetland tvpes). It is uncertain whether a similar
modeling approach would be more successful in identifving
forested wetlands compared to conventional methods (e.g. hybrid)
in other states or regions of U.S.A..
The conventional classifications were based on class grouping
derived from signature evaluation. The integrated model was
based on class grouping derived from weighted GIS layer
combinations. The analysis matrix technique can be used to
analvze a multivariate discrete data set for optimal aggregation to
category assignment, For example, an alternative approach can
be simulated by analyzing spectral signatures derived from
clustering or training samples to guide appropriate land cover
class assignment. A reference data set is required to apply the
analysis matrix technique. Consequently, the results avoid
subjective signature evaluation and benefits from the formulated
algorithm derived in the integrated model.
The integrated model approach will require further research and
development efforts. There are many important research issues
related to the acquisition, processing, and joint analysis of remote
sensing and GIS. Error is one of the primary issues of concern in
the integrated model. For example, the geometric accuracy of the
collective GIS layers is probably worse than the corrected
Landsat TM data. Integration of remote sensing and GIS data
required digitizing, converting, geo-referencing, and registration.
These transformations have potential errors deeply embedded in
the overall data processing flow. On the other hand, the current
accuracy assessment procedures have been adapted from
statistical procedures to give a quantity measure of overall
accuracy. However, the quality (e.g. spatial distribution) error
was not evaluated. Techniques need to be developed for
assessing the spatial structure of error in the integrated model
product. Further research into the integration of remote sensing
and GIS along with improvements in spectral and spatial
resolution of remotely sensed data may lead to advancements in
wetland inventory and monitoring in the future.
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