dominant tree species and/or the hvdrologic characteristic of 
wetlands are the primary features used to separate upland from 
forested wetlands (Tiner. 1990). Due to the limitations of the 
photo interpretation process, certain wetland types such as 
evergreen forested wetlands. temporarily flooded deciduous 
forested wetlands. and hvdrologically altered forested wetlands 
arc among the most difficult wetlands to detect. In such cases, 
subtle photo signatures, topographic position on the landscape, 
collateral hydric soil information and field work must be closely 
examined to aid in the interpretation process (The Federal 
Geographic Data Committee. 1992). 
2.2 Satellite Imagery for Wetland Mapping 
The use of satellite imagery for wetland mapping provides a 
number of advantages over conventional aerial photographs. For 
example, wetlands are very dynamic and have tremendous 
seasonal or yearly changes. Furthermore. satellite sensors cover 
broader wavelengths through their optical scanner systems. Each 
detector of the satellite scanner is positioned to record specific 
wavelength of energy. Although aerial photography may be 
appropriate for high resolution cartography, satellite imagery is 
better suited and less costly for rapid, repeated observations over 
broad regions assuming equipment and experienced human 
analysts are available to process the data (Ferguson et al., 1993). 
Besides, the major advantage of satellite data is it's digital 
format, making automatic analvses possible and the integration 
with GIS. 
Satellite imagery may offer an efficient means of identifying 
forested wetlands in the future. Landsat MSS, TM and SPOT 
HRV imagery have been used successfully to detect major 
categories of wetlands (Haddad and Ekberg, 1987; Jensen et al, 
1993). However. they have not been used previously to map or 
monitor forested wetlands for regional or national coverage. 
2.3 Modelling Approaches 
The primary reason for employing an integration of GIS and 
expert system ( ES ) is to reduce human labor and improve the 
consistency of results. A knowledge-based GIS and ES to 
manage wetland was developed by the USFWS (Wei et al, 
1992). The model, based on Mitchell's (1991) wetland value 
assessment ~~ methodology, integrated ~~ knowledge-based 
capabilities into a GIS. However. the approach was limited to 
monitoring, producing maps, and analyzing changes in wetland 
habitat and indicated a need to integrate knowledge of wetland 
expertise (Wei et al., 1992). 
Hepner et al. (1990) compared neural networks ( NN ) 
techniques with conventional supervised classification method. 
They concluded that NN offered a potential approach to land 
cover classification. The advantage of NN is it's abilitv to handle 
multispectral, multitemporal, and multisource spatial data (Civco 
and Wang, 1994). A NN model which incorporated multidate 
Landsat TM image with ancillary spatial information had an 
accuracy of approximately eight percent greater than a traditional 
single date image classification approach. However, the result for 
forested wetland were rather poor (Civco and Wang, 1994). 
420 
3.0 METHODS 
3.1 Equipment and Data Acquisition 
The experiments utilized three different commercial image 
processing and GIS computer software packages: ERDAS 
(1992), ARC/INFO (ESRI, 1990), IDRISI (Eastman, 1992) ang 
three statistical programs: Cart methodology (Breiman ef 4 
1984), LIMDEP (Greene, 1992), Kappa (Congalton, 1983). The 
Landsat TM July, 1991 images were acquired to delineate 
forested wetland and other vegetation types using three image 
classification methods. Aerial photographs were obtained to 
support standard plot creation for data analysis and accuracy 
assessment. Land characteristics data derived from various 
sources were essential components in the development of the GIS 
data base. 
3.2 Conventional Image Classification 
Conventional techniques of image classification were employed 
to understand the limitations of classification based on spectral 
signature ^ characteristics. ~~ Unsupervised, ^ tasseled cap 
transformation, and hybrid classification approaches were 
conducted. The generalized land cover classification. scheme 
includes the following classification types: 
1. Urban / Open / Agriculture — 6. Softwood Wetland 
2. Softwood Upland 7. Mixed Wetland 
3. Mixed Forested Upland 8. Hardwood Wetland 
4. Hardwood Upland 9. Shrub Upland 
5. Non-forested Wetland 
3.3 Sample Design for Data Analysis and Accuracy 
Assessment 
The purpose of the sample design was to create a set of stratified 
random plots as reference for data analysis and accuracy 
assessment. Three pure pixels on a side (3X3) were suggested a 
a minimum area to identify an object on an image (The Federal 
Geographic Data Committee, 1992). À plot size of 3x3 pixels 
was defined as the sample unit. In order to facilitate analysis, the 
nine land cover classification scheme was grouped into fou 
super categories as follows: 
1. Forested Wetland 3. Forested Upland 
Softwood Wetland Softwood Upland 
Mixed Wetland Mixed Upland 
Hardwood Wetland Hardwood Upland 
2. Other Wetlands 4. Other Upland 
Non-forested Wetland ~~ Urban / Open / Agriculture 
Shrub Upland 
3.4 GIS Data Base Development 
The GIS layers were derived from existing map Sources and 
selected under the assumption that the combination of these 
layers indicate a physical environment that may support wel 
conditions. Consequently, this investigation selected NWI maps, 
hydric soils mapped by the SCS, a digital elevation model (DEM) 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996