Table 1 Comparison of Classification and Model Results for Two Study Sites 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Accuracy Sm 
Method Site Forested | Other | Forested | Other Overall Overall | 
Wetland | Wetland Upland Upland Kappa 
Acadia user 0.70 0.50 0.74 0.72 74% 0.46 
Unsuper- (312) producer 0.12 0.06 0.96 0.85 
vised Orono user 0.58 0.62 0.81 0.81 72% 0.60 
(431) producer 0.71 0.42 0.73 0.82. 
Acadia user 0.41 0.41 0.81 0.94 75% 0.53 
Tasseled (309) producer 0.25 0.75 0.91 0.72 
Cap Orono user 0.64 0.41 0.84 0.84 74% 063 | 
(362) producer 0.65 0.47 0.73 0.97 
Acadia user 0.80 0.41 0.84 0.91 78% 0.61 
Hybrid (282) producer 0.35 0.91 0.95 0.56 
Orono user 0.75 0.82 0.80 0.86 81% 972 73 
(253) producer 0.57 0.64 0.87 0.98 
Existing GIS Orono user 0.82 0.64 0.83 0.80 80% 0.71 
Model (406) producer 0.66 0.71 0.87 0.92 
Acadia user 0.62 0.90 0.90 0.81 82% 0.70 
Integrated (309)” producer 0.77 0.45 0.86 0.89 
(Revised) Orono user 0.78 0.67 0.86 0.80 81% 0.72 
Model (402) producer 0.78 0.70 0.87 0.76 
Notes: The classification results of Orono were derived from Ahl (1994) except the result of the integrated model. 
"Sample size is in parenthesis. 
4.3 Integrated Model 
The integrated model combined one classified image (forest 
recoded as 16 and non-forest recoded as 0) and four weighted 
GIS layers in a series of 2%”, where k indicated the order of layer. 
An analysis image with 32 combination levels were created by 
map algebra using an additive approach. An analysis matrix was 
generated (Table 2). The columns represent the reference data in 
four classes which were separated by row into two primary 
groups of forest and non-forest. The rows represented the 32 
combination levels and indicated the distribution of reference 
data within levels. The aggregation of 32 combination levels into 
four classes was defined by the accumulated ratio and controlled 
by the balanced ratio. 
In the non-forest group, the threshold for other wetland and other 
upland is obvious (Table 2), even the balance ratios, 0.48 and 
0.58, are not comparable. It indicated that combination level 0 
was a source of confusion and a poor outcome for the other 
wetland class as expected. On the other hand, the threshold for 
forested wetland and forested upland was set between 
combination level 20 and 21. It was observed that increasing the 
ratio of forested wetland will dramatically drop the ratio of 
forested upland. Besides, the final ratios would reflect in the 
producer agreement of the confusion matrix except in the other 
upland category ( Table 1 ). The producer and user accuracy for 
forested wetland were 0.77 and 0.62 respectively. The overall 
agreement was 82% and the Kappa was 0.70. These results are 
the best among the two and four classes approach conducted in 
the Acadia study site. 
International Archives of Photogrammetry and 
4.4 Integrated Model Applied in the Orono Study Site 
The same algorithm was applied using the Orono data set (Al, 
1994). First, the slope percent layer was redefined by examining 
an analysis matrix. Second, individual GIS layers were evaluated 
by cross tabulation. The contribution of four GIS layers was the 
same as defined in the existing model (Ahl 1994). They were 
NWI, hydric soil, slope, and stream buffer layer. Similarly to 
Acadia, an analysis matrix can be generated for the four groups 
aggregation analysis. The final ratios reveal a homogeneous 
confusion matrix. Forested wetland producer and user accuracy 
were both at 0.78, and the overall agreement and the Kappa were 
81% and 0.72 respectively. These results are the best among al 
the approaches that had been conducted in Orono study sit 
(Table 1). 
The integrated model improved the results at Orono by 
equalizing the accuracy between user and producer agreement. 
This can be observed by comparing the Orono integrated mod! 
results with other approaches (Table 1). This emphasizes the 
utility of the analysis matrix and balance ratio in the aggregation 
process. Beyond the majority editing rule, an expected result 
might be revealed through examining the final balance ratio. The 
integrated model makes both overall accuracy and Kappa 
correction more comparable at two study sites (Table 1). This 
result suggests that the integrated model was less influenced by 
the size of study area. Moreover, the significance test of Kap! 
correction performed consistently in both study sites when the 
integrated model was involved (Table 1). Although the integrated 
model provided the best result among all experiments on both 
study sites, it was not determined to be a significan 
improvement over the conventional hybrid approach. 
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Remote Sensing. Vol. XXXI, Part B7. Vienna 1996