International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
reported R? values are therefore valid only for the transformed 
space. A scatterplot of field-based In(canopy bulk density) 
measurements versus predicted values is shown in Figure 7. 
  
Field In(Canopy Bulk Density) 
  
  
  
  
4.5 
T T T 
-4.0 -3.5 „3.0 -2.5 -2.0 
Predicted In(Canopy Bulk Density) 
Figure 7. Field-based (y) versus predicted (log-transformed) 
canopy bulk density measurements (x) (with 1:1 line shown). 
4.4 Canopy fuel weight 
The regression model for (log-transformed) canopy fuel weight 
had an R? of 0.77 and an adjusted R^ of 0.75. A scatterplot of 
field-based In(canopy fuel weight) measurements versus 
predicted values is shown in Figure 8. 
  
Field In(Canopy Fuel Weight) 
  
  
7.5 
o 
  
  
T T T T T 
8.0 8.5 9.0 9.5 10.0 
Predicted In(Canopy Fuel Weight) 
Figure 8. Field-based (y) versus predicted (log-transformed) 
canopy fuel weight measurements (x) (with 1:1 line shown). 
5. DISCUSSION 
The results indicate that information related to three- 
dimensional geometry, interferometric observables, and 
backscatter characteristics of the forest canopy provided by 
multi-frequency, polarimetric IFSAR systems can be used to 
obtain accurate measurements of canopy fuel parameters. 
Although the mature conifer forest stands within the study area 
are of fairly uniform height (see Figure 5), the interferometric 
observables and polarimetric backscatter data explain 
variability in the vertical structural characteristics of these 
forest stands, as evidenced by high correlation between field- 
based and predicted canopy base heights (see Figure 6). 
The use of model-based estimates for the various fuel 
parameters will also introduce a significant source of variability 
in the regression model. The fuel models use a number of 
assumptions (i.e. uniform vertical distribution of crown fuels) 
that are clearly over-simplifications of reality. More detailed 
crown fuel models will be needed in the future if they are to be 
used as ground truth in comparison to high-resolution remotely- 
sensed estimates. 
Although each predictive model contained a large number of 
independent variables, the adjusted R? values were not 
significantly lower than the R? for any of the models, indicating 
that none of the models contained a large number of extraneous 
variables working to artificially inflate this measure of fit. 
Correlation matrices for the regression models also indicate that 
there is little multicollinearity in these models. Admittedly, the 
large number of predictor variables make it difficult to establish 
a physical interpretation for these regression models. Further 
work will certainly be needed to fully understand the physical 
relationships between IFSAR observables and the array of 
forest structural parameters. 
It should also be noted that at this time, most IFSAR processing 
algorithms are (perhaps not surprisingly) optimized for 
extraction of bare earth elevations, not for accurate 
measurement of canopy surface structure. For example, some 
errors in the X-band measurement of canopy height were due to 
filtering of the interferogram (Mercer, 2004). Although it is 
unclear what effect this may have on the results, future work 
will look at optimizing X-band processing for the forest 
measurement application. 
6. CONCLUSIONS 
Results of this analysis indicate that IFSAR can be used to 
generate accurate estimates of canopy fuel parameters 
efficiently and economically over extensive areas of forest 
Given the capability of IFSAR to penetrate clouds and smoke, 
there may be potential for near real-time collection of fuels data 
in fire-prone areas. As extensive areas of the world begin to be 
covered through large-scale terrain mapping campaigns 
(NextMapBritain, NextMapUSA, etc.) IFSAR data will be 
increasingly available for forest mapping applications. 
Once the regression analysis have been carried out and the 
predictive models developed, canopy fuel parameters can be 
mapped over the entire extent of the IFSAR coverage, resulting 
in a GIS coverage that could be used to support fire behavior 
modelling or fuels management programs. 
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