Calderwood Fuel Classes 
  
  
  
  
  
  
  
  
  
  
  
  
Figure 9. A portion of the fuel class database in Great 
Smoky Mountains National Park corresponding to the USGS 
7.5-minute Calderwood topographic quadrangle. 
The fire fuel class maps and GIS data sets for Great Smoky 
Mountains National Park are being used for fire management 
decisions and long-term planning for the protection of park 
resources. As a demonstration of the use of the fuel maps for 
further fire analysis, Dukes (2001) assigned risk factors 
based on fuel classes, topography (isolating relatively dry 
slopes, aspects and elevations) and ignition sources (e.g. 
distance to roads, campsites and areas of potential lightning 
strikes). Since ignition risks were found to be important 
predic ors of 24 previous forest fires located in the 
Calderwood quad area, this risk data layer was given a 
weight of 2x in the model. A combination of all risk factors 
resulted in an overall map of fire ignition risk ranked as high 
medium and low (Fig. 10). An overlay of six withheld fire 
locations indicted all previous fires corresponded with 
designations of medium and high risk. 
4. LANDSCAPE METRICS RELATED TO 
VEGETATION PATTERNS 
Landscape metrics comparing vegetation patterns due to 
interpreter differences and human influence were derived 
using the Patch Analyst, an ArcView extension that 
interfaces grids and shapefiles with Fragstats Spatial Pattern 
Analysis program (McGarigal and Maraks, 1995; Elkie et al., 
1999). An area corresponding to four 7.5-minute USGS 
topographic quadrangles was selected to examine differences 
in landscape metrics. Overstory vegetation in the Wear Cove 
(WECO) and Thunderhead Mountain (THMO) quadrangles 
was mapped by Interpreter #1, while the vegetation in the 
Gatlinburg (GATL) and Silers Bald (SIBA) quadrangles was 
mapped by Interpreter #2 (Fig. 11). (Also indicted by "b", 
c". *d" and "e", respectively, in Fig. l). In addition to 
interpreter differences, WECO and GATL quadrangles are 
located on the outside boundary of the park and the 
vegetation in these quads is subject to greater human 
influence than the interior quads, THMO and SIBA (Fig. 12). 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 
1290 
These four quads, therefore, provide a good test for whether 
interpreter differences or human influence is having a greater 
impact on vegetation patterns as measured by landscape 
metrics (Madden 2003). 
| Fuel Risk 
Topography Risk 
+ 
2X | Ignition Sources Risk 
  
  
—— m 
  
Fire Ignition Risk Map 
  
  
  
| 
| 
L 
  
  
  
Figure 10. A schematic diagram of the GIS data layers 
combined in a cartographic model to assess the risk of forest 
fire and a map of fire ignition risk in the Calderwood area of 
Great Smoky Mountains National Park (Dukes, 2001). 
Landscape metrics, such as Shannon’s Diversity Index, 
computed at the landscape level (i.e. considering all pixels in 
the grid) indicate that there is very little difference that can 
be attributed to the two interpreters (Fig. 13). Exterior 
quads (WECO and GATL) showed a slight decrease in 
diversity compared to interior quads: SIBA and THMO. 
Groups of adjacent pixels with the same overstory vegetation 
class were then identified using an 8N-diagonals clumping 
method of the Patch Analyst (Fig. 14). Since resource 
managers in Great Smoky Mountains National Park are 
extremely interested in preventing wide-spread destruction of 
old growth forests due to an infestation of an exotic insect 
known as the hemlock wooly adelgid (Adelges Isugae), 
patches representing areas containing Eastern hemlock were 
isolated from the overstory vegetation database and analyzed 
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