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Table 13.4 Values of FOR% and INT20 for 10 Watersheds in the Mid-Atlantic Region
FOR%
INT20
Watershed
Crisp
0.10-cut
0.25-cut
0.50-cut
Crisp
0.10-cut
0.25-cut
0.50-cut
Schuylkill
47.5
55.4
47.7
45.4
23.6
31.1
24.0
22.8
Lower West Branch
Susquehanna
68.8
73.0
69.0
68.8
54.9
60.3
55.3
54.9
Lower Susquehanna
29.0
36.3
29.2
28.1
10.8
16.2
10.9
10.7
Nanticoke
30.1
57.5
31.2
21.9
6.8
37.6
8.0
4.5
Cacapon-Town
84.9
96.0
84.9
84.0
72.0
92.3
72.6
71.1
Pamunkey
64.2
78.4
65.2
60.1
39.1
61.9
40.5
36.4
Upper James
86.9
95.3
87.1
86.9
77.4
91.4
77.8
77.3
Hampton Roads
16.2
35.0
7.3
4.4
2.4
14.0
1.6
1.1
Connoquenessing
55.4
65.2
54.1
50.3
25.0
39.4
25.0
23.3
Little Kanawha
86.2
90.4
86.4
86.2
71.8
80.5
72.4
71.8
window using a threshold of 90% to determine interior habitat suitability (i.e., suitable if ≥ 90%
forest coverage). Then, the proportion of watershed with suitable interior habitat was determined as
INT20 (based on a 450- ¥ 450-m window). Various values of FOR% and INT20 at three a-cut maps
provided possible values of these landscape indicators for the watersheds under study.
For the Schuylkill watershed (2040203) located in an urbanized area with moderate accuracy
for forested upland pixels, FOR% ranged from 55.4 to 45.4 with a 10% change from 0.1- to 0.5-
cut. Also, the FOR% value at 0.25-cut was very close to those for the crisp binary forested upland
map (i.e., 47.7 vs. 47.5) and INT20 values at this watershed changed about 8.3% from 0.1- to 0.5-
cut. The Lower Susquehanna watershed (2050306), also located in an urbanized area, had a
relatively higher accuracy level; 0.10- and 0.25-cut variations of FOR% and INT20 were only 8.2%
and 5.5%, respectively. Conversely, for the Little Kanawha watershed (5030203), located in a
forested area with a high accuracy level, FOR% changed only 4.2% from 0.1- to 0.5-cut (from
90.4 to 86.2%). However, the INT20 0.10- to 0.25-cut variation increased to 8.7%. These analyses
can be applied to other watersheds, providing valuable insights into the accuracy of the landscape
indicators across the region. These two landscape indicators serve as an example of how landscape
indicators derived from thematic LC maps can be analyzed to reveal their spatial accuracy and
possible value in the study area.
13.4 CONCLUSIONS
We have developed a fuzzy set-based method to map the spatial accuracy of thematic maps
and compute landscape indicators while taking into account the spatial variation of accuracy
associated with different LC types.
This method provides valuable information not only on the
spatial patterns of accuracy associated with various cover types but also on the possible values of
landscape indicators across the study area. Such insights have not previously been incorporated
into any of the existing thematic map-related accuracy assessment methods. We believe that
including a spatial assessment in the accuracy assessment process would greatly enhance the user's
capability to evaluate map suitability for numerous environmental applications.
13.5 SUMMARY
This chapter presented a fuzzy set-based method of mapping the spatial accuracy of thematic
maps and computing landscape indicators while taking into account the spatial variation of accuracy
associated with different LC types. First, a multilevel agreement was defined, providing a framework
to accommodate different levels of matching between sampled pixels and mapped pixels. Then, the
