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different matrices. However, it does not account for fuzzy class membership and variation in
interpretation of the reference data. From a map user's perspective, individual fuzzy assessment
class accuracies vary from 30% (for water) to 96% (for deciduous forest). Producer's accuracies
range from 0% (for barren/sparse vegetation and wet, permanent herbaceous) to 100% (for water
and urban). The highest combined user's and producer's accuracies occur in the urban class (100%
and 91.7%, respectively).
A useful comparison is the total number of sites for a particular class by row and by column.
For example, for deciduous forest there are a total of 113 reference sites and a total of 56 map
sites. This indicates that the map underestimates deciduous forest. Another underestimated class is
agriculture-other (51 vs. 82). Conversely, for evergreen forest there are a total of 50 map sites and
26 reference sites, indicating that the map overestimates evergreen forest. Other overestimated
classes include shrub (47 vs. 31) and grassland (50 vs. 24).
12.5 DISCUSSION AND CONCLUSIONS
The following text discusses and analyzes the major sources of confusion and agreement in the
LC map for the initial prototype study. The highest user's accuracy occurs in the deciduous forest
class (96.4%). However, producer's accuracy in deciduous forest is low (63.7%), indicating that
there is more deciduous forest in the area than is indicated on the map. The highest producer's
accuracy is in water and urban (100%). While the urban user's accuracy is also high (91.7%)
(indicating that urban is a very reliable class), the user's accuracy for water is low (30.3%),
indicating that significant commission errors may exist in the water class. For example, 18 water
map sites were determined to be deciduous in the reference data. After the matrix was generated,
these sites were reviewed. In each case, the sites were small, scattered polygons in forested areas.
Because the water was maintained at full resolution (no filtering was performed), any scattered
pixels of water were maintained in the polygon coverage. Many of these polygons came from one
or two pixels of water. Because there are many of these small polygons, more than half of the
accuracy assessment sites for water came from these polygons.
Confusion also existed in the agriculture-other class, which tends to be confused with
shrub/scrub, grassland, or deciduous forest. User's class accuracy for agriculture-other is estimated
at 71% (36/51). Eleven sites were labeled as deciduous forest. These sites were also reexamined.
In most all cases, the polygons came from small groups of pixels (greater than the minimum
mapping unit of 1.4 ha) labeled as agriculture within forested areas. The matrix also identifies
confusion between agriculture and shrub and between agriculture and grasslands. For the
shrub/scrub map class, 22 sites were labeled as agriculture in the reference data, with 15 sites rated
as “poor.” Subsequent review of the maps revealed scattered pixels and polygons of shrub within
agricultural areas and scattered agriculture within shrub. For grasslands, 24 sites were labeled as
agriculture in the reference data, with 18 sites labeled as “acceptable.” This reflects the uncertainty
with separating grassland from agriculture in many cases. Often, they have identical spectral
responses, and unless there are distinct geometric spatial patterns or other contextual features, it is
very difficult to distinguish these classes from TM imagery alone.
Map error is often the result of scattered polygons in otherwise homogeneous areas. For
example, scattered small polygons of water (particularly in forested areas) accounted for the low
estimate of class accuracy for water. Likewise, scattered polygons of agriculture in shrub and
grassland and scattered polygons of shrub and grassland in agriculture influenced the accuracies
of these classes. This type of error points to the need for increased precision in the image classi-
fication algorithms, additional map editing, and/or refinement of the polygon-generating algorithms.
Finally, it should be noted that the first-stage sample units contained no polygons of bar-
ren/sparse vegetation, agriculture-rice, ice/snow, mangrove, cloud/shadow or wet, permanent her-
baceous. Therefore, these map classes were not sampled for accuracy assessment. Because the first-
