Geoscience Reference
In-Depth Information
Understanding the accuracy of meso-scale (1:100,000 to 1:500,000 scale) digital maps produced
by government agencies is especially important because of the potential for broad dissemination
and use. Meso-scale maps encompass large areas, and thus the information may affect significantly
large populations. Additionally, digital information can be shared much more easily than hard-copy
maps in the rapidly growing technological world. Finally, information produced by public agencies
is freely available and sometimes actively disseminated. These combined factors highlight that a
thorough understanding of the thematic accuracy of a map is essential for proper use.
A rigorous assessment of a map allows users to determine the suitability of the map for particular
applications. For example, estimates of thematic accuracy are needed to assist land managers in
providing a defensible basis for use of the map in conservation decisions (Edwards et al., 1998).
Errors can occur and accumulate throughout a land-cover (LC) mapping project (Lunetta et al.,
1991). The final map can have spatial (positional) and/or thematic (classification) errors. Spatial
errors may occur during the registration of the spatial data to ground coordinates or during sequential
analytical processing steps, while thematic errors occur as a result of cover-type misclassifications.
Thematic errors may include variation in human interpretation of a complex classification scheme
or an inappropriate classification system for the data used (e.g., understory classification when
satellite imagery can only visualize the overstory).
This chapter focuses on analysis and estimation of thematic accuracy of a LC map containing
105 cover types. Using a single reference data set, three methods of analysis were conducted to
illustrate the increase in accuracy information portrayed by fuzzy set theory and spatial visualization.
This added information allows a user to better evaluate use of the map for any given application.
14.1.2
Analysis of Reference Data
14.1.2.1
Binary Analysis
The analysis and estimation of thematic accuracy of meso-scale LC maps has traditionally been
limited to a binary analysis (i.e., right/wrong) (Congalton, 1996; Congalton and Green, 1999). This
type of assessment provides information about agreement between cover types as mapped (classified
data) and corresponding cover types as determined by an independent data source (reference data).
The binary assessment is summarized in an error matrix (Congalton and Green, 1999), also referred
to as a confusion or contingency table. In the matrix, the cover type predicted by the classified data
(map) is assigned to rows and the observed cover type (reference data) is displayed in columns.
The values in each cell represent the count of sample points matching the combination of classified
and reference data (Congalton, 1996). Errors of inclusion (commission errors) and errors of exclu-
sion (omission errors) for each cover type and overall map accuracy can be calculated using the
error matrix. “User's accuracy” corresponds to the area on the map that actually represents that
LC type on the ground. “Producer's accuracy” represents the percentage of sampling points that
were correctly classified for each cover type.
A binary analysis of accuracy data using an error matrix omits information in two ways: (1) it
does not take into account the degree of agreement between reference and map data and (2) it
ignores spatial information from the reference data. The error matrix forces each map label at each
reference point into a correct or incorrect classification. However, a LC classification is often not
discrete (i.e., one type is exclusive of all others). Instead, types grade from one to another and may
be related, justifying one or more map labels for the same geographic area. The binary assessment
does not take into account that the reference data may be incorrect. In addition, the error matrix
does not use the locations of the reference points directly, and accuracy is assumed to be spatially
constant within each LC type. Instead, accuracy may vary spatially across the landscape in a manner
partially or totally unrelated to LC type (Steele et al., 1998). This has led to the utilization of two
additional analysis techniques, fuzzy set analysis and spatial analysis, to describe the thematic
accuracy of a LC map.
