Geoscience Reference
In-Depth Information
Table 12.2
Classification Labels
Class Number
Class Name
1
Forest, Deciduous
2
Forest, Evergreen
3
Shrub/Scrub
4
Grassland
5
Barren/Sparsely Vegetated
6
Urban/Built-Up
7
Agriculture, Other
8
Agriculture, Rice
9
Wetland, Permanent Herbaceous
10
Wetland, Mangrove
11
Water
12
Ice/Snow
13
Cloud/Cloud Shadow/No Data
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How should the samples be chosen? The choice and distribution of samples, or sampling scheme,
is an important part of any inventory design. Selection of the proper scheme is critical to generating
results that are representative of the map being assessed. First, the samples must be selected without
bias. Second, further data analysis will depend on which sampling scheme is selected. Finally, the
sampling scheme will determine the distribution of samples across the landscape, which will
significantly affect accuracy assessment costs.
This chapter addresses all of the above considerations relative to the NIMA GeoCover study.
Major study elements included (1) the finalization of the NIMA GeoCover classification scheme,
(2) accuracy assessment sample design and selection, (3) accuracy assessment site labeling, and
(4) the compilation of the deterministic and fuzzy error matrix
12.3.1
Classification Scheme
The first task in this project was to specify the NIMA GeoCover classification system rules. A
classification scheme has two critical components: (1) a set of labels (e.g., deciduous forest, urban,
shrub/scrub, etc.) and (2) a set of rules or definitions such as a dichotomous key for assigning
labels. Without a clear set of rules, the assignment of labels to types can be arbitrary and lack
consistency. In addition to having labels and a set of rules, a classification scheme should be
mutually exclusive and totally exhaustive. All study partners worked together to develop and finalize
a classification scheme with the necessary labels and rules. Table 12.2 presents the labels; the
classification rules can be found in Appendix A of this chapter.
12.3.2
Sampling Design
Sample design often requires trade-offs between the need for statistical rigor and the practical
constraints of budget and available reference data. To achieve statistically reliable results and keep
costs to a minimum, a multistaged, stratified random sample design was employed for this project.
Research by Congalton (1988) indicates that random and stratified random samplings are the optimal
sampling designs for accuracy assessment.
One of the most important aspects of sample design is that the reference data must be inde-
pendent from data used to create the map. The need for independence posed a dilemma for the
assessment of the NIMA GeoCover prototype because the National Technical Means (NTM) used
for reference data development were not available for the entire study area. NTM can be defined
as classified intelligence gathering systems and the data they generate.
As a result of this limited NTM availability, a choice needed to be made to either (1) constrain
the accuracy assessment sample to the areas with existing NTM data, and thereby risk sampling
