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were obtained using the three alternative methods. Aerial photography provided reference data to
assess the 1973 and 1986 LC maps with overall classification accuracies of 70% (1973) and 67%
(1986). Assignments of class labels to sample points on 1992 reference DOQQs were verified by
comparison with higher-resolution airborne video data, with overall 1992 map classification accu-
racy of 75%. Accuracy assessment of the 1997 products used contemporaneous airborne color
video data and resulted in an overall map accuracy of 72%. There was no evidence of positive bias
in accuracy resulting from use of homogeneous vs. heterogeneous pixel contexts in sampling the
LC maps.
The use of historical aerial photography, high-resolution DOQQs, and airborne videography as
a proxy for actual ground sampling for satellite image classification accuracy has merit. Selection
of a reference data set for this study depended on the date of image acquisition. For example, prior
to 1992, historical aerial photographs were the only data available. DOQQs covered the period
since initiation of the high-resolution NAPP in 1992, and high-resolution airborne videography
provided a cost-effective means of acquiring many reference sample points near the time of image
acquisition. Problems that were difficult to avoid included inadequate sampling of rare classes and
reconciling cover changes between acquisition dates of aerial photography or DOQQs and satellite
image data. Other issues, including the need for consistent geometric rectification and criteria for
mutually exclusive and reproducible LC class descriptions, need special attention when satellite
image classification and subsequent LC map accuracy assessment are performed by different teams.
ACKNOWLEDGMENTS
The U.S. Environmental Protection Agency, Office of Research and Development provided fund-
ing for this work. The authors wish to thank participants from U.S. EPA, Lockheed Martin Environ-
mental Services, Instituto del Medio Ambiente y el Desarrollo Sustentable del Estado de Sonora
(IMADES), and the Arizona Remote Sensing Center at the University of Arizona for their assistance.
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