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MONITORING LARGE AREAS FOR FOREST CHANGE USING LANDSAT: GENERALIZATION ACROSS SPACE, TIME AND LANDSAT SENSORS. (R828309)
Citation:
Woodcock, C. E., S. A. Macomber, M. Pax-Lenney, AND W. B. Cohen. MONITORING LARGE AREAS FOR FOREST CHANGE USING LANDSAT: GENERALIZATION ACROSS SPACE, TIME AND LANDSAT SENSORS. (R828309). REMOTE SENSING OF ENVIRONMENT 78(1-2):194-203, (2001).
Description:
Landsat 7 ETM+ provides an opportunity to extend the area and frequency with
which we are able to monitor the Earth's surface with fine spatial resolution
data. To take advantage of this opportunity it is necessary to move beyond the
traditional image-by-image approach to data analysis. A new approach to monitoring
large areas is to extend the application of a trained image classifier to data
beyond its original temporal, spatial, and sensor domains. A map of forest change
in the Cascade Range of Oregon developed with methods based on such generalization
shows accuracies comparable to a map produced with current state-of-the-art methods.
A test of generalization across sensors to monitor forest change in the Rocky
Mountains indicates that Landsat 7 ETM+ data can be combined with earlier Landsat
5 TM data without retraining the classifier. Methods based on generalization
require less time and effort than conventional methods and as a result may allow
monitoring of larger areas or more frequent monitoring at reduced cost. One key
component to achieving this goal is the improved availability and affordability
of Landsat 7 imagery. These results highlight the value of the existing Landsat
archive and the importance for continuity in the Landsat Program.