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    Author(s): Yohay Carmel; Dennis J. Dean; Curtis H. Flather
    Date: 2001
    Source: Photogrammetric Engineering and Remote Sensing. 67(7): 865-872.
    Publication Series: Scientific Journal (JRNL)
    Station: Rocky Mountain Research Station
    PDF: Download Publication  (2.0 MB)


    Detection and quantification of temporal change in spatial objects is the subject of a growing number of studies. Much of the change shown in such studies may be an artifact of location error and classification error. The basic units of these two measures are different (distance units for location error and pixel counts for classification error). The lack of a single index summarizing both error sources poses a constraint on assessing and interpreting the apparent change. We present an error model that addresses location and classification error jointly. Our approach quantifies location accuracy in terms of thematic accuracy, using a simulation of the location error process. We further develop an error model that combines the location and classification accuracy matrices into a single matrix, representing the overall thematic accuracy in a single layer. The resulting time-specific matrices serve to derive indices for estimating the overall uncertainty in a multi-temporal dataset. In order to validate the model, we performed simulations in which known amounts of location and classification error were introduced into raster maps. Our error model estimates were highly accurate under a wide range of parameters tested. We applied the error model to a study of vegetation dynamics in California woodlands in order to explore its value for realistic assessment of change, and its potential to provide a means for quantifying the relative contributions of these two error sources.

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    Carmel, Yohay; Dean, Dennis J.; Flather, Curtis H. 2001. Combining location and classification error sources for estimating multi-temporal database accuracy. Photogrammetric Engineering and Remote Sensing. 67(7): 865-872.


    temporal change, location error, classification error, error model, multi-temporal datasets, vegetation

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