Fuzzy reliability assessment of multi-period land-cover change maps

Lowell, K, Richards, G, Woodgate, P, Jones, S and Buxton, L 2005, 'Fuzzy reliability assessment of multi-period land-cover change maps', Photogrammetric Engineering and Remote Sensing, vol. 71, pp. 939-946.

Document type: Journal Article
Collection: Journal Articles

Title Fuzzy reliability assessment of multi-period land-cover change maps
Author(s) Lowell, K
Richards, G
Woodgate, P
Jones, S
Buxton, L
Year 2005
Journal name Photogrammetric Engineering and Remote Sensing
Volume number 71
Start page 939
End page 946
Total pages 8
Publisher American Society for Photogrammetry and Remote Sensing
Abstract A fuzzy methodology is presented for evaluating the reliability of satellite imagery-derived, continent-wide (Australia) land-cover deforestation/regrowth maps covering the period of 1972 to 2000 in ten discrete time periods. The methodology uses aerial photographs as its reference data and accommodates the difficulty inherent in determining definitively from an aerial photograph, whether a sample point is Forest or Non-forest by permitting interpreters to identify their level of certainty, i.e., Definitely Forest, Probably Forest, Uncertain, Probably Non-forest, or Definitely Nonforest. This information is then cross-tabulated against the Forest/Non-forest classification for the classified image closest in date to the photo date. Information from several photographs is summarized over a larger geographic area and over all time periods. Subsequently, temporal lineage information for each sample pixel is extracted from the 1972 to 2000 series of classified images to determine if a pixel's lineage is Forest Throughout, Non-forest Throughout, Deforestation, Regrowth, or Cyclic. The fuzzy evaluation for individual pixels is then tabulated against this lineage information to identify if pixels of any particular lineage have an elevated tendency to be misclassified. The methodology provides a means by which problems in the map production methodology can be improved as future time slices are added.
Subject Photogrammetry and Remote Sensing
Keyword(s) Uncertainty
multi-temporal image classification
fuzzy assessment
ISSN 0099-1112
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