Assimilation of spectral information and temporal history into a statewide woody cover change classification
The Statewide Landcover and Trees Study (SLATS) is a major vegetation monitoring initiative of the Queensland Department of Natural Resources and Water (NRW). SLATS gathers accurate woody vegetation cover and land cover change information for vegetation management planning and compliance, and for greenhouse gas inventory purposes. The SLATS land cover change is produced annually over the state of Queensland and is required to map changes in woody vegetation at a sub-hectare scale. Detailed operational mapping of such a large area requires robust and automated methods wherever possible in the image processing chain. The SLATS woody vegetation change classification relies on the analysis of Landsat TM and ETM+ satellite imagery but this imagery is limited in its ability to reliably detect minor clearing and more subtle changes in woody vegetation such as thinning and drought related death using only two image dates. However, by accessing the SLATS archive of Landsat imagery it is possible to develop change detection algorithms that account for the historical variability of woody cover to improve the classification estimator. This paper describes the development of the classifier, which uses the statewide change classification from previous eras as training data to derive both time series and spectrally based indices. The output from the classifier is a woody change probability and an interpretation image that is then checked by an operator before field validation.
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