23 January 2001 Mapping koala habitat and eucalyptus trees: integration and scaling of field and airborne hyperspectral data
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The spatial extent of Australia's forest estate lends itself to the application of an integrated multi-scale approach for the identification of koala habitat extent and condition. A multiple-scale methodology initially identifies forests using AVHRR, TM or predictive modelling to give a regional-state coverage. Then, the application of calibrated geometrical-optical models is used within forests to identify communities and structural properties such as projected crown cover, stem density and biomass. Finally, habitat quality at scale of individual trees is evaluated. This work reports on approaches used to map the location of trees that indicate high habitat quality for koalas and possibly other foliovores such as possums and greater gliders. An approach has been developed for using canopy level field spectrometer measurements to transform hyperspectral data into four components representing within-pixel proportions of target and non-target forest species, grasses and other non-tree components, and shade. A canopy detection routine is then used to produce canopy scale maps of individual species. This approach was tested using an Analytical Spectral Devices spectrometer in conjunction with CASI image data in a mixed coastal eucalypt forest at Koala beach, Pottsville, New South Wales. The maps of target species location were found to be 90.1% accurate when compared to field located species.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Peter Scarth, Stuart R. Phinn, Alex Held, Dave Mitchell, "Mapping koala habitat and eucalyptus trees: integration and scaling of field and airborne hyperspectral data", Proc. SPIE 4171, Remote Sensing for Agriculture, Ecosystems, and Hydrology II, (23 January 2001); doi: 10.1117/12.413946; https://doi.org/10.1117/12.413946


Associative arrays

Chemical elements



Target detection



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