Comparison of EO-1 Hyperion to AVIRIS for mapping forest composition in the Appalachian Mountains, USA

We used classification and regression trees (CART) to map forest composition with Hyperion and AVIRIS in the Central Appalachian Mountains. Imagery from both sensors exhibited strong topographic effects, with AVIRIS also having a view-angle dependent brightness gradient across the image swath. A DEM-based empirical adjustment to reflectance levels was implemented to reduce apparent topographic effects in the imagery. In general, classification accuracy improved using the topographically normalized imagery, although it is possible that the adjustments to the AVIRIS imagery diminished the superior signal:noise performance of the AVIRIS imagery. Subtle distinctions in forest composition were detectable from both AVIRIS and Hyperion imagery, and despite the superior S:N and spatial resolution of AVIRIS, classification of Hyperion images was as accurate or more accurate than AVIRIS for most species. We therefore demonstrate the utility of Hyperion imagery, but note that further comparisons are still required. In particular, the effects of sensor artifacts (such as striping and "smile") must still be addressed when using Hyperion data.