Integrating spectral, spatial, and terrain variables for forest ecosystem classification
Photogrammetric Engineering and Remote Sensing
Sets of spectral, spectral-spatial, textural, and geomor- phometric variables derived fram high spatial resolution Compact Airborne Spectrographic Imager (CASI)and elevation
data are tested to determine their ability to discriminate landscape-scale forest ecosystem classes for a study area in northern Ontario, Canada. First, linear discriminant analysis
for various spectral and spectral-spatial variables indicated that a spatial resolution of approximately 6 m was optimal
for discriminating six landscape-scaleforest ecosystem classes. Second, texture features, using second-order spatial statistics, significantly improved discrimination of the classes over the originalreflectance data. Finally, addition of terrain descriptors improved discrimination of the six forest ecosystem classes. It has been demonstrated that, in a low- to moderate-relief boreal environment,addition of textural and terrain variables to high- resolution CASI reflectance data provides improved discrim- ination of forest ecosystem classes.
Treitz, P. and Howarth, P., "Integrating spectral, spatial, and terrain variables for forest ecosystem classification" (2000). Aspen Bibliography. Paper 744.