FOREST CANOPY CLOSURE FROM CLASSIFICATION AND SPECTRAL UNMIXING OF SCENE COMPONENTS - MULTISENSOR EVALUATION OF AN OPEN CANOPY

Three types of remote sensing data, color infrared aerial photography (CIR), compact airborne spectrographic imager (CASI) imagery, and airb orne visible/infrared imaging spectrometer (AVIRIS) imagery, have been used to estimate forest canopy closure for an open-canopy forest envi ronment. The high-spatial-resolution CIR and CASI images were classifi ed to generate forest canopy closure estimates. These estimates were u sed to validate the forest canopy closure estimation accuracy obtained using the AVIRIS image. Reflectance spectra extracted from the spectr al-mode CASI image were used to normalize the raw AVIRIS image to a re flectance image. Classification and spectral unmixing methods have bee n applied to the AVIRIS image. Results indicate that both the classifi cation and the spectral unmixing methods can produce reasonably accura te estimates of forest canopy closure (within 3 percent agreement) whe n related statistics are extracted from the AVIRIS image and relativel y pure reflectance spectra are extracted from the CASI image. However, it is more challenging to use the spectral unmixing technique to deri ve subpixel-scale components whose reflectance spectra cannot be direc tly extracted from the AVIRIS image.