Forest canopy closure from classification and spectral unmixing of scene components - multisensor evaluation of an open canopy

Abstract

Three types of remote sensing data, color infrared aerial photography (CIR), compact airborne spectrographic imager (CASI) imagery, and airborne visible/infrared imaging spectrometer (AVIRIS) imagery, have been used to estimate forest canopy closure for an open-canopy forest environment. The high-spatial-resolution CIR and CASI images were classified to generate forest canopy closure estimates. These estimates were used to validate the forest canopy closure estimation accuracy obtained using the AVIRIS image. Reflectance spectra extracted from the spectral-mode CASI image were used to normalize the raw AVIRIS image to a reflectance image. Classification and spectral unmixing methods have been applied to the AVIRIS image. Results indicate that both the classification and the spectral unmixing methods can produce reasonably accurate estimates of forest canopy closure (within 3 percent agreement) when related statistics are extracted from the AVIRIS image and relatively pure reflectance spectra are extracted from the CASI image. However, it is more challenging to use the spectral unmixing technique to derive subpixel-scale components whose reflectance spectra cannot be directly extracted from the AVIRIS image.