Forest canopy closure from classification and spectral mixing of scene components: Multi-sensor evaluation of application to an open canopy

Abstract

Three types of remote sensing data, colour infrared aerial photograph (CIR), compact airborne spectrographic imager (CASI) images and airborne visible/infrared imaging spectrometer (AVIRIS) image, have been used in estimating 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 accuracies obtained using the AVIRIS image. Reflectance spectra extracted from the spectral-mode CASI image were used to atmospherically calibrate the raw AVIRIS image and converted it into 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% agreement) when relatively pure reflectance spectra and related statistics are extracted from the image. However, it is more challenging to use the spectral unmixing technique to derive subpixel-scale scene components whose reflectance spectra cannot be directly extracted from the AVIRIS image.