Knyazikhin, Y, Schull, M, Hu, L, Mynenil, R, Carmona, PL (2009). CANOPY SPECTRAL INVARIANTS FOR REMOTE SENSING OF CANOPY STRUCTURE. 2009 FIRST WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING, 251-254.
The concept of canopy spectral invariants expresses the observation that simple algebraic combinations of leaf and canopy spectral reflectances become wavelength independent and determine two canopy structure specific variables - the recollision and escape probabilities. The recollision probability (probability that a photon scattered from a phytoelement will interact within the canopy again) is a measure of the multi-level hierarchical structure in a vegetated pixel and can be obtained from hyperspectral data. The escape probability (probability that a scattered photon will escape the vegetation in a given direction) is sensitive to canopy geometrical properties and can be derived from multi-angle spectral data. The escape and recollision probabilities have the potential to separate forest types based on crown shape and the number of hierarchical levels within the landscape. This paper introduces the concept and demonstrates how this approach can be used to monitor forest structural parameters with multi-angle and hyperspectral data.