Date of this Version
Remote Sensing of Environment 95 (2005) 517–531; doi:10.1016/j.rse.2005.01.004
In the past decade, lidar (light detection and ranging) has emerged as a powerful tool for remotely sensing forest canopy and stand structure, including the estimation of aboveground biomass and carbon storage. Numerous papers have documented the use of lidar measurements to predict important aspects of forest stand structure, including aboveground biomass. Other papers have documented the ability to transform lidar measurements to approximate common field measures, such as cover, stand height, and vertical distributions of foliage density and light transmittance. However, only a small number of existing works have thoroughly examined relationships between comprehensive assemblages of forest canopy and forest stand structure indices. In this work, canonical correlation analysis of coincident lidar and field datasets in western Oregon and Washington is used to define seven statistically significant pairs of canonical variables, each defining an axis of variation that stand and canopy structure have in common. The first major axis relates mean stand height, and related variables, to aboveground biomass. The second relates canopy cover and volume to leaf area index and stem density. The third relates canopy height variability to mean stem diameter and the basal area of deciduous species. Of the four remaining axes, three are related to contrasts between mature and old-growth stands. Canonical correlation analysis provides a method for ranking the importance of these effects, and for placing both canopy and stand structure indices within the overall covariance structure of the two datasets. In this sense, and for the study area involved, the first three factors (mean height, cover or leaf index area, height variability) represent the same kind of enhancement of lidar data that the tasseled cap indices [Crist, C.P., R.C. Cicone, 1984. A physically-based transformation of thematic mapper data—the TM tasseled cap. IEEE Transactions on Geoscience and Remote Sensing 22, 256–263.] represent for optical remote sensing.