Lidar remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests

Scanning lidar remote sensing systems have recently become available for use in ecological applications. Unlike conventional microwave and optical sensors, lidar sensors directly measure the distribution of vegetation material along the vertical axis and can be used to provide three-dimensional, or volumetric, characterizations of vegetation structure. Ecological applications of scanning lidar have hitherto used one-dimensional indices to characterize canopy height. A novel three-dimensional analysis of lidar waveforms was developed to characterize the total volume and spatial organization of vegetation material and empty space within the forest canopy. These aspects of the physical structure of canopies have been infrequently measured, from either field or remote methods. We applied this analysis to 22 plots in Douglas-fir/western hemlock stands on the west slope of the Cascades Range in Oregon. Each plot had coincident lidar data and field measurements of stand structure. We compared results from the novel analysis to two earlier methods of canopy description. Using the indices of canopy structure from all three methods of description as independent variables in a stepwise multiple regression, we were able to make nonasymptotic predictions of biomass and leaf area index (LAI) over a wide range, up to 1200 Mg ha(-1) of biomass and art LAI of 12, with 90% and 75% of variance explained respectively. Furthermore, we were able to make accurate estimates of other stand structure attributes, including the mean and standard deviation of diameter at breast height, the number of steins greater than 100 cm in diameter and independent estimates of the basal area of Douglas-fir and western hemlock. These measurements can be directly related to indices of forest stand structural complexity, such as those developed for old-growth forest characterisation. Indices of canopy structure developed using the novel, three-dimensional analysis accounted for most of the variables used in predictive equations generated by the stepwise multiple regression. Published by Elsevier Science Inc.