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

Scanning lidar remote sensing systems have recently become available for us e in ecological applications. Unlike conventional microwave and optical sen sors, lidar sensors directly measure the distribution of vegetation materia l along the vertical axis and can be used to provide three-dimensional, or volumetric, characterizations of vegetation structure. Ecological applicati ons of scanning lidar have hitherto used one-dimensional indices to charact erize 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, fro m either field or remote methods. We applied this analysis to 22 plots in D ouglas-fir/western hemlock stands on the west slope of the Cascades Range i n Oregon. Each plot had coincident lidar data and field measurements of sta nd structure. We compared results from the novel analysis to two earlier me thods of canopy description. Using the indices of canopy structure from all three methods of description as independent variables in a stepwise multip le regression, we were able to make nonasymptotic predictions of biomass an d 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. Fur thermore, 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 independe nt estimates of the basal area of Douglas-fir and western hemlock. These me asurements can be directly related to indices of forest stand structural co mplexity, such as those developed for old-growth forest characterisation. I ndices of canopy structure developed using the novel, three-dimensional ana lysis accounted for most of the variables used in predictive equations gene rated by the stepwise multiple regression. Published by Elsevier Science In c.