Using airborne and ground-based ranging lidar to measure canopy structure in Australian forests

Airborne and ground-based lidars are useful tools to probe the structure of forest canopies. Such information is not readily available from other remote sensing methods but is essential for modern forest inventory in which growth models and ecological assessment are becoming increasingly important. This study was undertaken to investigate the capacity of current airborne and ground-based ranging systems to provide data from which useful forest inventory parameters can be derived. Additional data collected included standard forest inventory, hemispherical photography, and optical point-quadrat sampling. Four contrasting study sites were established within an existing study area in the Bago and Maragle State Forests, New South Wales, Australia. A simple and standard set of models was fitted to the data to establish consistency between methods and current practice. Methods to reduce the bias induced by interaction of the size of the airborne laser scanner (ALS) footprint and thresholding used in ranging systems are demonstrated by the use of first and last returns and the intensity of the returns. A measure analogous to predominant height was calculated from an average of a number of the highest ALS returns within an area. This estimate agreed with field measured predominant heights within the uncertainty of the measurements. Data from a ground-based scanning rangefinder system were used to model leaf area index (LAI). These LAI estimates coincided with those from hemispherical canopy photographs. The validation work presented in this paper justifies further development of the instrumentation and analyses to combine results from multi-angular systems with data from airborne systems to alleviate some of the problems associated with the vertical view. Current laser ranging systems can be used to derive canopy structural parameters such as height, cover, and foliage profile provided information based on multiple returns or the intensity of returns is used to minimize the bias induced by the size of the footprint and the detection threshold.