Measuring individual trees can provide valuable information about forests, and airborne light detection and ranging (LiDAR) sensors have been used recently to identify individual trees and measure structural tree parameters. Past results, however, have been mixed because of reliance on interpolated (image) versions of the LiDAR measurements and search methods that do not adapt to variations in canopies. In this work, an adaptive clustering method is developed using airborne LiDAR data acquired over two distinctly different managed pine forests in North-Central Florida, USA. A crucial issue in isolating individual trees is determining the appropriate size of the moving window (search radius) when locating seed points. The proposed approach works directly on the three-dimensional (3D) ‘cloud’ of LiDAR points and adapts to irregular canopies sizes. The region growing step yields collectively exhaustive sets in an initial segmentation of tree canopies. An agglomerative clustering step is then used to merge clusters that represent parts of whole canopies using locally varying height distribution. The overall tree detection accuracy achieved is 95.1% with no significant bias. The tree detection enables subsequent estimation of tree height and vertical crown length to an accuracy better than 0.8 and 1.5 m, respectively.

Ground-based LIDAR (also known as laser ranging) is a novel technique that may precisely quantify fuelbed characteristics important in determining fire behavior. We measured fuel properties within a south-eastern US longleaf pine woodland at the individual plant and fuelbed scale. Data were collected using a mobile terrestrial LIDAR unit at sub-cm scale for individual fuel types (shrubs) and heterogeneous fuelbed plots. Spatially explicit point-intercept fuel sampling also measured fuelbed heights and volume, while leaf area and biomass measurements of whole and sectioned shrubs were determined from destructive sampling. Volumes obtained by LIDAR and traditional methods showed significant discrepancies. We found that traditional means overestimated volume for shrub fuel types because of variation in leaf area distribution within shrub canopies. LIDAR volume estimates were correlated with biomass and leaf area for individual shrubs when factored by species, size, and plant section. Fuelbed heights were found to be highly variable among the fuel plots, and ground LIDAR was more sensitive to capturing the height variation than traditional point intercept sampling. Ground LIDAR is a promising technology capable of measuring complex surface fuels and fuel characteristics, such as fuel volume.

Improved fire management of savannas and open woodlands requires better understanding of the fundamental connection between fuel heterogeneity, variation in fire behaviour and the influence of fire variation on vegetation feedbacks. In this study, we introduce a novel approach to predicting fire behaviour at the submetre scale, including measurements of forest understorey fuels using ground-based LIDAR (light detection and ranging) coupled with infrared thermography for recording precise fire temperatures. We used ensemble classification and regression trees to examine the relationships between fuel characteristics and fire temperature dynamics. Fire behaviour was best predicted by characterising fuelbed heterogeneity and continuity across multiple plots of similar fire intensity, where impacts from plot-to-plot variation in fuel, fire and weather did not overwhelm the effects of fuels. The individual plot-level results revealed the significance of specific fuel types (e.g. bare soil, pine leaf litter) as well as the spatial configuration of fire. This was the first known study to link the importance of fuelbed continuity and the heterogeneity associated with fuel types to fire behaviour at metre to submetre scales and provides the next step in understanding the complex responses of vegetation to fire behaviour.