Tree species differentiation using intensity data derived from leaf-on and leaf-off airborne laser scanner data

Tree species identification is important for a variety of natural resource management and monitoring activities including riparian buffer characterization, wildfire risk assessment, biodiversity monitoring, and wildlife habitat assessment. Intensity data recorded for each laser point in a LIDAR system is related to the spectral reflectance of the target material and thus may be useful for differentiating materials and ultimately tree species. The aim of this study is to test if LIDAR intensity data can be used to differentiate tree species. Leaf-off and leaf-on LIDAR data were obtained in the Washington Park Arboretum, Seattle, Washington, USA. Field work was conducted to measure tree locations, tree species and heights, crown base heights, and crown diameters of individual trees for eight broadleaved species and seven coniferous species. LIDAR points from individual trees were identified using the field-measured tree location. Points from adjacent trees within a crown were excluded using a procedure to separate crown overlap. Mean intensity values of laser returns within individual tree crowns were compared between species. We found that the intensity values for different species were related not only to reflective properties of the vegetation, but also to a presence or absence of foliage and the arrangement of foliage and branches within individual tree crowns. The classification results for broadleaved and coniferous species using linear discriminant function with a cross validation suggests that the classification rate was higher using leaf-off data (83.4%) than using leaf-on data (73.1%), with highest (90.6%) when combining these two LIDAR data sets. The result also indicates that different ranges of intensity values between two LIDAR datasets didnʹt affect the result of discriminant functions. Overall results indicate that some species and species groups can be differentiated using LIDAR intensity data and implies the potential of combining two LIDAR datasets for one study.