Fusion of Remotely Sensed Multispectral Imagery and Lidar Data for Forest Structure Assessment at the Tree Level

A new feature-level fusion is presented for modelling individual trees by applying watershed segmentation and subsequent classification, using tree heights and tree crown signatures derived from light detection and ranging (lidar) data and multispectral imagery. The study area is part of the Moira State Forest, New South Wales, Australia where the dominant tree species are native eucalypts. In this study, airborne lidar data and four band multispectral imagery were acquired. A digital surface model (DSM) was generated from the lidar first return data and a digital terrain model (DTM) was derived from the lidar last return data. A tree crown model was computed as the difference between DSM and DTM using appropriate height thresholds. A marker-controlled watershed segmentation algorithm was used to extract individual tree crowns from the lidar data. The resulting crown polygons were overlaid on the four band multispectral imagery to extract the spectral signatures of the tree crowns. A principal components transformation was applied to the four-band dataset to replace the highly correlated original bands with those of reduced correlation. In addition, two lidar derived texture and height layers were included in the fusion procedure. The application of the maximum likelihood technique led to a high classification accuracy. An average classification accuracy of 86 percent was achieved and this procedure outperformed the original four-band maximum likelihood classification by 23 percent. The success of the tree crown extraction algorithm in old growth areas was higher than in more juvenile areas where the crowns were more scattered. It was also observed that large crowns were better delineated than small ones. The results indicate that this fusion modelling strategy may prove suitable for estimating and mapping the crown area, height and species of each tree. * Corresponding author.