A COMPARATIVE STUDY OF POLARIMETRIC AND NON-POLARIMETRIC LIDAR IN DECIDUOUS-CONIFEROUS TREE CLASSIFICATION

As an important active remote sensing tool in forest remote sensing, lidar is able to provide information on tree height, canopy structure, aboveground biomass, among other parameters. It has become desirable to be able to classify tree species using lidar data during recent years. Research has been performed using commercial non-polarimetric lidar in tree species classification, at either dominant species level or individual tree level. The objective of this research is to classify deciduous and coniferous trees using the newly developed polarimetric lidar system. Lidar data from five different tree species were collected in the field. These included ponderosa pine, Austrian pine, blue spruce, green ash and maple. Data were preprocessed and artificial neural network method was developed for classification. Data analysis demonstrated that the classification performance using polarimetric lidar data was far better than that using the non-polarimetric lidar data.