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The overarching aim of this paper is to enhance the visualisations and classifications of airborne remote sensing data for remoteforest surveys. A new open source tool is presented for aligning hyperspectral and full-waveform LiDAR data. The tool producescoloured polygon representations of the scanned areas and aligned metrics from both datasets. Using data provided by NERC ARSF,tree coverage maps are generated and projected into the polygons. The 3D polygon meshes show well-separated structures and aresuitable for direct rendering with commodity 3D-accelerated hardware allowing smooth visualisation. The intensity profile of eachwave sample is accumulated into a 3D discrete density volume building a 3D representation of the scanned area. The 3D volume isthen polygonised using the Marching Cubes algorithm. Further, three user-defined bands from the hyperspectral images are projectedinto the polygon mesh as RGB colours. Regarding the classifications of full-waveform LiDAR data, previous work used extraction ofpoint clouds while this paper introduces a new approach of deriving information from the 3D volume representation and thehyperspectral data. We generate aligned metrics of multiple resolutions, including the standard deviation of the hyperspectral bandsand width of the reflected waveform derived from the volume. Tree coverage maps are then generated using a Bayesian probabilisticmodel and due to the combination of the data, higher accuracy classification results are expected

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

ALIGNMENT OF HYPERSPECTRAL IMAGERY AND FULL-WAVEFORM LIDAR DATA FOR VISUALISATION AND CLASSIFICATION PURPOSES