There is an increasing demand for urban vegetation mapping, and airborne laser\ud scanning (ALS) has the unique ability to provide geo-referenced three-dimensional\ud data useful for mapping of surface features. This thesis examines the ability of\ud full-waveform and discrete return ALS point data to distinguish urban surface\ud features, and represent the three-dimensional attributes of vegetation at\ud different scales in a vector-based GIS environment. Two full-waveform datasets,\ud at a wavelength of 1550 nm, and a discrete return dataset, at 1064 nm, are used.\ud Points extracted from the first full-waveform dataset are classified with k-means\ud clustering and decision tree into vegetation, buildings and roads, based on the\ud attributes of individual points and the relationships between neighbouring points.\ud A decision tree is shown to perform significantly better (74.62%) than k-means\ud clustering (51.59%) based on the overall accuracies. Grass and paved areas could\ud be distinguished better using intensity from discrete return data than amplitude\ud from full-waveform data, both values proportional to the strength of the return\ud signal. The differences in the signatures of surfaces could be related to the\ud wavelengths of the lasers, and need to be explored further. Calibration of\ud intensity is currently possible only with full-waveform data. When the decision\ud tree is applied on the second full-waveform dataset, the backscatter coefficient\ud proves to be a more useful attribute than amplitude, pointing to the need for\ud calibration if a classification method using intensity is to be applied on datasets\ud with different scanning geometries. A vector-based approach for delineating tree\ud crowns is developed and implemented at three scales. The first scale provides a\ud good estimation of the tree crown area and structure, suitable for estimating\ud biomass and canopy gaps. The third scale identifies the number of trees and their\ud locations and can be used for modelling individual trees
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