Urban environments are particularly challenging to classify and map using remote sensing techniques as they are geometrically, texturally, and spectrally complex. Compared to other remote sensing methods for mapping vegetation (e.g. statistical approximations, supervised classifications and Machine Learning / AI based two-dimensional classifiers), LiDAR explicitly measures the location of trees in three dimensions and therefore has the potential to generate more accurate measurements of the proportion and spatial distribution of tree canopy coverage across urban landscapes. This survey technology, and the information derived from it, has become a critical asset for strategic environmental planning within local government organisations.
