Remote sensing of urban vegetation life form by spectral mixture analysis of high-resolution IKONOS satellite images

This paper evaluates the techniques of linear spectral unmixing (LSU), comparing high- and medium-resolution images for their ability to obtain separate estimates of tree and grassy surfaces in urban areas. It demonstrates that, unlike on medium-resolution images, tree and grassy surfaces each constitute distinct endmembers on high- resolution images. This is because at high resolution, shadows in the urban scene approximate pixel size and therefore can be separately masked, thus avoiding the spectral similarities between shadow and tree canopies on the one hand, and low albedo surfaces on the other. In this study, the ability to mask shadow on IKONOS VHR images removes these spectral overlaps. Spatial autocorrelation, applied to find the characteristic scale lengths of vegetated patches in the study area, demonstrated that at the 4m spatial resolution of IKONOS almost two thirds of pixels would be mixed, and at the 20m resolution of SPOT all pixels would be mixed. Accuracies of the tree and grass fractions were found to be very high in the case of IKONOS, with 87% confidence that both the grass and tree fractions within each pixel were within 10% of the actual amount. The somewhat lower accuracy for SPOT supports previous studies based on medium-resolution sensors, which have noted that trees do not constitute an endmember.