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Vegetation height is a crucial feature in various applications related to ecological mapping, enhancing the discrimination among different land cover or habitat categories and facilitating a series of environmental tasks, ranging from biodiversity monitoring and assessment to landscape characterization, disaster management and conservation planning. Primary sources of information on vegetation height include in situ measurements and data from active satellite or airborne sensors, which, however, may often be non-affordable or unavailable for certain regions. Alternative approaches on extracting height information from very high resolution (VHR) satellite imagery based on texture analysis, have recently been presented, with promising results. Following the notion that multispectral image bands may often be highly correlated, data transformation and dimensionality reduction techniques are expected to reduce redundant information, and thus, the computational cost of the approaches, without significantly compromising their accuracy. In this paper, dimensionality reduction is performed on a VHR image and textural characteristics are calculated on its reconstructed approximations, to show that their discriminatory capabilities are maintained up to a large degree. Texture analysis is also performed on the projected data to investigate whether the different height categories can be distinguished in a similar way.

A vegetation height classification approach based on texture analysis of a single VHR image