Characterizing sub-pixel landscape patterns from remotely sensed imagery with sub-pixel mapping methods

Abstract

Landscape pattern is a mixture of natural and human-managed patches that vary in size, shape, arrangement and is the result of complex interactions of physical, biological, and social forces. At present, landscape pattern indices (LPIs) have become increasingly popular for characterizing landscape pattern and they are commonly calculated from land cover maps extracted from remotely sensed imagery. Pixel-based hard classification methods assign each pixel to one class and always suffer from the mixed pixel problem, and make the resultant land cover maps and extracted LPIs inaccuracy. Moreover, as the pixel size of hard classification maps is identical with the resolution of the remotely sensed imagery, spatial patterns of patches which are smaller than the resolution of remotely sensed imagery are usually ignored, and hard classification maps thus can hardly represent the spatial pattern of finer scale land cover classes due to the grain size effect. In general, soft classification techniques can estimate the class composition of land cover classes, however, their output fraction images provide no indication of how such classes are spatially distributed. Sub-pixel mapping (SPM) is a technology to transform the resulting fraction images of soft classification technologies into a finer scale hard classification map, which can solve the mixed pixel problem and eliminate the influence of grain size effect to a certain extent while providing the spatial representation of land cover targets at the sub-pixel scale. In this research, 33 landscape-level LPIs (for all land cover classes altogether) and 28 class-level LPIs (for individual land cover class) were applied to assess the performance of SPM method in characterizing landscape pattern. Ikonos imagery of Dujiangyan (Sichuan Province, China) representing urban landscape pattern and Landsat TM imagery of suburb of Wuhan (Hubei Province, China) representing rural landscape pattern were used in this study. We derived LPIs from both SPM maps and hard classification maps and found that SPM can better depict landscape pattern than hard classification methods at both the landscape level and class level in most cases.