Local spatial autocorrelation characteristics of remotely sensed imagery assessed with the Getis statistic
To enable data collection by remote sensing instruments the Earth's continuously varying surface is regularized into a grid of consistently sized and shaped pixels. Remotely sensed data, as a result, is often highly spatially autocorrelated. The characterization and quantification of spatial autocorrelation can provide a valuable source of information for both theoretical and applied studies in remote sensing. Consequently, various techniques have been developed to assess the spatial dependence characteristics of remotely sensed imagery. Typically such techniques yield summary measures which enable the identification of distinctive regions of spatial dependency within the image. In contrast, local indicators of spatial association (LISA) measures, focus upon variations within the regions of spatial dependence. This letter provides an introduction to one such LISA measure, the Getis statistic, and indicates how it may be used in remote sensing research and applications as a complement to existing approaches. The Getis statistic provides a measure of spatial dependence for each pixel while also indicating the relative magnitudes of the digital numbers in the neighbourhood of the pixel.