Land-cover change detection enhanced with generalized linear models

This paper explores the use of generalized linear models (GLMs) for enhancing standard methods of satellite-based land-cover change detection. It starts by generalizing satellite-based change-detection algorithms in a modelling context and then gives an overview of GLMs. It goes onto describe how GLMs can fit into the context of existing change-detection methods. By way of example, using a change detection over two locations in North Carolina, USA, using Landsat Thematic Mapper data, it shows how the models provide a quantitative approach to image-based change detection. The application of GLMs requires special consideration of the spatial correlation of geographical data and how this effects the use of GLMs. The paper describes the use of preliminary variogram analysis on the image data for initial sampling considerations. For the binary response (change/no-change) derived from the reference data, a 'joint-count' test is used to assess their independence. Finally, the model error term is checked through the empirical variogram of the residuals. It is concluded that GLMs can be helpful in examining different change metrics and useful by applying the resulting model throughout the image to get a probability of change estimate as well as pixel-specific estimates of the variability of change estimate. However, as presented here, this application should respect the assumption of independent response data used for the modelling.