Mapping Land Cover Changes with Landsat Imagery and Spatio-temporal Geostatistics

Abstract

Satellite images are the principal medium to detect and map changes in the landscape, both in space and time. Current image processing techniques do not fully exploit the data in that they do not take simultaneously into account the spatial and the temporal relations between the various land cover types. The method proposed here aims to accomplish that. At each pixel of the landscape, the time series of land cover type is modeled as a Markov Chain. That time series at any specific location is estimated jointly from the local satellite information, the neighboring ground truth land cover data, and any neighboring previously estimated time series deemed well-informed by the satellite measurements. The method is applied to detect anthropogenic changes in the Pearl River Delta, China. The prediction accuracy of the time series improves significantly, the accuracy almost double, when both spatial and temporal information are considered in the estimation process. The introduction of spatial continuity through indicator kriging also reduced unwanted speckles in the classified images, removing the need for post-processing.