Potential effects in multi-resolution post-classification change detection

Change detection is one of the primary applications of remote-sensing data, and many techniques have been developed during the past three decades. Although frequently criticized and despite many alternatives, due to its simplicity and intuitive manner, post-classification change detection still remains one of the most applied techniques. Many studies in the field of change detection analysis acknowledge, for instance, the impact of misregistration, inconsistencies in classification schemes or differences in methods for image interpretation. However, there are additional, rarely studied influences that can cause large errors in change detection results, including integrating multi-resolution data, the adjacency effect and the minimum mapping units (MMUs) that are applied to the classification before change detection. This study demonstrates these effects for the complex land cover of the Alvarado mangrove area at the Mexican Gulf Coast, employing 10 m Système Pour l'Observation de la Terre 5 (SPOT-5) high geometric resolution (HRG)‐based and 57 m Landsat Multispectral Scanner (MSS) classifications. As a baseline, the proportion of the fine spatial resolution classes within the coarse spatial resolution cells were derived, from which proportional change matrices were computed.