Potential effects in multi-resolution post-classification change detection

Abstract

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. The analysis employs difference measures to compare change matrices and proportional maps. The impact of various tested resampling functions was negligible if coarse resolution data were refined. For coarsening fine spatial resolution data, change matrix comparison was comparatively small but yielded differences of approximately 20% in spatially explicit measures. Incorrect positional alignment indicated differences of up to 5% in the change matrix for a misregistration of 100 m and even higher spatially explicit differences (28%). The discrepancies due to the adjacency effect were rather low. MMUs of 25 ha resulted in differences of up to 36% in the change matrix. The magnitude of the discrepancies of all studied effects depends on the class diversity in the map, and some can also be related to the difference in spatial resolution.

Acknowledgements

We thank the anonymous reviewer for thoughtful and detailed comments that substantially improved our article. We also acknowledge our colleagues, Abigail Uribe Martínez, Arturo Torres Vázquez, Carlos Troche Souza, Daniel Ocaña Nava, Margarita Ascención Merino and Sergio Cerdeira Estrada (CONABIO), who contributed to the project ‘Los manglares de México: Estado actual y establecimiento de un programa de monitoreo a largo plazo’ in the field of remote sensing and GIS.

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