LAND-COVER MAPS USING MULTIPLE CLASSIFIER SYSTEM FOR POST-DISASTER LANDSCAPE MONITORING

Abstract. Recently, land cover maps created from high resolution satellite images have been used for landscape analysis, in order to understand the impact of natural disasters on biodiversity and ecosystems. Conventional land cover classification methods, however, suffer from problems with isolated pixels (salt and pepper effect). Filtering can remove the isolated pixels, but can also result in loss of accurate information. The purpose of this study is to create a land cover map for landscape analysis of large-scale disturbances caused by the Great East Japan Earthquake of 2011, utilizing a Multiple Classifier System (MCS), which allows for reduction of isolated pixels while maintaining classification accuracy. RapidEye satellite images covering the Pacific Ocean side of the Tohoku district damaged by the earthquake and subsequent tsunami were obtained for 2010, 2011, 2012 and 2016, and land cover classification was implemented using individual classifiers and the MCS method. The results showed that the MCS land cover map was able to reduce the number of isolated pixels significantly (61-71%) compared with the individual classifiers, while maintaining very high accuracy (0.976-0.986) for all four years. These results indicate that MCS land cover maps have a great potential for analyzing disturbances following infrequent largescale natural disasters such as earthquakes and tsunami, and for monitoring the process of recovery afterwards. We expect that the results of this research will be useful in managing the recovery process in the region disturbed by the Great Eastern Japan Earthquake and Tsunami of 2011, and also for developing future Ecosystem-based Disaster Risk Reduction programs for the region.