Change-detection analysis using bitemporal satellite imagery is a reliable method for providing and assessing information about flood-induced changes over a wide area in a timely and cost-effective manner. Accurate radiometric normalization between bitemporal imagery is a critical component in the application of change-detection techniques to flood mapping because the accuracy of the change detection is directly affected by the quality of radiometric normalization. A methodology based on multivariate alteration detection (MAD) is introduced as an approach that enables reliable radiometric normalization of bitemporal very high-resolution (VHR) images for detecting flood-induced changes. The method uses a weighting function to adaptively identify weights based on open water features, which are estimated by the normalized difference water index, in the computation of the covariance matrices of the MAD transform. To quantitatively evaluate and test the performance of the proposed method, a comparison is made between it and the iteratively reweighted (IR)-MAD method based on statistical tests and the accuracy of flood change detection. Change vector analysis- and MAD-based change-detection methods were used for the comparison of the proposed and IR-MAD methods. Experimental results on KOMPSAT-2 bitemporal VHR images prove that the proposed method produced better results than the IR-MAD method in the statistical tests and also increased the overall accuracy of flood change detection by 1.8% and 12.6% for the two study sites.
Heat wave is one of the phenomena stemmed from abnormal climate caused by climate change. This phenomenon which occurs strongly and frequently worldwide has been threatening the heath-vulnerable classes in the urban and suburb area. To reduce the damage from the heat wave, the current research attempts to perform data assimilation between highresolution images and ground observation data based on middle infra-red satellite imagery. We use an integrated approach involving compilation of both spatial and non-spatial data from government agencies and institutions, application of spatial and temporal analyses using remote sensing data. The near real-time temperature retrievals of selected areas are performed and analyzed using thermal data from COMS, Landsat, and in-situ data. And, the computational complexity and storage were discussed. Seven major land-use categories (Built-up, Road, Agriculture (green house, paddy fields, and dry fields), Field of construction work, Vegetation (forests), Wasteland and Water bodies) frequently are used in Korea. The four land-uses were selected as the most strongly areas affected by heat waves according to the survey of National Emergency Management Agency. In the future, we will estimate the precise wide area temperature of life space and promote the application of the heat/health watch/warning system.