In this study, spatiotemporal changes in Lake Burdur from 1987 to 2011 were evaluated using multi-temporal Landsat TM and ETM+ images. Support Vector Machine (SVM) classification and spectral water indexing, including the Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI) and Automated Water Extraction Index (AWEI), were used for extraction of surface water from image data. The spectral and spatial performance of each classifier was compared using Pearson's r, the Structural Similarity Index Measure (SSIM) and the Root Mean Square Error (RMSE). The accuracies of the SVM and satellitederived indexes were tested using the RMSE. Overall, SVM followed by the MNDWI, NDWI and AWEI yielded the best result among all the techniques in terms of their spectral and spatial quality. Spatiotemporal changes of the lake based on the applied method reveal an intense decreasing trend in surface area between 1987 and 2011, especially from 1987 to 2000, when the lake lost approximately one fifth of its surface area compared to that in 1987. The results show the effectiveness of SVM and MNDWI-based surface water change detection, particularly in identifying changes between specified time intervals.

In this paper, the performance of four different image pan-sharpening methods, the Brovey, the Gram-Schmidt (GS), the Intensity-Hue-Saturation (IHS) and the Principle Component Analysis (PCA), are investigated based on spectral and spatial distortions. In the study, the Brovey, the GS, the IHS, and the PCA pan-sharpening algorithms are applied to multispectral (MS) bands of Ikonos and QuickBird images. The spectral and spatial qualities of pansharpened images are tested using the Correlation Coefficient (CC), the Root Mean Square Error (RMSE), and the Structural Similarity Index (SSIM). A comparative performance analysis of the CC, the RMSE, and the SSIM shows that the PCA followed by the GS, the Brovey, and the IHS perform the best among all the techniques, except a swap in the PCA and the GS in the SSIM.

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This paper presents an image analysis of the Van Erciş earthquake, and demonstrates how efficiently the orthophoto images and point clouds from stereo matching data can be used for automatic detection of buildings and changes in buildings. The proposed method contains three basic steps. The first step is to classify the high-resolution pre and post event RedGreen-Blue (RGB) orthophoto images (orthoRGB) using Support Vector Machine (SVM) classification procedure to extract the building areas. In the second step, normalized Digital Surface Model (nDSM) band derived from point clouds and Digital Terrain Model (DTM) is integrated with the SVM classification (nDSM+orthoRGB). In the last step, building damage assessment is performed through a comparison between two independent classification results from pre-and post-event data. It was observed that using the nDSM band in the classification process as additional bands the accuracy of classification increases significantly.