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The 2005 northern Pakistan earthquake (magnitude 7.6) of 8 October 2005 occurred in the northwestern part of the Himalayas. We interpreted landslides triggered by the earthquake using black-and-white 2.5-m-resolution System Pour l'Observation de la Terre 5 (SPOT 5) stereo images. As a result, the counts of 2,424 landslides were identified in the study area of 55 by 51 km. About 79% or 1,925 of the landslides were small (less than 0.5 ha in area), whereas 207 of the landslides (about 9%) were large (1 ha and more in area). Judging from our field survey, most of the small landslides are shallow rock falls and slides. However, the resolution and whitish image in the photos prevented interpreting the movement type and geomorphologic features of the landslide sites in detail. It is known that this earthquake took place along preexisting active reverse faults. The landslide distribution was mapped and superimposed on the crustal deformation detected by the environmental satellite/synthetic aperture radar (SAR) data, active faults map, geological map, and shuttle radar topography mission data. The landslide distribution showed the following characteristics: (1) Most of the landslides occurred on the hanging-wall side of the Balakot-Garhi fault; (2) greater than one third of the landslides occurred within 1 km from the active fault; (3) the greatest number of landslides (1,147 counts), landslide density (3.2 counts/km 2 ), and landslide area ratio (2.3 ha/km 2 ) was found within Miocene sandstone and siltstone, Precambrian schist and quartzite, and Eocene and Paleocene limestone and shale, respectively; (4) there was a slight trend that large landslides occurred on vertically convex slopes rather than on concave slopes; furthermore, large landslides occurred on steeper (30°and more) slopes than on gentler slopes; (5) many large landslides occurred on slopes facing S and SW directions, which is consistent with SAR-detected horizontal dominant direction of crustal deformation on the hanging wall.

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Polygon-based terrain classification data were created globally using 280 m digital elevation models (DEMs) interpolated from the multi-error-removed improved-terrain DEM (MERIT DEM). First, area segmentation was performed globally with the logarithmic value of slope gradient and the local convexity calculated from the DEM. Next, by adding surface texture, k-means clustering was performed globally and the polygons were grouped into 40 clusters. Then, we tried to reclassify these 40 clusters into geomorphologic terrain groups. In this study, we attempted reclassification and grouping using local information from Japan as a test case. The 40 clusters were compared with Japanese geological and geomorphological data and were then reclassified into 12 groups that had different geomorphological and geological characteristics. In addition, large shape landforms, mountains, and hills were subdivided by using the combined texture. Finally, 15 groups were created as terrain groups. Cross tabulations were performed with geological or lithological maps of California and Australia in order to investigate if the Japanese grouping of the clusters was also meaningful for other regions. The classification is improved from previous studies that used 1-km DEMs, especially for the representation of terrace shapes and landform elements smaller than 1 km. The results were generally suitable for distinguishing bedrock mountains, hills, large highland slopes, intermediate landforms (plateaus, terraces, large lowland slopes), and plains. On the other hand, the cross tabulations indicate that in the case of gentler landforms under different geologic provinces/climates, similar topographies may not always indicate similar formative mechanisms and lithology. This may be solved by locally replacing the legend; however, care is necessary for mixed areas where both depositional and erosional gentle plains exist. Moreover, the limit of the description of geometric signatures still appears in failure to detect narrow valley bottom plains, metropolitan areas, and slight rises in gentle plains. Therefore, both global and local perspectives regarding geologic province and climate are necessary for better grouping of the clusters, and additional parameters or higher resolution DEMs are necessary. Successful classification of terrain types of geomorphology may lead to a better understanding of terrain susceptibility to natural hazards and land development.

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