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Extraction of landslides in the 2016 Kumamoto earthquake using multi-temporal Lidar data

机译:利用多时间激光雷达数据提取2016年熊本地震的山体滑坡

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Extraction of landslides from a pair of Lidar data taken before and after the 2016 Kumamoto, Japan, earthquake was carried out. The spatial correlation coefficient of the two Lidar data was calculated, and the horizontal shift of the April-23 DSM with the maximum correlation coefficient was considered as the crustal movement by the April-16 main-shock. By taking the difference of the co-registered DSMs, the change of the surface elevation was calculated. This elevation change includes many effects due to the earthquake, such as landslides and building collapses, and the other temporal changes, such as parking cars and construction/rescue activities. Thus in this study, only large-scale elevation changes more than plus and minus 2.0 m and the areas of larger than 200 square meters were extracted as possible landslides. The extracted areas were compared with aerial photos taken after the Kumamoto earthquake and other soil movement maps made for this event. The result shows that large-scale landslides were easily extracted by the difference of the DSMs and even ground deformations along surface ruptures, where trees were torn down, could be identified.
机译:从2016年熊本,日本,地震前后采集了一对潮羊里的数据的山体滑坡。计算了两个LIDAR数据的空间相关系数,并且4月23日DSM的水平偏移与最大相关系数被认为是4月16日主冲击的地壳运动。通过取得共同登记的DSM的差异,计算表面高度的变化。这种高程变化包括由于地震,例如山体滑坡和建筑物折叠等许多效果,以及其他时间变化,例如停车车和施工/救援活动。因此,在这项研究中,只有大规模的高度变化超过加上2.0米,并且如可能的滑坡提取大于200平方米的区域。将提取的区域与熊本地震和其他土壤运动图之后拍摄的空中照片进行比较。结果表明,通过DSM的差异且甚至沿着表面破裂的差异差异,可以识别大规模滑坡,可以识别树木的裂缝。

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