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摘要: 地质灾害识别是灾害易发性评价以及监测预警的基础，采用传统的人工地面调查和卫星遥感方式在地形条件复杂的高植被覆盖山区进行地质灾害识别具有较大困难。机载激光雷达技术(light detection and ranging, LiDAR)的发展为高植被复杂山区的地质灾害识别提供了新的解决方案。利用获取的机载LiDAR点云数据，通过点云滤波、空间插值生成了高分辨率数字高程模型(digital elevation model, DEM)，结合天空视域因子(sky view factor，SVF)的DEM可视化方法，开展了中国四川省丹巴县城周边135 km2的地质灾害识别研究工作。共解译出地质灾害146处，总面积约46.48 km2，占研究区总面积的33.4%，并通过现场实地调查验证了机载LiDAR识别结果的可靠性。在此基础上分析了该区地质灾害的空间分布规律及其影响因素。研究结果为高植被复杂山区的地质灾害识别提供了一定参考，并为丹巴县地质灾害防治与风险评价提供数据支撑。Abstract:Objectives Geohazard recognition and inventory mapping are the basis for geohazard susceptibility mapping, monitoring and early warning.However, it has been challenging to implement geohazard recognition and inventory mapping in mountainous areas with complex topography and vegetation cover though manual field survey or satellite remote sensing.Progress in the light detection and ranging(LiDAR) technology provides a new possibility for geohazard recognition in such areas.Methods A high-resolution digital elevation model (DEM) was generated through the LiDAR point filtering and spatial interpolation, and combined with the DEM visualization method of sky view factor(SVF). Subsequently, the geohazard recognition work of Danba County and its surrounding area with a total area of 135 km2 was carried out.Results A total of 146 geohazards are remotely mapped and classified as slides, rock fall, debris flows based on morphologic characteristics, it shows nearly one-third of the study area is dominated by geohazards. Field validation indicate the success rate of LiDAR-derived DEM in recognition and mapping landslides with higher precision and accuracy. On the basis of this, the spatial distribution characteristics and influencing factors of the geohazards were analyzed and it indicate these mapped geohazards lie along both sides of the river, and their spatial distributions are related highly to human engineering activities, such as road excavation and slope cutting.Conclusions The research results provide references for geohazard recognition and mapping in mountainous areas with complex topography and vegetation cover and provide data support for the geohazard prevention and risk assessment for Danba county.
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图 1 研究区入库地质灾害发育分布图
Figure 1. Geohazards Distribution Map of Study Area Based on Field Investigation
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图 3 不同DEM可视化方法对滑坡特征呈现的差异
Figure 3. Differences of DEM Visualization Methods on Showing Landslide Characteristics
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图 5 地质灾害地貌特征及解译结果示例
Figure 5. Geohazard Morphological Characteristics in SVF Map and Interpreted Results for a Sample Area
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图 6 研究区地质灾害机载LiDAR识别分布图
Figure 6. LiDAR Based Geohazard Inventory Map of the Study Area
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图 7 滑坡、崩塌、泥石流的二维、三维展示图
Figure 7. Two- and Three-Dimensional Examples of Landslide, Rock Fall, Debris Flow
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图 8 滑坡L01的LiDAR影像及现场复核照片
Figure 8. LiDAR and Field Investigation Result of Landslide L01
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图 9 滑坡L02的LiDAR影像及现场复核照片
Figure 9. LiDAR and Field Investigation Result ofLandslide L02
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