Shenzhen Key Laboratory of Spatial Smart Sensing and Services and the Key Laboratory for Geo-Environment Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen, Guangdong, China.
Shenzhen Integrated Geotechnical Investigation and Surveying Co., Ltd., Shenzhen, China.
Sci Rep. 2017 Mar 3;7:43351. doi: 10.1038/srep43351.
The time-series topography change of a landfill site before its failure has rarely been surveyed in detail. However, this information is important for both landfill management and early warning of landslides. Here, we take the 2015 Shenzhen landslide as an example, and we use the radar shape-from-shading (SFS) technique to retrieve time-series digital elevation models of the landfill. The results suggest that the total filling volume reached 4,074,300 m in the one and a half years before the landslide, while 2,817,400 m slid down in the accident. Meanwhile, the landfill rate in most areas exceeded 2 m/month, which is the empirical upper threshold in landfill engineering. Using topography captured on December 12, 2015, the slope safety analysis gives a factor of safety of 0.932, suggesting that this slope was already hazardous before the landslide. We conclude that the synthetic aperture radar (SAR) SFS technique has the potential to contribute to landfill failure monitoring.
垃圾填埋场在发生失稳破坏前的时间序列地形变化很少被详细探测到。然而,这些信息对于填埋场管理和滑坡预警都很重要。在这里,我们以 2015 年深圳滑坡为例,利用雷达阴影构形(SFS)技术获取了垃圾填埋场的时间序列数字高程模型。结果表明,在滑坡发生前的一年半时间里,总填埋量达到了 4074300 立方米,而在事故中有 2817400 立方米滑下。同时,大部分地区的填埋率超过了 2 米/月,这是填埋工程中的经验上限。利用 2015 年 12 月 12 日获取的地形,可以对边坡安全进行分析,得出安全系数为 0.932,表明在滑坡发生前,该边坡已经处于危险状态。我们得出结论,合成孔径雷达(SAR)SFS 技术有可能有助于监测垃圾填埋场的失稳破坏。