摘要:
湘北常澧山地-丘陵地区地理地质环境复杂,滑坡地质灾害点多、面广、零散、频发,是造成人员伤亡和经济损失最主要的地质灾害类型。InSAR、光学遥感、LiDAR、GIS多源遥感综合技术,是目前可行性高、精度良好的滑坡地灾隐患识别和监测技术方法,能够满足宏观大范围、时效性等要求。该文基于InSAR形变速率数据、多光谱影像和DEM数据对湖南常澧地区的滑坡地灾隐患进行了识别和提取: 首先用2种决策树分类方法对多光谱图像进行了土地利用分类,以便于观察研究区的用地类别及分布情况; 然后运用DEM数据提取了高程、坡度、坡向、起伏度和曲率等5项地形地貌因子对研究区进行了滑坡危险性评价; 再基于SBAS-InSAR技术对研究区进行地表时序微形变测量; 最后在GIS系统内综合危险性评价结果和形变速率对研究区滑坡隐患进行提取和圈定,并基于CART决策树分类结果和研究区水系分布情况,对研究区内除圈定的滑坡隐患点以外的形变速率大于-0.01 m/a的区域进行了危险性推断。本次研究在植被覆盖区和裸露区识别出了数处隐蔽性高、规模小的滑坡隐患,并圈定了滑坡隐患的空间分布范围,面积0.126 km2,证明了技术方法的有效性,具有一定的实践应用价值。
Abstract:
Due to the intricate geographical and geological environment, the mountainous-hilly area of Changli in northern Hunan Province is challenged by numerous, widespread, scattered, and frequent landslide hazards, which constitute the most significant geologic hazard that causes casualties and economic losses. The multi-source remote sensing technology integrating InSAR, optical remote sensing, LiDAR, and GIS is currently a high-feasibility and high-precision landslide hazard identification and monitoring technology, meeting the requirements for macroscale and timeliness. This study identified and extracted landslide hazards in the Changli area based on InSAR deformation rate data, multispectral images, and DEM data. First, two decision tree classification methods were employed to classify the land use types based on multispectral images, facilitating the observation of land use types and their distributions in the Changli area. Then, five topographic factors, including elevation, slope, aspect, undulation, and curvature, were extracted from DEM data to evaluate the landslide risk in the Changli area. Then, five topographic factors, such as elevation, slope, aspect, undulation and curvature, are extracted from DEM data to evaluate the landslide risk in the study area. Furthermore, the time-series surface microdeformation of the Changli area was measured based on SBAS-InSAR technology. Finally, landslide hazards were extracted and delineated in the GIS by combining risk assessment results and deformation rates. Additionally, based on the classification and regression tree (CART) results and the river system distribution in the Changli area, risk inference was conducted on zones with deformation rates exceeding -0.01 m/a except the delineated landslide hazard sites. This study identified several small-scale landslide hazards with high concealment in vegetation-covered and bare zones, delineating their spatial distribution ranges, which covered an area of 0.126 km2. The multi-source remote sensing technology proved effective, demonstrating certain practical application value.
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