| 结合SBAS-InSAR技术及信息熵的苍山地质滑坡隐患识别 |
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| 引用本文： | 朱智富,甘淑,张荐铭,袁希平,王睿博,张晓伦.结合SBAS-InSAR技术及信息熵的苍山地质滑坡隐患识别[J].测绘通报,2022,0(11):13-19. |
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| 作者姓名： | 朱智富 甘淑 张荐铭 袁希平 王睿博 张晓伦 |
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| 作者单位： | 1. 昆明理工大学国土资源工程学院, 云南 昆明 650093;2. 云南省高校高原山地空间信息测绘技术应用工程研究中心, 云南 昆明 650093;3. 滇西应用技术大学云南省高校山地实景点云数据处理及应用重点实验室, 云南 大理 671006 |
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| 基金项目： | 国家自然科学基金(41861054) |
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| 摘 要： | 针对西南地区滑坡隐患高位隐蔽,传统技术难以全面识别的问题,本文以大理苍山为研究对象,首先利用SBAS-InSAR技术对苍山2019年1月-2021年4月间的滑坡隐患进行识别;然后结合随机概率信息熵模型,对不同坡度等级与边坡稳定性之间的关联性进行定量分析;最后根据典型隐患区的遥感影像以及采样点的形变时序图,探讨了边坡形变时空演化特征及沉降诱因。试验结果表明:①2019年1月-2021年4月,研究区的形变速率为-155.6～92.4mm/a,13个超过-30mm/a的不稳定滑坡隐患被识别;②坡度等级为Ⅳ、Ⅴ级时,信息熵大于0.8,边坡稳定性较弱,不均匀形变严重,与已有研究保持高度一致,证实了该模型的可靠性;③典型隐患区形变趋势呈明显的季节性变化,降雨和冰雪消融是导致边坡失稳的主要因素。
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| 关 键 词： | 苍山 SBAS-InSAR技术 结合 信息熵 滑坡隐患识别 |
| 收稿时间： | 2021-12-01 |
| 修稿时间： | 2022-07-22 |
Identification of geological potential landslides in Cang Mountain by combining SBAS-InSAR technique and information entropy |
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| ZHU Zhifu,GAN Shu,ZHANG Jianming,YUAN Xiping,WANG Ruibo,ZHANG Xiaolun.Identification of geological potential landslides in Cang Mountain by combining SBAS-InSAR technique and information entropy[J].Bulletin of Surveying and Mapping,2022,0(11):13-19. |
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| Authors: | ZHU Zhifu GAN Shu ZHANG Jianming YUAN Xiping WANG Ruibo ZHANG Xiaolun |
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| Institution: | 1. Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China;2. Application Engineering Research Center of Plateau and Mountainous Spatial Information Surveying and Mapping Technology in Yunnan Universities, Kunming 650093, China;3. Key Laboratory of Cloud Data Processing and Application of Mountain Scenic Spot in Yunnan Universities, West Yunnan University of Applied Technology, Dali 671006, China |
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| Abstract: | In response to the problem of high hidden landslide hazards in southwest China, it is difficult to identify the problem comprehensively by traditional technology. This paper took Dali Cang Mountain as the research object and used SBAS-InSAR technique to identify landslide potential in Cang Mountain between January 2019 and ation between different slope grades and slope stability. Finally, based on the remote sensing images of typical potential landslide areas and the deformation time series maps of sampling points, the spatial and temporal evolution trends of slope stability and deformation inducing factors are discussed. The experimental results show that ① The deformation rate in the study area is -155.6 to 92.4mm/a during January 2019 to April 2021, and 13 unstable potential landslides exceeding -30mm/a are identified. ② The information entropy is greater than 0.8 when the grade of slope is Ⅳ,Ⅴ, the slope stability is weak and the uneven deformation is serious, which maintains high consistency with the existing literature conclusions, confirming the reliability of the model. ③ The deformation trend of typical potential landslide area shows obvious seasonal changes, and rainfall and snow and ice melt are the main factors leading to slope instability. |
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| Keywords: | Cang Mountain SBAS-InSAR technology combination information entropy identification of potential landslides |
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