邓博, 张会, 柏君, 董秀军, 金典琦, 金松燕, 张少标. 利用机载LiDAR的深圳斜坡类地质灾害危险性评价[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220141
引用本文: 邓博, 张会, 柏君, 董秀军, 金典琦, 金松燕, 张少标. 利用机载LiDAR的深圳斜坡类地质灾害危险性评价[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220141
DENG Bo, ZHANG Hui, BAI Jun, DONG Xiujun, JIN Dianqi, JIN Songyan, ZHANG Shaobiao. Hazard evaluation of the slope in Shenzhen based on airborne LiDAR data[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220141
Citation: DENG Bo, ZHANG Hui, BAI Jun, DONG Xiujun, JIN Dianqi, JIN Songyan, ZHANG Shaobiao. Hazard evaluation of the slope in Shenzhen based on airborne LiDAR data[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220141

利用机载LiDAR的深圳斜坡类地质灾害危险性评价

Hazard evaluation of the slope in Shenzhen based on airborne LiDAR data

  • 摘要: 深圳是我国改革开放后兴起的超大城市,经历了快速城市化过程,土地改造频繁,同时受亚热带季风气候影响,辖区雨量充沛,植被覆盖密集,致使难以查明广泛分布于人工边坡和自然斜坡的地质灾害风险。首先,本文以机载LiDAR获取的深圳全域高精度DEM为数据源,通过多源遥感识别共建立深圳市3500多处斜坡类地质灾害典型标记;其次,综合考虑地形、地质构造、人类工程活动3大因素(8个因子),并根据降雨诱发灾害机制提出降雨汇集因子RCF(Rainfall collection factor),运用证据权重法完成了降雨诱发条件下的地质灾害危险性评价;最后,提出了在单体灾害实际背景下的“关键点控制”危险性阈值等级划分方法。结果表明:基于机载LiDAR技术能够更广泛的发现植被层下的不稳定斜坡体,将原有计算样本库数量扩大约330%,且危险性评价模型的受试者工作特征曲线(ROC)检验有效值达0.903,评价结果与实际相符,能够有效评估由降雨诱发的斜坡失稳概率,形成了适用于植被茂密城区的斜坡类地质灾害危险性精细化评价体系。

     

    Abstract: Objectives: With the development of Shenzhen, land renovation is frequent. At the same time, affected by the subtropical monsoon climate, the area under the jurisdiction has abundant rainfall and dense vegetation coverage, making it difficult to identify the hidden dangers of geological hazards widely distributed on artificial slopes and natural slopes. Therefore, it is necessary to develop a set of hazard evaluation system of geological disaster that can solve the unique terrain and climate conditions in Shenzhen, so as to achieve the purpose of preventing disasters in advance and reducing casualties. Methods: (1) we use the high-precision DEM of Shenzhen city obtained by airborne LiDAR as the data source, and Obtained about 3500 slope disaster prone points in Shenzhen through data collection, remote sensing interpretation, and field verification. The sample library expanded to 330% after proofreading;(2) Comprehensive consideration of three major factors (8 factors) of terrain, geological structure and human engineering activities, and based on the rainfall-induced disaster mechanism, a rainfall collection factor RCF (Rainfall collection factor) is proposed, and the weight of evidence method is used to complete the geological disaster hazard evaluation under rainfall-induced conditions.;(3) The threshold determination method of "key point control" under the actual background of single disaster is proposed, and the classification of the risk assessment model is completed. Results: The AUC value of ROC model test reached 0.903, indicating that the model has a good effect on disaster forecasting. LiDAR technology can improve the identification accuracy of geological hazards in cities under dense vegetation coverage. Conclusions: Based on airborne LiDAR data, through a series of means such as expansion of disaster database, analysis of disaster distribution law, establishment of disaster evaluation factors, and classification of risk levels, it can form a refined evaluation system for the hazard evaluation of the slope in densely vegetated areas under the influence of the subtropical monsoon climate in the southeast.

     

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