利用机载LiDAR的深圳市斜坡类地质灾害危险性评价

Hazard Evaluation of the Slope Based on Airborne LiDAR Data in Shenzhen,China

深圳市是中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,致使广泛分布于人工边坡和自然斜坡的地质灾害风险难以查明。首先,以机载激光雷达(light detection and ranging,LiDAR)获取的深圳市全域高精度数字高程模型为数据源,通过多源遥感识别共建立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,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“关键控制点”危险性阈值等级划分方法。结果表明,基于机载LiDAR技术能够更广泛地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。

Objectives:With the development of Shenzhen city,China,land renovation is more 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)On the basis of highprecision digital elevation model of Shenzhen city obtained by airborne light detection and ranging(LiDAR),about 3500 slope disaster prone points in Shenzhen are obtained through data collection,remote sensing interpretation and field verification.The sample library expanded 330%after proofreading.(2)Taking 3 major factors(8 factors)of terrain,geological structure and human engineering activities into comprehensive consideration,and based on the rainfall-induced disaster mechanism, a rainfall collection factor is proposed, and the weight of evidencemethod is used to complete the geological disaster hazard evaluation model under rainfall-induced conditions.(3) The threshold determination method of“key point control”under the actual background of single disasteris proposed, and the classification of the risk assessment model is completed. Results: The area under curvevalue of receiver operating characteristic curve model reaches 0.903, indicating that the model has a good effecton disaster forecasting. LiDAR technology can improve the identification accuracy of geological hazards in citiesunder dense vegetation coverage. Conclusions: Based on airborne LiDAR data, through a series of means suchas expansion of disaster database, analysis of disaster distribution law, establishment of disaster evaluationfactors, and classification of risk levels, it can form a refined evaluation system for the hazard evaluation of theslope in densely vegetated areas under the influence of the subtropical monsoon climate.