滑坡地质灾害信息监测方法研究

滑坡监测是滑坡地质灾害治理中对滑坡进行实施监控、事故预警的关键技术,是保障工程安全、人民生命和财产安全的重要手段,也为由滑坡引起的地表环境影响评价提供重要的数据支撑。本文在现代滑坡监测技术背景下,以信息化测绘技术为支撑,对滑坡信息化监测的新内涵进行探讨,对滑坡监测中的关键技术与方法、信息管理与反馈机制进行论述与总结,并以某地的滑坡监测为例,对相关技术方法进行检验,证明了其可行性和实用性。     

TLS技术及其在滑坡监测中的应用进展

为总结地面激光扫描(terrestrial laser scanning,TLS)技术的应用特点及关键问题,强调TLS技术在滑坡监测应用中的重要性及优势,介绍了TLS技术的原理和数据处理方法,回顾了TLS技术在国内外滑坡监测领域的应用历史和现状,并将应用进行了分类;总结了TLS技术及其在滑坡监测应用中的关键问题,对该技术的未来发展进行了展望。分析认为,虽然TLS技术还未成为滑坡监测中的常规手段,但毋庸置疑,该技术已为滑坡调查与监测开辟了一条新的途径。     

InSAR技术在滑坡灾害中的应用研究进展

InSAR技术作为重要的对地观测技术之一,已在城市、矿山、地质灾害等地表形变监测领域得到广泛应用与探索,特别是在滑坡灾害形变监测中具有很强的实用性.为全面、准确及深入认识和梳理InSAR技术在滑坡灾害应用中的前沿科学问题、局限性、面临挑战及未来发展趋势,以期更好地服务于滑坡灾害的防治与监测.以InSAR技术滑坡灾害应用...     

GPS及InSAR数据支持下的甘肃黑方台滑坡监测云平台设计与分析

通过对主流云计算平台技术的深入研究和思考,针对滑坡灾害监测数据量大、数据类型多这一特点,设计了基于GPS及InSAR数据的滑坡监测云平台;并以甘肃黑方台滑坡为例,使用ArcGIS对该滑坡进行了风险评估和分析。Hadoop技术的应用明显提高了滑坡监测中海量数据存储和处理的效率,为云计算技术在灾害监测方面的进一步应用进行了有益的探索。     

中国滑坡遥感

我国滑坡遥感已有20多a的历史,作为区域性滑坡宏观调查的主要手段曾为山区大型工程建设的滑坡灾害调查及防灾减灾工作 作出了重要贡献。上世纪末以来,由于采用了“数字滑坡技术”和高分辨率遥感数据,滑坡遥感成为能更准确的定性、定量的调查手 段,甚至可进行大型个体滑坡的详细调查和监测研究。“数字滑坡”技术的实现主要依赖于遥感技术、数字摄影测量及图像处理技术 、GIS技术和计算机技术的支持。该技术大致可分为3大部分： 滑坡基本信息获取、信息存贮和管理及专题服务技术。本文以三峡库 区、四川天台乡滑坡、金龙山滑坡及易贡滑坡遥感调查及监测说明“数字滑坡”技术的专题服务应用。     

舟曲县\

利用存档光学遥感影像对灾前演变情况进行分析是目前常用的方法,但往往受限于获取时间密度、云量等因素。随着雷达遥感卫星数据质量的不断提升,合成孔径雷达干涉测量（interferometric syntheticaperture radar,InSAR）技术可以为滑坡灾前形变探测提供新的技术途径。基于欧洲空间局哨兵一号（Sentinel-1）雷达卫星数据,同时结合升轨与降轨视线向形变结果提取沿坡向与垂直向二维形变,对2018年7月12日甘肃南峪乡滑坡灾前二维形变进行追溯分析。时序结果显示,该滑坡自2017年6月起便已经开始缓慢的变形,至滑坡发生前13个月时最大累积形变量达77 mm。结合降雨量数据对比分析,发现该滑坡灾前变形与降雨量变化高度吻合,说明降雨是该滑坡发生的主要诱因之一。该InSAR追溯结果展示了星载雷达干涉测量技术在滑坡探测方面的应用潜力,为滑坡诱因分析、防灾减灾乃至滑坡监测预警工作提供了新的思路与参考。     

雷达干涉测量对甘肃南峪乡滑坡灾前二维形变追溯

利用存档光学遥感影像对灾前演变情况进行分析是目前常用的方法,但往往受限于获取时间密度、云量等因素。随着雷达遥感卫星数据质量的不断提升,合成孔径雷达干涉测量（interferometric syntheticaperture radar,InSAR）技术可以为滑坡灾前形变探测提供新的技术途径。基于欧洲空间局哨兵一号（Sentinel-1）雷达卫星数据,同时结合升轨与降轨视线向形变结果提取沿坡向与垂直向二维形变,对2018年7月12日甘肃南峪乡滑坡灾前二维形变进行追溯分析。时序结果显示,该滑坡自2017年6月起便已经开始缓慢的变形,至滑坡发生前13个月时最大累积形变量达77 mm。结合降雨量数据对比分析,发现该滑坡灾前变形与降雨量变化高度吻合,说明降雨是该滑坡发生的主要诱因之一。该InSAR追溯结果展示了星载雷达干涉测量技术在滑坡探测方面的应用潜力,为滑坡诱因分析、防灾减灾乃至滑坡监测预警工作提供了新的思路与参考。     

无人机遥感数据支持下滑坡VR场景探索分析方法

滑坡灾害是最常见的地质灾害之一,无人机遥感和虚拟现实（virtual reality,VR）技术的快速发展为滑坡灾害沉浸式模拟与可视化分析提供了重要的数据资源和技术支持。拟重点开展滑坡灾害VR场景动态构建与探索分析研究,探讨了滑坡灾害数据多样化组织、VR场景动态融合表达等关键技术,提出了基于手柄射线的VR场景交互方法,在此基础上进行了原型系统研发与案例试验分析。试验结果表明,所提方法在无人机遥感数据支持下能够动态构建滑坡灾害VR场景,并且能够支持用户沉浸式交互与滑坡灾情信息分析。     

三维激光扫描测量技术在滑坡监测中的应用

介绍了三维激光扫描测量的原理并对其理论测量误差进行了分析。采用三维激光扫描技术对广东省五华县崩岗一滑坡进行了6次观测,分析结果表明,三维激光扫描技术与传统的滑坡监测相比具有速度快、精度高、能实时三维动态显示滑坡变化及滑坡量等优点,在实时动态监测中具有广泛的应用前景。     

地基InSAR技术在门头沟滑坡灾害监测中的应用

InSAR技术在地表监测应用方面被广泛研究,相比传统监测手段有其独特优势。本文通过对门头沟区二斜井地基INSAR滑坡监测的数据采集、处理与分析等过程的论述,介绍了地基InSAR技术在门头沟区二斜井滑坡灾害监测的应用,充分验证了地基INSAR技术在滑坡灾害应急监测应用中的优势,展示了地基InSAR系统在恶劣环境下的较强适应力,肯定了地基InSAR技术监测的应用前景,为今后同类应急监测项目提供一定的参考。     

基于GIS的西攀高速公路沿线滑坡灾害管理

滑坡是西攀高速公路主要的地质灾害之一,本文采用GIS技术建立了西攀高速公路沿线滑坡灾害管理系统,提高了滑坡灾害的管理效率。详细讨论了各种滑坡灾害专题制图和滑坡体三维可视化建模的过程和方法。这些是滑坡治理过程中重要的非工程措施,也为滑坡治理的工程措施的优化提供决策依据。     

Spatial Prediction of Landslide Hazard Using Logistic Regression and ROC Analysis

An empirical modeling of road related and non‐road related landslide hazard for a large geographical area using logistic regression in tandem with signal detection theory is presented. This modeling was developed using geographic information system (GIS) and remote sensing data, and was implemented on the Clearwater National Forest in central Idaho. The approach is based on explicit and quantitative environmental correlations between observed landslide occurrences, climate, parent material, and environmental attributes while the receiver operating characteristic (ROC) curves are used as a measure of performance of a predictive rule. The modeling results suggest that development of two independent models for road related and non‐road related landslide hazard was necessary because spatial prediction and predictor variables were different for these models. The probabilistic models of landslide potential may be used as a decision support tool in forest planning involving the maintenance, obliteration or development of new forest roads in steep mountainous terrain.     

地质灾害GPS实时监测预警系统关键技术探讨

利用GPS形变监测、一机多天线（GMS）及CORS等技术,探讨基于GDCORS的GPS监测数据的实时处理、灾害因子分析、灾害趋势分析等关键技术,用于高精度滑坡形变在线监测研究和应用。通过系统设计与集成,开展地质灾害隐患点滑坡地表形变远程动态监测示范站建设,为地质灾害发生的可能性分析与预报提供科学依据。     

Spatial Prediction of Landslide Hazard Using Fuzzy k-means and Dempster-Shafer Theory

Landslide databases and input parameters used for modeling landslide hazard often contain imprecisions and uncertainties inherent in the decision‐making process. Dealing with imprecision and uncertainty requires techniques that go beyond classical logic. In this paper, methods of fuzzy k‐means classification were used to assign digital terrain attributes to continuous landform classes whereas the Dempster‐Shafer theory of evidence was used to represent and manage imprecise information and to deal with uncertainties. The paper introduces the integration of the fuzzy k‐means classification method and the Dempster‐Shafer theory of evidence to model landslide hazard in roaded and roadless areas illustrated through a case study in the Clearwater National Forest in central Idaho, USA. Sample probabilistic maps of landslide hazard potential and uncertainties are presented. The probabilistic maps are intended to help decision‐making in effective forest management and planning.     

新疆大区域滑坡地质灾害危险性评价研究

文中通过定性和定量相结合的方法,从灾害形成机理孕灾和致灾两个角度选取因子,最后将实际筛选出的高程、坡度、断层、岩性、道路及降雨等因子纳入到新疆滑坡地质灾害危险性评价中,并针对新疆大区域因子量化的问题上给出解决思路,最后将GIS技术和统计学方法相融合构建新疆滑坡地质灾害评价模型,并对其进行评估。实验结果表明,综合GIS技术和统计学方法相融合可以解决大区域地质灾害危险性评价的问题,评价效果较好,具有实践价值。     

Integrating a fuzzy <Emphasis Type="Italic">k</Emphasis>-means classification and a Bayesian approach for spatial prediction of landslide hazard

A robust method for spatial prediction of landslide hazard in roaded and roadless areas of forest is described. The method is based on assigning digital terrain attributes into continuous landform classes. The continuous landform classification is achieved by applying a fuzzy k-means approach to a watershed scale area before the classification is extrapolated to a broader region. The extrapolated fuzzy landform classes and datasets of road-related and non road-related landslides are then combined in a geographic information system (GIS) for the exploration of predictive correlations and model development. In particular, a Bayesian probabilistic modeling approach is illustrated using a case study of the Clearwater National Forest (CNF) in central Idaho, which experienced significant and widespread landslide events in recent years. The computed landslide hazard potential is presented on probabilistic maps for roaded and roadless areas. The maps can be used as a decision support tool in forest planning involving the maintenance, obliteration or development of new forest roads in steep mountainous terrain.     

武汉市三维数字地图系统建设与应用示范

三维数字地图系统建设是武汉市近年来开展科技创新和管理创新、深化"数字城市"建设的一项重要举措。介绍了武汉市三维数字地图的概念、建设思路与内容,探讨了建模单元划分、模型命名、模型细节层次确定以及建模方法选择等关键技术问题,介绍了三维数字地图系统在武汉的应用试点情况。     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.