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1.
焦学军 《测绘与空间地理信息》2013,(10):198-201,204
采用3维激光扫描技术快速采集滑坡体地形点云信息,并提取滑坡体微地貌信息,为滑坡监测提供基础技术支持。3维激光扫描技术的数据处理主要包括外业数据采集、点云数据配准、地貌数据获取与非地貌点云数据过滤、地形图生成等过程,并着重介绍其工作原理与处理方法。采用3维激光扫描技术能够高效准确实时的监测地质灾害,对预防灾害的发生提供了决策作用。 相似文献
2.
Evaluation of Lidar and Medium Scale Photogrammetry for Detecting Soft-Cliff Coastal Change 总被引：3，自引：0，他引：3
Lidar and photogrammetry have both been evaluated for detecting shortterm coastal change using the Black Ven mudslide, Dorset as a case study. A lidar-generated digital elevation model (DEM) was obtained and initially compared with a DEM generated using available 1:7500 scale aerial photography and automated digital photogrammetry. The quality of these two data sets was assessed using a third DEM, derived using a total station and conventional ground survey methods. The vertical accuracies (rms error) of the lidar and photogrammetry were 0.26m and 0.43m respectively, although both data sets displayed a tendency to generate heights slightly lower than the elevation of the terrain surface. The quality of the two data sets was then assessed with respect to local slope angle. The accuracy of photogrammetrically derived elevations varied with slope and more so than in the case of lidar
From these basic tests, lidar has proved to be more accurate than photogrammetry for soft-cliff. monitoring. Further research is required to establish whether this trend is applicable to other data sets and specifically for photogrammetric data acquired using larger scale imagery 相似文献
From these basic tests, lidar has proved to be more accurate than photogrammetry for soft-cliff. monitoring. Further research is required to establish whether this trend is applicable to other data sets and specifically for photogrammetric data acquired using larger scale imagery 相似文献
3.
山体滑坡对人类安全造成严重影响,准确识别滑坡变形对预防滑坡灾害具有重要意义。利用SBAS-InSAR技术可以进行空间连续地表变形监测,但无法精确获取滑坡边界的变化。为了综合监测滑坡,本文首先采用SBAS-InSAR技术与无人机影像结合的滑坡变形监测方法,利用2018年1月1日—2020年12月24日,共计80幅升轨Sentinel-1A SAR影像,进行了VV极化和VH极化数据处理;然后通过SBAS-InSAR技术获取滑坡区地表雷达视线(LOS)方向变形速率,选取了若干变形点进行滑坡体变形时序分析;最后采用无人机获取滑坡影像并提取滑坡边界,分析了滑坡边界的变形。试验结果表明,利用SBAS-InSAR技术获取的滑坡变形和无人机获取的滑坡变形趋势基本吻合,通过该方法可以获取滑坡的综合变形情况,对滑坡活动性的判断具有重要意义。 相似文献
4.
Application of drone for landslide mapping,dimension estimation and its 3D reconstruction 总被引：2，自引：0，他引：2
Sharad Kumar Gupta Dericks P. Shukla 《Journal of the Indian Society of Remote Sensing》2018,46(6):903-914
Dimension estimation of landslides is a major challenge while preparing the landslide inventory map, for which very high-resolution satellite data/aerial photography is required, which is very expensive. An alternative is the application of drones for such mapping. This study presents the utility of drone/unmanned aerial vehicle (UAV) for mapping and 3D reconstruction of two landslides near IIT Mandi, Himachal Pradesh. In this study, we used DJI Phantom 3 Advanced drone to collect high-resolution images of landslides. Features in the images were automatically detected, described, and matched among photographs using scale invariant feature transform (SIFT) technique. The 3D position and orientation of the cameras and the XYZ location of each feature in the photographs was estimated using bundle block adjustment. This resulted in sparse 3D point cloud, which was densified using Clustering View for Multi-View Stereo (CMVS) algorithm. Finally, surface reconstruction was done using Poisson Surface Reconstruction method, which was visualised in open source software CloudCompare. The 3D model, generated from drone images, was validated using field measurements of some objects, and 3D surface created from total station. Various quantities i.e. width (length), height and perimeter were measured in the 3D drone model and verified with total station data. The differences among all the measured quantities for both the landslides are less than 5% keeping the measurements of total station as reference. The 3D reconstructed from the sets of photographs is very accurate giving the measurements up to cm level and even small objects could be easily identified. In addition, digital elevation model (DEM) of sub meter resolution could be easily generated and used for various applications. Hence drone-based imagery in combination with 3D scene reconstruction algorithms provide flexible and effective tools to map and monitor landslide apart from accurately assessing the dimensions of the landslides. 相似文献
5.
滑坡变形的产生是坡体自身地质条件和外部诱发条件共同作用的结果,滑坡变形定量预测是滑坡监测预警的关键。传统的基于滑坡累计位移-时间曲线分析滑坡变形的方法,忽略了滑坡变形演化的影响因素,难以对滑坡变形进行准确预测。三峡库区滑坡研究多集中在滑坡时空分布特征和滑坡整体稳定性分析方面,亟需开展单体滑坡综合变形分析。以三峡库区白水河滑坡为例,基于滑坡宏观变形和位移监测数据,利用重标方差（rescaled variance statistic,V/S）分析法对滑坡整体和局部变形趋势进行分析,进而构建考虑库水位波动和降雨滞后性影响因素的可有效利用长期依赖信息的长短记忆（long short-term memory,LSTM）神经网络模型,定量预测滑坡位移。研究结果表明,滑坡体属牵引式滑坡,北东部稳定性较差,西部和后缘相对稳定,预测值的均方根误差为8.95 mm,证明该模型是一种高性能的滑坡变形分析方法。 相似文献
6.
数据处理是滑坡变形监测的一项重要工作,目前应用的滑坡变形监测数据处理方法很多。针对一些方法的不足之处,文中介绍了构造断面分析法处理滑坡体变形监测数据的原理和方法,通过举证在积石峡水电站I^#滑坡体变形监测数据处理中的应用,证实该方法的可行性。 相似文献
7.
滑坡是人类面临的主要地质灾害之一,而滑坡监测是减少滑坡灾害的有效方法之一,其中外观监测具有能监测滑坡体的运动特征等优势,在滑坡监测系统中占据了重要的地位。从滑坡监测控制网布设、监测点布置以及观测频率和周期的确定3个方面,结合滑坡的等级、变形阶段、滑坡类型以及变形特征等方面进行针对性布设实施,使其滑坡监测不仅经济合理且同时能较好地反映滑坡变形趋势,为后期预测预报分析提供可靠数据支撑。 相似文献
8.
李建 《测绘与空间地理信息》2020,(2):216-218
近年来,国内外山区滑坡地质灾害频发,传统调查手段受客观条件影响而作用有限。本文介绍了无人机低空航摄技术的基本原理,总结了利用无人机航摄技术进行山区滑坡灾害调查的基础流程,以川东北某石膏矿矿区为例开展了山区无人机航摄调查应用分析,结果表明无人机低空航摄技术在保障地灾调查精度的基础上极大地提高了工作效率,弥补了传统地面调查方法易"重局部、轻整体"的缺陷。 相似文献
9.
高分三号卫星全极化SAR影像九寨沟地震滑坡普查 总被引：1，自引：1，他引：0
基于光学遥感影像的区域滑坡普查易受云雾天气的影响,存在滑坡体调查不全面的问题,无法满足震后应急调查与恢复重建的需求。本文提出了一种极化SAR卫星数据滑坡普查方法,采用高分三号全极化SAR卫星影像数据,以九寨沟地震震区为实验区,在深入分析滑坡体和其他地物类型散射特征的基础上,融合极化特征、纹理特征和地形特征等多维特征信息,结合高分二号影像获取的训练样本,构建基于BP神经网络的全极化SAR数据滑坡自动识别模型,实现滑坡体的自动快速识别。与高分辨率光学影像与无人机航空影像目视解译结果相比较,总体识别精度为92.8%,Kappa系数为0.715,识别准确度满足地震应急实际应用的需求。研究成果可用于震区大区域滑坡体的普查,为后续开展无人机高分辨率影像滑坡体详查、灾后应急与景区恢复提供辅助信息支撑,并促进国产高分SAR卫星数据在防震减灾中的应用。 相似文献
10.
Landslide susceptibility mapping of a catchment area using frequency ratio,fuzzy logic and multivariate logistic regression approaches 总被引：30，自引：0，他引：30
B. Pradhan 《Journal of the Indian Society of Remote Sensing》2010,38(2):301-320
Geospatial database creation for landslide susceptibility mapping is often an almost inhibitive activity. This has been the
reason that for quite some time landslide susceptibility analysis was modelled on the basis of spatially related factors.
This paper presents the use of frequency ratio, fuzzy logic and multivariate regression models for landslide susceptibility
mapping on Cameron catchment area, Malaysia, using a Geographic Information System (GIS) and remote sensing data. Landslide
locations were identified in the study area from the interpretation of aerial photographs, high resolution satellite images,
inventory reports and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed
into a spatial database using GIS and image processing tools. There were nine factors considered for landslide susceptibility
mapping and the frequency ratio coefficient for each factor was computed. The factors chosen that influence landslide occurrence
were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database;
lithology and distance from lineament, taken from the geologic database; land cover from TM satellite image; the vegetation
index value from Landsat satellite images; and precipitation distribution from meteorological data. Using these factors the
fuzzy membership values were calculated. Then fuzzy operators were applied to the fuzzy membership values for landslide susceptibility
mapping. Further, multivariate logistic regression model was applied for the landslide susceptibility. Finally, the results
of the analyses were verified using the landslide location data and compared with the frequency ratio, fuzzy logic and multivariate
logistic regression models. The validation results showed that the frequency ratio model (accuracy is 89%) is better in prediction
than fuzzy logic (accuracy is 84%) and logistic regression (accuracy is 85%) models. Results show that, among the fuzzy operators,
in the case with “gamma” operator (λ = 0.9) showed the best accuracy (84%) while the case with “or” operator showed the worst
accuracy (69%). 相似文献
11.
针对当前滑坡稳定性评价方法难以准确获取评价结果的突出问题,本文提出了一种融合地下水、工程地质钻孔信息及灌溉资料等工程地质资料与GNSS观测的黄土滑坡稳定性评价方法。首先,基于高分辨率影像、高精度DEM、地层地貌等多源异构数据,建立滑坡精细三维地质模型体;然后,将滑坡外部高精度GNSS监测数据作为模型外部约束条件,进一步构建起融合工程地质资料与GNSS观测的黄土滑坡稳定性综合评价模型。本文方法能够将滑坡外部大地测量高精度监测数据与工程地质数值模拟手段有机融合,实现了滑坡外部形变信息与内部变形机制的有效耦合。通过我国典型黄土滑坡域甘肃黑方台党川实际发生的两起滑坡失稳事件验证表明,本文方法可有效地提高滑坡稳定性评价结果的精度及可靠性,获取了与试验区域滑坡实际失稳情况相一致的结果:HF06/07 GNSS监测点首先失稳,其次是HF09监测点失稳,最后是HF05监测点失稳。基于本文方法获取的滑坡失稳顺序与实际滑坡发生顺序高度一致,显著优于现有的滑坡失稳数值模拟法。 相似文献
12.
13.
为提升城市级大范围、高分辨率、高精度的海量倾斜摄影航测数据的建模效率,在分析当前建模软件、设备配置、集群架构的基础上,搭建倾斜摄影实景三维数据快速处理集群。该集群以ContextCapture为倾斜摄影数据处理软件,选择最优的计算节点设备配置,合理规划集群架构,布设万兆光纤,搭建100个计算节点的倾斜摄影实景三维数据网格处理集群;同时,研发集群监控平台,实时监控集群运行状态。通过济南市倾斜摄影实景三维数据的生产验证了集群的有效性。 相似文献
14.
以滑坡蠕变阶段坡体的蠕变会引起环境条件的改变，进而影响植被生长状况的野外考察客观现实为依据，提出一种间接监测滑坡变化的新方法。利用高分辨率光学遥感技术，对滑坡蠕变阶段遥感影像上坡体上覆植被的异常特征进行判识，建立遥感影像上植被异常与滑坡蠕变的关系，反映滑坡的演化过程，弥补GPS技术、InSAR技术及部分地面监测手段在地势高、地形陡峭、植被茂盛等条件下监测工作的不足，为后续的滑坡预测研究提供帮助。以植被覆盖度较高的新磨村山体高位滑坡为例，首先，对研究区域进行分区；其次，计算各分区的植被覆盖度；最后，利用植被覆盖度分析遥感影像上的植被异常与滑坡蠕变的关系，并根据滑后遥感影像和实地考察情况进行验证。结果显示，2014年—2016年，滑坡的主要物源区、变形体上方细长局部崩滑区和泉眼及冲沟周边的植被覆盖度出现明显的下降，即随着滑坡发生时间的临近，植被受滑坡蠕变的影响变大，植被生长状况变差；而且随着距裸地等滑坡风险较大区域的距离增大，植被受滑坡蠕变的影响变小，植被生长状况变好。这表明，植被异常与滑坡蠕变存在明显的时空相关性，体现了滑坡蠕变阶段遥感影像上植被异常与滑坡蠕变的内在联系，反映了滑坡逐步失稳的演化过程，为进一步预测滑坡的发生提供依据。 相似文献
15.
数码航摄与传统摄影的比较探讨 总被引：2，自引：1，他引：1
结合DMC航空摄影数据和UCD像机的测试数据,将数码航摄与传统摄影模式进行了比较,总结了影响航摄数据质量的主要原因,并结合相关航空摄影规范,分析了数码航空摄影的数据质量及应用于传统航测生产中的优势与不足。 相似文献
16.
为了避免灾情误判和误报,准确探测和剔除滑坡形变监测数据中的粗差已经成为提高监测数据质量亟待解决的问题。已有方法主要针对单一传感器数据独立处理,且过度依赖数据变化本身的突变-平滑关系,难以有效区分粗差和外界因素突变引起的奇异值。介绍了一种知识引导的滑坡监测数据粗差剔除方法,通过粗糙集属性约简筛选具有相关关系的多源滑坡观测数据,并结合多元统计理论挖掘粗差影响因素间的时空约束关系,利用不同类型滑坡监测数据变化间的相关性规律,将多因素影响下的滑坡形变抽象为多模式的组合,根据不同模式自适应选择多因子模型以此引导卡尔曼滤波模型更新,从而实现滑坡形变监测粗差的定位与剔除。实验证明,该方法不仅能够有效甄别因环境变化引起的突变,并且能显著提高滑坡形变监测数据粗差自适应剔除的准确性、可靠性与智能化水平。 相似文献
17.
Mazlan Hashim Syarifuddin Misbari Amin Beiranvand Pour 《Journal of the Indian Society of Remote Sensing》2018,46(2):233-248
Integration of satellite remote sensing data and GIS techniques is an applicable approach for landslide mapping and assessment in highly vegetated regions with a tropical climate. In recent years, there have been many severe flooding and landslide events with significant damage to livestock, agricultural crop, homes, and businesses in the Kelantan river basin, Peninsular Malaysia. In this investigation, Landsat-8 and phased array type L-band synthetic aperture radar-2 (PALSAR-2) datasets and analytical hierarchy process (AHP) approach were used to map landslide in Kelantan river basin, Peninsular Malaysia. Landslides were determined by tracking changes in vegetation pixel data using Landsat-8 images that acquired before and after flooding. The PALSAR-2 data were used for comprehensive analysis of major geological structures and detailed characterizations of lineaments in the state of Kelantan. AHP approach was used for landslide susceptibility mapping. Several factors such as slope, aspect, soil, lithology, normalized difference vegetation index, land cover, distance to drainage, precipitation, distance to fault, and distance to the road were extracted from remotely sensed data and fieldwork to apply AHP approach. The excessive rainfall during the flood episode is a paramount factor for numerous landslide occurrences at various magnitudes, therefore, rainfall analysis was carried out based on daily precipitation before and during flood episode in the Kelantan state. The main triggering factors for landslides are mainly due to the extreme precipitation rate during the flooding period, apart from the favorable environmental factors such as removal of vegetation within slope areas, and also landscape development near slopes. Two main outputs of this study were landslide inventory occurrences map during 2014 flooding episode and landslide susceptibility map for entire Kelantan state. Modeled/predicted landslides with a susceptible map generated prior and post-flood episode, confirmed that intense rainfall throughout Kelantan has contributed to produce numerous landslides with various sizes. It is concluded that precipitation is the most influential factor for landslide event. According to the landslide susceptibility map, 65% of the river basin of Kelantan is found to be under the category of low landslide susceptibility zone, while 35% class in a high-altitude segment of the south and south-western part of the Kelantan state located within high susceptibility zone. Further actions and caution need to be remarked by the local related authority of the Kelantan state in very high susceptibility zone to avoid further wealth and people loss in the future. Geo-hazard mitigation programs must be conducted in the landslide recurrence regions for reducing natural catastrophes leading to loss of financial investments and death in the Kelantan river basin. This investigation indicates that integration of Landsat-8 and PALSAR-2 remotely sensed data and GIS techniques is an applicable tool for Landslide mapping and assessment in tropical environments. 相似文献
18.
基于DEM的机助航空摄影技术设计 总被引：1，自引：0，他引：1
In aerial photography, the primary factor is terrain undulation. However, most of the external aerial photography software used for aerial photography design do not take terrain undulation influence into consideration. Therefore, the design result has comparative randomicity and ＂gaps＂ are expected. An aerial photography design system is developed by analyzing the terrain undulation influence to the design result with DEM data so that the forward overlap and side overlap can be justified according to the block terrain undulation to meet specifications or standards. The data designed by this system is compared with the real flying data. The results show that making use of DEM to assist in aerial photography design can ensure that the designed result fits the real terrain better. 相似文献
19.
在滑坡位移综合预测研究中,常因滑坡随机位移分量无法准确提取、最优训练数据集及时效性无法确定等,造成多源监测数据利用不充分、位移预测结果不稳定。鉴于此,引入变分模态分解,在滑坡位移时序分析的基础上,结合门控循环单元递归神经网络,提出一种新型滑坡位移综合预测模型。以三峡库区白水河滑坡为例,选取2003-07—2012-12的位移监测数据和同时期库水位及降雨数据进行分析研究,综合模型预测结果的均方根误差为9.715 mm,判定系数为0.967。对比实验分析表明,该模型在保证高预测精度的同时,在有效预测时长和时效性上同样优势明显,在库岸滑坡位移预测研究中具有很强的应用和推广价值。 相似文献
20.
Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model 总被引：10，自引：0，他引：10
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. 相似文献