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1.
Buildings are sensitive to movements caused by ground deformation. The mapping both of spatial and temporal distribution, and of the degree of building damages represents a useful tool in order to understand the landslide evolution, magnitude and stress distribution. The high spatial resolution of space-borne SAR interferometry can be used to monitor displacements related to building deformations. In particular, PSInSAR technique is used to map and monitor ground deformation with millimeter accuracy. The usefulness of the above mentioned methods was evaluated in San Fratello municipality (Sicily, Italy), which was historically affected by landslides: the most recent one occurred on 14th February 2010. PSInSAR data collected by ERS 1/2, ENVISAT, RADARSAT-1 were used to study the building deformation velocities before the 2010 landslide. The X-band sensors COSMO-SkyMed and TerraSAR-X were used in order to monitor the building deformation after this event. During 2013, after accurate field inspection on buildings and structures, damage assessment map of San Fratello were created and then compared to the building deformation velocity maps. The most interesting results were obtained by the comparison between the building deformation velocity map obtained through COSMO-SkyMed and the damage assessment map. This approach can be profitably used by local and Civil Protection Authorities to manage the post-event phase and evaluate the residual risks. 相似文献
2.
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
3.
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
4.
2017年8月8日发生的7.0级九寨沟地震诱发九寨沟熊猫海附近产生大量的滑坡体,造成道路阻塞,严重影响地震应急救援进度。为快速准确地识别滑坡分布范围,本文在深入分析滑坡遥感影像特征的基础上,引入面向对象分析方法,实现了基于无人机影像的震后滑坡体的自动识别。通过多尺度分割算法获取滑坡多层次影像对象,利用SEaTH算法自动构建每一层次特征规则集,实现基于不同层次分析的滑坡体自动识别。分析滑坡体在地形、活动断层等因子中的空间分布特征,为地震滑坡预测与危险性评价奠定基础。与人工目视解译结果相比较,基于面向对象的滑坡自动识别方法提取精度可达94.8%,Kappa系数为0.827,在电脑配置相同的情况下,自动识别方法的效率是人工目视解译效率的一倍。空间分布特征分析表明,地震滑坡的空间分布与斜坡坡度、地形起伏度呈正相关关系,与地表粗糙度存在负相关关系,研究区滑坡体分布存在明显的断层效应。 相似文献
5.
《地理信息系统科学与遥感》2013,50(4):498-513
The objective of the present study, developed in a mountainous region in Brazil where many landslides occur, is to present a method for detecting landslide scars that couples image processing techniques with spatial analysis tools. An IKONOS image was initially segmented, and then classified through a Batthacharrya classifier, with an acceptance limit of 99%, resulting in 216 polygons identified with a spectral response similar to landslide scars. After making use of some spatial analysis tools that took into account a susceptibility map, a map of local drainage channels and highways, and the maximum expected size of scars in the study area, some features misinterpreted as scars were excluded. The 43 resulting features were then compared with visually interpreted landslide scars and field observations. The proposed method can be reproduced and enhanced by adding filtering criteria and was able to find new scars on the image, with a final error rate of 2.3%. 相似文献
6.
We manually detected and mapped 66 landslides from Landsat imagery over a 33-year period from 1985 to 2017 in the Buckinghorse River region, British Columbia, Canada. We semi-automatically determined landslide timing using the cumulative difference (CD) between the normalized difference vegetation index (NDVI) and a fitted harmonic sinusoidal curve (CDNDVI). The semi-automated dating method was capable of determining the timing of 80% of the landslides using CDNDVI and 85% of the landslides after detrending CDNDVI (dCDNDVI). The CDNDVI method generally detects landslides too early and the dCDNDVI method is generally too late. Mean absolute errors (in days) are lower for the dCDNDVI (208 and 188) than the CDNDVI (227 and 267), respectively. This study, however, has many examples of extreme outliers with very large errors (>1000 days). Our method is portable to other remote regions as long as vegetation anomalies can be used as an indicator for landslide activity. We conclude that the timeseries of images available in the Landsat Archive are useful for landslide mapping, but the pixel size limits the size of the landslides that can be mapped. 相似文献
7.
采用合成孔径雷达干涉测量（interferometric synthetic aperture radar,InSAR）技术对甘肃黑方台地区潜在的黄土滑坡开展了多时相编目、长时序监测以及失稳模式识别研究。首先,采用不同空间分辨率、不同波长的历史存档合成孔径雷达（synthetic aperture radar,SAR）数据对黑方台地区2006-12至2017-11间的潜在滑坡开展了识别研究,在2006-12至2011-03和2016-01至2016-11两个时间段均识别出数10处不稳定坡体,实地调查和光学遥感影像验证了InSAR技术识别结果的可靠性与准确性。然后,对典型不稳定滑坡体采用高空间与高时间分辨率的TerraSAR-X数据开展了长时序监测,结果表明,在InSAR监测期间,累积形变最大的滑坡体在随后的时间里均发生了滑动,并成功地捕获到滑坡体形变加速的时间点。最后,利用升降轨SAR数据开展了黄土滑坡二维形变监测研究,基于滑坡的二维形变特征并结合地形图以及光学遥感影像进一步研究了滑坡的失稳模式,现场调查结果验证了所获得滑坡失稳模式的准确性。 相似文献
8.
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
9.
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
10.
滑坡敏感性评价是地质灾害预测预报的关键环节。针对BP神经网络易陷入局部最小值、收敛速度慢等问题,该文以三峡库区秭归县境内为研究区,采用粒子群优化(PSO)算法对BP神经网络的初始权值和阈值进行优化,构建PSO-BP神经网络滑坡敏感性预测模型,实现研究区滑坡敏感性评价。采用受试者工作特征曲线分析模型预测精度,得到PSO-BP神经网络预测精度为0.931,预测结果与实际滑坡总体空间分布具有良好的一致性,且预测能力优于BP神经网络。实验结果表明,PSO-BP神经网络耦合模型在实现滑坡敏感性评价上具有理想的预测精度和良好的适用性。 相似文献
11.
Rainfall-triggered shallow landslide is very common in Korean mountains and the socioeconomic impact is much higher than in the past due to population pressure in hazardous zones. Present study is an attempt toward the development of a methodology for the integration of shallow landslide susceptibility zones and runout zones that could be reached by mobilized mass. Landslide occurrence areas in Yongin were determined based on the interpretation of aerial photographs and extensive field surveys. Nineteen landslide-related factors maps were collected and analysed in geographic information system environment. Among 109 identified landslides, about 85% randomly selected training landslide data from inventory map was used to generate an evidential belief function model and remaining 15% landslides were used to validate the shallow landslide susceptibility map. The resulting susceptibility map had a success rate of 89.2% and a predictive accuracy of 92.1%. A runout propagation from high susceptible area was obtained from the modified multiple-flow direction algorithm. A matrix was used to integrate the shallow landslide susceptibility classes and the runout probable zone. Thus, each pixel had a susceptibility class in relation to its failure probability and runout susceptibility class. The study of landslide potential and its propagation can be used to obtain a spatial prediction for landslides, which could contribute to landslide risk mitigation. 相似文献
12.
On Oct. 11 and Nov. 3, 2018, two large-scale landslides occurred in the same location in Baige Village, Tibet, and massive rocks fell and encroached into the Jingsha River. These landslides posed a severe risk to the upstream and downstream areas. The occurrence, development and evolution of landslides are accompanied by a large number of changes in measurable variables. The deformation data are one of most important parameters for characterizing change and development trends of a landslide. This paper is centered on the results derived from ground-based radar and space-borne Synthetic Aperture Radar (SAR) images in the post-event phase to monitor the Baige landslides and to assess their residual risk. Two technologies play important roles in identifying and characterizing impending catastrophic slope failures: ground-based radar reveals the horizontal deformation, and satellite SAR images reveal the azimuth and range offset deformation. By combining satellite and ground-based SAR observations, we obtained high-precision three-dimensional (3D) deformation results and found that the vast majority of the instability regions mainly occur in the source area of the slope failures and that the direction of collapse converges from all sides to the middle. Additional information from UAV orthophoto maps and GNSS measurements also reveal that several cracks are distributed on the trailing edge of the landslide and are still moving. The comprehensive results revealed that the moving rock mass has still been remarkably active after the two landslide events. This study combined ground-based and space-borne SAR data to develop a long-term monitoring and stability evaluation process for implementation after a large landslide disaster. Based on the distribution characteristics of the 3D deformation fields, the present and future stability of the Baige Landslide was analyzed. 相似文献
13.
14.
区域性地震滑坡信息获取目前主要通过遥感目视解译和计算机提取,存在主观性强、耗时费力、提取精度低等问题,导致难以满足灾后应急调查、灾情评估等方面的应用需求。采用资源三号、高分一号高分辨率遥感影像,以汶川震区为实验区,在地震滑坡灾害特征分析的基础上,通过多尺度最优分割方法构建多层次滑坡对象,融合光谱、纹理、几何等影像特征和地形特征信息建立多维滑坡识别规则集合,基于高分辨率影像认知模式与场景理解过程提出滑坡分层识别模型,从而实现地震滑坡空间分布及其滑源区、滑移区和堆积区的准确识别。实验区分析结果显示最低识别精度为81.89%,而滑坡的堆积区最容易被分辨,识别方法具有可推广性。研究成果可为灾后应急调查提供技术支撑,并促进国产高分辨率遥感卫星的地质灾害应用。 相似文献
15.
The 2008 Mw 7.9 Wenchuan earthquake triggered plenty of coseismic giant landslides, which resulted in almost one third of total fatalities and economic losses during the event. Previous studies investigated the spatial relations between landslide distribution and topographic and seismic factors such as elevation, slope aspect, distance from rupture trace and seismic intensity. However, few studies are performed exploring the effects of coseismic surface deformation and Coulomb stress change on triggering landslides due to lack of adequate deformation observation data and stress calculation model for slope failure. In this study, we develop an envelope method to map an entire coseismic deformation field in both near- and far-field areas of seismic faults through the data fusion from InSAR and pixel offset-tracking (POT) techniques. The change in static Coulomb stress (SCS) acting on coseismic landsliding surface caused by the event is determined using the faulting model derived from the joint inversion of InSAR and GPS data, and also with the use of the elastic half-space dislocation theory and the generalized Hook’s law. The analysis suggests the spatial response pattern of seismic landslides to the coseismic ground motion and stress change, especially in the vicinity of fault rupture trace. The landslide density dramatically rises with the stress increase within the range from Yingxiu to Beichuan areas along the major surface rupture. Moving further and eastward along the fault strike, most of large landslides are triggered as the zone of positive SCS change narrows. Moreover, the high-magnitude surface displacements are possibly responsible for the giant landsliding events in the easternmost section. From the analysis of the stress transfer, the occurrence of landslides in the study area is largely controlled by the Yingxiu-Beichuan fault with overwhelming rupture length and fault slip, yet the Pengguan fault indeed shows dominance in the area between the two faults. The results show that coseismic surface deformation (derived from InSAR data in this study) and static Coulomb stress change can serve as two significant controlling factors on seismic landslide distribution and that the stress factor seems more significant in the vicinity of surface rupture. 相似文献
16.
Application of frequency ratio,weights of evidence and evidential belief function models in landslide susceptibility mapping 总被引：2，自引：1，他引：1
The landslide hazard occurred in Taibai County has the characteristics of the typical landslides in mountain hinterland. The slopes mainly consist of residual sediments and locate along the highway. Most of them are in the less stable state and in high risk during rainfall in flood season especially. The main purpose of this paper is to produce landslide susceptibility maps for Taibai County (China). In the first stage, a landslide inventory map and the input layers of the landslide conditioning factors were prepared in the geographic information system supported by field investigations and remote sensing data. The landslides conditioning factors considered for the study area were slope angle, altitude, slope aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, normalized difference vegetation index, lithological unit, rainfall and land use. Subsequently, the thematic data layers of conditioning factors were integrated by frequency ratio (FR), weights of evidence (WOE) and evidential belief function (EBF) models. As a result, landslide susceptibility maps were obtained. In order to compare the predictive ability of these three models, a validation procedure was conducted. The curves of cumulative area percentage of ordered index values vs. the cumulative percentage of landslide numbers were plotted and the values of area under the curve (AUC) were calculated. The predictive ability was characterized by the AUC values and it indicates that all these models considered have relatively similar and high accuracies. The success rate of FR, WOE and EBF models was 0.9161, 0.9132 and 0.9129, while the prediction rate of the three models was 0.9061, 0.9052 and 0.9007, respectively. Considering the accuracy and simplicity comprehensively, the FR model is the optimum method. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose. 相似文献
17.
18.
《International Journal of Digital Earth》2013,6(8):688-708
In this paper, GIS-based ordered weighted averaging (OWA) is applied to landslide susceptibility mapping (LSM) for the Urmia Lake Basin in northwest Iran. Nine landslide causal factors were used, whereby the respective parameters were extracted from an associated spatial database. These factors were evaluated, and then the respective factor weight and class weight were assigned to each of the associated factors using analytic hierarchy process (AHP). A landslide susceptibility map was produced based on OWA multicriteria decision analysis. In order to validate the result, the outcome of the OWA method was qualitatively evaluated based on an existing inventory of known landslides. Correspondingly, an uncertainty analysis was carried out using the Dempster–Shafer theory. Based on the results, very strong support was determined for the high susceptibility category of the landslide susceptibility map, while strong support was received for the areas with moderate susceptibility. In this paper, we discuss in which respect these results are useful for an improved understanding of the effectiveness of OWA in LSM, and how the landslide prediction map can be used for spatial planning tasks and for the mitigation of future hazards in the study area. 相似文献
19.
黄河上游干流地区由于特殊的地形地貌和地质构造使得滑坡灾害频发,对其开展滑坡灾害监测、分析研究,具有十分重要的意义。本文利用2015年间Google Earth遥感数据,提取并分析了该地区的滑坡灾害分布信息,取得了如下成果及认识:1)研究区的空间展布形态主要有7种,滑体性质类型有6种,岩质滑坡数量最多。2)从空间分布特征看,共发现研究区有各类滑坡162处,滑坡主要集中分布在群科-尖扎盆地;从滑坡类型看,研究区滑坡主要为大型滑坡和巨型滑坡。3)滑坡体长、宽主要集中在0~1 500 m和500~1 500 m之间,且长、宽呈两极化方向延伸,滑坡体面积分布不均,滑坡数量随着方量的增大呈现减少的趋势,发生的滑坡主要是滑坡体厚度在25~50 m的深层滑坡。4)滑坡数量在0°~90°之间有峰值出现,然后向两端逐渐减少。 相似文献
20.
滑坡遥感调查、监测与评估 总被引：17，自引：2，他引：17
滑坡遥感调查包括滑坡识别、基本信息获取和滑坡空间分析等，本文以天台乡滑坡遥感调查中用特征点法确定滑坡边界、影响带及滑坡运动特征及规模为例说明。滑坡遥感监测可分为直接监测和间接监测。由于突发的高速超高速崩塌、滑坡及泥石流活动时间难以预测，滑坡运动的规模相对于遥感地面分辨率较小，获取遥感数据的不连续性及价格昂贵等原因，目前较少应用遥感技术直接监测滑坡活动； 遥感监测滑坡运动引起的环境变化，称为间接滑坡监测，以遥感监测易贡大滑坡引起的易贡湖水面变化及溃坝造成的下游灾害为例说明。滑坡遥感评估指在获取滑坡及其发育环境基本信息的基础上，评估滑坡的稳定性，预测其未来活动性，评估区域滑坡的影响因子和进行区域滑坡危险性评价，文中以天台乡滑坡、千将坪滑坡稳定性评估及三峡库区中前段区域滑坡危险性评价为例说明。 相似文献