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1.
A GIS-based statistical methodology for landslide susceptibility zonation is described and its application to a study area
in the Western Ghats of Kerala (India) is presented. The study area was approximately 218.44 km2 and 129 landslides were identified in this area. The environmental attributes used for the landslide susceptibility analysis
include geomorphology, slope, aspect, slope length, plan curvature, profile curvature, elevation, drainage density, distance
from drainages, lineament density, distance from lineaments and land use. The quantitative relationship between landslides
and factors affecting landslides are established by the data driven-Information Value (InfoVal) — method. By applying and
integrating the InfoVal weights using ArcGIS software, a continuous scale of numerical indices (susceptibility index) is obtained
with which the study area is divided into five classes of landslide susceptibility. In order to validate the results of the
susceptibility analysis, a success rate curve was prepared. The map obtained shows that a great majority of the landslides
(74.42%) identified in the field were located in susceptible and highly susceptible zones (27.29%). The area ratio calculated
by the area under curve (AUC) method shows a prediction accuracy of 80.45%. The area having a high scale of susceptibility
lies on side slope plateaus and denudational hills with high slopes where drainage density is relatively low and terrain modification
is relatively intense. 相似文献
2.
In this study, landslide susceptibility assessments were achieved using logistic regression, in a 523 km2 area around the Eastern Mediterranean region of Southern Turkey. In reliable landslide susceptibility modeling, among others, an appropriate landslide sampling technique is always essential. In susceptibility assessments, two different random selection methods, ranging 78–83% for the train and 17–22% validation set in landslide affected areas, were applied. For the first, the landslides were selected based on their identity numbers considering the whole polygon while in the second, random grid cells of equal size of the former one was selected in any part of the landslides. Three random selections for the landslide free grid cells of equal proportion were also applied for each of the landslide affected data set. Among the landslide preparatory factors; geology, landform classification, land use, elevation, slope, plan curvature, profile curvature, slope length factor, solar radiation, stream power index, slope second derivate, topographic wetness index, heat load index, mean slope, slope position, roughness, dissection, surface relief ratio, linear aspect, slope/aspect ratio have been considered. The results showed that the susceptibility maps produced using the random selections considering the entire landslide polygons have higher performances by means of success and prediction rates. 相似文献
3.
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
4.
We propose a framework to systematically generate event landslide inventory maps from satellite images in southern Taiwan, where landslides are frequent and abundant. The spectral information is used to assess the pixel land cover class membership probability through a Maximum Likelihood classifier trained with randomly generated synthetic land cover spectral fingerprints, which are obtained from an independent training images dataset. Pixels are classified as landslides when the calculated landslide class membership probability, weighted by a susceptibility model, is higher than membership probabilities of other classes. We generated synthetic fingerprints from two FORMOSAT-2 images acquired in 2009 and tested the procedure on two other images, one in 2005 and the other in 2009. We also obtained two landslide maps through manual interpretation. The agreement between the two sets of inventories is given by the Cohen’s k coefficients of 0.62 and 0.64, respectively. This procedure can now classify a new FORMOSAT-2 image automatically facilitating the production of landslide inventory maps. 相似文献
5.
文中通过对长江河型、河谷地貌及水动力状态的分析研究, 指出长江上游的岸坡稳定性除了 与岩性、构造条件有关外, 特别强调了河谷地貌和水动力作用在滑坡形成中的作用。并以1982年7月再次滑动的四川省云阳县鸡扒子滑坡为例, 指出现在的治理措施没有考虑河流地质作用是不适当的。 相似文献
6.
The main aim of this study was to produce landslide susceptibility maps using statistical index (SI), certainty factors (CF), weights of evidence (WoE) and evidential belief function (EBF) models for the Long County, China. Firstly, a landslide inventory map, including a total of 171 landslides, was compiled on the basis of earlier reports, interpretation of aerial photographs and supported by extensive field surveys. Thereafter, all landslides were randomly separated into two data sets: 70% landslides (120 points) were selected for establishing the model and the remaining landslides (51 points) were used for validation purposes. Eleven landslide conditioning factors, such as slope aspect, slope angle, plan curvature, profile curvature, altitude, distance to faults, distance to roads, distance to rivers, lithology, NDVI and land use, were considered for landslide susceptibility mapping in this study. Then, the SI, CF, WoE and EBF models were used to produce the landslide susceptibility maps for the study area. Finally, the four models were validated using area under the curve (AUC) method. According to the validation results, the EBF model (AUC = 78.93%) has a higher prediction accuracy than the SI model (AUC = 77.72%), the WoE model (AUC = 77.62%) and the CF model (AUC = 77.72%). Similarly, the validation results also indicate that the EBF model has the highest training accuracy of 80.25%, followed by SI (79.80%), WoE (79.71%) and CF (79.67%) models. 相似文献
7.
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
8.
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
9.
A GIS-based comparative study of Dempster-Shafer,logistic regression and artificial neural network models for landslide susceptibility mapping 总被引：4，自引：0，他引：4
The main aim of present study is to compare three GIS-based models, namely Dempster–Shafer (DS), logistic regression (LR) and artificial neural network (ANN) models for landslide susceptibility mapping in the Shangzhou District of Shangluo City, Shaanxi Province, China. At First, landslide locations were identified by aerial photographs and supported by field surveys, and a total of 145 landslide locations were mapped in the study area. Subsequently, the landslide inventory was randomly divided into two parts (70/30) using Hawths Tools in ArcGIS 10.0 for training and validation purposes, respectively. In the present study, 14 landslide conditioning factors such as altitude, slope angle, slope aspect, topographic wetness index, sediment transport index, stream power index, plan curvature, profile curvature, lithology, rainfall, distance to rivers, distance to roads, distance to faults and normalized different vegetation index were used to detect the most susceptible areas. In the next step, landslide susceptible areas were mapped using the DS, LR and ANN models based on landslide conditioning factors. Finally, the accuracies of the landslide susceptibility maps produced from the three models were verified using the area under the curve (AUC). The validation results showed that the landslide susceptibility map generated by the ANN model has the highest training accuracy (73.19%), followed by the LR model (71.37%), and the DS model (66.42%). Similarly, the AUC plot for prediction accuracy presents that ANN model has the highest accuracy (69.62%), followed by the LR model (68.94%), and the DS model (61.39%). According to the validation results of the AUC curves, the map produced by these models exhibits the satisfactory properties. 相似文献
10.
金沙江流域因两岸地势陡峭、软弱岩层发育、降雨集中等,使得流域内滑坡灾害分布密集。高分辨率遥感是滑坡识别的重要手段,但通过目视解译法开展的大范围滑坡灾害识别,具有工作量大、效率低的特点。针对此问题,本文采用基于面向对象的分类方法,提出了利用滑坡灾害的光谱、形状、空间等特征进行区域内滑坡灾害的快速识别。同时,选取金沙江流域巴塘县王大龙村区段进行了滑坡识别提取试验,区域内利用面向对象分类方法识别出滑坡18处,其中12处与目视解译结果相同,一致性为75%;发现3处目视解译未识别出的隐蔽性滑坡。结果表明,该方法识别效果较好,可为后续的金沙江流域乃至川藏铁路沿线的大范围滑坡识别提取及滑坡编目工作提供参考。 相似文献
11.
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
12.
13.
Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio,certainty factor,and index of entropy 总被引：3，自引：0，他引：3
The main objective of the study was to evaluate and compare the overall performance of three methods, frequency ratio (FR), certainty factor (CF) and index of entropy (IOE), for rainfall-induced landslide susceptibility mapping at the Chongren area (China) using geographic information system and remote sensing. First, a landslide inventory map for the study area was constructed from field surveys and interpretations of aerial photographs. Second, 15 landslide-related factors such as elevation, slope, aspect, plan curvature, profile curvature, stream power index, sediment transport index, topographic wetness index, distance to faults, distance to rivers, distance to roads, landuse, NDVI, lithology and rainfall were prepared for the landslide susceptibility modelling. Using these data, three landslide susceptibility models were constructed using FR, CF and IOE. Finally, these models were validated and compared using known landslide locations and the receiver operating characteristics curve. The result shows that all the models perform well on both the training and validation data. The area under the curve showed that the goodness-of-fit with the training data is 79.12, 80.34 and 80.42% for FR, CF and IOE whereas the prediction power is 80.14, 81.58 and 81.73%, for FR, CF and IOE, respectively. The result of this study may be useful for local government management and land use planning. 相似文献
14.
Xiaojun Yang Liding Chen 《International Journal of Applied Earth Observation and Geoinformation》2010
Landslides are a major type of geohazards claiming thousands of casualties and billions of dollars in property damages every year. Catastrophic landslide activities are often triggered by some extreme events such as earthquakes, excessive precipitations, or volcanic eruptions. Quickly identifying the spatial distribution of landslides induced by these extreme events is crucial for coordinating rescue efforts and planning in situ investigations. In this study, we propose an automated method for detecting the spatial distribution of earthquake-triggered landslides by examining after-event vegetation changes. Central to this method is the use of pre- and post-event remote sensor images covering the same area. Geometric correction and radiometric normalization are performed before deriving a vegetation index from each image. Then, an image differencing procedure is applied to the two derived indices. With the resultant difference image, an initial landslide distribution map is generated by highlighting the pixels with a threshold percentage decrease in the brightness values as a direct result of the image subtraction. The threshold percentage value is interactively determined by using a visual interpretation method. The final landslide distribution map is produced after using a modal filter to suppress boundary errors in the initial map. This method has been implemented in a test site, approximately 30 km from the epicenter of the Sichuan earthquake (7.9 Ms) that struck on 12 May 2008. A pre-event Thematic Mapper image and a post-event Advanced Spaceborne Thermal Emission and Reflection Radiometer scene are used. The thematic accuracy assessment indicates that 90% of the landslides have correctly been mapped. Given the relatively simple procedures and the good mapping accuracy, the image processing and change detection method identified in this study seems to be promising from an operational perspective. 相似文献
15.
自2003年蓄水以来,三峡库区已查明的滑坡或潜在滑坡高达5000余处,这些灾害对三峡水库的持续运营、大坝、航道及库区居民的安全造成了严重的威胁。通过研究滑坡的变形特征、诱发因素及失稳机制,有助于开展滑坡的稳定性评价,并构建预警预报模型。以三峡库区八字门滑坡为研究对象,综合分析降雨、库水位、人工和自动GNSS监测等数据,结合勘查资料及野外宏观巡查,研究了滑坡的变形特征及失稳机理,并确定合理的预警判据及阈值。研究表明:①八字门滑坡整体变形明显,处于蠕动变形阶段,滑坡变形主要集中于每年5—9月,滑坡累积曲线呈现典型的“阶跃”状变形特性。②滑坡的变形受斜坡结构、岩性等因素的控制,水库水位下降是滑坡变形的主要驱动因素,并与库水下降速率正相关。另外,特大暴雨和持续降雨在水位下降阶段、水库低水位运行期及水位上升期会促进滑坡变形,是滑坡的次要驱动因素。③通过精细化数据分析以及改进切线角法获取的八字门滑坡出现“阶跃”变形的位移速率阈值为4.6 mm/d,7 d累积降雨量阈值为60 mm,库水位阈值为159 m,库水位下降速率阈值0.4 m/d。 相似文献
16.
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
17.
M. A. A. Patwary P. K. Champati ray Irshad Parvaiz 《Journal of the Indian Society of Remote Sensing》2009,37(3):493-509
Structurally disturbed zones of Himalaya are among the worst landslide affected regions in the world. Although landslides
are induced/triggered either by torrential rain during monsoon or by seismic activity in the region, the inherent terrain
conditions characterize the prevailing basic conditions susceptible to landslides. Using remotely sensed data and Geographic
Information System (GIS), geological and terrain factors can be integrated for preparation of factor maps and demarcation
of areas susceptible to landslides. Moderate to high resolution data products available from Indian Remote Sensing satellites
have been utilized for deriving geological and terrain factor maps, which were integrated using knowledge driven heuristic
approach in Integrated Land and Water Information System (ILWIS) GIS. The resultant map shows division of the area into landslide
susceptibility classes ranked in terms of hazard potential in one of the structurally disturbed zones in western Himalaya
around Rishikesh. 相似文献
18.
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
19.
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
20.
On 3 August 2014, an earthquake struck Ludian County, Yunnan Province, China, which has caused a large number of coseismic landslides. Visual interpretation permitted 284 and 1053 landslides before and after the event to be mapped, respectively. This work attempted to analyse these two kinds of landslides. Conditioning factors, such as slope angle, aspect, curvature, elevation, distance from drainages, intensity and lithology, and the index of Landslide Number Density (LND) were used to describe the spatial distribution of these landslides. Then, the pre-earthquake and coseismic landslide susceptibility maps were produced using the information value model. The areas under curve are 84.73 and 77.05%, for the pre-earthquake and coseismic landslides, respectively. The results show that the LND values as well as the information values of coseismic landslides are larger than those of the pre-earthquake case. This also indicates that this Ludian earthquake has a relatively larger ability to trigger landslides. 相似文献