基于多特征面向对象区域滑坡现象识别

高空间分辨率卫星遥感数据的发展为滑坡灾害数据获取和更新提供了新的途径。以西北黄土高原为研究区,提出了一种基于多特征面向对象区域滑坡现象的识别方法,基于单期高空间分辨率遥感数据,利用集合和特征组合进行区域滑坡现象识别,实验结果表明:该方法是识别滑坡现象有效的技术方法之一,对开展滑坡监测、影像理解和地学分析具有重要的研究意义。     

Research on Wenchuan Earthquake-induced LandslidesRapid Recognition from ZY-3 Imagery

Presently,regional earthquake-induced landslides is mainly obtained by field survey and visual interpretation from remote sensing images; but these methods are objective,and time-consuming.In this study,with a main data source of domestic high-resolution remote sensing images from ZY-3 satellite as well as the study area of the Wenchuan earthquake region,objects of multilevel landslides were established using the multi-scale optimum partition method based on in-depth analysis of landslide features.A recognition rule set of multi-dimensional landslides was also built through the combination of topographic features and image features,such as spectrum,texture,and geometry.Additionally,recognition models for landslide stratification were proposed based on the recognition models of high-resolution images and an understanding of the scenes.Through all of the aforementioned efforts,the spatial distribution of the seismic landslide as well as the sliding source area,transport area,and depositional area can be identified intelligently.The analysis results of the experimental area showed a minimum recognition accuracy of 82.97%,with the depositional zone of landslides being the easiest zone to recognize,and the effectiveness of the proposed method as well as ZY-3 data.These findings may provide technical support for regional earthquake-induced landslides investigations and further promote geological hazard application of domestic high-resolution satellites.      

Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data

The aim of this study is to evaluate the hazard of landslides at Penang, Malaysia, using a Geographical Information System (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed and constructed into a spatial database using GIS and image processing. 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 use from Thematic Mapper (TM) satellite images; and the vegetation index value from Système Probatoire de l'Observation de la Terre (SPOT) satellite images. Landslide hazardous areas were analysed and mapped using the landslide‐occurrence factors by logistic regression model. The results of the analysis were verified using the landslide location data and compared with probabilistic model. The validation results showed that the logistic regression model is better in prediction than probabilistic model.     

Landslide mapping in the coastal area between the Strymonic Gulf and Kavala (Macedonia,Greece) with the aid of remote sensing and geographical information systems

The main aim of this study is to photointerpret land cover change along hill slopes in order to detect existing landslides with the aid of methodologies such as false colour composites (FCCs), principal component analysis (PCA) and the normalized difference vegetation index (NDVI). Then, by combining geological data (lithology, vegetation cover), and geomorphologic factors (slope, aspect, distance from the rivers), landslide susceptibility maps were produced. The region where the study took place was the coastal area between the Strymonic Gulf and southwest Kavala prefecture, which was selected because of its intense landslide activity. The identification of locations where landslides occurred was achieved with the use and processing of TERRA/ASTER satellite images, while the data, which were mainly collected from the digitization of contours from topographical maps at 1 : 50 000 scale, were used to construct the final landslide susceptibility maps. The resulting FCC images provided satisfactory information about the locations of landslide sites and the landslide susceptibility maps indicated areas that were more prone to produce such phenomena.     

Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun,Korea

The aim of this study is to evaluate the hazard of landslides at Boun, Korea, using a Geographic Information System (GIS) and remote sensing. Landslide locations were identified in the Boun area from interpretation of aerial photographs and field surveys. The topographic, soil, forest, geologic, lineament and land cover data were collected, processed and constructed into a spatial database using GIS and remote sensing data. The factors that influence landslide occurrence, such as slope, aspect and curvature of the topography, were calculated from the topographic database. Texture, material, drainage and effective soil thickness were extracted from the soil database, and type, age, diameter and density of timber were extracted from the forest database. The lithology was extracted from the geological database and lineaments were detected from Indian Remote Sensing (IRS) satellite images. The land cover was classified based on the Landsat Thematic Mapper (TM) satellite image. Landslide hazard areas were analysed and mapped, using the landslide-occurrence factors, by the probability–likelihood ratio method. The results of the analysis were verified using actual landslide location data. The validation results showed satisfactory agreement between the hazard map and the existing data on landslide locations.     

Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling

Data collection for landslide susceptibility modeling is often an inhibitive activity. This is one reason why for quite some time landslides have been described and modelled on the basis of spatially distributed values of landslide-related attributes. This paper presents landslide susceptibility analysis in the Klang Valley area, Malaysia, using back-propagation artificial neural network model. A landslide inventory map with a total of 398 landslide locations was constructed using the data from various sources. Out of 398 landslide locations, 318 (80%) of the data taken before the year 2004 was used for training the neural network model and the remaining 80 (20%) locations (post-2004 events) were used for the accuracy assessment purpose. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Eleven landslide occurrence related factors were selected as: slope angle, slope aspect, curvature, altitude, distance to roads, distance to rivers, lithology, distance to faults, soil type, landcover and the normalized difference vegetation index value. For calculating the weight of the relative importance of each factor to the landslide occurrence, an artificial neural network method was developed. Each thematic layer's weight was determined by the back-propagation training method and landslide susceptibility indices (LSI) were calculated using the trained back-propagation weights. To assess the factor effects, the weights were calculated three times, using all 11 factors in the first case, then recalculating after removal of those 4 factors that had the smallest weights, and thirdly after removal of the remaining 3 least influential factors. The effect of weights in landslide susceptibility was verified using the landslide location data. It is revealed that all factors have relatively positive effects on the landslide susceptibility maps in the study. The validation results showed sufficient agreement between the computed susceptibility maps and the existing data on landslide areas. The distribution of landslide susceptibility zones derived from ANN shows similar trends as those obtained by applying in GIS-based susceptibility procedures by the same authors (using the frequency ratio and logistic regression method) and indicates that ANN results are better than the earlier method. Among the three cases, the best accuracy (94%) was obtained in the case of the 7 factors weight, whereas 11 factors based weight showed the worst accuracy (91%).     

Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model

This paper presents the application of remote sensing techniques, digital image analysis and Geographic Information System tools to delineate the degree of landslide hazard and risk areas in the Balik Pulau area in Penang Island, Malaysia. Its causes were analysed through various thematic attribute data layers for the study area. Firstly, landslide locations were identified in the study area from the interpretation of aerial photographs, satellite imageries, field surveys, reports and previous landslide inventories. Topographic, geologic, soil and satellite images were collected and processed using Geographic Information System and image processing tools. There are 12 landslide-inducing parameters considered for the landslide hazard analyses. These parameters are: topographic slope, topographic aspect, plan curvature, distance to drainage and distance to roads, all derived from the topographic database; geology and distance to faults, derived from the geological database; landuse/landcover, derived from Landsat satellite images; soil, derived from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value, derived from SPOT satellite images. In addition, hazard analyses were performed using landslide-occurrence factors with the aid of a statistically based frequency ratio model. Further, landslide risk analysis was carried out using hazard map and socio-economic factors using a geospatial model. This landslide risk map could be used to estimate the risk to population, property and existing infrastructure like transportation networks. Finally, to check the accuracy of the success-rate prediction, the hazard map was validated using the area under curve method. The prediction accuracy of the hazard map was 89%. Based on these results the authors conclude that frequency ratio models can be used to mitigate hazards related to landslides and can aid in land-use planning.     

Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images

We present a method for the semi-automatic recognition and mapping of recent rainfall induced shallow landslides. The method exploits VHR panchromatic and HR multispectral satellite images, and was tested in a 9.4 km² area in Sicily, Italy, where on 1 October 2009 a high intensity rainfall event caused shallow landslides, soil erosion, and inundation. Pre-event and post-event images of the study area taken by the QuickBird satellite, and information on the location and type of landslides obtained in the field and through the interpretation of post-event aerial photographs, were used to construct and validate a set of terrain classification models. The models classify each image element (pixel) based on the probability that the pixel contains (or does not contain) a new rainfall induced landslide. To construct and validate the models, a procedure in five steps was adopted. First, the pre-event and the post-event images were pan-sharpened, ortho-rectified, co-registered, and corrected for atmospheric disturbance. Next, variables describing changes between the pre-event and the post-event images attributed to landslide occurrence were selected. Next, three classification models were calibrated in a training area using different multivariate statistical techniques. The calibrated models were then applied in a validation area using the same set of independent variables, and the same statistical techniques. Lastly, combined terrain classification models were prepared for the training and the validation areas. The performances of the models were evaluated using four-fold plots and receiver operating characteristic curves. The method proved capable of detecting and mapping the new rainfall induced landslides in the study area. We expect the method to be capable of detecting analogous shallow landslides caused by similar (rainfall) or different (e.g. earthquake) triggers, provided that the event slope failures leave discernable features captured by the post-event satellite images, and that the terrain information and satellite images are of adequate quality. The proposed method can facilitate the rapid production of accurate landslide event-inventory maps, and we expect that it will improve our ability to map landslides consistently over large areas. Application of the method will advance our ability to evaluate landslide hazards, and will foster our understanding of the evolution of landscapes shaped by mass-wasting processes.     

Object-oriented Detection of Landslides based on the Spectral，Spatial and Morphometric Properties of Landslides

Earthquakes in mountain area often induce hundreds of thousands of landslides resulting in destructive casualties and economic damage.It is urgent needed to rapidly detect the extent areas of the landslides.With the advent of very high resolution satellite remote sensing,the application of the object\|oriented classification method in this area have significant advantage comparing to those of visual interpretation and pixel\|based methods.However,the study of object\|oriented landslide detection is relatively few,and the study usually has a small study area.The method of object\|oriented rapid identification of landslides based on the spectral,spatial and morphometric properties of landslides and a 2.5m SPOT5 multi\|spectral image is proposed in this paper and is applied in a relatively large study area.The normalized difference vegetation index (NDVI) threshold was set to remove vegetation objects and obtain landslide candidates.Then,the spectral characteristics,texture,terrain features and context of the image were used to build indicators to gradually separate the landslide from false positives.The small scale chessboard segmentation was conducted to further eliminate vegetation objects and get the landslide objects.The object\|oriented detection results show that the adopted method can recognize about 95% of the landslides in the study area.When considering the landslide excessive detection and omissions,the landslide detection quality percentage of the proposed method is 74.04%.Hence,the method proposed in the article can help to rapid assess landslide disasters caused by earthquakes or heavy rainfalls,providing a reference for post\|disaster emergency relief and reconstruction work.     

A methodology for improving landslide PSI data analysis

In this work, we present a methodology for improving persistent scatterer interferometry (PSI) data analysis for landslide studies. This methodology is a revision of previously described procedures with several improved and newly proposed aspects. To both evaluate and validate the results from this methodology, we used various persistent scatterer (PS) datasets from different satellites (ERS – ENVISAT, Radarsat, TerraSAR-X, and ALOS PALSAR) that were processed using three PSI techniques (stable point network – SPN, permanent scatterer interferometry – PSInSAR?, and SqueeSAR?) to map and monitor landslides in various mountainous environments in Spain and Italy. This methodology consists of a preprocessing model that predicts the presence of a PS over a certain area and a post-processing method used to determine the stability threshold, project the line of sight (LOS) velocity along the slope, estimate the E–W and vertical components of the velocity, and identify anomalous areas.     

Comparison of GIS-based methodologies for the landslide susceptibility assessment

In this paper, two simple GIS-based methodologies have been used to assess the landslide susceptibility in a basin located in Southern Italy. The methodologies at issue, based on the spatial distribution of landslides and/or of causal factors, are bivariate statistics-based and expert-based, respectively. The spatial distribution of both the landslides and the causal factors has been investigated by integrating pre-existing and original data, which have been processed using ArcView GIS 3.2 software. The obtained results, consisting of landslide susceptibility maps have been compared and discussed. The bivariate statistics-based method has provided the most satisfying results. On the contrary, the expert-based method has provided a classification of the study area in terms of landslide susceptibility which does not completely fit with the surveyed spatial distribution of the landslides.

西藏墨脱县甘登乡滑坡遥感应急调查

利用4个类型11个时相的卫星数据，采用“数字滑坡”技术进行处理及解译获取灾害特征信息，基于地学原理进行的动态空间分析认为：最近发生在我国雅鲁藏布江大拐弯下游右岸，西藏甘登乡菊汤蒙的堵江性质为原已存在的一崩滑群的局部复活，为一自然重力侵蚀现象。自2008年汛期以来曾有过3次较大规模的活动堵江，崩滑活动的规模约为500×104m3 。卫星监测表明，菊汤蒙崩塌群正处于活动期，会经常发生堵江。该段河流位于高山峡谷，滑坡坝堵江后可在较短时间冲开，溃坝可能在下游造成一定的灾害，建议作为重大地质灾害卫星监测区域。     

Semi-automatic approach for identifying locations of shallow debris slides/flows based on lidar-derived morphological features

Identification of landslides at the regional scale has always been a challenging problem. Various automatic landslide identification methods, mainly relying on spectral information from aerial photographs or satellite imagery, have been developed. This paper proposes a semi-automatic approach to identify locations of small-sized shallow debris slides and flows using airborne lidar (light detection and ranging) data. Cells related to landslide components were first extracted by using a new method based on local Moran’s I (LMI). Subsequently, cell clusters representing landslide components and other terrain objects were discriminated through geometric and contextual analysis at cluster level. The approach was tested in a study area in Hong Kong and the identification result was verified by a landslide inventory. Locations of 93.5% of recent landslides and 23.8% of old landslides were identified by the proposed approach. The result indicates that the proposed approach is able to identify both recent and old landslides with distinct morphological features. However, the proposed approach also identified a large number of locations (77.6% of all locations) unrelated to landslides. These locations may correspond to terrain objects with similar morphology to debris slides or flows, and indicate rough terrain in the study area. In addition, the effects of DEM (digital elevation model) resolution on landslide identification were analysed by applying the LMI-based method to digital elevation models (DEMs) at different resolutions. The results indicate that the smoothing effect caused by lowering DEM resolution led to extraction of fewer landslide components.     

基于NDVI变化检测的滑坡遥感精细识别

随着遥感技术的发展,高分辨率的卫星影像数据逐渐丰富,滑坡灾害的信息提取被进一步推进,当前滑坡灾害应急调查主要以目视解译和野外调查为主,费时费力,难以满足灾后救援的迫切需求。面向像元和面向对象的单时相滑坡遥感信息提取方法等存在着滑坡过识别、误识别的问题。因此,在此提出以滑坡前后多时相遥感影像为数据源的变化检测滑坡识别方法,首先根据归一化植被指数（NDVI）进行基于像元的变化检测确定滑坡预选区,再结合面向对象的几何规则完成滑坡的精细识别,这种基于变化检测和几何规则相结合的方法能有效排除道路、建筑、裸地等光谱特征与滑坡相似的非滑坡部分。以九寨沟滑坡为例,采用高分一号8 m分辨率多光谱相机2015年8月1日的影像（滑前）以及2017年8月16日的影像（滑后）作为数据源,进行滑坡识别实验。结果表明,和面向对象的单时相方法相比,基于变化检测和几何规则相结合的多时相方法滑坡提取的精度较高,制图精度高达88.80%,用户精度高达81.19%,都大幅超过面向对象单时相法的精度,漏分误差及错分误差分别下降23.22%和11.72%,可为有效组织滑坡灾后救援与重建工作提供可靠依据。     

PSI technique for analysing slow landslides: a useful tool to handle with care

Persistent scatterers interferometry (PSI) based on the analysis of satellite synthetic aperture radar (SAR) data in the field of landslide mapping is becoming a widespread tool, commonly used together with traditional geomorphological survey techniques and other monitoring instruments. Having acquired permanent scatterers interferometry SAR (PSInSAR?) data since 2005, the Region of Liguria has in recent years carried out several operational tests to define the correct procedures to provide appropriate interpretations of PSI data sets with respect to landslide mapping and state of activity definition. These experiences have resulted in the elaboration of a semi-automatic procedure using spatial analysis tools provided by any commercial geographic information system (GIS) software to allow a quick overview of huge data and obtain indications of potentially unstable areas. An analysis of the results shows a good general congruity between the potentially unstable areas detected and landslide inventory maps, but also some anomalies.     

Persistent Scatterers Interferometry Hotspot and Cluster Analysis (PSI-HCA) for detection of extremely slow-moving landslides

The synthetic aperture radar (SAR) interferometry (InSAR) technique has already shown its importance in landslide mapping and monitoring applications. However, the usefulness of traditional differential InSAR applications is limited by disturbing factors such as temporal decorrelation and atmospheric disturbances. The Persistent Scatterers Interferometry (PSI) technique is a recently developed InSAR approach. It generates stable radar benchmarks (namely persistent scatterers, PSI point targets) using a multi-interferogram analysis of SAR images. The PSI technique has the advantage of reducing temporal decorrelation and atmospheric artefacts. The PSI technique is suitable for the investigation of extremely slow-moving landslides due to its capability to detect ground displacements with millimetre precision. However, the interpretation of PSI outputs is sometimes difficult for the large number of possible persistent scatterers (PSs). A new approach of PSI Hotspot and Cluster Analysis (PSI-HCA) is introduced here in order to develop a procedure for mapping landslides efficiently and automatically. This analysis has been performed on PSs in hilly and mountainous areas within the Arno river basin (Italy). The aim is to use PSs processed from 4 years (2003–2006) of Radarsat images for identifying areas preferentially affected by extremely slow-moving landslides. The Getis–Ord G_i *?statistic is applied in the study for the PSI-HCA approach. The velocity of PSs is used as weighting factor and the G_i *?index is calculated for each single point target. The results indicate that both high positive and low negative G_i *?values imply the clustering of potential mass movements. High positive values suggest the moving direction towards the sensor along the satellite line-of-sight (LOS), whereas low negative values imply the movement away from the sensor. Furthermore, the kernel function is used to estimate PS density based on these derived G_i *?values. The output is a hotspot map which highlights active mass movements. This spatial statistic approach of PSI-HCA is considered an effective way to extract useful information from PSs at a regional scale, thus providing an innovative approach for rapid mapping of extremely slow-moving landslides over large areas.     

Traffic monitoring with spaceborne SAR—Theory, simulations, and experiments

This paper reviews the theoretical background for upcoming dual-channel radar satellite missions to monitor traffic from space and exemplifies the potentials and limitations by real data. In general, objects that move during the illumination time of the radar will be imaged differently than stationary objects. If the assumptions incorporated in the focusing process of the synthetic aperture radar (SAR) principle are not met, a moving object will appear both displaced and blurred. To study the impact of these (and related) distortions in focused SAR images, the analytic relations between an arbitrarily moving point scatterer and its conjugate in the SAR image have been reviewed and adapted to dual-channel satellite specifications. Furthermore, a specific detection scheme is proposed that integrates complementary detection and velocity estimation algorithms with knowledge derived from external sources as, e.g., road databases. Results using real SAR data are presented to validate the theory.     

Application of binary logistic regression analysis and its validation for landslide susceptibility mapping in part of Garhwal Himalaya,India

Landslides cause heavy damage to property and infrastructure, in addition to being responsible for the loss of human lives, in many parts of the Himalaya. It is possible to take appropriate management measures to reduce the risk from potential landslide hazard with the help of landslide hazard zonation (LHZ) maps. The present work is an attempt to utilize binary logistic regression analysis for the preparation of a landslide susceptibility map for a part of Garhwal Himalaya, India, which is highly prone to landslides, by taking the geological, geomorphological and topographical parameters into consideration. Remote sensing and the geographic information system (GIS) were found to be very useful in the input database preparation, data integration and analysis stages. The coefficients of the predictor variables are estimated using binary logistic regression analysis and are used to calculate the landslide susceptibility for the entire study area within a GIS environment. The receiver operator characteristic curve analysis gives 88.7% accuracy for the developed model.     

Pre-survey suitability evaluation of the differential synthetic aperture radar interferometry method for landslide monitoring

The active remote-sensing technique differential radar interferometry (D-InSAR) is a powerful method for detection and deformation monitoring of landslides. But the radar-specific imaging geometry causes specific spatial distortions in radar images (as e.g. the layover and shadowing effect), which have a negative impact on the suitability of these images for D-InSAR applications. To address this issue, we present a geographical information system (GIS) procedure to accurately predict the areas in which layover and shadowing will occur, before the area of interest is recorded by radar. Additionally, the percentage of measurability of movement of a potential landslide can be ascertained. In the third part of the GIS procedure, the main types of land cover are classified in regard to their influence on applicability of the D-InSAR technique, depending on the characteristics of the sensor used. The results of the analyses are objective pre-survey estimation of the potential applicability of the D-InSAR technique for landslide monitoring prior to the costly investment of a radar survey.     

Detailed rockslide mapping in northern Norway with small baseline and persistent scatterer interferometric SAR time series methods

Rockslides have a high socioeconomic and environmental importance in many countries. Norway is particularly susceptible to large rockslides due to its many fjords and steep mountains. One of the most dangerous hazards related with rock slope failures are tsunamis that can lead to large loss of life. It is therefore very important to systematically identify potential unstable rock slopes. Traditional landslide monitoring techniques are expensive and time consuming. Differential satellite interferometric synthetic aperture radar (InSAR) is an invaluable tool for land displacement monitoring. Improved access to time series of satellite data has led to the development of several innovative multitemporal algorithms. Small baseline (SB) methods are based on combining and inverting a set of unwrapped interferograms that are computed with a small perpendicular baseline in order to reduce spatial phase decorrelation. Another well proven technique is the persistent scatterer interferometric method (PSI) that is based on analysis of persistent point targets. In this paper, we apply both approaches to study several rockslide sites in Troms County in the far north of Norway. Moreover, we take the opportunity to address the difference and similarities between the SB and the PSI multitemporal InSAR methods for displacement studies in rural terrain.