Assessment of Land Surface Subsidence Due to Underground Metal Mining Using Integrated Spaceborne Repeat-Pass Differential Interferometric Synthetic Aperture Radar (DInSAR) Technique and Ground Based Observations

Differential interferometric synthetic aperture radar (DInSAR) is a novel remote sensing technique to measure earth surface deformation. It is capable of obtaining dense information related to the deformation of a large area efficiently, economically and effectively. Therefore, DInSAR is a promising technology for monitoring the earth surface deformation related to some natural hazardous events, such as earthquake, volcano eruption, land subsidence, landslide. In present study, Conventional DInSAR technique have been applied to a mineral rich zone, coming under the Khetri copper belt, a part of Northern Aravali range of hillocks in India, predominant with mining activities since late 1960’s to address the possibility of deformation phenomena due to hard rock underground metal mining. Four interferometric SAR data sets of Radarsat-2 was used for the study area to address the subsidence/uplift phenomena. Further, results obtained from conventional DInSAR technique using Radarsat-2 data sets compared with results obtained from ground based observation technique for its validity. In both the techniques, deformation results obtained in terms of average subsidence rate in mm (quarterly basis) of points under study within mining zone of Mine-A has well agreed to each other. Further, it has been observed that average subsidence rate in mm (quarterly basis) obtained from space based observation and ground based observation are 5.6 and 6.67, respectively over the points under study in mining zone of Mine-A.     

Landslide susceptibility mapping in a hilly terrain using remote sensing and GIS

Remote sensing and Geographic Information System (GIS) are well suited to landslide studies. The aim of this study is to prepare a landslide susceptibility map of a part of Ooty region, Tamil Nadu, India, where landslides are common. The area of the coverage is approximately 10 × 14 km in a hilly region where planting tea, vegetables and cash crops are in practice. Hence, deforestation, formation of new settlements and changing land use practices are always in progress. Land use and land cover maps are prepared from Indian Remote Sensing Satellite (IRS 1C - LISS III) imagery. Digital Elevation Model (DEM) was developed using 20 m interval contours, available in the topographic map. Field studies such as local enquiry, land use verification, landslide location identification were carried out. Analysis was carried out with GIS software by assigning rank and weights for each input data. The output shows the possible landslide areas, which are grouped for preparation of landslide susceptibility maps.     

Using Sentinel-1A DInSAR interferometry and Landsat 8 data for monitoring water level changes in two lakes in Crete,Greece

Differential Interferometric Synthetic Aperture Radar (DInSAR) methodology has been successfully employed to detect water level changes and produce corresponding water level variation maps. In this study, Agia and Kournas lakes, located in Western Crete, Greece, were used as pilot areas to monitor water level change with means of SAR interferometry and auxiliary Earth Observation (EO) data. The water level variation was monitored for the period 2015–2016, using Sentinel-1A imageries and corresponding stage water level data. Landsat 8 data were additionally used to study vegetation regime and surface water extent and how these parameters affect interferograms performance. The results highlighted the fact that the combination of SAR backscattering intensity and unwrapped phase can provide additional insight into hydrological studies. The overall analysis of both interferometric characteristics and backscattering mechanism denoted their potential in enhancing the reliability of the water-level retrieval scheme and optimizing the capture of hydrological patterns spatial distribution.     

Monitoring ground surface deformation over the North China Plain using coherent ALOS PALSAR differential interferograms

North China Plain (Huabei Plain) is one of the most densely populated regions on earth. Due to excessive underground water extraction, the North China Plain has been experiencing severe ground deformation over the last three decades. Therefore, for the purpose of hazard mitigation, it is necessary to monitor the ground displacement occurred in this region. As an extension of the differential radar interferometry (DInSAR) technique, advanced DInSAR techniques involving multiple images have demonstrated the potential to effectively map ground displacement. Such techniques are able to measure the temporal evolution of ground deformation with millimetre-level accuracy by using a stack of differential interferograms. In this study, the ALOS PALSAR data acquired over the North China Plain, which cover an area of approximately 16,000 km $^2$ , were processed based on the concept of advanced DInSAR techniques. Because of the large size of the PALSAR images, a targeted processing strategy was designed. This strategy is able to reduce required disk storage space and I/O operations, leading to the improvement of the computational efficiency. The resulting mean deformation velocity map demonstrates that a large portion of the area covered by the data was affected by various degrees of ground deformation between January 2007 and April 2010. The ground deformation is mostly distributed in rural areas, while the downtown areas are generally stable.     

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.     

Monitoring surface changes in discontinuous permafrost terrain using small baseline SAR interferometry,object-based classification,and geological features: a case study from Mayo,Yukon Territory,Canada

Permafrost-induced deformation of ground features is threating infrastructure in northern communities. An understanding of permafrost distribution is therefore critical for sustainable adaptation planning and infrastructure maintenance. Considering the large area underlain by permafrost in the Yukon Territory, there is a need for baseline information to characterize the permafrost in this region. In this study, the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique was used to identify areas of ground movement likely caused by changes in permafrost. The DInSAR technique was applied to a series of repeat-pass C-band RADARSAT-2 observations collected in 2015 over the Village of Mayo, in central Yukon Territory, Canada. The conventional DInSAR technique demonstrated that ground deformation could be detected in this area, but the resulting deformation maps contained errors due to a loss of coherence from changes in vegetation and atmospheric phase delay. To address these limitations, the Small BAseline Subset (SBAS) InSAR technique was applied to reduce phase error, thus improving the deformation maps. To understand the relationship between the deformation maps and land cover types, an object-based Random Forest classification was developed to classify the study area into different land cover types. Integration of the InSAR results and the classification map revealed that the built-up class (e.g., airport) was affected by subsidence on the order of ?2 to ?4 cm. The spatial extent of the surface displacement map obtained using the SBAS InSAR technique was then correlated with the surficial geology map. This revealed that much of the main infrastructure in the Village of Mayo is underlain by interbedded glaciofluvial and glaciolacustrine sediments, the latter of which caused the most damage to human made structures. This study provides a method for permafrost monitoring that builds upon the synergistic use of the SBAS InSAR technique, object-based image analysis, and surficial geology data.     

A GIS-based comparative study of Dempster-Shafer,logistic regression and artificial neural network models for landslide susceptibility mapping

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.     

角反射器差分干涉方法及反距离加权最小费用流相位展开算法

合成孔径雷达差分干涉测量(Differential Interferometric Synthetic Aperture Radar,DInSAR)在低相关区由于受时间空间去相关的影响,无法得到有效应用。角反射器DInSAR方法能在长时间段内保持幅度和相位稳定性,可以最大程度地减小去相关的影响。但由于反射器在空间上一般形成不规则稀疏网络分布,在平地相位、高程相位计算及相位展开方法上都带来新的挑战。研究三角反射器的DInSAR技术,重点分析基于不规则离散点的最小费用流相位展开算法。对费用流算法权重的选择,通过分析残差的产生来源,提出以弧所穿越的边长度倒数作为弧费用的权重设置方法,解决费用流算法中具有相同费用路径的选择问题。最后将角反射器DInSAR技术应用于滑坡移动的监测,通过对140d时间段的监测,得到与实测值具有较好一致性的结果。     

DInSAR技术对地震同震形变场的研究

合成孔径雷达差分干涉测量（DInSAR）可用于监测厘米级或更微小的地表形变,以揭示许多物理现象,如地震形变、火山运动、大气变化、冰川漂移、地面沉降以及山体滑坡等。DInSAR作为一种新型的空间对地观测技术,具有不受时间和空间的限制、对地物具有一定的穿透性等传统测量所不可比拟的优势,已得到较为广泛的应用。简要介绍DInSAR技术的基本原理及其处理流程,以Barn地震为例提取Bam地震的同震形变场。     

IRS-LISS-III and PAN data analysis for landslide susceptibility mapping using heuristic approach in active tectonic region of Himalaya

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.     

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.     

Spatial and temporal change in landslide hazard by future climate change scenarios using probabilistic-based frequency ratio model

The aims of this study were to apply, verify and compare a frequency ratio model for landslide hazards, considering future climate change and using a geographic information system in Inje, Korea. Data for the future climate change scenario (A1B), topography, soil, forest, land cover and geology were collected, processed and compiled in a spatial database. The probability of landslides in the study area in target years in the future was then calculated assuming that landslides are triggered by a daily rainfall threshold. Landslide hazard maps were developed for the two study areas, and the frequency ratio for one area was applied to the other area as a cross-check of methodological validity. Verification results for the target years in the future were 82.32–84.69%. The study results, showing landslide hazards in future years, can be used to help develop landslide management plans.     

Landslide inventory validation and susceptibility mapping in the Gerecse Hills,Hungary

Landslides pose a threat to property both in the populated and cultivated areas of the Gerecse Hills (Hungary). The currently available landslide inventory database holds the records from many sites in the area, but the database is out-of-date. Here we address the problem of revising the National Landslides Cadastre landslide inventory database by creating a landslide suscept- ibility map with a multivariate model based on likelihood ratio functions. The model is applied to the TanDEM-X DEM (0.4″ res.), the current landslide inventory of the area, and data acquired from geological maps. By comparing the distributions of four variables in the landslide and non-landslide area with grid computation methods, the model yields landslide susceptibility estimates for the study area. The estimations show to what extent a certain area is similar to the sample areas, therefore, its likelihood to be affected by landslides in the future. The accuracy of the model predictions was checked in the field and compared to the results of our previous study using the SRTM-1 DEM for a similar analysis. The model gave accurate estimates when certain correction measures were applied to the input datasets. The limitations of the model, the input datasets, and the suggested correction measures are also discussed.     

Effects of Landslide Sampling Strategies on the Prediction Skill of Landslide Susceptibility Modelings

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.     

Analytical optimization of a DInSAR and GPS dataset for derivation of three-dimensional surface motion

A revised method for derivation of three-dimensional surface motions maps from sparse global positioning system (GPS) measurements and two differential interferometric synthetic aperture radar (DInSAR) interferograms based on a random field theory and Gibbs-Markov random fields equivalency within Bayesian statistical framework is proposed. It is shown that the Gibbs energy function can be optimized analytically in the absence of a neighboring relationship between sites of a regular lattice. Because the problem is well posed, its solution is unique and stable, and additional regularization in the form of smoothness is not required. The proposed algorithm is simple in realization, does not require extensive computer power, and is very quick in execution. The results of inverse computer modeling are presented and show a drastic improvement of accuracy when both GPS and DInSAR data are used.     

Coseismic liquefaction phenomenon analysis by COSMO-SkyMed: 2012 Emilia (Italy) earthquake

The liquefaction phenomenon that occurred in the coseismic phase of the May 20, 2012 Emilia (Italy) earthquake (ML 5.9) is investigated. It was induced by the water pressure increase in the buried and confined sand layers. The level-ground liquefaction was the result of a chaotic ground oscillation caused by the earthquake shaking and the observed failures were due to the upward water flow caused by the excess of pore pressures. We exploited the capability of the differential synthetic aperture radar interferometry (DInSAR) technique to detect soil liquefactions and estimate their surface displacements, as well as the high sensitivity to surface changes of complex coherence, SAR backscattering and intensity correlation. To this aim, a set of four COSMO-SkyMed X-band SAR images, covering the period April 1–June 6, 2012, was used. Geological–geotechnical analysis was also performed in order to ascertain if the detected SAR-based surface effects could be due to the compaction induced by liquefaction of deep sandy layers. In this regards, the results obtained from 13 electrical cone penetrometer tests show the presence of a fine to medium sandy layer at depths, ranging between 9 and 13 m, which probably liquefied during the earthquake, inducing vertical displacements between 3 and 16 cm. The quantitative results from geological–geotechnical analysis and the surface punctual effects measured by DInSAR are in good agreement, even if some differences are present, probably ascribable to the local thickness and depth variability of the sandy layer, or to lack of deformation detection due to DInSAR decorrelation. The adopted approach permitted us to define the extent of the areas that underwent liquefaction and to quantify the local subsidence related to these phenomena. The latter achievement provides useful information that must be considered in engineering practices, in terms of expected vertical deformations.     

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.     

Detecting the slope movement after the 2018 Baige Landslides based on ground-based and space-borne radar observations

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.     

The influence correction of boreal forest vegetation on SRTM data

The digital elevation model based on SRTM is very convenient for a wide range of studies but requires correction due to the influence of forest vegetation. The present study was conducted to analyse the effect of boreal forests on altitudes, aspects and slopes calculated from the SRTM. A DEM based on topographic maps at 1:100 000 scale was used as a reference. The linear regression analysis showed low data correlation in forested areas. The presence of different types of forests and felling in the woods leads to a complex distribution of deviations from the SRTM. A simple correction method was proposed, using a forest mask, built according to Landsat, and forest heights indicated on the topographic maps. After correction, the correlation coefficient between the altitudes increased by 0.05–0.14, the share of matching aspects by 1–4% and the share of matching slopes by 2–8%.