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Although the atmospheric compensation models developed to date have been generally robust and effective for InSAR, how to choose the right atmospheric correction method for ground-based InSAR is worth studying. This paper uses different methods for atmospheric phase screen (APS) compensation based on studies of ground-based radar (GBR) to zero-baseline acquisitions over the Woda landslide in Sichuan, China. This landslide has a steep topography, thick fog, turbulent rivers and strong rains. Data were acquired at the Ku-band using a GAMMA Portable Radar Interferometer II (GPRI-II) in multiple campaigns (4 July 2019–13 July 2019). In 2019, the Miyun open-pit mine was also studied; the maximum deformation reached 4.03 mm/hour. The collected data were processed within one unit based on the small baseline set (SBAS) approach. The experimental results show the following (1) For long-distance monitoring with complex atmospheric disturbances, range- and/or height-dependent models fail. (2) The iterative decomposition (ITD) method can effectively address complex atmospheric disturbances. (3) If the threshold of wavelength is set to be large, the ITD method becomes a stratified model. [ABSTRACT FROM AUTHOR]

Landslide mapping is the primary step for landslide investigation and prevention. At present, both the accuracy and the degree of automation of landslide mapping with remote sensing (LMRS) are still lower than those of general remote sensing classification. In order to improve the performance, previous attempts have been made to develop new features, classifiers, and rules, whereas few studies have investigated the in-depth causes and the corresponding solutions. In this paper, after reviewing the related literature, some of the fundamental difficulties hindering the improvement of LMRS are disclosed and discussed. Firstly, landslides do not have distinguishable spectral, spatial, or temporal characteristics, as they may actually be covered by other land covers. Secondly, the surface features of a landslide can vary greatly, affected by the different geological factors, geomorphological factors, hydrological factors, weather conditions, and other factors. Thirdly, the differences in the surface features are often remarkable and nonnegligible, and thus it is difficult to identify a landslide with only a few simple criteria. Finally, some solutions to the above difficulties are suggested. It is expected that the accuracy and applicability of LMRS could be greatly improved, by exploiting big data, utilizing the deep learning technique, and modelling the surface spatial structure of the landslide. [ABSTRACT FROM AUTHOR]

Inherent complex topography and drastic weather patterns together have concocted various natural disasters worldwide. In difficult terrains such as those prevalent in the North-Eastern regions of India, coupled with the factors such as population explosion and improper land use, lead them to witness some of the world's most drastic landslides with an astonishing frequency, reckoning landslide susceptibility assessment crucial in such regions. This paper focuses on exploring a promising machine learning ensemble technique of Majority-based voting which has seldom been employed for landslide susceptibility assessment. The ensemble comprises Logistic Regression (LR), Gradient Boosted Decision Trees (GBDT) and Voting Feature Interval (VFI) to prepare landslide susceptibility zonation maps for the Brahmaputra valley region (Assam & Nagaland) and its close vicinity. In the first stage of the study, a landslide inventory for the area comprising 436 landslide locations was prepared in geographic information system (GIS), substantiated by news reports and remote sensing data. In the second stage, 16 landslide causative thematic maps including Elevation, Slope, Slope Aspect, General Curvature, Plan Curvature, Profile Curvature, Surface Roughness, Topographic Wetness Index (TWI), Stream Power Index (SPI), Slope Length, Normalized Difference Vegetation Index (NDVI), Land Use/Land Cover (LULC), Distance from Roads, Rivers, Faults and Railways were prepared. In the third stage, the landslide inventory was annexed with the causative factor maps to obtain a dataset comprising coordinates of the locations and the values of aforementioned causative factors on the corresponding coordinates. The proposed model was then trained and tested on the prepared dataset (70%:30% split). Finally, the efficiency of the new model was tested using the area under receiver operating characteristic curve (AUC of ROC). The validation results demonstrate the mettle of the proposed majority-based voting ensemble LR-GBDT-VFI (AUC: 0.98) against the conventional techniques such as Decision Trees, Support Vector Machines, Random Forest, etc. Altogether, the study offers an approach with wide scope across the field of landslide hazard assessment. [ABSTRACT FROM AUTHOR]

Automatic DEM deformation detection without ground control points (GCPs) with its application in debris flow change analysis is an attractive but very difficult research topic. Based on the existing methods of M‐LZD and LMS‐LZD, this paper presents an improved method that takes both magnitude and relationship into account through allocating an appropriate weight to each observation and evaluating the contribution of each observation to deformation with the allocated weight. Such improvement enhances greatly the ability of DEM deformation detection. Compared with the M‐LZD and LMS‐LZD algorithm, using the multi‐temporal DEMs of the Puwaigou valley, along the Hengduan mountain ranges in the southwest of China, where debris flows occur frequently during each summer due to heavy rainfall and each Autumn due to rapid snowmelt, the experimental results demonstrate that our method has the highest detection accuracy (RMS) and detection percentage (capability) of over 50%. The advantages of our method are (1) it does not need GCPs and prior information and knowledge of the terrain surface, and (2) it can calculate simultaneously the volume and determine the spatial distribution of terrain surface changes of multi‐temporal DEMs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Although the differential interferometric synthetic aperture radar (DInSAR) technique has the potential for monitoring ground deformation with millimetric accuracy, it still suffers from temporal and geometric decorrelation. In this paper, the corner reflectors differential interferometry synthetic aperture radar (CRDInSAR) technique was used to overcome the limitations of conventional DInSAR. We studied the basic principles of CRDInSAR, discussed the calculation of the flat earth and topographic phases based on the geometry of satellite and corner reflectors, presented the phase unwrapping approach for the sparse grid of corner reflectors, then investigated the construction of, and solution to, the unwrapped phase system equation. Subsequently, we applied CRDInSAR to monitor the displacement of the Shuping landslide in Hubei Province, China. In this study, we processed five SAR images on a descending pass acquired by the Environmental Satellite (ENVISAT) Advanced Synthetic Aperture Radar (ASAR) sensor from September 2005 to March 2006, and compared the achieved results of CRDInSAR with Global Positioning System (GPS) measurements at the same time of the SAR data acquisition assumed as reference. The result indicates a good agreement between the measurements provided by the two different techniques, which shows that CRDInSAR allows monitoring of slow landslide deformation in low coherence areas and provides accurate results. [ABSTRACT FROM AUTHOR]

Two large tropical cyclones struck Taiwan in the summer of 2004 and landslides triggered by these events caused not only casualties and housing damage but also produced large volumes of sediment that entered rivers and reservoirs. For reservoir and watershed management it is important to quickly identify the location and areal extent of new landslides for coordinating mitigation efforts. In this study, two automated methods, supervised and unsupervised classification of 10 m multi-spectral SPOT-5 imagery, were tested for their ability to identify and map landslide areas before and after the two storm events. A slope map was applied to mask roads, riverbeds and agricultural fields erroneously commissioned as landslides. The automated classification results were compared with manually delineated landslides using SPOT-5 supermode satellite imagery with a resolution of 2.5 m. Statistical testing and spatial analysis of the mapping results were performed. Finally, the results from all three methods were validated by using 0.35 m orthophotographs. This paper reports the results and discusses the salient differences between the automated and manual methods. [ABSTRACT FROM AUTHOR]

In recent years, SAR interferometry has become one of the most popular emerging techniques for the assessment of ground displacements, and, as such, it is of great interest as a possible operational tool for civil protection institutions having to deal with landslide risk. The paper presents some of the results obtained in northern Italy during a research project aimed at testing the potentiality of the application of C‐band space‐borne interferometry and Ku‐band ground based interferometry during different specific civil protection activities. Main research objectives were the detection of the movements of complex earth and rock slides affecting built‐up areas during the 1990s, and the near real‐time monitoring of a reactivated rotational earth slide over an emergency period of 15 days. Results of space‐borne interferometry did qualitatively fit with the geological interpretation of the mass movements and with ground truths such as damaged buildings and in situ monitoring systems. However, this was not achieved in quantitative terms, suggesting that this technique should be used limitedly for displacement recognition and not monitoring. On the other hand, ground‐based interferometry proved valuable both for a qualitative and a quantitative estimate of slope movements. Nonetheless, the research has also enabled the limitations that are still to be tackled in order to bring these systems to an operational usage in civil protection to be highlighted. [ABSTRACT FROM AUTHOR]

Landslides are some of the most destructive and recurrent natural hazards worldwide. Landslides are triggered by natural phenomena such as extreme rainfall and earthquakes, causing human and economic losses. A rapid response to landslide events is necessary to assess damage mitigation and save lives and property. This study developed a landslide detection model using differential spectral indices and amplitude ratio changes with a classification and regression tree (CART), aiming to detect landslide scars after the occurrence of these events in Asian regions for testing different environment condition. The multi-temporal SAR and optical stack images were pre-processed to reduce speckle noise, seasonal noise, and atmospheric noise. This study explored change detection approaches with a minimum threshold of amplitude ratio change (Aratio), using Sentinel−1 images and the relative difference in the normalized difference vegetation index (rdNDVI), differential bare soil index (dBSI), and differential brightness index (dBI) was obtained using Sentinel−2 images. The accuracy of the model was examined by F1-scores. The accuracy of the model for landslide detection was considered moderately good to excellent. As a result of the landslide detection model, amplitude ratio change detection improved the model as revealed by the F1-scores. Moreover, this study found that differential spectral indices could be used to classify the types of landslides (deep-seated and shallow landslides) according to the level of surface changes and texture of the collapsed material after landslide events. [ABSTRACT FROM AUTHOR]

Landslides are destructive and recurrent natural disasters on steep slopes and represent a risk to lives and properties. Knowledge of relict landslides' location is vital to understand their mechanisms, update inventory maps and improve risk assessment. However, relict landslide mapping is complex in tropical regions covered with rainforest vegetation. A new CNN framework is proposed for semi-automatic detection of relict landslides, which uses a dataset generated by a k-means clustering algorithm and has a pre-training step. The weights computed in the pre-training are used to fine-tune the CNN training process. A comparison between the proposed and the standard framework is performed using CBERS-04A WPM images. Three CNNs for semantic segmentation are used (Unet, FPN and Linknet) with two augmented datasets. A total of 42 combinations of CNNs are tested. Values of precision and recall were very similar between the combinations tested. Recall was higher than 75% for every combination, but precision values were usually smaller than 20%. False positives (FP) samples were addressed as the cause for these low precision values. Predictions of the proposed framework were more accurate and correctly detected more landslides. This work demonstrates that there are limitations for detecting relict landslides in areas covered with rainforest, mainly related to similarities between the spectral response of pastures and deforested areas with Gleichenella sp. ferns, commonly used as an indicator of landslide scars. Relict landslide detection has limitations in rainforest environments The proposed framework for CNN semantic segmentation proved to be useful for relict landslide detection Land cover features spectral characteristics, and input data technical specificities are crucial to the accuracy of CNN models No optimal combination of parameters has been identified No CNN proved to be superior [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) came into practical use as a geohazard monitoring tool in the 1990s. The uptake of this remote sensing technique however is geographically varying, and Australia has been one of many continents not fully utilizing the available InSAR data. As a result, limited knowledge exists on the application of InSAR data to obtain insights and benefits in geohazard risk monitoring and management. Due to the said limitation, this research carried out a concise technical background study and investigated a suite of case studies to demonstrate the potential for geohazard monitoring and assessment in Australia using InSAR. The differential InSAR workflow was applied to sites that experienced heaving and subsiding of the ground surface, mining, earthquake, landslides, and tunnelling in West Metro Melbourne (VIC), Musgrave (SA), Crinum mine (QLD), Pelham (TAS), Lake Muir (WA), and Sydney (NSW). The results of the Differential InSAR analysis resulted in acceptable ground movement and deformation estimates when compared with measured field data. Future opportunities related to new applications of the InSAR technique on geohazard reduction considering climate change contributions and probable coupling with artificial intelligence techniques were also discussed. This research has formed a conducive and scientific learning medium, which may encourage the growth of multidisciplinary research combining the fields of remote sensing and geotechnical engineering to investigate the impact of geohazards. [ABSTRACT FROM AUTHOR]

A landslide susceptibility map (LSM) is a valuable tool for landslide assessment and land use management. This research proposes a landslide susceptibility dynamic map (DLSM) to increase LSM utility and update the predicted map in a time series. Slope units, as basic mapping units, were produced to define the landslide boundaries and simplify the mapping in the DLSM. The permanent scatterer interferometric synthetic aperture radar (PSInSAR) technique was used to estimate the line of sight velocity (Vlos). This was then reprojected into the velocity in the steepest slope direction (Vslope) to avoid the influence of geometric distortion. The DLSM was produced by integrating, using slope unit aggregate values, the susceptibility (probability) of landsliding predicted by logistic regression based on eight spatial covariates and the Vslope predicted using the PSInSAR technique. The DLSM is a dynamically changing susceptibility map in which susceptibility is increased in certain months, particularly where surface velocity increases following the rainy season. The proportion of the study area classified with extremely high susceptibility increased from 22.2% to 44.8% after the rainy season. The DLSM, thus, potentially improves the prediction reliability for slow-moving landslides and, in particular, can help to avoid false negatives. The DLSM can be applied in areas for which radar data are available and can provide more reliable and readily interpretable results to decision-makers. [ABSTRACT FROM AUTHOR]

Landslides, threatening millions of human lives, are geological phenomena on earth, occurred frequently. An increasing number of techniques are being used to monitor landslide deformation. Among these, Light Detection and Ranging (LiDAR) stands out for its high efficiency and accuracy in displacement detection, particularly for giant landslides. In this work, we collected two temporal datasets of terrain laser scanning and proposed a flowchart for giant landslide displacement analysis using the point cloud set conflict（PCSC） technique. First, the terrestrial points were obtained by performing registration and off-terrain point filtering. Second, the landslide displacement field was acquired using the proposed method based on its surface roughness. The displacement results from our established methodological system are comparable with the ones of Interferometric Synthetic Aperture Radar (InSAR)-derived deformations. The differences estimated from two systems are at the centimetre level. Cross-analysis on the trigger factor with landslide occurred mechanism could be achieved based on the results as well. Therefore, this work provides a novel system to analyse the displacement of a giant landslide in the future study. [ABSTRACT FROM AUTHOR]

In this article, a method for landslides identification based on polarimetric decomposition techniques and the Wishart classifier is presented. Several parameters of Cloude decomposition and Freeman–Durden decomposition are introduced to analyse the radar scattering mechanisms over landslides and their surroundings. By analysing the polarimetric characteristics of three typical landslides triggered by the 2008 Wenchuan earthquake, the result demonstrates that the dominant scattering mechanism of the landslides is the surface scattering component, and the scattering entropy is centred between 0.6 and 0.8. Besides, the result also indicates that the surroundings show the dominance of the volume scattering component, whereas their scattering entropy is almost similar to the landslides. According to the experimental analysis, two temporal SAR images are classified into nine categories, respectively, based on polarimetric decomposition techniques and the Wishart classifier. By comparing the surface scattering areas in the second image with the volume scattering areas in the first image, and combining the scattering entropy, the new slope failures are obtained using the change detection method. In addition, in order to reduce the errors introduced by the registration and noises, the identification map of new landslides is processed by the morphology algorithm. Finally, a field experiment is carried out to verify the existence of the new slope failures and thereby the result shows the validity of the proposed method. [ABSTRACT FROM PUBLISHER]

On 12 May 2008 the magnitude 8.0 Wenchuan Earthquake erupted on the eastern fringe of the Tibetan Plateau in China, triggering many landslides onto towns and villages, leading to substantial loss of life and damage to property and the infrastructure. Local characteristic cloudy weather posed serious difficulties upon regional remote observation for disaster rescue in the mountainous region. To overcome weather limits to remote sensing, the study advanced one quick methodology to extract primary regional landslides with 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and terrain slope information. The threshold of temporal NDVI jump (value of 0.4) was introduced to discriminate landslide induced sharp NDVI decline from seasonal variations of vegetation cover, while slope information can be used to further confirm land cover change caused by a landslide. The study indicated that the Wenchuan Earthquake induced about 170 km2 of landslides in the southern half of the Longmenshan Fault Zone, most of which was formerly covered by forest or shrub. An ecological restoration of the areas affected by landslides will therefore be quite difficult. In addition, the result emphasizes that regions with slopes over 15° are exposed to high landslide risk, which should be carefully taken into account for settlement and transportation planning. [ABSTRACT FROM AUTHOR]

As a geological hazard, landslides cause extensive property damage and sometimes result in loss of life. Thus, it is necessary to assess areas that are vulnerable to future landslide events to mitigate potential damage. For this purpose, change detection analysis and a generalized additive model were applied to investigate potential landslide occurrences within the Sacheoncheon area, Korea. An unsupervised change detection analysis based on multi-temporal object-based segmentation of high-resolution remote sensing data and thresholding wad adopted to detect landslide-prone areas. Landslide susceptibility was predicted on the basis of detected landslide areas and GIS-based spatial databases. The generalized additive model, which can deal with categorical and continuous data as well as model the continuous data as a nonlinear smoothing function, was used for landslide susceptibility analysis. As a result, the unsupervised change detection scheme was able to detect 83% of actual landslide areas. The generalized additive model provided a superior predictive capability compared with the traditional generalized linear model. [ABSTRACT FROM AUTHOR]

The purpose of this study was to develop landslide susceptibility analysis techniques using artificial neural networks and then to apply these to the selected study areas of Gangneung in Korea. We aimed to verify the effect of data selection at training sites. Landslide locations were identified by the change detection technique of Korea Multipurpose Satellite (KOMPSAT-1) electro-optical camera (EOC) images, checked in the field, and a spatial database of topography, soil, forest, and land use was constructed. Sixteen landslide-related factors were extracted. These factors were used with an artificial neural network to analyse landslide susceptibility. Each factor weight was determined by the back-propagation training method. Five different data sets were applied to analyse and verify the effect of training. Landslide susceptibility indices were then calculated using the trained back-propagation weights, and susceptibility maps were constructed from Geographic Information System (GIS) data for five cases of difference training. The landslide susceptibility map was then compared with known landslide locations and verified. The result showed a prediction accuracy varying from 82.72% to 86.10%. The landslide susceptibility map can be used for land use planning to reduce hazards associated with landslides. [ABSTRACT FROM AUTHOR]

Drawing upon the recent advances of satellite remote-sensing technology and landslide modelling techniques, a framework is proposed to attempt an early-warning system for landslide hazards after heavy rainfall and/or earthquake, the two major triggers for landslides. This framework includes three major components: (1) a landslide susceptibility information database, including geology, elevation, topography, soil, and land-cover types; (2) a real-time space-borne precipitation estimation system (http://trmm.gsfc.nasa.gov); and (3) a near-real-time ground-shaking prediction system after earthquakes (http://earthquake.usgs.gov/eqcenter/shakemap/). The ultimate goal of this framework is to rapidly predict landslide potential after large earthquakes and/or heavy rainfall by combing the dynamic triggers with landslide susceptibility information derived from high-resolution geospatial datasets. However, the challenge for integrating these real-time systems into an operational landslide prediction network and quickly disseminating alerts around the world is tremendous. It requires continued efforts and interdisciplinary collaboration in the next 2-5 years in order to realize such a system, providing early warning for landslides around the globe in a day-to-day decision-making operation. [ABSTRACT FROM AUTHOR]

The Kashmir earthquake of 8 October 2005 that epicentred 10 km northeast of Muzaffarabad town caused widespread slope failures (landslides) extending from Balakot in the northwest to Khudpura in the southeast through Muzaffarabad and Kuroli, with the slides concentrated mostly along the river valley. The southeasterly (sun-facing) hill slopes were affected predominantly. These landscape changes and the sudden increase in turbidity in the Neelam River have been delineated on earth-observing satellite data. The occurrence of the landslides is observed linearly along a NW-SE line extending for about 40 km in and around the epicentral zone. From the damage pattern it is deduced that this linear zone might be the maximum displaced block during the earthquake, generated by reverse thrusting. [ABSTRACT FROM AUTHOR]

In the past few years differential synthetic aperture radar (SAR) interferometry (DInSAR) from ground‐based installations has provided multi‐temporal surface deformation maps of landslides. Experimental data have demonstrated its effectiveness for remote monitoring of terrain slopes and as an early‐warning system to assess the risk of rapid landslides. Following a brief description of the principles of operation of SAR interferometry, the use of a portable ground‐based radar to survey the large active landslide of Tessina, near Belluno in north‐eastern Italy, is described. This landslide was monitored for the first time in 2000. The current experimental study, carried out with a different instrumental configuration, confirms the effectiveness of this technique for estimating the evolution of landslide movements. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Landslides cause widespread damage in the Himalayas. Landslide Hazard Zonation is important to take quick and safe mitigation measures and make strategic planning for the future. A part of the Bhagirathi Valley in the Garhwal Himalaya was selected for landslide hazard zonation. The study utilized different types of data including Survey of India topographic maps, geological (lithological and structural) maps, IRS-1B and-1D multispectral and PAN satellite sensor data and field observations. The processing of multi-geodatasets was carried out in a raster GIS environment. The various data layers generated and co-registered were: landuse/landcover, buffer map of thrusts, buffer map of photo-lineaments, lithology, buffer map of drainage, slope angle and relative relief. Data integration was carried out using the ordinal scale (qualitative) relative weighting rating technique to give a Landslide Hazard Index (LHI) value. The breaks in the LHI frequency diagram were used to delineate various landslide hazard zones, namely, very low, low, moderate, high and very high. Field data on landslides were employed to evaluate and validate landslide hazard zonation map. It is interpreted that the distribution of landslides is largely governed by a combination of geoenvironmental conditions like proximity (>500 m) to the thrust zone, presence of Munsiari Formation (Higher Himalayan Crystalline) and barren or less-vegetated areas. [ABSTRACT FROM AUTHOR]