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The surface deformation of Yangshuo County is extracted by small baselines subset interferometry synthetic aperture radar(SBAS-InSAR) and persistent scatterer interferometry synthetic aperture radar(PS-InSAR) based on 30 Sentinel-1A views covering Yangshuo County from January 2018 to December 2019. The results show that the deformation rate monitored by SBAS is from -20. 1 to 16. 7 mm / a, and that monitored by PS is from -24. 4 to 24. 6 mm / a. The results obtained by the two methods are consistent. Landslide hazards are mainly distributed in highway side slope and rural mountains which are exploited by human beings. The subside is mostly affected by human activities such as production and construction. Because of the geological characteristics of complex structure in Guangxi, the influence of continuous rainfall may accelerate the subsidence. This case can provide effective data and technical support for disaster prevention and reduction in Yangshuo, and provide ideas and reference for landslide monitoring and analysis in hills and mountainous areas. It also proves that interferometric synthetic aperture radar, as a new radar measurement means, can accurately and efficiently identify landslide hazards in the area. [ABSTRACT FROM AUTHOR]

The Baihetan reservoir region is characterized by complex geomorphology, significant altitude differences, and rugged terrain. Geological hazards in such areas are often characterized by high concealment, wide distribution, and difficulty in field investigation. Traditional identification techniques are unable to detect and monitor geological hazards on a large scale with high efficiency and accuracy. In recent decades, interferometric synthetic aperture radar (InSAR) techniques, such as small baseline subset InSAR (SBAS-InSAR), have been widely applied to landslide identification. However, due to factors such as vegetation and the degree of landslide deformation, single-band synthetic aperture radar (SAR) still has certain limitations in detecting landslides. In this study, SBAS-InSAR was conducted based on ALOS-2 and Sentinel-1 ascending-descending images covering the Baihetan reservoir region. Deformation identification results were utilized to conduct a statistical analysis of the SAR detection performance and landslide characteristics, and the effect of vegetation on the detection effectiveness of different SAR bands was discussed. The study revealed that when surface vegetation coverage reaches a high degree, the percentage of areas with coverage greater than 0.6 is greater than 95%, the SAR coherence is mainly affected by vegetation thickness; the comparison of the difference change in the average coherence of the C/L bands among the four vegetation types shows that the ratio of the average coherence of the L-bands to the C-bands increases by a factor of three with the increase in thickness and the transition from crops to shrubs and trees. The results showed that the L-band has better detectability than the C-band in alpine-canyon terrain with vegetation coverage and complex vegetation composition. However, considering the high temporal resolution and accessibility of Sentinel-1 SAR data, it is still the main data choice for wide-area identification of landslides in the reservoir area, while other satellite-borne SAR data with different wavelengths and resolutions, such as ALOS, can be used to assist in the identification and monitoring of landslide hazards with significant magnitude of deformations and dense vegetation coverage. Therefore, the combined utilization of multi-band SAR data has the potential to enhance the dependability of landslide identification and monitoring, resulting in more accurate detection results. [ABSTRACT FROM AUTHOR]

Objectives: In view of the problem that it is difficult to identify the high hidden landslides by traditional ground investigation methods, the author's team have proposed the space-air-ground investigation system of landslide detection and monitoring. It has been applied nationwide with good results. However, there are few academic reports about it. In the study area of Danba County, where landslide disasters are frequent. Therefore, the detection and monitoring of potential landslides based on the space-air-ground investigation system was carried out. Methods: First, stacking-interferometric synthetic aperture radar(StackingInSAR) and small baseline subsets-InSAR(SBAS-InSAR) were combined with high-resolution optical remote sensing to achieve a wide-area census of active landslides in the whole study area, and the results of Stacking-InSAR and SBAS-InSAR were compared and analyzed. Second, light detection and ranging (LiDAR) technology and unmanned aerial vehicle aerial photography technology were used to conduct a detailed investigation of landslides in high-risk areas around Danba County, and further verification of major landslides through on-site investigation. Finally, SBAS-InSAR technology was used to monitor and analyze the deformation of typical landslides. Results: 41 active landslides were detected in the study area by using the space-air-ground investigation system. They are mainly local resurgence of large ancient land-slide accumulations under long-term gravity, river scouring and human engineering activities, etc. Most of the landslide deformations have a good correlation with rainfall. Conclusions: In the process of identifying landslides by using the space-air-ground investigation system, it is necessary to give full play to the advantages of each technical means. [ABSTRACT FROM AUTHOR]

Early discovery and monitoring of the active deformation areas of potential landslides are important for geohazard risk prevention. The objective of the study is to propose a one-step strategy for automatically mapping the active deformation areas of potential landslides from a Sentinel-1 SAR dataset. First, we built a generalized convolutional neural network (CNN) based on activity and topographic characteristics. Second, we conducted a comparative analysis of the performance of various multi-channel combiners for detecting the active deformation areas of the potential landslides. Third, we verified the transferability of the pretrained CNN model for an unknown region. We found that by incorporating topographic characteristics into a generalized convolutional neural network, we were able to enhance the accuracy of identifying the active deformation areas of potential landslides, rapidly mapping these areas. The methodology is robust and efficient, and it has the capability to automatically detect the active deformation areas of potential landslides, even in unknown or unfamiliar regions. This product can facilitate automated pipelines, updating and mapping active deformation areas for final users who are not InSAR experts. This implementation can be used for providing support to risk management activities. [ABSTRACT FROM AUTHOR]

The magnitude (spatial location, area, and volume) of landslides is a crucial parameter for estimating the quantitative risk and preventing landslide hazards. This study proposes a novel approach to forecast the volume of potential landslides in alpine-canyon terrain by combining the empirical relationship linking landslide volume (V) to characteristics and synthetic aperture radar interferometry (InSAR) technology in the northeast of the Qinghai-Tibet Plateau, China. First, a historical landslide inventory with detailed characteristics was established by reviewing literature and field investigation. Then, the relationship between volume and characteristics of landslides was analyzed based on the least-squares linear regression of the logarithmic transformation. Finally, the relationships of the colluvial landslides (V = 0.170 × L 0.982 × W 1.589 × H 0.471) and loess landslides (V = 0.170 × L 0.796 × W 2.151 × H 0.048) were obtained after verifying several landslides. The ground deformation rate was generated, and 217 potential landslides were detected using 47 descending Sentinel-1A images acquired from January 11, 2020, to July 16, 2021, and 40 ascending Sentinel-1A images acquired from January 11, 2020, to April 23, 2021. Through the established relationships, the total volumes of all potential colluvial and loess landslides were estimated to be 8.77 × 108 m3 and 5.74 × 108 m3, respectively. This study provides an improved empirical relationship to better forecast the volume of potential landslides in alpine-canyon terrain. Furthermore, the study helps in understanding the future dynamic evolution of potential landslides using InSAR technology, which provides scientific understanding to assess and prevents potential landslide hazards and risks in the Bailong River basin. [ABSTRACT FROM AUTHOR]

Landslides in high-order position areas pose a serious threat to residents located below such areas. Therefore, research on the evolution process and underlying dynamic mechanisms is crucial. The majority of relevant studies are based on landslides that have already occurred; however, the investigation of potential landslides is of higher value. In this study, a progressive potential landslide identification method is proposed with the application of SBAS-InSAR (Small Baseline Subset Interferometric Synthetic Aperture Radar) and subsequently combined with Google Earth, GF-1 (Gaofen-1), GF-2 (Gaofen-2), ZY-3 (Ziyuan-3) and UAV (Unmanned Aerial Vehicle) imagery, and DEM (Digital Elevation Model) for further validation. The proposed method is employed to determine the potential landslide in Tuanjiecun by integrating the multi-period data to simultaneously analyze the evolution and mechanism of the potential landslide. The long short-term memory method is then adopted to predict the evolution trend based on accumulative deformation from SBAS-InSAR. The results suggest that the deformation of Tuanjiecun potential landslide will increase to −238.57 mm. This article provides a new approach for disaster prevention and mitigation by determining a potential landslide using composite remote sensing and predicting the development of the potential landslide on indirect contact monitoring.

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Baihetan reservoir region is characterized by complex geomorphology, significant altitude differences, and rugged terrain. Geological hazards in such areas are often characterized by high concealment, wide distribution, and difficulty in field investigation. Traditional identification techniques are unable to detect and monitor geological hazards on a large scale with high efficiency and accuracy. In recent decades, interferometric synthetic aperture radar (InSAR) techniques, such as small baseline subset InSAR (SBAS-InSAR), have been widely applied to landslide identification. However, due to factors such as vegetation and the degree of landslide deformation, single-band synthetic aperture radar (SAR) still has certain limitations in detecting landslides. In this study, SBAS-InSAR was conducted based on ALOS-2 and Sentinel-1 ascending-descending images covering the Baihetan reservoir region. Deformation identification results were utilized to conduct a statistical analysis of the SAR detection performance and landslide characteristics, and the effect of vegetation on the detection effectiveness of different SAR bands was discussed. The study revealed that when surface vegetation coverage reaches a high degree, the percentage of areas with coverage greater than 0.6 is greater than 95%, the SAR coherence is mainly affected by vegetation thickness; the comparison of the difference change in the average coherence of the C/L bands among the four vegetation types shows that the ratio of the average coherence of the L-bands to the C-bands increases by a factor of three with the increase in thickness and the transition from crops to shrubs and trees. The results showed that the L-band has better detectability than the C-band in alpine-canyon terrain with vegetation coverage and complex vegetation composition. However, considering the high temporal resolution and accessibility of Sentinel-1 SAR data, it is still the main data choice for wide-area identification of landslides in the reservoir area, while other satellite-borne SAR data with different wavelengths and resolutions, such as ALOS, can be used to assist in the identification and monitoring of landslide hazards with significant magnitude of deformations and dense vegetation coverage. Therefore, the combined utilization of multi-band SAR data has the potential to enhance the dependability of landslide identification and monitoring, resulting in more accurate detection results.

Early discovery and monitoring of the active deformation areas of potential landslides are important for geohazard risk prevention. The objective of the study is to propose a one-step strategy for automatically mapping the active deformation areas of potential landslides from a Sentinel-1 SAR dataset. First, we built a generalized convolutional neural network (CNN) based on activity and topographic characteristics. Second, we conducted a comparative analysis of the performance of various multi-channel combiners for detecting the active deformation areas of the potential landslides. Third, we verified the transferability of the pretrained CNN model for an unknown region. We found that by incorporating topographic characteristics into a generalized convolutional neural network, we were able to enhance the accuracy of identifying the active deformation areas of potential landslides, rapidly mapping these areas. The methodology is robust and efficient, and it has the capability to automatically detect the active deformation areas of potential landslides, even in unknown or unfamiliar regions. This product can facilitate automated pipelines, updating and mapping active deformation areas for final users who are not InSAR experts. This implementation can be used for providing support to risk management activities.

The Yellow River Basin has active geological structure, rapid geomorphic evolution and significant regional differentiation of climate, resulting in many types of major disasters in the basin, wide distribution and strong paroxysm. Taking the Loess Hilly Region in the south of Ningxia as the research area, this paper introduces the concept of loess potential landslide of terrace. By combining historical data collection, remote sensing image interpretation, field investigation and mathematical statistics, it analyzes the development characteristics and distribution rules of loess potential landslide of terrace, and analyzes its formation reasons. The results show that: （1）There are 26 loess potential landslide of terrace in the study area, which are mainly distributed in the Quaternary loess, with a larger number of medium and long slopes, mainly loess potential landslide of terrace of shallow and middle-level, and the scale is mainly small. （2）The loess potential landslide of terrace are mainly distributed in loess hilly areas and large undulating mountain areas, the elevation interval of 1 800~2 000 m, and the slope direction is southeast, South and southwest in space; It is mainly distributed in rainy season or seismicity period in time. （3）Topography, stratum lithologic, rainfall, surface water and human engineering activities are the main factors for the loess potential landslide of terrace. The research results have guiding and reference significance for the risk identification and risk management of loess potential landslide of terrace in the Loess Hilly Region in Southern Ningxia and the Yellow River Basin.

It is normally difficult to identify the ground deformation of potential landslides in highly vegetation-covered areas in terms of field investigation or remote sensing interpretation. In order to explore a methodology to effectively identify potential landslides in highly vegetation-covered areas, this paper established an integrated identification method, including sliding prone area identification based on regional geological environment analysis, target area identification of potential landslides in terms of comprehensive remote sensing methods, and landslide recognition through engineering geological survey. The Miaoyuan catchment in Quzhou City, Zhejiang Province, southeastern China, was taken as an example to validate the identification methods. Particularly, the Shangfang landslide was successfully studied in terms of comprehensive methods, such as geophysical survey, drilling, mineral and chemical composition analysis, and microstructure scanning of the sliding zone. In order to assess the landslide risk, the potential runout of the Shangfang landslide was evaluated in a quantitative simulation. This paper suggests a methodology to identify potential landslides from a large area to a specific slope covered by dense vegetation. [ABSTRACT FROM AUTHOR]

Since 2012, a mega-project, “Mountain Excavation and City Construction” (MECC), has been underway on the Chinese Loess Plateau, aiming to create extra flat ground for urban growth. However, this large-scale land creation has led to dramatic changes in the local geological environment, forming a large number of engineered loess slopes. This study aims to map and assess the boundaries and stability of these potentially unstable engineered slopes (potential landslides). Based on spatio-temporal deformation patterns of landslides prior to failure, a novel interferometric synthetic aperture radar (InSAR)-based procedure for potential landslide mapping and stability evaluation was constructed in this study. The proposed procedure was employed to map and evaluate the potential landslides in the Yan’an New District (YND), the largest land-creation area in the Loess Plateau. First, the spatial locations and boundaries of the potential landslides were determined by combining optical image interpretation with differential deformation maps derived from Sentinel-1 small baseline subset InSAR (SBAS-InSAR) analysis. Then, the kinematic evolution trend of the mapped potential landslide was evaluated by quantitative analysis of the time-series displacements using an exponential model. Finally, the potential influencing factors and failure modes of the potential landslide were examined and discussed. The results indicate that the InSAR-derived differential deformation maps reflect the external boundary of the potential landslides well, and a total of 18 potential landslides are identified and mapped for the first time in the large-scale land-creation area of the YND. Over 82% of the coherent targets (CTs) within these potential landslides showed primary or steady creep during the InSAR observation period; however, tertiary creep was also observed on several slopes. Thus, it is crucial to utilize multitemporal InSAR and/or ground-based equipment for the continuous monitoring of engineered slopes in large-scale land-creation areas. The high correlation between spatiotemporal deformation patterns and environmental factors indicates that the activity of potential landslides is mainly controlled by the thickness of filling loess, while rainfall accelerates the displacement of slopes. These findings can contribute to the development of landslide disaster prevention and mitigation measures in the YND.

More than 70% of catastrophic landslides were previously unknown and brought tremendous losses to human life and property in urban regions; therefore, there is an urgent need for early identification of active landslides to eliminate landslide risk at the early stage. However, early identification of landslides has always been a worldwide challenge due to high concealment, steep topography, inaccessible location, and sudden onset. This work suggests a new set of comprehensive criteria for the early identification of landslides by integrating surface deformation, geological, topographic, geomorphological, and disaster-failure features. This set of criteria is universally applicable with no use of the prior knowledge of landslide locations (blind identification) and is successfully validated by a field survey. This work selects the Xuecheng region, a hard-hit area of landslides, as the study area and employs multisource data (seismic, geological, topographic, meteorological, SAR, and optical remote sensing data) and time-series InSAR technology to identify active landslides and reveal their deformation rules. Some new viewpoints are suggested. (1) The new comprehensive criteria synthesize the surface deformation, disaster-controlling, and disaster-inducing characteristics and achieve relatively high accuracy by field validation. (2) Forty-seven active landslides are identified in Xuecheng with no use of the prior knowledge of landslides. The soft rocks or soft-hard interbeddings, tectonic movement, fluvial undercutting and eroding, precipitation, earthquakes, and human engineering activity control or induce the development of these active landslides. (3) Two giant landslides that significantly threaten human lives and properties and exhibit different movement modes are selected to highlight the deformation rules of active landslides under the coupled action of poor lithologic condition, tectonic movement, river erosion, precipitation, and human engineering activity. The suggested new criteria can be applied to other landslide hard-hit urban regions and contribute to the timely and effective prevention and control of catastrophic landslides, reduction of enormous disaster losses, and rational management of the environment. [ABSTRACT FROM AUTHOR]

Landslides in high-order position areas pose a serious threat to residents located below such areas. Therefore, research on the evolution process and underlying dynamic mechanisms is crucial. The majority of relevant studies are based on landslides that have already occurred; however, the investigation of potential landslides is of higher value. In this study, a progressive potential landslide identification method is proposed with the application of SBAS-InSAR (Small Baseline Subset Interferometric Synthetic Aperture Radar) and subsequently combined with Google Earth, GF-1 (Gaofen-1), GF-2 (Gaofen-2), ZY-3 (Ziyuan-3) and UAV (Unmanned Aerial Vehicle) imagery, and DEM (Digital Elevation Model) for further validation. The proposed method is employed to determine the potential landslide in Tuanjiecun by integrating the multi-period data to simultaneously analyze the evolution and mechanism of the potential landslide. The long short-term memory method is then adopted to predict the evolution trend based on accumulative deformation from SBAS-InSAR. The results suggest that the deformation of Tuanjiecun potential landslide will increase to −238.57 mm. This article provides a new approach for disaster prevention and mitigation by determining a potential landslide using composite remote sensing and predicting the development of the potential landslide on indirect contact monitoring. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Geological Hazard & Control is the property of China Institute of Geological Environmental Monitoring (CIGEM) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Longde County, Ningxia Hui Autonomous Region with massive fossil landslides and a strong cutting action of river to slope, is a typical loess hilly and medium⁃risk of geological disasters area. Based on 47 Sentinel⁃1data covering 17 months, this study used two InSAR methods, Stacking and IPTA to detect potential landslides in Longde County. Through comparing with the existing archives, Stacking can effectively detect 44 potentiallandslides, with an effective detection rate of 93.6%, IPTA can effectively detect 37 potential landslides, with an effective detection rate of 78.7%. The maximum deformation rates of two InSAR methods had a significant linear correlation (R2=0.56) and the measurements is coincidence for each other. Stacking and IPTA had the similar ability to identify potential landslides with different deformation rates and aspects, and the identified potential landslides were mainly distributed in gentle and moderate slopes. Meanwhile, IPTA results showed that there are four typical potential landslide characteristics in Longde county. This study proved that combining the two InSAR methods can carry out potential landslide detection well, and realized a comprehensive detection in Longde County. The next work is the conduction of the accuracy verification and the improvement of IPTA method. [ABSTRACT FROM AUTHOR]

Shuicheng county is located in the west of Guizhou province. Under the comprehensive effect of natural environment and human activities，landslides occur frequently in Shuicheng county，which has the characteristics of strong concealment，high suddenness and strong destructiveness，which threatens the safety of local people′s life and property. It is urgent to identify potential landslides in Shuicheng county by effective means，so as to provide strong information support for disaster prevention and mitigation. In view of the fact that Shuicheng county is mostly mountainous and canyon areas，and traditional manual investigation methods are difficult to implement，this paper proposes an early landslide identification method based on interferometry synthetic aperture radar technology. In this method，the effective deformation points in the slope direction of Shuicheng county are obtained by the improved small baseline set interferometry technology. The spatial outliers of these deformation points are analyzed and clustered by Anselin Local Moran′s I index，and get the low value gathering area. Finally，combined with the surface features of Google Image，the potential landslide is identified.The results show that：the surface deformation rate of Shuicheng county is uneven，and there are multiple deformation centers，and the maximum deformation rate of slope direction reaches 142.3mm/a. Through the method of this paper，the potential landslides in Shuicheng county are identified，and combined with the field survey data，14 potential landslides are successfully identified， and 8 landslides are located in the invisible area of radar，so the identification is not successful [ABSTRACT FROM AUTHOR]

It is normally difficult to identify the ground deformation of potential landslides in highly vegetation-covered areas in terms of field investigation or remote sensing interpretation. In order to explore a methodology to effectively identify potential landslides in highly vegetation-covered areas, this paper established an integrated identification method, including sliding prone area identification based on regional geological environment analysis, target area identification of potential landslides in terms of comprehensive remote sensing methods, and landslide recognition through engineering geological survey. The Miaoyuan catchment in Quzhou City, Zhejiang Province, southeastern China, was taken as an example to validate the identification methods. Particularly, the Shangfang landslide was successfully studied in terms of comprehensive methods, such as geophysical survey, drilling, mineral and chemical composition analysis, and microstructure scanning of the sliding zone. In order to assess the landslide risk, the potential runout of the Shangfang landslide was evaluated in a quantitative simulation. This paper suggests a methodology to identify potential landslides from a large area to a specific slope covered by dense vegetation.

A growing number of slope hazard and engineering case studies have shown that only one safety factor (SF) can predict landslide occurrence, but cannot identify potentially dangerous landslides, which is the major source of the most destructive landslides. To solve the problem, a safety factor of cohesion (SF c) is proposed, and an improved slope stability analysis based on two safety factors is presented. Taking a sloping rock mass as a case study, the results showed that when SF c was less than 1 and the ratio of the cohesive force to the sliding resistance force drops to less than 100%, the slope became a potential landslide. The study suggests a new method for quantitative identification of potential landslides and provides a more reliable safety evaluation index, thus can solve the difficulty of adjustment of potential landslides and provide a more reliable safety evaluation index for engineering. [ABSTRACT FROM AUTHOR]

Simulation of the process of landslide occurrence, including deformation and failure, material movement and energy dissipation of slopes, can help in predicting the features, affected area and risk of potential landslides induced by earthquakes, thus providing support to decision-makers, which is of great practical significance in hazard prevention and mitigation. Taking the Chenjiaying potential landslide in Mian County, Shaanxi Province, China, as the scenario, this study used the discrete element method to develop a numerical simulation. The results showed that deformation of the slope started from the trailing edge, then gradually extended to the middle and leading edge until the failure occurred, via a progressive process. With the increase of the friction coefficient, the movement distance, velocity and failure time of the landslide were shortened, reduced and delayed, respectively, but the peak velocity remained at about 7.5 s after the seismic loading. The proportions of kinetic energy and collision energy in the total energy dissipation also decreased as the friction coefficient increased, but friction energy increased gradually, becoming dominant. The simulation also suggested that the landslide could not only bury houses to a depth of more than 3.8 m but also generate an impact of between 1.18 × 105 and 5.50 × 105 kN on the unit width of houses at the leading edge, which would pose a major threat to life and property safety for more than 50 households. These findings will aid in the assessment of landside risk caused by earthquakes. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research is aimed to map potential landslide areas, identify types of landslide, and draw a map of potential landslide level. This research was carried out in Batu, East Java. The research design that was used in the study was image interpretation of Landsat 7 Band 432 Satellite and primary data of the landslide causes (the data of soil and rocks, map interpretation, and variable scoring of landslide. ArcGis 9.3 and ArcView 3.2 software was used for map analysis. The researcher also analyzed the soil texture, permeability and land plasticity index, bedding, weathering level, the density of joints, the depth of weathering, the existence of springs, slopes, and slope cutting. The research findings showed that the image of Landsat 7 Bands 432 in 1:11.000 - 1:15.000 scale could be used to interpret land use and land cover. Interpreted land use included housing, bare soil, irrigated rice field, rain fed rice field, moor, nursery area, garden, slope failure, natural forest, sengon forest, pine forest, and agro forestry. [ABSTRACT FROM AUTHOR]