Active landslides pose a significant threat globally,endangering lives and property.Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events.Interferom...Active landslides pose a significant threat globally,endangering lives and property.Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events.Interferometric synthetic aperture radar(InSAR)stands out as an efficient and prevalent tool for monitoring landslide deformation and offers new prospects for displacement prediction.However,challenges such as inherent limitation of satellite viewing geometry,long revisit cycles,and limited data volume hinder its application in displacement forecasting,notably for landslides with near-north-south deformation less detectable by InSAR.To address these issues,we propose a novel strategy for predicting three-dimensional(3D)landslide displacement,integrating InSAR and global navigation satellite system(GNSS)measurements with machine learning(ML).This framework first synergizes InSAR line-of-sight(LOS)results with GNSS horizontal data to reconstruct 3D displacement time series.It then employs ML models to capture complex nonlinear relationships between external triggers,landslide evolutionary states,and 3D displacements,thus enabling accurate future deformation predictions.Utilizing four advanced ML algorithms,i.e.random forest(RF),support vector machine(SVM),long short-term memory(LSTM),and gated recurrent unit(GRU),with Bayesian optimization(BO)for hyperparameter tuning,we applied this innovative approach to the north-facing,slow-moving Xinpu landslide in the Three Gorges Reservoir Area(TGRA)of China.Leveraging over 6.5 years of Sentinel-1 satellite data and GNSS measurements,our framework demonstrates satisfactory and robust prediction performance,with an average root mean square deviation(RMSD)of 9.62 mm and a correlation coefficient(CC)of 0.996.This study presents a promising strategy for 3D displacement prediction,illustrating the efficacy of integrating InSAR monitoring with ML forecasting in enhancing landslide early warning capabilities.展开更多
Yangbajain contains the largest geothermal energy power station in China.Geothermal explorations in Yangbajain first started in 1976,and two plants were subsequently built in 1981 and 1986.A large amount of geothermal...Yangbajain contains the largest geothermal energy power station in China.Geothermal explorations in Yangbajain first started in 1976,and two plants were subsequently built in 1981 and 1986.A large amount of geothermal fluids have been extracted since then,leading to considerable surface subsidence around the geothermal fields.In this paper,InSAR time series analysis is applied to map the subsidence of the Yangbajain geothermal fields during the period from December 2011 to November 2012 using 16 senses of TerraSAR-X stripmap SAR images.In the case of the TerraSAR-X data,most orbital fringes were removed using precise orbits during the interferometric processing.However,residual orbital ramps remain in some interferograms due to the uncertainties in the TerraSAR-X orbits.To remove the residual orbital ramps,we estimated a best-fit‘twisted plane’for each epoch interferogram using quadratic polynomial models based on a network approach.This method removes most of the long-wavelength signals,including orbit ramps and atmospheric effects.The vertically stratified component(Topography Correlated Atmospheric Delay,TCAD)was also removed using a network approach.If the influence of seasonal frozen ground(SFG)is not taken into consideration,our results show that the subsidence rate around power plant I(the south plant)is approximately 20 mm/yr with a peak of 30 mm/yr.The subsidence rate around power plant II(the north plant)is approximately 10 mm/yr,when accounting for the influence of SFG on the power plant and its surrounding ground surface.Our results show that ground motion is caused by seasonal frozen ground and is strongly related to the temperature change.展开更多
Remote sensing,particularly satellite-based,can play a valuable role in monitoring areas prone to geohazards.The high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and...Remote sensing,particularly satellite-based,can play a valuable role in monitoring areas prone to geohazards.The high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential hazards.This paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data,with a particular emphasis on:(1)detecting mining subsidence,where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar(InSAR)technology.The results showed that the Efficient Channel Attention(ECA)U-Net model performed better than the U-Net(baseline)model in terms of Mean Intersection over Union(MIoU)and Intersection over Union(IoU)indicators;(2)monitoring water conservancy and hydropower engineering.The Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets(SBAS)InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north side.The dam body also showed obvious deformation with a velocity exceeding 60 mm/a;(3)the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural preservation.The overall outcome of these methods showed that the use of Artificial Intelligence(AI)techniques in combination with InSAR data leads to more efficient analysis and interpretation,resulting in improved accuracy and prompt identification of potential hazards;and(4)finally,this study also presents a method for detecting landslides in mountainous regions,using optical imagery.The new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods,this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time such as decades.展开更多
ABSTRACT Conducting long measurements of infrastructure deformation is a critical engineering task.Conventional methods are both timeconsuming and expensive,limiting their use for large-scale applica-tions.The synergy...ABSTRACT Conducting long measurements of infrastructure deformation is a critical engineering task.Conventional methods are both timeconsuming and expensive,limiting their use for large-scale applica-tions.The synergy of synthetic aperture radar(SAR)and geographic information systems(GIS)offers a complementary approach.This study focuses on the feasibility of using time series analysis of L-band PALSAR-2 images to discover land displacements in Istanbul and Kocaeli,significant industrial and residential areas in Turkey.PALSAR-2 phase and intensity information were analyzed.For phase analysis,14 L-band images from 2014 to 2021 were taken into account.Small baseline subset(SBAS)analysis was performed using 44 pairs,and results of the velocity,coherence and back-scattering values are presented.Coherence of all pairs and their correlations were calculated.Principal Component Analysis(PCA)reduced the dimension of coherence pairs,enhancing feature extraction and the final geocoded velocity map revealed a fastest subsidence rate of−58 mm/yr and a mean subsidence of−20 mm/yr.These findings were confirmed through mean vertical velocity from Sentinel-1 datasets and field observations.The results showed that immature land subsidence in the mentioned areas are growing slowly,which can be taken as a serious risk in future.展开更多
基金jointly supported by the International Research Center of Big Data for Sustainable Development Goals(Grant No.CBAS2022GSP02)the National Natural Science Foundation of China(Grant Nos.42072320 and 42372264).
文摘Active landslides pose a significant threat globally,endangering lives and property.Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events.Interferometric synthetic aperture radar(InSAR)stands out as an efficient and prevalent tool for monitoring landslide deformation and offers new prospects for displacement prediction.However,challenges such as inherent limitation of satellite viewing geometry,long revisit cycles,and limited data volume hinder its application in displacement forecasting,notably for landslides with near-north-south deformation less detectable by InSAR.To address these issues,we propose a novel strategy for predicting three-dimensional(3D)landslide displacement,integrating InSAR and global navigation satellite system(GNSS)measurements with machine learning(ML).This framework first synergizes InSAR line-of-sight(LOS)results with GNSS horizontal data to reconstruct 3D displacement time series.It then employs ML models to capture complex nonlinear relationships between external triggers,landslide evolutionary states,and 3D displacements,thus enabling accurate future deformation predictions.Utilizing four advanced ML algorithms,i.e.random forest(RF),support vector machine(SVM),long short-term memory(LSTM),and gated recurrent unit(GRU),with Bayesian optimization(BO)for hyperparameter tuning,we applied this innovative approach to the north-facing,slow-moving Xinpu landslide in the Three Gorges Reservoir Area(TGRA)of China.Leveraging over 6.5 years of Sentinel-1 satellite data and GNSS measurements,our framework demonstrates satisfactory and robust prediction performance,with an average root mean square deviation(RMSD)of 9.62 mm and a correlation coefficient(CC)of 0.996.This study presents a promising strategy for 3D displacement prediction,illustrating the efficacy of integrating InSAR monitoring with ML forecasting in enhancing landslide early warning capabilities.
基金This work was supported by Research grant from Institute of Crustal Dynamics,China Earthquake Administration[grant numbers ZDJ2015-15 and ZDJ2013-22]National Natural Science Foundation of China[grant numbers 41104028 and 41204004]and the TerraSAR-X data we used were provided by the DLR in the frame of the General AO project LAN0208.
文摘Yangbajain contains the largest geothermal energy power station in China.Geothermal explorations in Yangbajain first started in 1976,and two plants were subsequently built in 1981 and 1986.A large amount of geothermal fluids have been extracted since then,leading to considerable surface subsidence around the geothermal fields.In this paper,InSAR time series analysis is applied to map the subsidence of the Yangbajain geothermal fields during the period from December 2011 to November 2012 using 16 senses of TerraSAR-X stripmap SAR images.In the case of the TerraSAR-X data,most orbital fringes were removed using precise orbits during the interferometric processing.However,residual orbital ramps remain in some interferograms due to the uncertainties in the TerraSAR-X orbits.To remove the residual orbital ramps,we estimated a best-fit‘twisted plane’for each epoch interferogram using quadratic polynomial models based on a network approach.This method removes most of the long-wavelength signals,including orbit ramps and atmospheric effects.The vertically stratified component(Topography Correlated Atmospheric Delay,TCAD)was also removed using a network approach.If the influence of seasonal frozen ground(SFG)is not taken into consideration,our results show that the subsidence rate around power plant I(the south plant)is approximately 20 mm/yr with a peak of 30 mm/yr.The subsidence rate around power plant II(the north plant)is approximately 10 mm/yr,when accounting for the influence of SFG on the power plant and its surrounding ground surface.Our results show that ground motion is caused by seasonal frozen ground and is strongly related to the temperature change.
基金supported by the National Key Research and Development Program of China[grant number 2021YFE0116800]ESA-MOST China Dragon-5 Program[grant number 56796]+1 种基金the National Natural Science Foundation of China[grant number 41977415]the SIAP Project[grant number 1/SAMA/2020/2019(POCI-62-2019-01)]by AMA IP(Portuguese Administrative Modernization Agency).
文摘Remote sensing,particularly satellite-based,can play a valuable role in monitoring areas prone to geohazards.The high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential hazards.This paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data,with a particular emphasis on:(1)detecting mining subsidence,where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar(InSAR)technology.The results showed that the Efficient Channel Attention(ECA)U-Net model performed better than the U-Net(baseline)model in terms of Mean Intersection over Union(MIoU)and Intersection over Union(IoU)indicators;(2)monitoring water conservancy and hydropower engineering.The Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets(SBAS)InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north side.The dam body also showed obvious deformation with a velocity exceeding 60 mm/a;(3)the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural preservation.The overall outcome of these methods showed that the use of Artificial Intelligence(AI)techniques in combination with InSAR data leads to more efficient analysis and interpretation,resulting in improved accuracy and prompt identification of potential hazards;and(4)finally,this study also presents a method for detecting landslides in mountainous regions,using optical imagery.The new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods,this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time such as decades.
基金funded by the TUBITAK#2221 project and the University of Tabriz,and the Japanese Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(KAKENHI)grant number#23H01654。
文摘ABSTRACT Conducting long measurements of infrastructure deformation is a critical engineering task.Conventional methods are both timeconsuming and expensive,limiting their use for large-scale applica-tions.The synergy of synthetic aperture radar(SAR)and geographic information systems(GIS)offers a complementary approach.This study focuses on the feasibility of using time series analysis of L-band PALSAR-2 images to discover land displacements in Istanbul and Kocaeli,significant industrial and residential areas in Turkey.PALSAR-2 phase and intensity information were analyzed.For phase analysis,14 L-band images from 2014 to 2021 were taken into account.Small baseline subset(SBAS)analysis was performed using 44 pairs,and results of the velocity,coherence and back-scattering values are presented.Coherence of all pairs and their correlations were calculated.Principal Component Analysis(PCA)reduced the dimension of coherence pairs,enhancing feature extraction and the final geocoded velocity map revealed a fastest subsidence rate of−58 mm/yr and a mean subsidence of−20 mm/yr.These findings were confirmed through mean vertical velocity from Sentinel-1 datasets and field observations.The results showed that immature land subsidence in the mentioned areas are growing slowly,which can be taken as a serious risk in future.
