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Detecting the slow motions of highly elevated landslides in remote mountain areas has always been a problem. This paper takes the Woda landslide along the Jinsha River as an example to monitor landslide movement. Although some parts of the landslide body have been found to have moved in recent years, the timing and magnitude of motion have not been systematically monitored or interpreted. Here, we apply the SBAS time series strategy using 65-scene Sentinel-1A/B satellite InSAR images and study the spatial distribution and temporal behaviour of landslide movements between July 4, 2018, and August 29, 2020. Our research results show that the cumulative deformation on the left side of the landslide body with concentrated deformation was approximately 200 mm during the 2-year observation period. By calculating the relationship between the InSAR time series and the precipitation around the landslide, it is found that the landslide deformation is closely related to precipitation. GNSS technology is also deployed on the landslide mass and effectively complements InSAR technology. Simultaneously, based on the results of field surveys and hydrological data analysing the landslide's spatial deformation characteristics and deformation factors, the landslide deformation can also be inferred to be related to precipitation. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) technology has the potential to reveal ground surface deformation at high temporal and spatial resolutions, and the InSAR image processing field is progressing toward big data analytics. In this era of big data, as InSAR is usually employed for natural hazard research, processing large InSAR images under time constraints is a fundamental challenge. Graphics processing units (GPUs) have been widely adopted for high-performance computing (HPC) due to their parallel computing capability and low power consumption. Accordingly, we explore the interconnection between InSAR time series processing and GPU hardware for parallel Sentinel-1 InSAR processing. We build a rapid GPU-assisted InSAR processing chain and apply this chain to accelerate the time-consuming optimization steps in Sentinel-1 InSAR processing. This processing chain plays a pivotal role in rapidly calculating InSAR deformation maps using large quantities of Sentinel-1 SAR data and thus can be used in practical applications. We also perform some experiments to demonstrate that the processing chain is appropriate for the deformation monitoring of various natural disasters and is suitable for emergency monitoring and wide-area investigations of potential hazards involving geological disasters. [ABSTRACT FROM AUTHOR]

Detecting the slow motions of high and distant landslides in remote mountain areas has always been a problem. This paper takes the Woda landslide along the Jinsha River as an example to monitor landslide movement. Although some parts of the landslide body have been found to have moved in recent years, the timing and magnitude of motion have not been systematically monitored or interpreted. Here, we apply the SBAS time series strategy using 65-scene Sentinel-1A/B satellite InSAR images and study the spatial distribution and temporal behaviour of landslide movements between July 4, 2018, and August 29, 2020. Our research results show that the cumulative deformation on the left side of the landslide body with concentrated deformation was approximately 200 mm during the 2-year observation period. By calculating the relationship between the InSAR time series and the precipitation around the landslide, it is found that the landslide deformation is closely related to rainfall. GNSS technology is also deployed on the landslide mass and effectively complements InSAR technology. Simultaneously, based on the results of field surveys and hydrological data analysing the landslide's spatial deformation characteristics and deformation factors, the landslide deformation can also be inferred to be related to precipitation. The method used in this paper can be used for early recognition and early warning of high and remote landslides. [ABSTRACT FROM AUTHOR]

For prediction of urban remote sensing surface temperature, cloud, cloud shadow and snow contamination lead to the failure of surface temperature inversion and vegetation‐related index calculation. A time series prediction framework of urban surface temperature under cloud interference is proposed in this paper. This is helpful to solve the problem of the impact of data loss on surface temperature prediction. Spatial and temporal variation trends of surface temperature and vegetation index are analyzed using Landsat 7/8 remote sensing data of 2010 to 2019 from Beijing. The geographically weighed regression (GWR) method is used to realize the simulation of surface temperature based on the current date. The deep learning prediction network based on convolution and long short‐term memory (LSTM) networks was constructed to predict the spatial distribution of surface temperature on the next observation date. The time series analysis shows that the NDBI is less than −0.2, which indicates that there may be cloud contamination. The land surface temperature (LST) modeling results show that the precision of estimation using GWR method on impervious surface and water bodies is superior compared to the vegetation area. For LST prediction using deep learning methods, the result of the prediction on surface temperature space distribution was relatively good. The purpose of this study is to make up for the missing data affected by cloud, snow, and other interference factors, and to be applied to the prediction of the spatial and temporal distributions of LST. This article is categorized under:Technologies > Machine Learning [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

Due to temporal and spatial decorrelation and other noise, the integer-cycle jumps always appear in the unwrapped phase. It is well known that phase unwrapping errors represent one of the major limitations of Interferometric SAR (InSAR). A phase closure method is presented to detect and correct phase unwrapping errors for discrete coherent points in InSAR time series analysis. Its application to ENVISAT Advanced Synthetic Aperture Radar (ASAR) data over the coastal zone of Cang-zhou, Hebei Province showed a significant improvement in the InSAR time series results, with the number of valid coherent pixels increasing from 68 000 to 186 400 and the root mean square (RMS) decreasing from 13. 1 mm to 5. 3 mm and 17. 5 mm to 4. 3 mm for two deforming areas respectively. [ABSTRACT FROM AUTHOR]