Monitoring surface deformation associated with geohazards is a prerequisite for geological disaster prevention. Interferometric synthetic aperture radar (InSAR) has the ability to capture ground ...deformation of landslides with high precision over a large area. However, in mountainous regions this capability is often limited by decorrelation noise and atmospheric phase artifacts. Over Eldorado National Forest, California, where many landslides need to be monitored and investigated, InSAR images are severely affected by atmospheric noise and the coherence is highly variable throughout the year, challenging InSAR techniques to effectively detect movement of active landslides. In order to obtain reliable measurements, we have designed an interferogram selection method and an InSAR segment processing (SP) technique to improve the deformation measurement. Compared with the traditional non-segment processing (NSP), the SP technique has demonstrated advantages in reducing the impact of atmospheric noise. Our results from both the ascending and descending InSAR datasets based on SP indicate that many landslides along the Highway 50 corridor were creeping at a rate of less than 10 cm/year during the investigation period. We have found that landslide movements in the study region present obvious seasonal patterns. The precipitation and pore-water measurements and our hydrogeological diffusion models suggest that the seasonal movements of these landslides are primarily driven by the pore-water pressures, and the peak deformation of the landslides may occur in the dry season (May to October) due to the time lag of precipitation infiltration. In addition, we have observed subtle upward movement of the landslides after the precipitation begins, which is likely caused by the swelling of clay-rich landslide body due to an increase in the pore pressure. Furthermore, several other localized unstable regions which may contain potential landslide hazards were also detected and mapped in the study area, and their dynamics need further investigation. We conclude that InSAR is capable of detecting slow landslide motions over difficult terrains if associated artifacts in the interferograms are suppressed. InSAR time-series measurements along with hydrogeological models enable us to characterize the time delay between peaks of landslide motions and precipitation.
•InSAR segment processing is used to mitigate atmospheric noise in mountainous areas.•Slow-creeping landslides in Eldorado National Forest in California are mapped.•Hydrogeological diffusion models suggest landslides are driven by pore-water pressure.•Peak deformation occurs in dry season due to time lag of precipitation infiltration.•Subtle upward movement is likely caused by swelling of clay-rich landslide body.
This letter presents a novel change detection method for multitemporal synthetic aperture radar images based on PCANet. This method exploits representative neighborhood features from each pixel using ...PCA filters as convolutional filters. Thus, the proposed method is more robust to the speckle noise and can generate change maps with less noise spots. Given two multitemporal images, Gabor wavelets and fuzzy c-means are utilized to select interested pixels that have high probability of being changed or unchanged. Then, new image patches centered at interested pixels are generated and a PCANet model is trained using these patches. Finally, pixels in the multitemporal images are classified by the trained PCANet model. The PCANet classification result and the preclassification result are combined to form the final change map. The experimental results obtained on three real SAR image data sets confirm the effectiveness of the proposed method.
To address increasing water demands in expanding cities, many aquifers in Mexico are overexploited and deplete. The resulting land subsidence often combines with ground faulting/fracturing and damage ...to infrastructure. This study provides the longest Synthetic Aperture Radar (SAR) survey ever undertaken for the Aguascalientes Valley, aimed to constrain its structurally-controlled subsidence process and the induced risk. 275 ERS-1/2 1996–2002, ENVISAT 2003–2010 and Sentinel-1 2014–2020 C-band SAR images are processed with change detection, differential Interferometric SAR (InSAR) and Small Baseline Subset (SBAS) methods. Aguascalientes notably expanded over the last four decades, as revealed by Seasat 1978 L-band SAR, Landsat 1985–2010 and Sentinel-2 2020 optical imagery. The observed subsidence pattern involves alluvial/fluvial deposits within the N-S trending graben. Maximum settlement rates are −14 cm/year in 1996, −10 cm/year in 2000–2010 and over −12 cm/year in 2015–2020. An acceleration (−0.70 cm/year2) is recorded in 2015–2020 close to recently developed industrial plants and housing districts. Satellite estimates agree with in-situ observations, static GPS surveying and continuous GPS monitoring data. Rough correlation is found with piezometric level drop rates, whereas aquifer thickness plays a stronger role in the subsidence process. While these outcomes align with the existing literature, this InSAR survey: (i) unveils previously unknown E-W deformation affecting two N-S oriented bands within the valley, with up to ~ ±3 cm/year in 2015–2020 towards its center; (ii) identifies zones of sagging and hogging with horizontal strain (ε) of up to 0.05–0.1%; (iii) retrieves differential rates reaching 6–8 cm/year and angular distortions (β) of 1/500 along the Oriente fault; and (iv) investigates the statistical distribution of β across field surveyed faults and fissures, and marks areas with potentially yet-unmapped ground discontinuities. A new surface faulting risk matrix embedding β and ε is therefore proposed to estimate subsidence impact on properties and population. Given its scale-dependency, the risk assessment provides a lower bound to the percentage of urban areas at risk within the Aguascalientes state: at least 2% of the urban areas were at high and very high risk in 2003–2010 (involving ~12,000 properties and ~39,000 inhabitants), but this increased to 6% in 2015–2020 (~25,600 properties, ~85,200 inhabitants). The evidence of a subsidence process evolving spatially and temporally highlights the need for continuous updating of hazard information.
•Subsidence in Aguascalientes is unveiled with ERS-1/2, ENVISAT and Sentinel-1 InSAR.•Up to −12 cm/year vertical and ±3 cm/year E-W rates are observed in 2015–2020.•Subsidence acceleration is recorded near new industrial/housing developments.•Angular distortions reach 1/500 and horizontal strain 0.1% along surface faults.•6% urban areas are at high to very-high risk in 2015–2020, i.e. ~85,200 inhabitants.
A broad range of studies have been conducted so far to quantify the effect of soil moisture on synthetic aperture radar (SAR) intensity and interferometric synthetic aperture radar (InSAR) phase. The ...introduced models are either intensity or interferometric models, and there is no single scattering model that can estimate both intensity and phase changes, indicating the subject is poorly understood. Here, we quantify the influence of soil moisture on InSAR phase and SAR intensity by employing a volume scattering model. We model soil as a collection of randomly distributed independent point scatterers embedded in a homogeneous background. Our volume scattering model successfully estimates SAR intensity and InSAR phase changes due to soil moisture changes. In addition to soil moisture changes, the model also takes into account the scatterers' size and their volumetric fraction. This may open a new window in the study of soil structure using SAR images and InSAR methods. Our results indicate that the structure of soil manipulates the way soil moisture alters the SAR intensity and InSAR phase. The model has been evaluated against field soil moisture measurements and shown to be successful in modeling InSAR phase and SAR intensity.
Two-dimensional phase unwrapping (2-D PU) is one of the key processes in reconstructing the topography or displacement of the Earth surface from its interferometric synthetic aperture radar (InSAR) ...data. Estimating the absolute phase gradient information is an unavoidable step utilized by almost all the 2-D PU methods. Traditionally, the gradient estimation step relies on the phase continuity assumption, which requests that the observed area has spatial continuity. However, the abrupt topographic changes and system noise usually results in the failure of the phase continuity assumption in reality. Under this condition, it is difficult for the traditional 2-D PU to provide the correct absolute phase over the area with abrupt interferometric fringe change or with strong system noise. To solve the issue, we propose a novel deep convolutional neural network (DCNN), abbreviated as PGNet, to estimate the phase gradient information instead of the phase continuity assumption in this article. The major advantage of PGNet lies in its deep architecture to learn the characteristics of phase gradients from enormous training images with different noise levels and topographic features. Subsequently, the <inline-formula> <tex-math notation="LaTeX">L^{1} </tex-math></inline-formula>-norm objective function is used to minimize the difference between unwrapped phase gradients and the gradients estimated by PGNet for obtaining the final PU result. Taking the phase gradient pattern of the TerraSAR-X-TanDEM-X interferogram as the learning object, experimental results demonstrate the absolute phase gradient estimated by PGNet is more credible than that from the phase continuity assumption such that the corresponding PU result outperforms those obtained by the traditional 2-D PU methods.
Digital elevation models (DEMs) are vital in the geosciences and many other fields. Interferometric synthetic aperture radar (InSAR), an advanced earth observation technology, has shown its potential ...in DEM reconstruction. Multi baseline InSAR (MB-InSAR) is currently improving the precision of DEM reconstruction by combining multiple interferograms. However, MB-InSAR for DEM generation can result in severe decorrelation, which may cause significant gaps in the final DEM product. To solve this problem, an improved MB-InSAR DEM reconstruction method is proposed in this study, which we term as the dynamic DEM calculation algorithm. The proposed method can estimate the DEM pixel-by-pixel, which allowed us to select the interferograms dynamically and therefore minimize the void values. For the performance test of the proposed method, 25 ascending and 20 descending TerraSAR-X images over Heifangtai (China) were collected to form repeat-pass interferograms and produce the DEM using the proposed method. Results showed that the number of valid pixels increased by approximately 20% compared with the traditional MB-InSAR DEM reconstruction method without loss of precision, thereby illustrating the feasibility of the proposed method.
Mapping and monitoring landslides in remote areas with steep and mountainous terrain is logistically challenging, expensive, and time consuming. Yet, in order to mitigate hazards and prevent loss of ...life in these areas, and to better understand landslide processes, high-resolution measurements of landslide activity are necessary. Satellite-based synthetic aperture radar interferometry (InSAR) provides millimeter-scale measurements of ground surface deformation that can be used to identify and monitor landslides in remote areas where ground-based monitoring techniques are not feasible. Here we present a novel InSAR deformation detection approach, which uses double difference time-series with local and regional spatial filters and pixel clustering methods to identify and monitor slow-moving landslides without making a priori assumptions of the location of landslides. We apply our analysis to freely available Copernicus Sentinel-1 satellite data acquired between 2014 and 2017 centered on the Trishuli River drainage basin in Nepal. We found a minimum of 6 slow-moving landslides that all occur within the Ranimatta lithologic formation (phyllites, metasandstones, metabasics). These landslides have areas ranging from 0.39 to 1.66 km2 and long-term dry-season displacement rates ranging from 2.1 to 8.8 cm/yr. Due to periods of low coherence during the monsoon season (June – September) each year, and following the 25 April 2015 Mw7.8 Gorkha earthquake, our time series analysis is limited to the 2014–2015 and 2016–2017 dry seasons (September–May). We found that each of the landslides displayed slightly higher rates during the 2014 period, likely as a result of higher cumulative rainfall that fell during the 2014 monsoon season. Although we do not have high quality InSAR data to show the landslide evolution directly following the Gorkha earthquake, the similar rates of movement before (2014–2015) and after (2016–2017) Gorkha suggest the earthquake had negligible long-term impact on these landslides. Our findings highlight the potential for region-wide mapping of slow-moving landslides using freely available remote sensing data in remote areas such as Nepal and future work will benefit from expanding our methodology to other regions around the world.
•A novel method is developed to detect landslides in mountainous terrain.•InSAR time-series is used to identify and monitor slow-moving landslides.•6 slow-moving landslides in Trishuli, Nepal, unaffected by the Gorkha earthquake.•Landslides have rates between 2 and 9 cm/yr and likely driven by monsoonal rainfall.
Recently, a novel design scheme of low-earth-orbit spaceborne mini-synthetic aperture radar (MiniSAR) system is proposed to exploit the integrated transceiver to collect the azimuth periodic block ...sampling data by using alternated transmitting and receiving operations. Because such collected data are downsampled, the images recovered by the typical matched filtering (MF)-based methods have the problems of obvious azimuth ambiguities, ghosts, and energy dispersion. To find a suitable method for such data, with the help of sparse signal processing technique, we first introduce sparse synthetic aperture radar (SAR) imaging with <inline-formula> <tex-math notation="LaTeX">\ell _{1} </tex-math></inline-formula>-norm regularization-based approximated observation method to recover the large-scale considered scene. To further improve the imaging performance, a novel approximated observation unambiguous sparse SAR imaging method via <inline-formula> <tex-math notation="LaTeX">\ell _{2,1} </tex-math></inline-formula>-norm is proposed. Compared with <inline-formula> <tex-math notation="LaTeX">\ell _{1} </tex-math></inline-formula>-norm -based method, the recovered image by the proposed one achieves better imaging quality with reduced azimuth ambiguities and ghosts. Experimental results on simulated and real data validate the proposed method.
Sentinel-1 mission with its wide spatial coverage (250 km), short revisit time (6 days), and rapid data dissemination opened new perspectives for large-scale interferometric synthetic aperture radar ...(InSAR) analysis. However, the spatiotemporal changes in troposphere limits the accuracy of InSAR measurements for operational deformation monitoring at a wide scale. Due to the coarse node spacing of the tropospheric models, like ERA-Interim and other external data like Global Navigation Satellite System (GNSS), the interpolation techniques are not able to well replicate the localized and turbulent tropospheric effects. In this study, we propose a new technique based on machine learning (ML) Gaussian processes (GP) regression approach using the combination of small-baseline interferograms and GNSS derived zenith total delay (ZTD) values to mitigate phase delay caused by troposphere in interferometric observations. By applying the ML technique over 12 Sentinel-1 images acquired between May–October 2016 along a track over Norway, the root mean square error (RMSE) reduces on average by 83% compared to 50% reduction obtained by using ERA-Interim model.
•Proposing a tropospheric correction method on large-scale InSAR using machine learning•Successful implementation of the method for country-scale InSAR map of Norway•Performance assessment of the method against external observations
Accurate flood mapping is important for both planning activities during emergencies and as a support for the successive assessment of damaged areas. A valuable information source for such a procedure ...can be remote sensing synthetic aperture radar (SAR) imagery. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground. For this reason, a data fusion approach of remote sensing data with ancillary information can be particularly useful. In this paper, a Bayesian network is proposed to integrate remotely sensed data, such as multitemporal SAR intensity images and interferometric-SAR coherence data, with geomorphic and other ground information. The methodology is tested on a case study regarding a flood that occurred in the Basilicata region (Italy) on December 2013, monitored using a time series of COSMO-SkyMed data. It is shown that the synergetic use of different information layers can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which may affect algorithms based on data from a single source. The produced flood maps are compared to data obtained independently from the analysis of optical images; the comparison indicates that the proposed methodology is able to reliably follow the temporal evolution of the phenomenon, assigning high probability to areas most likely to be flooded, in spite of their heterogeneous temporal SAR/InSAR signatures, reaching accuracies of up to 89%.
PDF