Sarmap (Switzerland)

The intensive development of both interferometric technology and sensors in recent years allows Interferometric Synthetic Aperture Radar (InSAR)-based applications to be accessible to a growing number of users. InSAR-based services now cover entire countries and soon even the whole of Europe. These InSAR systems require massive amounts of computer processing power and significant time to generate a final product. Most, if not all, of these projects have a limited “monitoring component”, aimed at historical analysis but are rarely, if ever, updated. Consequently, the results do not necessarily meet every purpose or specific user requirement. It is now clear that the increasing computing capacity and big data provided by the sensors have initiated the development of new InSAR services. However, these systems are only useful when linked to specific real-world operational problems. Continuous monitoring of a country’s ageing water management infrastructure has become an increasingly critical issue in recent years, in addition to the threats posed by climate change. Our article provides a comprehensive overview of a nationwide, dedicated, operational InSAR application, which was developed to support the operational work of the Hungarian Disaster Management Service (HDMS). The objective was to provide monthly monitoring of 63 water facilities, including 83 individual objects, distributed throughout Hungary, in combination with the development of a near real-time warning system. Our work involved the compilation of a completely new InSAR System as a Service (SaaS) which incorporates user requirements, preparatory work, the compilation of the Sentinel-1 automatic processing pipeline, the installation of corner reflectors, a special early warning system, and a dedicated user interface. The developed system can automatically start to evaluate the S1 measurements within 24 h of downloading the data into the system storage forward the results toward the warning system before the next image arrives. Users are provided with detailed information on the stability of 70% of the 83 water facility objects monitored through the dedicated user interface. The additional early warning system currently operates as a preliminary “spatial decision support system”, but the HDMS is willing to make it fully operational over the next few years.

This paper presents a machine learning method to map groundwater potential in crystalline domains. First, a spatially-distributed set of explanatory variables for groundwater occurrence is compiled into a geographic information system. Twenty machine learning classifiers are subsequently trained on a sample of 488 boreholes and excavated wells for a region of eastern Chad. This process includes collinearity, cross-validation, feature elimination and parameter fitting routines. Random forest and extra trees classifiers outperformed other algorithms (test score > 0.80, balanced score > 0.80, AUC > 0.87). Fracture density, slope, SAR coherence (interferometric correlation), topographic wetness index, basement depth, distance to channels and slope aspect proved the most relevant explanatory variables. Three major conclusions stem from this work: (1) using a large number of supervised classification algorithms is advisable in groundwater potential studies; (2) the choice of performance metrics constrains the relevance of explanatory variables; and (3) seasonal variations from satellite images contribute to successful groundwater potential mapping.

Landslides threaten more than before the urbanized areas and are a worldwide growing problem for the already affected communities and the local authorities committed to landslide risk management and mitigation. For this reason, it is essential to analyze landslide dynamics and environmental conditioning factors. Various techniques and instruments exist for landslide investigation and monitoring. Out of these, Multi-temporal Synthetic Aperture Radar Interferometry (MT-InSAR) techniques have been widely used in the last decades. Their capabilities are enhanced by the availability of the active Sentinel-1 mission, whose 6-day revisiting time enables near real-time monitoring of landslides. Interferometric results, coupled with ground measurements or other approaches such as numerical modeling, significantly improve the knowledge of the investigated surface processes. In this work, we processed the C-band SAR images of the available European Space Agency (ESA) satellite missions, using MT-InSAR methods to identify the surface deformations related to landslides affecting the Iași Municipality (Eastern Romania). The results (i.e., velocity maps) point out the most active landslides with velocities of up to 20 mm/year measured along the satellite Line of Sight (LOS). Following, we focused on the most problematic landslide that affects the Țicău neighborhood and is well-known for its significant implications that it had. To better understand its behavior and the sensitivity of the displacements to the environmental factors (i.e., rainfall), we carried out 2D numerical modeling using a finite difference code. The simulated displacement field is consistent with the InSAR displacements and reveals the most active sectors of the landslide and insights about its mechanism.

Mass movements represent a serious threat to the stability of human structures and infrastructures, and cause loss of lives and severe damages to human properties every year worldwide. Built structures located on potentially unstable slopes are susceptible to deformations due to the displacement of the ground that at worst can lead to total destruction. Synthetic aperture radar (SAR) data acquired by Sentinel-1 satellites and processed by multi-temporal interferometric SAR (MT-InSAR) techniques can measure centimeter to millimeter-level displacement with weekly to monthly updates, characterizing long-term large-scale behavior of the buildings and slopes. However, the spatial resolution and short wavelength weaken the performance of Sentinel-1 in recognizing features (i.e., single buildings) inside image pixels and maintaining the coherence in mountainous vegetated areas. We have proposed and applied a methodology that combines Sentinel-1 interferometry with ground-based geomatics techniques, i.e., global navigation satellite system (GNSS), terrestrial laser scanning (TLS) and terrestrial structure from motion photogrammetry (SfM), for fully assessing building deformations on a slope located in the north-eastern Italian pre-Alps. GNSS allows verifying the ground deformation estimated by MT-InSAR and provides a reference system for the TLS and SfM measurements, while TLS and SfM allow the behavior of buildings located in the investigated slope to be monitored in great detail. The obtained results show that damaged buildings are located in the most unstable sectors of the slope, but there is no direct relationship between the rate of ground deformation of these sectors and the temporal evolution of damages to a single building, indicating that mass movements cause the displacement of blocks of buildings and each of them reacts differently according to its structural properties. This work shows the capability of MT-InSAR, GNSS, TLS and SfM in monitoring both buildings and geological processes that affect their stability, which plays a key role in geohazard analysis and assessment.

With some complex surface structure, it is difficult to understand the surface displacements by using SAR data. In addition to the complex geometry, identifying and understanding the surface displacements becomes quite challenging when non-linear displacements exist. There are several types of the interferometric stacking approaches, and each approach has different characteristics to estimate the displacements. In this paper, the conventional PSI, NN-PSI, and SBAS techniques are applied to a PALSAR-1 dataset, and the resulting displacements are compared. By using the three approaches, it is possible to understand the surface displacements in the study area, and the strength and drawback in each techniques are recognized. It is also recognized that the spatial baseline distribution should influence the displacement estimation of PSI approaches according to the EV-spectrum.

Rock glaciers are widespread periglacial landforms in mountain regions like the European Alps. Depending on their ice content, they are characterized by slow downslope displacement due to permafrost creep. These landforms are usually mapped within inventories, but understand their activity is a very difficult task, which is frequently accomplished using geomorphological field evidences, direct measurements, or remote sensing approaches. In this work, a powerful method to analyze the rock glaciers’ activity was developed exploiting the synthetic aperture radar (SAR) satellite data. In detail, the interferometric coherence estimated from Sentinel-1 data was used as key indicator of displacement, developing an unsupervised classification method to distinguish moving (i.e., characterized by detectable displacement) from no-moving (i.e., without detectable displacement) rock glaciers. The original application of interferometric coherence, estimated here using the rock glacier outlines as boundaries instead of regular kernel windows, allows describing the activity of rock glaciers at a regional-scale. The method was developed and tested over a large mountainous area located in the Eastern European Alps (South Tyrol and western part of Trentino, Italy) and takes into account all the factors that may limit the effectiveness of the coherence in describing the rock glaciers’ activity. The activity status of more than 1600 rock glaciers was classified by our method, identifying more than 290 rock glaciers as moving. The method was validated using an independent set of rock glaciers whose activity is well-known, obtaining an accuracy of 88%. Our method is replicable over any large mountainous area where rock glaciers are already mapped and makes it possible to compensate for the drawbacks of time-consuming and subjective analysis based on geomorphological evidences or other SAR approaches.

The activity of Aso and Sakurajima volcanoes is monitored through interferometric analysis of SAR data, ALOS Palsar-2 and Sentinel-1, to obtain caldera displacements over the observed period, from 2014 to 2018 and to investigate deformations correlation with inflation and deflation cycles of magma plumbing system, eruptions and diking. The time series, calibrated through global navigation satellite System (GNSS) measurements, are used to identify the pre and post seismic effects of the Mw 7.0 Kumamoto earthquake, occurred in April 2016, to identify the effects on Aso volcano and the most dramatic events affecting the active Sakurajima area since 2015.

Reliable and regular rice information is essential part of many countries’ national accounting process but the existing system may not be sufficient to meet the information demand in the context of food security and policy. Synthetic Aperture Radar (SAR) imagery is highly suitable for detecting lowland paddy rice, especially in tropical region where pervasive cloud cover in the rainy seasons limits the use of optical imagery. This study uses multi-temporal X-band and C-band SAR imagery, automated image processing, rule-based classification and field observations to classify rice in multiple locations across Tropical Asia and assimilate the information into ORYZA Crop Growth Simulation model (CGSM) to generate high resolution yield maps. The resulting cultivated rice area maps had classification accuracies above 85% and yield estimates were within 81-93% agreement against district level reported yields. The study sites capture much of the diversity in water management, crop establishment and rice maturity durations and the study demonstrates the feasibility of rice detection, yield monitoring, and damage assessment in case of climate disaster at national and supra-national scales using multi-temporal SAR imagery combined with CGSM and automated methods.

In commercial forestry, regular terrestrial enumerations of the growing stock are required for the valuation, sustain-able management and planning of current and future timber supplies. In this study we examined whether the combination of synthetic aperture radar (ALOS PALSAR) and optical satellite (SPOT 4) image data can accurately predict the timber volume of even-aged Eucalyptus plantations located in South Africa. Results from this study show that the combination of ALOS PALSAR and SPOT 4 produces a R 2 value of 0.68 for the planted model, whereas the coppiced model produced a R 2 value of 0.55. However, by including stand age as an independent variable in the stepwise model, there was a 15% improvement for the planted model, whereas the coppiced model produced a 27% improvement. The final model developed in this study produced a R 2 value of 0.83 and a RMSE of 31.71 m3 ha−1 for planted stands, whereas the model for coppiced stands produced a R 2 value of 0.82 and a RMSE of 27.70 m3 ha−1. As it is not practical or financially feasible for commercial forestry companies to carry out terrestrial enumerations for all plantations on an annual basis, the model developed in this study presents an alternative and accurate method to calculate timber volume for even-aged Eucalyptus plantations.

Synthetic aperture radar (SAR) has been shown to be a useful tool for estimating aboveground biomass (AGB), due to the strong correlation between the biomass and backscatter. In particular, L-band SAR is effective for estimating the lower range of biomass that characterizes most boreal forests. Unfortunately, the topographic impact on backscatter can dominate the normal forest signal variation. Since many boreal environments have significant topography, we investigate several topographic correction techniques to determine their effect upon AGB prediction accuracy. Different approaches to addressing the topography include: 1) no correction, 2) local incidence angle (LIA) correction, 3) pixel-area correction, and 4) a novel empirical slope correction. The investigation was performed for a data-rich experimental area near Tok, Alaska, for which Advanced Land Observing Satellite Phased Array type L-Band Synthetic Aperture Radar (ALOS PALSAR), field plots, lidar acquisitions, and a high-quality digital elevation model (DEM) existed. Biomass estimations were performed using both single- and dual-polarization (HH and HV) regressions against field plot data. The biomass estimation for each of the topographic corrections was compared with the field plot biomass, as well as more extensive lidar biomass estimations. The results showed a clear improvement in AGB estimation accuracy from no correction, to LIA, to pixel-area, to the novel pixel-area plus empirical slope correction. Using the field plot data for validation, the SAR root mean square error (RMSE) derived from the best approach was found to be 37.3 Mg/ha over a biomass range of 0-250 Mg/ha, only marginally less accurate than the 33.5 Mg/ha accuracy of the much more expensive lidar technique.