Multi-temporal SAR Research Articles

Threshold-based inventory for flood susceptibility assessment of the world’s largest river island using multi-temporal SAR data and ensemble machine learning algorithms

Threshold-based inventory for flood susceptibility assessment of the world’s largest river island using multi-temporal SAR data and ensemble machine learning algorithms

Flood scenario spatio-temporal mapping via hydrological and hydrodynamic modelling and a remote sensing dataset: A case study of the Basento river (Southern Italy)

Today, hydrological and hydraulic modelling are essential tools for flood risk management, although these models are still affected by elements of uncertainty that needs to be reduced by optimizing their results. The present research aims to implement an operational mechanism on the Basento river basin in Southern Italy based on the cascading use of a physically based concentrated-parameter hydrological model for the estimation of flood hydrographs, and a two-dimensional hydraulic model for flood mapping. The calibration of the hydrological model uses physical information to reduce the initial range of the set parameter values, and an automated optimisation procedure based on a genetic algorithm to find optimal values of the model parameters by comparing simulated and observed data for the 2013 flood event. To calibrate the hydraulic model, a series of flood maps extracted from multi-temporal SAR images was used. In addition, validation of the hydrological and hydraulic models was carried out on March 2011 flood event. The results show the reliability of the models during both calibration and validation, with the hydrological model achieving a Nash-Sutcliffe Efficiency coefficient between 0.86 and 0.91, and the hydraulic model leading to results with an accuracy close to 70 %. Considering the significance of the results, the developed modelling chain was used to simulate future event scenarios for risk management assessment and could operate as an early warning system.

Detection and Mechanical Interpretation of Surface Displacement by Volcanic and Anthropogenic Interactions around Bandung Basin, through Multi Temporal Interferometric SAR (MT-InSAR) Technique

Abstract This study aims to evaluate capability of MT-InSAR (Multi Temporal interferometric synthetic aperture radar) technique for measuring the surface displacement caused by tectonic, volcanic, and anthropogenic activity around the Bandung Basin, West Java, Indonesia. For this, persistent scatterer (PS), small baseline subset (SBAS), and combined PS and SBAS techniques are adopted. The results reveal different displacement patterns within two areas anthropogenic-caused subsidence in the middle Bandung city and geothermal production field, and volcanic-caused uplift in the northern area around the Tangkuban Parahu volcano. In addition, each InSAR tehnique shows unique spatial deformation. A significant pattern is located around the Wayang Windu geothermal field that large displacements has been occurred around injection, production wells. Those detected displacements are crucial for mitigating geological hazards as well as geothermal production monitoring.

Monitoring activity in Mount Melbourne, Antarctica, by multi-temporal SAR interferometry based on the ICOPS algorithm

Monitoring activity in Mount Melbourne, Antarctica, by multi-temporal SAR interferometry based on the ICOPS algorithm

Detecting and Monitoring Artisanal Mining Operations in Semi-Arid Terrain Using Multitemporal SAR Data for InSAR Coherence Estimation and Unsupervised Classification

Abstract. The impact of artisanal mining in sub-Saharan Africa and other developing countries are often huge as they leave trails of environmental and socio-economic effects on the people and the environment. 123 Multi-temporal SAR data acquired between 2019 and 2023 was used. SARPROZ was used to co-register the images and other preprocessing steps like radiometric and geometric correction to transform the images from SAR coordinates to geographic coordinates after generating all the interferograms. A stack of the 122 coherence map was created. Unsupervised classification was implemented on the stack. Principal component analysis of dimensionality reductions method yielded a far better result than the other unsupervised cluster methods attempted, it showed a very high classification accuracy of the terrain. The principal component analysis worked by computing the covariance matrix of the stacked coherence map then performed eigen-decomposition on it to yield eigenvectors and eigenvalues, The eigenvectors corresponding to the largest eigenvalues represent the principal components. These principal components capture the directions of maximum variance, and the eigenvectors provides a reduced-dimensional representation of the image stack which is then used to reconstruct an approximation of the original image that captures the essential features of the original SAR data. Backscatter intensity of the SAR images processed for this study period for unsupervised change detection and land cover classification, delineated the different features and classes based on long term coherence values.

Collaborative Inversion of Soil Water Content in Alpine Meadow Area Based on Multitemporal Polarimetric SAR and Optical Remote Sensing Data

Soil water content is a critical environmental parameter in research and practice, though various technological and contextual constraints limit its estimation in arid areas with vegetation cover. This study combined the multitemporal remote sensing data of Sentinel-1 and Landsat 8 to conduct an inversion study on surface soil water content under low vegetation cover in Nagqu, central Tibetan Plateau. Four vegetation indices (NDVI, ARVI, EVI, and RVI) were extracted from optical remote sensing data. A water cloud model was used to eliminate the influence of the vegetation layer on the backscattering coefficient associated with vegetation cover, and a predictive model suitable for the Nagqu area was constructed. The water cloud model effectively incorporated a vegetation index instead of vegetation water content. We found that VV polarization was more suitable for soil water content inversion than VH polarization. Among the four vegetation indices, the soil water content inversion model constructed with RVI under VV polarization had the best fit (R2 = 0.8212; RMSE = 6.30). The second-best fit was observed for vegetation water content-NDVI (R2 = 0.8201). The soil water content inversion models all had an R2 > 0.6, regardless of the vegetation index used, though the RVI had the best fitting effect, indicating that this vegetation index is highly applicable in the water cloud model, as a substitute for vegetation water content, and is expected to perform well in similar study sites.

Multi-temporal SAR Images Change Detection Considering Ambiguous Co-registration Errors: A Unified Framework

Multi-temporal SAR Images Change Detection Considering Ambiguous Co-registration Errors: A Unified Framework

Ratio-Based Multitemporal SAR Image Despeckling With Low-Rank Approximation

Ratio-Based Multitemporal SAR Image Despeckling With Low-Rank Approximation

Kharif rice growth and area monitoring in Gosaba CD block of Indian Sundarbans region using multi-temporal dual-pol SAR data

Kharif rice growth and area monitoring in Gosaba CD block of Indian Sundarbans region using multi-temporal dual-pol SAR data

Inundation extend mapping for multi-temporal SAR using automatic thresholding and change detection: a case study on Kosi river of India

Inundation extend mapping for multi-temporal SAR using automatic thresholding and change detection: a case study on Kosi river of India

Interannual comparison of historical floods through flood detection using multi-temporal Sentinel-1 SAR images, Awash River Basin, Ethiopia

Interannual comparison of historical floods through flood detection using multi-temporal Sentinel-1 SAR images, Awash River Basin, Ethiopia

Multi-temporal SAR Interferometry (MTInSAR)-based study of surface subsidence and its impact on Krishna Godavari (KG) basin in India: a support vector approach.

The surface subsidence in the Krishna Godavari (KG) basin in India has increased with the discovery of crude oil and natural gas reserves since 1983. With private players coming up to bag the exploration and refining contracts, there must be timely monitoring of the surface subsidence of the region so that remedial measures for the resettlement of the populations can be taken promptly. Regular monitoring is necessary since the region is fertile and any seawater ingress results in the loss of valuable cultivable land. Multi-temporal SAR Interferometry (MTInSAR) technique has been applied successfully all over the world for the study and regular monitoring of land surface subsidence scenarios. This study utilizes data from Sentinel-1 C-band SAR sensor for MTInSAR-based surface subsidence and RADAR Vegetation Index (RVI)-based vegetation loss for the same season estimation between 2017 and 2022 for the KG basin region. It is inferred from the study that the region has shown surface subsidence of 80mm/year between April 2020 and June 2022. This study uses support vector regressor (SVR) to predict the loss in forest cover in terms of RVI using MTInSAR-based surface subsidence, VH, and VV backscatter as parameters. It is observed that the SVR gave R2-statistics of 0.89 and 0.873 in the training and testing phases with a mean absolute error (MAE) and root mean squared error (RMSE) of 0.08 and 0.02, respectively. It is also observed that the region showed a loss of 3.21 km2 of cultivable land between 2020 and 2022.

Measuring polycentric urban development with multi-temporal Sentinel-1 SAR imagery: A case study in Shanghai, China

Studies on polycentric urban development (PUD) have been done by using LandScan and point of interest data. So far, the quality of PUD products has been limited by the spatio-temporal resolution of these datasets. Using Sentinel-1 SAR missions with a 10 m pixel spacing and a weekly repeat cycle, this study, for the first time, explores the potential of using a time series of Sentinel-1 SAR images for measuring urban polycentricity. In particular, we develop a variance-based filtering method to mitigate speckle noise. We propose the Kittler and Illingworth with a Modified Model (KI-MM) method to accurately identify PUD-related changes. We focus on the mean distance of new-born patches and the mean patch area for a PUD-related change analysis. These allow us to associate SAR tailor-made output with PUDs. The proposed methods were implemented on Google Earth Engine platform using 304 Sentinel-1 SAR images on the city of Shanghai, China, acquired between 2015 and 2018. We have tested 80 distribution models and found that the Laplace distribution is the best model for the KI-MM method. Our results show 2526, 2409, 4232 new-born patches for 2015–2016, 2016–2017, 2017-2018, with areas equal to 18, 20, 36 km2, respectively. Using cross-validation with Sentinel-2 (optical) reference, we found that the matching rates and F1-score between detected changes and reference for the years 2015–2016, 2016–2017, 2017-2018 were equal to 89.79% and 91.67%, 100% and 100%, 88.64% and 94.06%, respectively. We conclude that Sentinel-1 SAR images are suited to PUD applications at an intra-city scale with a high spatio-temporal resolution.

Fine classification of rice paddy using multitemporal compact polarimetric SAR C band data based on machine learning methods

Fine classification of rice paddy using multitemporal compact polarimetric SAR C band data based on machine learning methods

Combination of optical images and SAR images for detecting landslide scars, using a classification and regression tree

ABSTRACT Landslides are some of the most destructive and recurrent natural hazards worldwide. Landslides are triggered by natural phenomena such as extreme rainfall and earthquakes, causing human and economic losses. A rapid response to landslide events is necessary to assess damage mitigation and save lives and property. This study developed a landslide detection model using differential spectral indices and amplitude ratio changes with a classification and regression tree (CART), aiming to detect landslide scars after the occurrence of these events in Asian regions for testing different environment condition. The multi-temporal SAR and optical stack images were pre-processed to reduce speckle noise, seasonal noise, and atmospheric noise. This study explored change detection approaches with a minimum threshold of amplitude ratio change (Aratio), using Sentinel−1 images and the relative difference in the normalized difference vegetation index (rdNDVI), differential bare soil index (dBSI), and differential brightness index (dBI) was obtained using Sentinel−2 images. The accuracy of the model was examined by F1-scores. The accuracy of the model for landslide detection was considered moderately good to excellent. As a result of the landslide detection model, amplitude ratio change detection improved the model as revealed by the F1-scores. Moreover, this study found that differential spectral indices could be used to classify the types of landslides (deep-seated and shallow landslides) according to the level of surface changes and texture of the collapsed material after landslide events.

Spatiotemporal Evolution of Ground Subsidence and Extensional Basin Bedrock Organization: An Application of Multitemporal Multi-Satellite SAR Interferometry

Since the early 1990s, the European (ESA) and Italian (ASI) space agencies have managed and distributed a huge amount of satellite-recorded SAR data to the research community and private industries. Moreover, the availability of advanced cloud computing services implementing different multi-temporal SAR interferometry techniques allows the generation of deformation time series from massive SAR images. We exploit the information provided by a large PS dataset to determine the temporal trend of ground deformation and the relative deformation rate with millimetric accuracy to analyze the spatial and temporal distribution of land subsidence induced by water pumping from a deep confined aquifer in the Northern Valle Umbra Basin (Central Italy), exploiting 24 years of Permanent Scatterers—interferometric SAR data archives. The SAR images were acquired between 1992 and 2016 by satellites ERS1/2 and ENVISAT, the Sentinel 1 ESA missions and the COSMO-SkyMed ASI mission. We observed ground velocities and deformation geometries between 1992 and 2016, with displacements of more than 70 cm and velocities of up to 55 mm/yr. The results suggest that the shape and position of the surface ground displacement are controlled by the fault activity hidden under the valley deposits.

EZ-InSAR: An easy-to-use open-source toolbox for mapping ground surface deformation using satellite interferometric synthetic aperture radar

Satellite Interferometric Synthetic Aperture Radar (InSAR) is a space-borne geodetic technique that can map ground displacement at millimetre accuracy. Via the new era for InSAR applications provided by the Copernicus Sentinel-1 SAR satellites, several open-source software packages exist for processing SAR data. These packages enable one to obtain high-quality ground deformation maps, but still require a deep understanding of InSAR theory and the related computational tools, especially when dealing with a large stack of images. Here we present an open-source toolbox, EZ-InSAR (easy-to-use InSAR), for a user-friendly implementation of InSAR displacement time series analysis with multi-temporal SAR images. EZ-InSAR integrates the three most popular and renowned open-source tools (i.e., ISCE, StaMPS, and MintPy), to generate interferograms and displacement time series by using these state-of-art algorithms within a seamless Graphical User Interface. EZ-InSAR reduces the user’s workload by automatically downloading the Sentinel-1 SAR imagery and the digital elevation model data for the user’s area of interest, and by streamlining preparation of input data stacks for the time series InSAR analysis. We illustrate the EZ-InSAR processing capabilities by mapping recent ground deformation at Campi Flegrei (> 100 mm·yr−1) and Long Valley (~ 10 mm·yr−1) calderas with both Persistent Scatterer InSAR and Small-Baseline Subset approaches. We also validate the test results by comparing the InSAR displacements with Global Navigation Satellite System measurements at those volcanoes. Our tests indicate that the EZ-InSAR toolbox provided here can serve as a valuable contribution to the community for ground deformation monitoring and geohazard evaluation, as well as for disseminating bespoke InSAR observations for all.

Integration of multi-sensor MTInSAR and ground-based geomatic data for the analysis of non-linear displacements affecting the urban area of Chieuti, Italy

Slow instability phenomena can turn into rapid events, showing sudden accelerations and potentially developing in a threat for structures and people. In such scenarios, an in-depth understanding of the spatial and temporal evolution of the ground surface displacement field becomes essential for preventing potential catastrophes. In this work, Multi-Temporal Interferometry SAR (MTInSAR) technique based on COSMO-SkyMed and Sentinel-1 SAR acquisitions and ground measurements have been used to study an ongoing instability occurrence, affecting the urban area of Chieuti, a town located in the Southern Italy. Archives of C and X-band SAR data and geomatic monitoring observations spanning seven, five and one year, respectively, have been analyzed exploiting the complementary characteristics of these datasets. This enabled the accurate spatial-temporal characterization of the ground displacement field in the study area, the identification of sectors evidencing instability problems and a comprehensive reliability assessment of the detected displacements trends, characterized by strong non-linearities. Moreover, the multi-geometry DInSAR analysis allowed to evaluate the horizontal and vertical components of the detected motion, confirming the nature of the instability process, related to a deep landslide mechanism affecting the western slope of the town.

Glacier facies characterisation in transboundary West Sikkim Himalaya from TerraSAR-X; GLCM based classification approach

ABSTRACT The proposed facies classification approach uses three features of the GLCM with multitemporal X-band SAR data of TerraSAR-X combined with k-means clustering. This approach was tested on 15 transboundary Himalayan glaciers. They were classified into six facies namely debris, dry snow, wet snow, bare ice, percolation zone 1 and percolation zone 2 and the covered areas for these classes are 20.56%, 12.50%, 20.26%, 13.16%, 25.49% and 8.03% of total area respectively. After classification, the signature for classes are validated using the benchmark outline of East Rathong glacier, India. The accuracy achieved is 89.56% with negligible variance and kappa of 0.87.

Visual and semi-automated interpretation methods for urban flood detection using SAR Sentinel-1A image: study case in North Aceh Regency, Indonesia

Flood is a natural disaster when causes damage and loss, especially in urban areas that are inhabited by many people and have many properties. The goal of this study is to map rapidly the flooded areas in North Aceh district on January, 5 2022 using multi-temporal SAR Sentinel-1A images through visual interpretation and semi-automated interpretation methods including thresholding and support vector machine (SVM). Visual interpretation relied on multi-polarization image of VH-VV with median filters of 3x3 and 5x5. This study produced the flooded area maps from three methods used with an area of each method: visual interpretation (422.86 ha), thresholding (791.22 ha) and SVM (1084.76 ha). According to the comparison with the result generated from visual interpretation using Planetscope-3A, SVM method has the similar result in the flooded areas with an area of 1001.52 ha. This study also analysed impact of flood event against land cover referring to Sentinel-2 10-Meter Land Use/Land Cover of ESRI and showed that class of cultivated crops is most affected. Results show that this study can be very helpful in damage and loss assessment of flood event and can be reference for urban planning management in order to face the climate change.