Synthetic Aperture Radar Technique Research Articles

INSAR-BASED DETECTION AND MAPPING OF SEISMICALLY INDUCED GROUND SURFACE DISPLACEMENT AND DAMAGE IN PAMPANGA, PHILIPPINES

On April 22, 2019, an earthquake with a magnitude MW 6.1 struck the municipality of Castillejos in Zambales, the Philippines, and severely affected the province of Pampanga, which caused damage to commercial and residential structures reaching over 40 victims. This paper presents an approach for creating a pixel-based proxy damage assessment and displacement field maps to delineate the extent of ground surface displacements due to an earthquake. Specifically, this paper explored two change detection methods: the interferometric synthetic aperture radar (InSAR) technique and the coherence difference analysis method, using an open-source remote sensing software package and free SAR image data acquired by Sentinel-1 missions. Ground truth data were collected to substantiate the findings of the generated maps after the earthquake. Out of 7 surveyed damaged structures that were included in the National Disaster Risk Reduction and Management Council (NDRRMC) of the Philippines Situational Report, four damaged structures were successfully targeted using the proxy damage assessment map that had a coherence difference value ranging from 0.7-0.9 and damage grades of 3-5 based on the European Macroseismic Scale 1998 (EMS-98) damage classification system. This study confirms that change detection methods applied to C-band Sentinel-1 SAR data are valuable for mapping damaged areas and estimating ground surface displacements toward better hazard mitigation and disaster response.

Monitoring Maize Growth and Calculating Plant Heights with Synthetic Aperture Radar (SAR) and Optical Satellite Images

The decrease in water resources due to climate change is expected to have a significant impact on agriculture. On the other hand, as the world population increases so does the demand for food. It is necessary to better manage environmental resources and maintain an adequate level of crop production in a world where the population is constantly increasing. Therefore, agricultural activities must be closely monitored, especially in maize fields since maize is of great importance to both humans and animals. Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical satellite images were used to monitor maize growth in this study. Backscatter and interferometric coherence values derived from Sentinel-1 images, as well as Normalized Difference Vegetation Index (NDVI) and values related to biophysical variables (such as Leaf Area Index (LAI), Fraction of Vegetation Cover (fCover or FVC), and Canopy Water Content (CW)) derived from Sentinel-2 images were investigated. Sentinel-1 images were also used to calculate plant heights. The Interferometric SAR (InSAR) technique was applied to calculate interferometric coherence values and plant heights. For the plant height calculation, two image pairs with the largest possible perpendicular baseline were selected. Backscatter, NDVI, LAI, fCover, and CW values were low before planting, while the interferometric coherence values were generally high. Backscatter, NDVI, LAI, fCover, and CW values increased as the maize grew, while the interferometric coherence values decreased. Among all Sentinel-derived values, fCover had the best correlation with maize height until maize height exceeded 260 cm (R2 = 0.97). After harvest, a decrease in backscatter, NDVI, LAI, fCover, and CW values and an increase in interferometric coherence values were observed. NDVI, LAI, fCover, and CW values remained insensitive to tillage practices, whereas backscatter and interferometric coherence values were found to be sensitive to planting operations. In addition, backscatter values were also sensitive to irrigation operations, even when the average maize height was about 235 cm. Cloud cover and/or fog near the study area were found to affect NDVI, LAI, fCover, and CW values, while precipitation events had a significant impact on backscatter and interferometric coherence values. Furthermore, using Sentinel-1 images, the average plant height was calculated with an error of about 50 cm.

SENTINEL-1 SAR FOR OPERATIONAL MONITORING OF SOUTHERN AFRICA OCEANS

Abstract. The introduction of Synthetic Aperture Radar (SAR) through the Oceans and Coasts Monitoring Information system (OCIMS) has therefore become a critical tool for operational coastal and ocean monitoring in South Africa. Natural/man-made ocean phenomena can be observed using satellite Synthetic Aperture Radar (SAR) techniques, making it an integral part of large ocean area monitoring for maritime domain awareness. SAR is an active instrument on-board a satellite platform that transmits electromagnetic waves and measures the returning signal to infer what is happening on the ocean surface. The objective of this paper was to provide information and present options to the decision-makers around sustainability of commercial SAR data acquisition in support of OCIMS IVT. The OCIMS IVT tool identifies ships from multiple types of SAR data, which can provide locations of vessels that have switched off their AIS and oil slicks caused by vessels. Although open-data offerings, such as Sentinel-1, can be considered alternatives to commercial sensors, they present severe limitations for an operational service. To meet OCIMS requirements, and address the imperative to reduce costs, a hybrid of commercial acquisition being used in conjunction with an open data source such as Sentinel-1 is recommended. This would ensure that an operational service with the desired latency as well as increasing the revisit time over the EEZ.

Predicting the Settlement of Mine Waste Dump Using Multi-Source Remote Sensing and a Secondary Consolidation Model

The settlement (or subsidence) of mine waste dump is likely to cause landslides, thereby imposing threats on the safety of human beings and other local properties. Hence, it is essential to accurately predict the settlement for the early-warning of settlement-induced geohazards. Traditional mechanical methods require in situ mechanical parameters and the geological structure of mine waste dump, narrowing their practical applications. In this paper, we proposed a remote sensing-based method for predicting the settlement of mine waste dump without the requirement for field observations. Firstly, the historical settlement and thickness of mine waste dump were measured by space-borne interferometric synthetic aperture radar (InSAR) and photogrammetry techniques, respectively. Secondly, the temporal evolution of the settlement of mine waste dump was described by a secondary consolidation model. Thirdly, the parameters of the secondary consolidation model were inverted based on the InSAR-measured historical settlements and photogrammetry-estimated thickness. Finally, the evolution trend of the settlement of the mine waste dump was predicted with the secondary consolidation model and its inverted parameters. The proposed method was tested over a mine waste dump in Weijiamao open-pit mine, China. The result shows that the root mean square error of the predicted time-series settlement is about 0.8 cm. The presented method will be beneficial to the assessment and early-warning of the settlement-related geohazards of mine waste dumps over a large area.

GROUND DEFORMATION ANALYSIS USING BASIC PRODUCTS OF THE COPERNICUS GROUND MOTION SERVICE

Abstract. Monitoring ground deformation at national and regional level with millimetre-scale precision, nowadays, is possible by using Advanced Differential Interferometric SAR (A-DInSAR) techniques. This study concerns the results of the European Ground Motion Service (EGMS), part of the Copernicus Land Monitoring Service, which detects and measures land displacement at European scale. This Service provides reliable and consistent information regarding natural ground motion phenomena such as landslides and subsidence. The ground motion is derived from Synthetic Aperture Radar (SAR) time-series analysis of Sentinel-1A/B data. These data, which provide full coverage of Europe from two different observation geometries (ascending and descending) every six days, are processed at full resolution. The paper is focused on the exploitation of the basic product of EGMS for both regional and local purposes. Analysing the slope and aspect of the deformation field is the novelty of this investigation. In particular, the focus is put on the generation of wide-area differential deformation maps. Such maps indicate the gradient of the deformation field. The obtained information is not only beneficial for monitoring anthropogenic phenomena but also vital for urban management and planning. Most of the significant damages to manmade structures and infrastructures are associated with high deformation gradient values. Thus, monitoring the temporal and spatial variations of deformation gradient is essential for dynamic analysis, early-warning, and risk assessment in urban areas. Although EGMS productions are prepared for monitoring at regional level, their resolutions are high enough to investigate at local level. Therefore, this paper considers the local deformations that affect single structures or infrastructures. Local differences in such deformation can indicate damages in the corresponding structures and infrastructures. We illustrate these types of analysis to generate differential deformation maps using datasets available at CTTC.

INFRASTRUCTURE MONITORING USING THE INTERFEROMETRIC SYNTHETIC APERTURE RADAR (INSAR) TECHNIQUE

Abstract. This paper focuses on the application of spaceborne radar interferometry as a tool for assisting the monitoring different types of infrastructures, including railways and highways. The Persistent Scatterer Interferometry (PSI) technique, the most advanced class of differential interferometric Synthetic Aperture Radar techniques (DInSAR), is used to generate the deformation maps, including the time series and the displacement velocity for each measured persistent scatterers. The dataset considered in this work consists of 261 SAR IW-SLC images acquired by the Sentinel-1 A/B satellites, between January 2016 and September 2021, over the metropolitan area of Barcelona. The infrastructures, especially railways and highways, not only cross the cities, but also connect them crossing non-urban areas. One main technical issue of monitoring infrastructures is the low density of persistent scatterers (PS) in rural areas. To improve the density, the processing chain combines the interferograms selection based on the coherence threshold. Also, for a better point selection, the Equivalent Spatial Coherence (Omega Factor) is tested.

FINE-TUNING CNNS FOR DECREASED SENSITIVITY TO NON-VOLCANIC DEFORMATION VELOCITY SIGNALS

Abstract. Monitoring volcanic deformations allows us to track dynamic states of a volcano and to know where an eruptions could happen. Spaceborne Synthetic Aperture Radar (SAR) and SAR interferometry (InSAR) techniques created an opportunity to track volcanoes globally, even in inaccessible regions without ground measuring stations.This paper proposes a convolutional neural network (CNN) for detection of volcanic deformations in InSAR velocity maps. We had only a small amount of velocity maps over the region of central South American Andes, therefore the synthetic data are used to train the model from scratch. In the region of interest, the velocity maps contain the patterns of salt lakes and slope induced signal which confuse CNN models trained on synthetic data.In order to bridge the gap between the synthetic and real data, the hybrid synthetic-real data set is used for fine-tuning the model. The hybrid set consists of the real background signal data and synthetic volcanic data. Four fine-tuning sets which were created by different combinations of the original hybrid data, the filtered hybrid data, and simulated data have been used and compared with each other. Besides, we compared four fine-tuning approaches to determine where and how to fine-tune the model. Results show significant improvement in performance by majority of the approaches, and training the last or last two layers have given the best results. In addition, using the FT1 (containing only hybrid set), and FT4 (containing all sets) improved the area under the curve receiver operating characteristic (AUC ROC) from 55% to 86% and 88% respectively.

Deformation monitoring of Surakachhar underground coal mines of Korba, India using SAR interferometry

Underground mines around the globe experience the deformation/subsidence due to the wornd practiced of the mining activity or by the geological failure. In this study deformation monitoring of Korba underground coal mines in Chhattisgarh, India was performed using the Synthetic aperture radar remote sensing. In this study, the Surakachhar 5/6 underground coal mining region of Korba area and its surrounding are considered for deformation/displacement monitoring. The displacement estimation was generated using Advanced Differential Interferometric Synthetic Aperture Radar (ADInSAR) technique: Persistent Scatterer SAR interferometry (PSInSAR) and Ground-based investigation for validation and impact assessment have also been performed. PSInSAR results, obtained by processing of 17 Sentinel-1A images (Feb-2015 to Feb-2017) showed that multiple areas above the underground mining have been deformed around 3 mm/year to −21 mm/year along the radar line of prospect. Deformed regions are mainly on the surface of active mining areas and settlements. Deformation mapping using PSInSAR with ground-based investigation provides an account learning of Surakachhar underground mining activities, consequences such as sag, pit and cracks in the area. This technique helps to predict the hazard-prone deformation/subsidence zone and to prevent serious damage due to roof failure in the near future.

Earthquake-induced landslide monitoring and survey by means of InSAR

Abstract. This study uses interferometric synthetic aperture radar (SAR) techniques to identify and track earthquake-induced landslides as well as lands prone to landslides, by detecting deformations in areas struck by earthquakes. The pilot study area investigates the Mila region in Algeria, which suffered significant landslides and structural damage (earthquake: Mw 5, 7 August 2020). DInSAR analysis shows normal interferograms with small fringes. The coherence change detection (CCD) and DInSAR analysis were able to identify many landslides and ground deformations also confirmed by Sentinel-2 optical images and field inspection. The most important displacement (2.5 m), located in the Kherba neighborhood, caused severe damage to dwellings. It is worth notice that CCD and DInSAR are very useful since they were also able to identify ground cracks surrounding a large zone (3.94 km2 area) in Grarem City, whereas the Sentinel-2 optical images could not detect them. Although displacement time-series analysis of 224 interferograms (April 2015 to September 2020) performed using LiCSBAS did not detect any pre-event geotechnical precursors, the post-event analysis shows a 110 mm yr−1 subsidence velocity in the back hillside of Kherba.

Interpretation of the Spatiotemporal Evolution Characteristics of Land Deformation in Beijing during 2003–2020 Using Sentinel, ENVISAT, and Landsat Data

Since the 1930s, due to the rapid development of the city and the increase of population, the demand from Beijing residents for water resources has gradually increased. Land deformation in the Beijing Plain is a serious issue. In order to warn of, and mitigate, disasters, it is urgently necessary to obtain the latest rate, extent, and temporal evolution of land subsidence in Beijing. Firstly, the temporal and spatial distribution characteristics of land deformation in Beijing during 2003–2020 were unveiled using the time-series interferometric synthetic aperture radar (InSAR) technique and two different satellite datasets, sentinel-1a/1b and ENVISAT ASAR. By means of combining calibration of InSAR results with the global positioning system (GPS), we studied the evolutionary process of long-term land subsidence in Beijing. The precision of our InSAR annual subsidence results is less than 10 mm. Land subsidence in Beijing is unevenly distributed, and so five main land subsidence zones were monitored. The time-series results showed that the rate of land subsidence rate continued to increase from 2003 to 2015, but has gradually shown a slowing trend from 2015 to 2020. Further, we used the quadratic polynomial fitting method to interpolate the time-series deformation results from 2010 to 2015, and compared these with GPS. The results demonstrated that although the InSAR observation method is not strictly registered with GPS in time, its deformation trend is consistent. In addition, the calibrated long time-series was consistent with the three deformation stages of land subsidence evolution in Beijing. Finally, we analyzed the deformation information obtained by InSAR technology in combination with land use type data, precipitation and groundwater data. The results demonstrated that the central area is mostly stable, and land deformation in the northeast is obvious and uneven. In addition, land use type and precipitation have little influence on land subsidence. Changes in land subsidence were closely related to changes in groundwater level, and seasonal variations in deformation correlated with precipitation.

Analysis and Correction of Antenna Pattern Effects in AMAO Spaceborne SAR Images

Azimuthal multi-angle observation (AMAO) is a novel spaceborne synthetic aperture radar (SAR) technique proposed recently, which significantly improves the acquisition performance of target information. It has highly flexible antenna beams with powerful beam-steering capability while affecting the consistency of radiometric measurements, especially in the high-squint case. This paper mainly focuses on the antenna pattern effect analysis and correction method of AMAO spaceborne SAR. The antenna pattern characteristics of the squint steering beam are first analyzed. On this basis, an analytical model is developed to quantify the antenna pattern effects in AMAO SAR images. Different from the conventional approach that corrects the elevation antenna pattern, an improved antenna pattern correction method, which accounts for the spatial variation in the two-dimensional antenna pattern, is then proposed for AMAO spaceborne SAR. Finally, experiments are conducted to verify the proposed correction method. The results show that the newly proposed method considering the spatial-variant antenna pattern has higher performance than the conventional approach using a reference elevation pattern.

Automatic Identification of Slope Active Deformation Areas in the Zhouqu Region of China With DS-InSAR Results

Zhouqu has narrow terrain and steep mountains on both sides of the Bailong River. It is one of the most serious landslide disaster areas in China due to the fracture development of rock and complex geological conditions. To identify the slope active deformation areas (SADAs) in the region accurately, the Distributed Scatter Interferometric Synthetic Aperture Radar (DS-InSAR) technique was used based on the process of the sentinel ascending and descending orbit data. Deformation results of the region from January 2019 to February 2021 were obtained. A new method for automatically identifying SADA while accounting for radar geometric distortion was established to quickly and efficiently identify SADA from the large-scale deformation results in the region. The SADA identified by this method can eliminate the region affected by geometric distortion. Based on the method, 62 SADAs including 12 in the upstream and 50 in the downstream of Zhouqu County were identified. Compared with 12 typical landslide areas in the study area, 9 typical landslides match with the detected SADA. The SADA is mainly concentrated in the section from the downstream of Zhouqu County to Lianghekou, and statistical analysis showed that the vegetation coverage and topographic slope angle are two main reasons for this difference. At the same time, the analysis of the typical landslide time-series deformation reveals that the slope deformation activity mainly takes place during the rainy season. It has high correlation with precipitation. These results of the study provided an important reference for geological disaster prevention in the Zhouqu region.

Monitoring of Maskun landslide and determining its quantitative relationship to different climatic conditions using D-InSAR and PSI techniques

Climate change has resulted in severe landslides in Maskun, Iran. This study evaluates and monitors the displacement caused by the landslide mass in Bam using Interferometry Synthetic Aperture Radar (DInSAR) and Persistent Scatterer Interferometry (PSI) techniques, as well as identifies relationships between climatic conditions and mass displacement. Temperature and precipitation data from 2007 to 2019 were combined with satellite images and the DInSAR method was used to determine the mass displacement differences after selecting eighteen radar images from the ASAR sensor of the ENVISAT satellite. Additionally, Sentinel 1 satellite images were acquired and analyzed using the PSI method from November 5, 2014, to June 24, 2019. The highest displacement level at the surface of the Maskun landslide mass was then extracted. The ASAR images show a monthly displacement rate of 7.3 mm. The smallest displacement, on the other hand, occurred between May and September 2009, at a rate of 3.1 mm/month. PSI results also revealed that the maximum Line Of Sight (LOS) velocities detected by PSI are −64.5 mm/yr (away from the satellite) and 32.45 mm/yr (toward the satellite). Rainfall is one of the main triggers for increasing the deformation of the Maskun landslide according to the time-series analysis.

InSAR Study of Landslides: Early Detection, Three-Dimensional, and Long-Term Surface Displacement Estimation—A Case of Xiaojiang River Basin, China

Landslides, a major natural geohazard, obstruct municipal constructions and may destroy villages and towns, at worst causing significant casualties and economic losses. Interferometric Synthetic Aperture Radar (InSAR) technique offers distinct advantages on landslide detection and monitoring. In this paper, a more systematic workflow is designed for InSAR study of landslides, in terms of three levels: (i) early detection on regional scale, (ii) three-dimensional (3D) surface displacement rates estimation on detailed scale, and (iii) time series analysis on long-term temporal scale. The proposed workflow is applied for landslide research on the Xiaojiang River Basin, China, using ascending and descending Sentinel-1 images acquired from March 2017 to May 2019. First, the landslide inventory has been mapped and updated using InSAR stacking method, supporting geohazard prevention on a regional scale. A total of 22 active landslides are identified, ranging from medium to super large scale. Compared with the existing inventory, three unrecorded landslides are newly detected by our approach, and five recorded landslides are detected significant expansion of their boundaries. Then, specific to a detected landslide, Baobao landslide, a Total Least Squares–Kalman Filter-based approach is presented. Two outcomes are provided for further spatial-temporal pattern analysis: 3D displacement rates, providing an intuitive insight on the spatial characteristics and sliding direction of landslide, which are analyzed to deep the understanding of its kinematic mechanism, and long-term time series, which contribute to deduce the dynamic evolution of landslide, presenting benefits in landslide forecasting.

Multi‐temporal InSAR analysis to monitor landslides using the small baseline subset (SBAS) approach in the Mila Basin, Algeria

AbstractHazardous ground deformation and landslides occur frequently in the Mila Basin, Algeria and this problem remains unsolved. However, the historical seismicity in the area indicates no severe damage from past earthquakes. For this reason, studies are needed to monitor the slow ground movements and their triggering factors. Since about two decades ago, satellite observations by interferometric synthetic aperture radar (InSAR) technique and the multi‐temporal (MT‐InSAR) technique have provided a tool for monitoring slow and extremely slow ground displacements. In this study, 2D decomposition of InSAR outputs revealed a sliding surface at two regions located 12 km apart, indicating slow motion rather than fast movement along the damaged area. We concluded that the factors leading to surface displacement in the investigated area include the triggering earthquakes, precipitation, terrain topography and soil moisture. This study contributes to landslide hazard identification and risk assessment in the Mila Basin.

An Interpretation Approach of Ascending–Descending SAR Data for Landslide Identification

The technique of interferometric synthetic aperture radar (InSAR) is increasingly employed for landslide detection over large areas, even though the limitations of initial InSAR analysis results have been well acknowledged. Steep terrain in mountainous areas may cause geometric distortions of SAR images, which could affect the accuracy of InSAR analysis results. In addition, due to the existence of massive ground deformation points in the initial InSAR analysis results, accurate landslide recognition from the initial results is challenging. To efficiently identify potential landslide areas from the ascending–descending SAR datasets, this paper presents a novel interpretation approach to analyze the initial time-series InSAR analysis results. Within the context of the proposed approach, SAR visibility analysis, conversion analysis of deformation rates obtained from the time-series InSAR analysis, and spatial analysis and statistics tools for cluster extraction are incorporated. The effectiveness of the proposed approach is illustrated through a case study of landslide identification in Danba, a county in Sichuan, China. The potential landslide regions in the study area are identified based on the interpretation of small baseline subset InSAR (SBAS-InSAR) results, obtained with ascending–descending Sentinel-1A datasets. Finally, on the basis of the field survey results, a total of 21 landslides are detected in the potential landslide regions identified, through which the results obtained from the proposed interpretation approach are tested.

Adaptive Square-Root Unscented Kalman Filter Phase Unwrapping with Modified Phase Gradient Estimation

Phase unwrapping (PU) is a key program in data processing in the interferometric synthetic aperture radar (InSAR) technique, and its accuracy directly affects the quality of final SAR data products. However, PU in regions with large gradient changes and high noise has always been a difficult problem. To overcome the limitation, this article proposes an adaptive square-root unscented Kalman filter PU method. Specifically, a modified phase gradient estimation (PGE) algorithm is proposed, in which a Butterworth low-pass filter is embedded, and the PGE window can be adaptively adjusted according to phase root-mean-square errors of pixels. Furthermore, the outliers of the PGE results are detected and revised to obtain high-precision vertical and horizontal phase gradients. Finally, the unwrapped phase is calculated by the adaptive square-root unscented Kalman filter method. To the best of our knowledge, this article is the first to combine the modified PGE with an adaptive square-root unscented Kalman filter for PU. Two sets of simulated data and a set of TerraSAR-X/TanDEM-X real data were used for experimental verification. The experimental results demonstrated that the various improvement measures proposed in this article were effective. Additionally, compared with the minimum-cost flow algorithm (MCF), statistical-cost network-flow algorithm (SNAPHU) and unscented Kalman filter PU (UKFPU), the proposed method had better accuracy and model robustness.

Different Ground Subsidence Contributions Revealed by Integrated Discussion of Sentinel-1 Datasets, Well Discharge, Stratigraphical and Geomorphological Data: The Case of the Gioia Tauro Coastal Plain (Southern Italy)

Groundwater is the main water supply for agricultural and industrial needs in many coastal plains worldwide. Groundwater depletion often triggers land subsidence, which threatens manmade infrastructure and activities and aggravates other geohazards. We applied a multi-temporal interferometric synthetic aperture radar technique to Sentinel-1 datasets to detect ground motion in the Gioia Tauro plain (Calabria, Southern Italy) from 2018 to 2021. The InSAR data were analysed through the integrated use of groundwater head, stratigraphical and geomorphological data, and land use information to distinguish the potential subsidence divers. The results show that subsiding areas, with a mean rate of about 10 mm/yr, are in the middle of the plain, and their location is influenced by the spatial distribution of compressible sediments included in the shallow aquifer. Furthermore, the subsidence arrangement is spatially accordant with the main groundwater depression area, which can be ascribed to the ongoing and increasing water pumping for predominantly agricultural usage. We also observed that subsidence (up to 10 mm/yr) affects the western dock of the Gioia Tauro harbour, in front of which, in very shallow water, are two submarine canyon heads already affected by slides in the past.

Reducing the Residual Topography Phase for the Robust Landscape Deformation Monitoring of Architectural Heritage Sites in Mountain Areas: The Pseudo-Combination SBAS Method

Monitoring deformation of architectural heritage sites is important for the quantitative evaluation of their stability. However, deformation monitoring of sites in mountainous areas remains challenging whether utilizing global navigation satellite system (GNSS) or interferometric synthetic aperture radar (InSAR) techniques. In this study, we improved the small baseline subset (SBAS) approach by introducing the pseudo-baseline combination strategy to avoid the errors caused by inaccurate external DEM, resulting in robust deformation estimations in mountainous areas where the architectural heritage site of the Great Wall is located. First, a simulated dataset and a real dataset were used to verify the reliability and effectiveness of the algorithm, respectively. Subsequently, the algorithm was applied in the landscape deformation monitoring of the Shanhaiguan section of the Great Wall using 51 Sentinel-1 scenes acquired from 2016 to 2018. A thematic stability map of the Shanhaiguan Great Wall corridor was generated, revealing that the landscape was generally stable save for local instabilities due to to unstable rocks and wall monuments. This study demonstrated the capabilities of adaptive multitemporal InSAR (MTInSAR) approaches in the preventive landscape deformation monitoring of large-scale architectural heritage sites in complex terrain.

Applying Machine Learning and Time-Series Analysis on Sentinel-1A SAR/InSAR for Characterizing Arctic Tundra Hydro-Ecological Conditions

Synthetic aperture radar (SAR) is a widely used tool for Earth observation activities. It is particularly effective during times of persistent cloud cover, low light conditions, or where in situ measurements are challenging. The intensity measured by a polarimetric SAR has proven effective for characterizing Arctic tundra landscapes due to the unique backscattering signatures associated with different cover types. However, recently, there has been increased interest in exploiting novel interferometric SAR (InSAR) techniques that rely on both the amplitude and absolute phase of a pair of acquisitions to produce coherence measurements, although the simultaneous use of both intensity and interferometric coherence in Arctic tundra image classification has not been widely tested. In this study, a time series of dual-polarimetric (VV, VH) Sentinel-1 SAR/InSAR data collected over one growing season, in addition to a digital elevation model (DEM), was used to characterize an Arctic tundra study site spanning a hydrologically dynamic coastal delta, open tundra, and high topographic relief from mountainous terrain. SAR intensity and coherence patterns based on repeat-pass interferometry were analyzed in terms of ecological structure (i.e., graminoid, or woody) and hydrology (i.e., wet, or dry) using machine learning methods. Six hydro-ecological cover types were delineated using time-series statistical descriptors (i.e., mean, standard deviation, etc.) as model inputs. Model evaluations indicated SAR intensity to have better predictive power than coherence, especially for wet landcover classes due to temporal decorrelation. However, accuracies improved when both intensity and coherence were used, highlighting the complementarity of these two measures. Combining time-series SAR/InSAR data with terrain derivatives resulted in the highest per-class F1 score values, ranging from 0.682 to 0.955. The developed methodology is independent of atmospheric conditions (i.e., cloud cover or sunlight) as it does not rely on optical information, and thus can be regularly updated over forthcoming seasons or annually to support ecosystem monitoring.