Global quantification of InSAR sensitivity for landslide deformation tracking

2021 ◽  
Author(s):  
Adriaan van Natijne ◽  
Roderik Lindenbergh ◽  
Thom Bogaard
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Radar Data ◽  
Google Earth ◽  
Project Planning ◽  
Deformation Monitoring ◽  
Radar Signal ◽  
Sensitivity Index ◽  
Potential Indicator ◽  
Landslide Deformation

<p>Landslides are lurking hazards, that often remains unnoticed. Fortunately, unstable slopes frequently show precursory deformation preceding more destructive accelerations. Thanks to satellite remote sensing, regional deformation monitoring is now available in near real-time.</p><p>Deformation time series are required for both training and validation of models for landslide nowcasting and forecasting. Various studies have shown that satellite Interferometric Synthetic Aperture Radar (InSAR) is capable of delivering the desired deformation time series. Although satellite radar data, such as from the Copernicus Sentinel-1 program, is freely available, application is not (yet) straightforward: InSAR processing is complex, computational intensive and requires specialist knowledge. Moreover, assessment of the potential of the technique on specific slopes requires experience.</p><p>Therefore, we present two concepts to a-priori assess the potential of InSAR landslide deformation tracking. First, the sensitivity index, available globally, indicates the minimum visibility of deformation in the radar signal on any slope. Second, the detection potential indicator, provided as Google Earth Engine application, performs a preliminary analysis of the Sentinel-1 data available at any specific location. Our analysis shows that on 89% of the world's slopes deformation is likely to be detectable with InSAR.</p><p>The detection potential indicator is a valuable tool in the project planning phase, while exploring the site specific possibilities for InSAR deformation monitoring. Furthermore, the sensitivity index provides overview of the slopes where large scale, machine learning driven, landslide nowcasting and forecasting are likely to succeed. We will present an analysis of the global sensitivity index, as well as demonstrate how to apply our detection potential application on a case study.</p>

scholarly journals Subsidence Monitoring of Fill Area in Yan’an New District Based on Sentinel-1A Time Series Imagery

2021 ◽  
Vol 13 (15) ◽  
pp. 3044
Author(s):  
Mingjie Liao ◽  
Rui Zhang ◽  
Jichao Lv ◽  
Bin Yu ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Ground Deformation ◽  
Dynamic Change ◽  
Deformation Monitoring ◽  
Chinese Loess Plateau ◽  
Ground Subsidence ◽  
Shanxi Province ◽  
Environment Change ◽  
Loess Area

In recent years, many cities in the Chinese loess plateau (especially in Shanxi province) have encountered ground subsidence problems due to the construction of underground projects and the exploitation of underground resources. With the completion of the world’s largest geotechnical project, called “mountain excavation and city construction,” in a collapsible loess area, the Yan’an city also appeared to have uneven ground subsidence. To obtain the spatial distribution characteristics and the time-series evolution trend of the subsidence, we selected Yan’an New District (YAND) as the specific study area and presented an improved time-series InSAR (TS-InSAR) method for experimental research. Based on 89 Sentinel-1A images collected between December 2017 to December 2020, we conducted comprehensive research and analysis on the spatial and temporal evolution of surface subsidence in YAND. The monitoring results showed that the YAND is relatively stable in general, with deformation rates mainly in the range of −10 to 10 mm/yr. However, three significant subsidence funnels existed in the fill area, with a maximum subsidence rate of 100 mm/yr. From 2017 to 2020, the subsidence funnels enlarged, and their subsidence rates accelerated. Further analysis proved that the main factors induced the severe ground subsidence in the study area, including the compressibility and collapsibility of loess, rapid urban construction, geological environment change, traffic circulation load, and dynamic change of groundwater. The experimental results indicated that the improved TS-InSAR method is adaptive to monitoring uneven subsidence of deep loess area. Moreover, related data and information would provide reference to the large-scale ground deformation monitoring and in similar loess areas.

scholarly journals The NDVI algorithm utilization on the google earth engine platform to monitor changes in forest density in mining area

2021 ◽  
Vol 886 (1) ◽  
pp. 012100
Author(s):  
Munajat Nursaputra ◽  
Siti Halimah Larekeng ◽  
Nasri ◽  
Andi Siady Hamzah
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Forest Degradation ◽  
Google Earth ◽  
Aerial Images ◽  
Forest Monitoring ◽  
Forest Density ◽  
Google Earth Engine

Abstract Periodic forest monitoring needs to be done to avoid forest degradation. In general, forest monitoring can be conducted manually (field surveys) or using technological innovations such as remote sensing data derived from aerial images (drone results) or cloud computing-based image processing. Currently, remote sensing technology provides large-scale forest monitoring using multispectral sensors and various vegetation index processing algorithms. This study aimed to evaluate the use of the Google Earth Engine (GEE) platform, a geospatial dataset platform, in the Vale Indonesia mining concession area to improve accountable forest monitoring. This platform integrates a set of programming methods with a publicly accessible time-series database of satellite imaging services. The method used is NDVI processing on Landsat multispectral images in time series format, which allows for the description of changes in forest density levels over time. The results of this NDVI study conducted on the GEE platform have the potential to be used as a tool and additional supporting data for monitoring forest conditions and improvement in mining regions.

scholarly journals Monitoring Coastline Changes of the Malay Islands Based on Google Earth Engine and Dense Time-Series Remote Sensing Images

2021 ◽  
Vol 13 (19) ◽  
pp. 3842
Author(s):  
Yaxin Ding ◽  
Xiaomei Yang ◽  
Hailiang Jin ◽  
Zhihua Wang ◽  
Yueming Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Large Scale ◽  
Dynamic Change ◽  
Google Earth ◽  
Tidal Range ◽  
Remote Sensing Images ◽  
Data Platform ◽  
Range Observation ◽  
Change Monitoring

The use of remote sensing to monitor coastlines with wide distributions and dynamic changes is significant for coastal environmental monitoring and resource management. However, most current remote sensing information extraction of coastlines is based on the instantaneous waterline, which is obtained by single-period imagery. The lack of a unified standard is not conducive to the dynamic change monitoring of a changeable coastline. The tidal range observation correction method can be used to correct coastline observation to a unified climax line, but it is difficult to apply on a large scale because of the distribution of observation sites. Therefore, we proposed a coastline extraction method based on the remote sensing big data platform Google Earth Engine and dense time-series remote sensing images. Through the instantaneous coastline probability calculation system, the coastline information could be extracted without the tidal range observation data to achieve a unified tide level standard. We took the Malay Islands as the experimental area and analyzed the consistency between the extraction results and the existing high-precision coastline thematic products of the same period to achieve authenticity verification. Our results showed that the coastline data deviated 10 m in proportion to a reach of 40% and deviated 50 m within a reach of 89%. The overall accuracy was kept within 100 m. In addition, we extracted 96 additional islands that have not been included in public data. The obtained multi-phase coastlines showed the spatial distribution of the changing hot regions of the Malay Islands’ coastline, which greatly supported our analysis of the reasons for the expansion and retreat of the coastline in this region. These research results showed that the big data platform and intensive time-series method have considerable potential in large-scale monitoring of coastline dynamic change and island reef change monitoring.

scholarly journals GROUND DEFORMATION MONITORING IN QINGDAO COASTAL AREAS BY TIME-SERIES TERRASAR-X IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 29-33
Author(s):  
A. Y. Hou ◽  
X. Qiao ◽  
D. Li
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Ground Deformation ◽  
Small Deformation ◽  
Coastal Areas ◽  
Deformation Monitoring ◽  
Rate Of Change ◽  
Short Period ◽  
Radar Satellite ◽  
Ground Deformation Monitoring

As a new generation of high resolution and short revisit period of radar satellite, TerraSAR-X is not only able to meet the requirements of monitoring large scale surface subsidence, but also make it possible to monitor the small deformation of the short period. This articles takes the coastal areas of the west coast of Qingdao as the research object. With Small baselines subsets interferometry synthetic aperture radar (SBASI), this paper obtained the period the average annual rate of change from the time series analysis of TerraSAR-X data from April 2015 to October 2014.In order to enrich the historical deformation data of the study area, it analyse the time series of ALOS images from December 2010 to October 2008 with the same method. Finally,it analyse and demonstrate the experimental results.

scholarly journals Landslide Deformation Monitoring by Adaptive Distributed Scatterer Interferometric Synthetic Aperture Radar

2019 ◽  
Vol 11 (19) ◽  
pp. 2273 ◽  
Author(s):  
Hongguo Jia ◽  
Hao Zhang ◽  
Luyao Liu ◽  
Guoxiang Liu
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Deformation Monitoring ◽  
Synthetic Aperture ◽  
Economic Losses ◽  
Interferometric Synthetic Aperture Radar ◽  
Mountainous Areas ◽  
Landslide Area ◽  
Landslide Deformation ◽  
Aperture Radar

Landslide is the second most frequent geological disaster after earthquake, which causes a large number of casualties and economic losses every year. China frequently experiences devastating landslides in mountainous areas. Interferometric Synthetic Aperture Radar (InSAR) technology has great potential for detecting potentially unstable landslides across wide areas and can monitor surface displacement of a single landslide. However traditional time series InSAR technology such as persistent scatterer interferometry (PSI) and small-baseline subset (SBAS) cannot identify enough points in mountainous areas because of dense vegetation and steep terrain. In order to improve the accuracy of landslide hazard detection and the reliability of landslide deformation monitoring in areas lacking high coherence stability point targets, this study proposes an adaptive distributed scatterer interferometric synthetic aperture radar (ADS-InSAR) method based on the spatiotemporal coherence of the distributed scatterer (DS), which automatically adjusts its detection threshold to improve the spatial distribution density and reliability of DS detection in the landslide area. After time series network modeling and deformation calculation of the ADS target, the displacement deformation of the landslide area can be accurately extracted. Shuibuya Town in Enshi Prefecture, Hubei Province, China, was used as a case study, along with 18 Sentinal-1A images acquired from March 2016 to April 2017. The ADS-InSAR method was used to obtain regional deformation data. The deformation time series was combined with hydrometeorological and related data to analyze landslide deformation. The results show that the ADS-InSAR method can effectively improve the density of DS distribution, successfully detect existing ancient landslide groups and determine multiple potential landslide areas, enabling early warning for landslide hazards. This study verifies the reliability and accuracy of ADS-InSAR for landslide disaster prevention and mitigation.

scholarly journals A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland

2020 ◽  
Vol 57 (8) ◽  
pp. 1102-1124
Author(s):  
M. Mahdianpari ◽  
H. Jafarzadeh ◽  
J. E. Granger ◽  
F. Mohammadimanesh ◽  
B. Brisco ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Landsat Imagery ◽  
Google Earth ◽  
Scale Change ◽  
Large Scale Change ◽  
Google Earth Engine ◽  
Change Monitoring

scholarly journals APPLICATION OF TIME SERIES INSAR TECHNIQUE FOR DEFORMATION MONITORING OF LARGE-SCALE LANDSLIDES IN MOUNTAINOUS AREAS OF WESTERN CHINA

2016 ◽  
Vol XLI-B1 ◽  
pp. 89-91
Author(s):  
T. Qu ◽  
P. Lu ◽  
C. Liu ◽  
H. Wan
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Deformation Monitoring ◽  
In Situ Monitoring ◽  
Western China ◽  
Surface Displacements ◽  
Landslide Hazards ◽  
Landslide Body ◽  
Small Baseline Subset ◽  
Small Baseline

Western China is very susceptible to landslide hazards. As a result, landslide detection and early warning are of great importance. This work employs the SBAS (Small Baseline Subset) InSAR Technique for detection and monitoring of large-scale landslides that occurred in Li County, Sichuan Province, Western China. The time series INSAR is performed using descending scenes acquired from TerraSAR-X StripMap mode since 2014 to get the spatial distribution of surface displacements of this giant landslide. The time series results identify the distinct deformation zone on the landslide body with a rate of up to 150mm/yr. The deformation acquired by SBAS technique is validated by inclinometers from diverse boreholes of in-situ monitoring. The integration of InSAR time series displacements and ground-based monitoring data helps to provide reliable data support for the forecasting and monitoring of largescale landslide.

River morphological changes detection from drone and radar satellite data

2020 ◽  
Author(s):  
Giulia Marchetti ◽  
Francesco Asaro ◽  
Simone Bizzi ◽  
Stefano Mariani ◽  
Barbara Lastoria ◽  
...  
Keyword(s):  
Satellite Data ◽  
Large Scale ◽  
Morphological Changes ◽  
Atmospheric Condition ◽  
Radar Data ◽  
Radar Signal ◽  
Catchment Scale ◽  
Natural Risk ◽  
Sediment Size ◽  
Radar Satellite

<p>The identification and quantification of morphological changes occurring in the river channel over time are essential to understand rivers behaviour, assess sediment budgets, evaluate effectiveness of river management strategies and support the production of natural risk map. Recently, river science has made a breakthrough thanks to emerging remote sensing technologies and today we can rely on an unparalleled amount of data, at spatial and temporal resolution not available in the past. This has opened new perspectives for river monitoring and fluvial survey practices, allowing to cover areas up to the catchment scale and get information almost in continuum. This research aims to investigate the potential of radar satellite data collected from Sentinel 1 mission to infer information about rivers morphodynamics processes (such as erosion and deposition), that may occur on medium-large river (e.g., active channel width > 50 m) after a flood that caused significant morphological adjustments. Drone and satellite data were collected in September 2017 and September 2018 on a selected site along the Po river, in northern Italy, characterized by a large exposed sediment bar. In March 2018 a flood caused an avulsion and a new secondary channel was opened. We used the sequential drone acquisitions to generate a Dem of Difference, that revealed geomorphic changes of the monitored sediment bar up to 2 m erosion and 1.5 m deposition. We then exploited the radar data of Sentinel 1 and conducted a seasonal analysis using both the coherence data between image pairs and the backscattered radar signal, by investigating the variability of the radar signals through the year and the correspondent condition of the bar. Results show that there is a significant correlation between morphological changes occurred in the site and the associated values of both the amplitude and the coherence of the radar data pre and post the event that caused the morphological changes measured. Further studies are needed to better discriminate the different contributions to changes in amplitude and coherence driven by soil water content, vegetation, sediment size, atmospheric condition for the various time windows analysed. Despite that, these initial evidences are encouraging and new applications to other sites and flood events are planned because these results prove the sensitiveness of the radar signal to geomorphic events. Even simply the ability to detect where channel morphological processes are occurring and their expected intensity through Sentinel 1 data would allow to prioritize more detailed field campaigns by, for instance, UAV technology providing a notable advance compared to the current ability to monitor river morphological changes on large scale.</p>

scholarly journals CHANGE DETECTION BASED ON PERSISTENT SCATTERER INTERFEROMETRY – A NEW METHOD OF MONITORING BUILDING CHANGES

2016 ◽  
Vol III-7 ◽  
pp. 243-250
Author(s):  
C. H. Yang ◽  
B. K. Kenduiywo ◽  
U. Soergel
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Ground Truth ◽  
Google Earth ◽  
Aerial Images ◽  
Radar Signal ◽  
Persistent Scatterer Interferometry ◽  
Small Surface ◽  
Persistent Scatterer ◽  
Main Station

Persistent Scatterer Interferometry (PSI) is a technique to detect a network of extracted persistent scatterer (PS) points which feature temporal phase stability and strong radar signal throughout time-series of SAR images. The small surface deformations on such PS points are estimated. PSI particularly works well in monitoring human settlements because regular substructures of man-made objects give rise to large number of PS points. If such structures and/or substructures substantially alter or even vanish due to big change like construction, their PS points are discarded without additional explorations during standard PSI procedure. Such rejected points are called big change (BC) points. On the other hand, incoherent change detection (ICD) relies on local comparison of multi-temporal images (e.g. image difference, image ratio) to highlight scene modifications of larger size rather than detail level. However, image noise inevitably degrades ICD accuracy. We propose a change detection approach based on PSI to synergize benefits of PSI and ICD. PS points are extracted by PSI procedure. A local change index is introduced to quantify probability of a big change for each point. We propose an automatic thresholding method adopting change index to extract BC points along with a clue of the period they emerge. In the end, PS ad BC points are integrated into a change detection image. Our method is tested at a site located around north of Berlin main station where steady, demolished, and erected building substructures are successfully detected. The results are consistent with ground truth derived from time-series of aerial images provided by Google Earth. In addition, we apply our technique for traffic infrastructure, business district, and sports playground monitoring.

Towards synergy of GNSS and InSAR mining deformation monitoring with Sentinel-1 data

2021 ◽  
Author(s):  
Damian Tondaś ◽  
Maya Ilieva ◽  
Witold Rohm ◽  
Jan Kapłon
Keyword(s):  
Time Series ◽  
Real Time ◽  
Laser Scanning ◽  
Large Scale ◽  
Ground Deformation ◽  
Dimensional Space ◽  
Satellite Navigation ◽  
Deformation Monitoring ◽  
North East ◽  
The Impact

<p>The determination of ground deformation may be carried out by applying various measurement methods such as levelling, laser scanning, satellite navigation systems, Synthetic Aperture Radar (SAR) and many others. In this work, we focus on the comparison of the deformation effects measured by Global Navigation Satellite Systems (GNSS) and satellite Interferometric SAR (InSAR) methods in the Upper-Silesian coal mining region (SW Poland).</p><p>An unquestionable advantage of GNSS technology is the possibility of continuous monitoring of deformations in three-dimensional space. Moreover, the evolution of real-time (RT) techniques such as: near real-time (NRT), ultra-fast NRT or RT allows to obtain a high precise position determination with a relatively slight latency (ranging from a few seconds to less than one hour). The limitation of the satellite navigation technology is the spatial range of the measurements. The deformation can only be observed at the point where the GNSS antenna is located. Furthermore, the acquisition, installation and maintenance of the equipment may also involve high costs.</p><p>In contrast to the GNSS technique, the InSAR methods enable measurement of the large-scale subsidence areas with possibility to use free products and software (e.g. Sentinel-1 and SNAP). The large-scale InSAR investigations provide a better overview of local terrain changes. Unfortunately, InSAR methods also have some limitations related to data acquisition technology:  </p><ul><li>a few days latency in acquiring a new image,</li> <li>insensitivity to changes in the northern component,</li> <li>acquiring deformation only in the LOS direction.</li> </ul><p>The main goal of this research is to analyse the deformation results obtained using GNSS and InSAR methods with respect to the capabilities and limitations of these two techniques. We investigated the level of agreement of eight GNSS and InSAR time series in areas with no subsidence, together with results acquired on seven regions of mining deformation where the maximum subsidence velocity exceeds 50 cm/year. The mean RMS time series fitting error obtained in subsidence basin is more than 5 cm and in non-deformed areas is equal to 2 cm. The study also investigated the effect of coherence threshold levels (0.3 ÷ 0.6) with introduction of the northern GNSS component on the InSAR decomposition process. Assuming the same GNSS deformation value in each directions (north, east, and up), the impact of the northern component was estimated as 10% of the total LOS subsidence.</p>

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