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Principles and Practices of Data Fusion in Multisensor Remote Sensing for Environmental Monitoring
Published in Ni-Bin Chang, Kaixu Bai, Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing, 2018
A synthetic aperture radar (SAR) may have longer wavelengths that are mostly unaffected by weather and clouds and may be deployed onboard a space- or air-borne platform. SAR is an active remote sensing system with a wavelength of C band or L band (see Chapter 3). The phase value of the SAR image is calculated by the apparent distance between the ground target and the radar antenna, providing more precious information. With a large virtual antenna and unique signal processing techniques, SAR products provide much higher spatial resolution than that of a regular aperture radar. By combining the phase components of two SAR co-registered images of the same area, Interferometric synthetic aperture radar (InSAR) can involve the information fusion of these two SAR images to extract the subtle patterns of Earth's surface changes through the phase change identification. In general, the environmental applications of InSAR include three categories for: (1) detecting ground deformation caused by natural hazards or anthropogenic activities, (2) mapping the extent and progression of natural hazards such as earthquakes, volcanic eruptions, landslides, mudflows, floods, and so on, and (3) monitoring tiny landscape changes by analyzing SAR intensity and coherence images.
Measuring and Monitoring Land Subsidence
Published in Frank R. Spellman, Land Subsidence Mitigation, 2017
Interferometric synthetic aperture radar (InSAR) is a remote sensing technique used to measure land surface elevation changes over wide areas, such as over the entire Hampton Roads area. InSAR can be used to determine and map critical areas of land subsidence, select locations for detailed geodetic surveying, and plan strategies for preventing and mitigating land subsidence (Bawden et al., 2003). Accuracy of InSAR subsidence estimates will be important in Hampton Roads, because subsidence rates in the area have been measured at 1.1 to 4.8 millimeters, as compared to typical error for InSAR of 5 to 10 mm. The high atmospheric humidity and dense vegetation found in Hampton Roads can reduce InSAR accuracy. Problems with error can be overcome by analyzing a large number of satellite scenes, applying persistent scatter analysis techniques, using InSAR data collected over multiple years, and by using L-band or X-band rather than C-band InSAR data (Eggleston, 2016). Probably the most valuable aspect of InSAR remote sensing is its capacity to input valuable data for detailed mapping of regional subsidence over time. The type of remote sensing data used to map subsidence, InSAR, has been collected for Hampton Roads by various satellites since 1992, and such data are currently being collected by several international satellites. In 2020, a new U.S. satellite, NISAR, is scheduled to begin collecting InSAR data over Hampton Roads.
Geomorphological Studies from Remote Sensing
Published in Prasad S. Thenkabail, Remote Sensing Handbook, 2015
James B. Campbell, Lynn M. Resler
InSAR, therefore, forms a valuable tool for assessing subsidence, displacement, and lateral motion of terrain surfaces associated with geomorphological analysis. Reported accuracies of InSAR analyses vary with specifics of terrain, sensor systems (including wavelength), atmospheric conditions, and validation strategies, among others. The European Space Agency’s TanDEM-X SAR system is specifically designed for terrain assessment at levels of detail and accuracies that support assessment of mass-wasting hazards (Bamler et al. 2009; Zebker et al. 2010). (In general, tabulation of summary information regarding accuracies of geomorphic RS applications represents so many different measures, methodologies, and variations in experimental design, which the data are often not comparable.)
Effectiveness evaluation of DS-InSAR method fused PS points in surface deformation monitoring: a case study of Hongta District, Yuxi City, China
Published in Geomatics, Natural Hazards and Risk, 2023
Yongfa Li, Xiaoqing Zuo, Fang Yang, Jinwei Bu, Wenhao Wu, Xinyu Liu
Although the traditional monitoring methods have relatively reliable monitoring results at the monitoring points, their spatial resolution is low, it is difficult to obtain the deformation at non-monitoring points, and the monitoring cost is high, so it is difficult to carry out fine monitoring of wide-area surface deformation, which limits our comprehensive understanding of the deformation field and the spatio-temporal evolution characteristics of deformation in the entire study area (Luo et al. 2014). Synthetic Aperture Radar Interferometry (InSAR) is a new type of earth observation technology that has been developed rapidly in the past 30 years. It is widely used in various kinds of deformation monitoring owing to its high spatial resolution, all-day, all-weather and other characteristics (Zhu et al. 2017; Yuan et al. 2020; Zhang et al. 2022). Compared with conventional geodetic techniques such as global positioning system (GPS) and levelling, InSAR technology can obtain wide-area surface deformation time series with significantly improved spatial resolution (Bai et al. 2016).
Potential landslides identification based on temporal and spatial filtering of SBAS-InSAR results
Published in Geomatics, Natural Hazards and Risk, 2023
Jiahui Dong, Ruiqing Niu, Bingquan Li, Hang Xu, Shunyao Wang
Identification of potential landslides is essential for disaster mitigation and prevention. InSAR is a new spatial surface measurement technology, that is unaffected by natural weather effects and can observe the surface at all hours of day and night, allowing it to be used for the identification of potential landslides. InSAR technology can collect high-resolution and high-precision information on small surface deformations over a large area of the ground. Applying InSAR to identify potential hidden hazards in key areas, we can realize the investigation of key areas from the perspective of time and space, thus providing enough reference basis for the prevention and control of regional geological hazards (Akbarimehr et al. 2013; Medhat et al. 2022; Milillo et al. 2022; van Natijne et al. 2022). Many researchers have already applied InSAR technology to identify regional landslides, update landslide inventory maps, determine potential landslide extent, and identify potential landslides precursor features (Intrieri et al. 2018; Devara et al. 2021; Guo et al. 2021; Su et al. 2021).
Ground infrastructure monitoring in coastal areas using time-series inSAR technology: the case study of Pudong International Airport, Shanghai
Published in International Journal of Digital Earth, 2023
Bei An, Yanan Jiang, Changcheng Wang, Peng Shen, Tianyi Song, Chihao Hu, Kui Liu
To solve these potential problems, traditional methods of land subsidence monitoring are mainly GNSS measurements (Benoit et al. 2015; Ito et al. 2016; Komac et al. 2015), level measurements (Stiros 2004; Vasco 2005), and extrapolation meters. These methods are mostly limited to point measures in a small spatial area, costly, time-consuming, and lacking in timeliness. With the increasing development of Interferometric Synthetic Aperture Radar (InSAR) technology, scholars from various countries have used differential interference, time-series InSAR, Pixel Offset tracking, and other techniques for seismic inversion (Ding et al. 2004; Hooper et al. 2012; Lindsey et al. 2015), landslides (Wasowski and Bovenga 2014; Intrieri et al. 2018; Rosi et al. 2018), glacier movement (Rignot et al. 2000; Luckman, Padman, and Jansen 2010), ground subsidence (Ding et al. 2004; Du et al. 2018; Wang et al. 2022), and other geohazard monitoring. The development of InSAR technology has an incomparable advantage compared with traditional ground-based methods in wide-area deformation monitoring. With the launch of multi-platform radar remote sensing satellites, the temporal resolution of the monitoring becomes increasingly higher, significantly improving the efficiency and reducing the monitoring cost.