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Journal articles on the topic 'Multitemporal InSAR'

Author: Grafiati
Published: 10 March 2023

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1

Shirzaei, Manoochehr. "A seamless multitrack multitemporal InSAR algorithm." Geochemistry, Geophysics, Geosystems 16, no. 5 (May 2015): 1656–69. http://dx.doi.org/10.1002/2015gc005759.

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2

Liang, Hongyu, Lei Zhang, Zhong Lu, and Xin Li. "Nonparametric Estimation of DEM Error in Multitemporal InSAR." IEEE Transactions on Geoscience and Remote Sensing 57, no. 12 (December 2019): 10004–14. http://dx.doi.org/10.1109/tgrs.2019.2930802.

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3

Manzoni, Marco, Monia Elisa Molinari, and Andrea Monti-Guarnieri. "Multitemporal InSAR Coherence Analysis and Methods for Sand Mitigation." Remote Sensing 13, no. 7 (April 2, 2021): 1362. http://dx.doi.org/10.3390/rs13071362.

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Dunes and sand sheets motion natural hazard affect many desertic areas worldwide and require careful assessment to develop effective mitigation plans to protect populated sites, infrastructure, and human activities. The study explores the suitability of Synthetic Aperture Radar (SAR) coherent methods to detect desert area instabilities and estimate sand accumulations displacements. The SAR methods have been applied to long time series of images provided by Sentinel-1. Moreover, the research introduces a novel robust index, named Temporal Stability Index, able to characterize the percentage of stability of a target with time. The work reports the experiments performed on the United Arab Emirates (UAE) and Egypt desertic areas and proves the usefulness of SAR coherent methods to support sand mitigation measures.
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Jiang, Mi, Xiaoli Ding, and Zhiwei Li. "Hybrid Approach for Unbiased Coherence Estimation for Multitemporal InSAR." IEEE Transactions on Geoscience and Remote Sensing 52, no. 5 (May 2014): 2459–73. http://dx.doi.org/10.1109/tgrs.2013.2261996.

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5

Hu, Jun, Xiao-Li Ding, Zhi-Wei Li, Jian-Jun Zhu, Qian Sun, and Lei Zhang. "Kalman-Filter-Based Approach for Multisensor, Multitrack, and Multitemporal InSAR." IEEE Transactions on Geoscience and Remote Sensing 51, no. 7 (July 2013): 4226–39. http://dx.doi.org/10.1109/tgrs.2012.2227759.

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6

Engdahl, M. E., and J. M. Hyyppa. "Land-cover classification using multitemporal ERS-1/2 insar data." IEEE Transactions on Geoscience and Remote Sensing 41, no. 7 (July 2003): 1620–28. http://dx.doi.org/10.1109/tgrs.2003.813271.

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7

Esmaeili, Mostafa, Mahdi Motagh, and Andy Hooper. "Application of Dual-Polarimetry SAR Images in Multitemporal InSAR Processing." IEEE Geoscience and Remote Sensing Letters 14, no. 9 (September 2017): 1489–93. http://dx.doi.org/10.1109/lgrs.2017.2717846.

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Liang, Hongyu, Lei Zhang, Zhong Lu, and Xin Li. "Correction of spatially varying stratified atmospheric delays in multitemporal InSAR." Remote Sensing of Environment 285 (February 2023): 113382. http://dx.doi.org/10.1016/j.rse.2022.113382.

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9

Liang, Hongyu, Xin Li, and Rou-Fei Chen. "Mapping Surface Deformation Over Tatun Volcano Group, Northern Taiwan Using Multitemporal InSAR." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021): 2087–95. http://dx.doi.org/10.1109/jstars.2021.3050644.

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10

Hu, Jiyuan, Mahdi Motagh, Jiming Guo, Mahmud Haghshenas Haghighi, Tao Li, Fen Qin, and Wenhao Wu. "Inferring subsidence characteristics in Wuhan (China) through multitemporal InSAR and hydrogeological analysis." Engineering Geology 297 (February 2022): 106530. http://dx.doi.org/10.1016/j.enggeo.2022.106530.

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11

Parker, Amy L., Juliet Biggs, and Zhong Lu. "Investigating long-term subsidence at Medicine Lake Volcano, CA, using multitemporal InSAR." Geophysical Journal International 199, no. 2 (September 4, 2014): 844–59. http://dx.doi.org/10.1093/gji/ggu304.

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12

Jiang, Mi, Xiaoli Ding, Ramon F. Hanssen, Rakesh Malhotra, and Ling Chang. "Fast Statistically Homogeneous Pixel Selection for Covariance Matrix Estimation for Multitemporal InSAR." IEEE Transactions on Geoscience and Remote Sensing 53, no. 3 (March 2015): 1213–24. http://dx.doi.org/10.1109/tgrs.2014.2336237.

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13

Jiang, Mi, Zelang Miao, Paolo Gamba, and Bin Yong. "Application of Multitemporal InSAR Covariance and Information Fusion to Robust Road Extraction." IEEE Transactions on Geoscience and Remote Sensing 55, no. 6 (June 2017): 3611–22. http://dx.doi.org/10.1109/tgrs.2017.2677260.

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14

Montazeri, Sina, Fernando Rodríguez González, and Xiao Zhu. "Geocoding Error Correction for InSAR Point Clouds." Remote Sensing 10, no. 10 (September 22, 2018): 1523. http://dx.doi.org/10.3390/rs10101523.

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Persistent Scatterer Interferometry (PSI) is an advanced multitemporal InSAR technique that is capable of retrieving the 3D coordinates and the underlying deformation of time-coherent scatterers. Various factors degrade the localization accuracy of PSI point clouds in the geocoding process, which causes problems for interpretation of deformation results and also making it difficult for the point clouds to be compared with or integrated into data from other sensors. In this study, we employ the SAR imaging geodesy method to perform geodetic corrections on SAR timing observations and thus improve the positioning accuracy in the horizontal components. We further utilize geodetic stereo SAR to extract large number of highly precise ground control points (GCP) from SAR images, in order to compensate for the unknown height offset of the PSI point cloud. We demonstrate the applicability of the approach using TerraSAR-X high resolution spotlight images over the city of Berlin, Germany. The corrected results are compared with a reference LiDAR point cloud of Berlin, which confirms the improvement in the geocoding accuracy.
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15

Rahmoune, Rachid, Mohammed Sulaimani, Jan Stammeijer, Saif Azri, Roeland van Gilst, Abir Mahruqi, Rawya Aghbari, and Abdesslam Belghache. "Multitemporal SAR interferometry for monitoring of ground deformations caused by hydrocarbon production in an arid environment: Case studies from the Sultanate of Oman." Leading Edge 40, no. 1 (January 2021): 45–51. http://dx.doi.org/10.1190/tle40010045.1.

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Time-lapse interferometric synthetic aperture radar (InSAR) remote sensing methods of surface deformation have proven their use in desert environments. The data are acquired frequently without the need to send personnel or equipment into the field. The quality and accuracy of the data is very high. The spatial resolution of the data is excellent and matches that of surface seismic data. These characteristics make the data well suited for a variety of time-lapse monitoring tasks. In this study, we describe the accuracy of the InSAR technique relative to other measurements such as the global positioning system and precise leveling (acquired at known stable locations). We illustrate two case studies of differing natures. In one case, gas production leads to reservoir compaction, which is tracked as surface subsidence with time using frequent InSAR data. The results are used to map zones of increased deformation and identify areas with localized changes. These insights are being used to influence decisions on new wells and well interventions, to provide support for management of facility integrity, and to advise building code and material selection that can withstand the expected rate of deformation. The second case of a shallow steam flood illustrates the use of InSAR data to identify areas of surface uplift following thermal expansion of the reservoir. These data are also used to support the monitoring of the steam chamber growth and confinement in the reservoir. The information from InSAR will become more valuable for reservoir management when the steam chamber matures and conventional downhole data acquisition consequently becomes challenging. In summary, oil and gas fields located in arid environments lend themselves well to remote sensing using the InSAR technique because (1) they are sizeable (from tens to hundreds of square kilometers); (2) they are free from vegetation, snow cover, and most atmospheric distortions, although cloud and pollution can affect the data quality; and (3) they benefit from highly repeatable long-term regular monitoring.
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16

Çomut, F. C., A. Ustun, M. Lazecky, and M. M. Aref. "MULTI BAND INSAR ANALYSIS OF SUBSIDENCE DEVELOPMENT BASED ON THE LONG PERIOD TIME SERIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 10, 2015): 115–21. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-115-2015.

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The SAR Interferometry (InSAR) application has shown great potential in monitoring of land terrain changes and in detection of land deformations such as subsidence. Longer time analysis can lead to understand longer trends and changes. Using different bands of SAR satellite (C- from ERS 1-2 and Envisat, L- from ALOS) over the study area, we achieve knowledge of movements in long-term and evaluation of its dynamic changes within observed period of time. Results from InSAR processing fit with the position changes in vertical direction based on GPS network established over the basin as an effective geodetic network. Time series (StaMPS PS+SB) of several points over Çumra County in eastern part of Konya City show a general trend of the deformation that is expected to be approximately between -13 to -17 mm/year. Northern part of Karaman is affected by faster subsidence, borders of the subsidence trough were identified from Envisat. <br><br> Presenting InSAR results together with GIS information about locations and time of occurrence of sudden subsidence, urban/industrial growth in time and climate changes helps in better understanding of the situation. This way, the impact of natural and man-made changes will be shown for urban planning thanks to InSAR and GIS comparisons with hydrogeological modeling. In this study we present results of differential and multitemporal InSAR series using different bands and GIS conjunction associated with seasonal and temporal groundwater level changes in Konya Closed Basin.
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17

Zhao, Jingwen, Jicang Wu, Xiaoli Ding, and Mingzhou Wang. "Elevation Extraction and Deformation Monitoring by Multitemporal InSAR of Lupu Bridge in Shanghai." Remote Sensing 9, no. 9 (August 30, 2017): 897. http://dx.doi.org/10.3390/rs9090897.

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18

Liang, Hongyu, Lei Zhang, Xiaoli Ding, Zhong Lu, and Xin Li. "Toward Mitigating Stratified Tropospheric Delays in Multitemporal InSAR: A Quadtree Aided Joint Model." IEEE Transactions on Geoscience and Remote Sensing 57, no. 1 (January 2019): 291–303. http://dx.doi.org/10.1109/tgrs.2018.2853706.

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19

Ebmeier, S. K. "Application of independent component analysis to multitemporal InSAR data with volcanic case studies." Journal of Geophysical Research: Solid Earth 121, no. 12 (December 2016): 8970–86. http://dx.doi.org/10.1002/2016jb013765.

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20

Palamà, R., M. Crosetto, O. Monserrat, A. Barra, B. Crippa, M. Mróz, N. Kotulak, M. Mleczko, and J. Rapinski. "ANALYSIS OF MINING-INDUCED TERRAIN DEFORMATION USING MULTITEMPORAL DISTRIBUTED SCATTERER SAR INTERFEROMETRY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 321–26. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-321-2022.

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Abstract. This work addresses a methodology based on the Interferometric Synthetic Aperture Radar (InSAR) to analyse and monitor ground motion phenomena induced by underground mining activities, in the Legnica-Glogow Copper District, south-western Poland. Two stacks of ascending and descending Sentinel-1 Synthetic Aperture Radar (SAR) images are processed with a small baseline multitemporal approach. A simple method to select interferograms with high coherence and eliminated images with low redundancy is implemented to optimize the interferogram netwrork. The estimated displacement maps and time series show the effect of both linear and impulsive ground motion and are validated against Global Navigation Satellite System (GNSS) measurements.
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21

Karimzadeh, Sadra, and Masashi Matsuoka. "A Weighted Overlay Method for Liquefaction-Related Urban Damage Detection: A Case Study of the 6 September 2018 Hokkaido Eastern Iburi Earthquake, Japan." Geosciences 8, no. 12 (December 14, 2018): 487. http://dx.doi.org/10.3390/geosciences8120487.

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We performed interferometric synthetic aperture radar (InSAR) analyses to observe ground displacements and assess damage after the M 6.6 Hokkaido Eastern Iburi earthquake in northern Japan on 6 September 2018. A multitemporal SAR coherence map is extracted from 3-m resolution ascending (track 116) and descending (track 18) ALOS-2 Stripmap datasets to cover the entire affected area. To distinguish damaged buildings associated with liquefaction, three influential parameters from the space-based InSAR results, ground-based LiquickMap (from seismic intensities in Japanese networks) and topographic slope of the study area are considered together in a weighted overlay (WO) analysis, according to prior knowledge of the study area. The WO analysis results in liquefaction potential values that agree with our field survey results. To investigate further, we conducted microtremor measurements at 14 points in Hobetsu, in which the predominant frequency showed a negative correlation with the WO values, especially when drastic coherence decay occurred.
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22

Zhao, Changjun, Zhen Li, Bangsen Tian, Ping Zhang, WU Wenhao, Shuo Gao, Yuechi Yu, and Yunyun Dong. "A statistically homogeneous pixel selection approach for adaptive estimation of multitemporal InSAR covariance matrix." International Journal of Applied Earth Observation and Geoinformation 110 (June 2022): 102792. http://dx.doi.org/10.1016/j.jag.2022.102792.

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23

Bakon, Matus, Irene Oliveira, Daniele Perissin, Joaquim Joao Sousa, and Juraj Papco. "A Data Mining Approach for Multivariate Outlier Detection in Postprocessing of Multitemporal InSAR Results." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 6 (June 2017): 2791–98. http://dx.doi.org/10.1109/jstars.2017.2686646.

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24

Zhang, Lei, Xiaoli Ding, Zhong Lu, Hyung-Sup Jung, Jun Hu, and Guangcai Feng. "A Novel Multitemporal InSAR Model for Joint Estimation of Deformation Rates and Orbital Errors." IEEE Transactions on Geoscience and Remote Sensing 52, no. 6 (June 2014): 3529–40. http://dx.doi.org/10.1109/tgrs.2013.2273374.

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25

Lee, Jui-Chi, and Manoochehr Shirzaei. "Novel algorithms for pair and pixel selection and atmospheric error correction in multitemporal InSAR." Remote Sensing of Environment 286 (March 2023): 113447. http://dx.doi.org/10.1016/j.rse.2022.113447.

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26

Montisci, Augusto, and Maria Cristina Porcu. "A Satellite Data Mining Approach Based on Self-Organized Maps for the Early Warning of Ground Settlements in Urban Areas." Applied Sciences 12, no. 5 (March 4, 2022): 2679. http://dx.doi.org/10.3390/app12052679.

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Structural failure prevention is a crucial issue in civil engineering. The causes of structure or infrastructure collapse include phenomena that slowly deform the ground and could affect the stability of foundations such as differential settlements, subsidence, groundwater changes, slope failure, or landslides. When large urban areas need to be monitored, such phenomena are hard to be mapped by means of classical structural health monitoring methods due to the unaffordable quantity of in situ measurements these methods would entail. A very effective alternative is exploiting multitemporal interferometric synthetic aperture radar (MT-InSAR) displacement timeseries which would enable the monitoring of wide geographical areas over a weekly basis and extended spatial coverage. Analyzing the enormous amount of data produced by MT-InSAR may help to assess the time evolution of phenomena but can barely highlight “anomalous” ground deformations in time, to prevent likely structural failure. This paper proposes a method which analyzes the InSAR data through an unsupervised learning paradigm with the purpose of detecting critical events at their early stage. On the basis of similarities among time sequences, this method allows the finding of precursors of anomalous ground settlement behaviors, the correct framing of which should be directed to specialist evaluation and in situ inspections.
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Zhang, Tengxu, Wen-Bin Shen, Wenhao Wu, Bao Zhang, and Yuanjin Pan. "Recent Surface Deformation in the Tianjin Area Revealed by Sentinel-1A Data." Remote Sensing 11, no. 2 (January 11, 2019): 130. http://dx.doi.org/10.3390/rs11020130.

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In this study, we employed multitemporal InSAR (Interferometric Synthetic Aperture Radar) (MT-InSAR) to detect spatial and temporal ground deformations over the whole Tianjin region in the North China Plain area. Twenty-five ascending Sentinel-1A terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR) scenes covering this area, acquired from 9 January 2016 to 8 June 2017, were processed using InSAR time series analysis. The deformation results derived from Sentinel-1A MT-InSAR were validated with continuously operating reference stations (CORS) at four sites and four stations of the Crustal Movement Observation Network of China (CMONOC). The overall results show good agreement, demonstrating the suitability of applying Doris with Sentinel-1A data to high-resolution monitoring of surface deformation. Significant deformation variations have been observed in different parts of Tianjin. These gradually increased from the central part of the metropolitan area to the nearby suburbs. The deformation rate of the main urban area is well-balanced and it is also relatively linear, with uplifting rates ranging from 0 to 20 mm/yr. However, due to the diversity of the geological conditions and anthropogenic activities, remarkable signs of subsidence were found in several parts of Tianjin. In particular, the south-western part of Wuqing District and western part of Beichen District showed subsidence rates of up to −136 mm/yr. We also found that, in addition to groundwater over-exploitation and lithological characteristics, additional factors also influence ground subsidence, including dynamic loads (e.g., railways), static loads (e.g., urban construction), and groundwater recharging.
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28

Pulvirenti, Luca, Marco Chini, and Nazzareno Pierdicca. "InSAR Multitemporal Data over Persistent Scatterers to Detect Floodwater in Urban Areas: A Case Study in Beletweyne, Somalia." Remote Sensing 13, no. 1 (December 24, 2020): 37. http://dx.doi.org/10.3390/rs13010037.

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A stack of Sentinel-1 InSAR data in an urban area where flood events recurrently occur, namely Beletweyne town in Somalia, has been analyzed. From this analysis, a novel method to deal with the problem of flood mapping in urban areas has been derived. The approach assumes the availability of a map of persistent scatterers (PSs) inside the urban settlement and is based on the analysis of the temporal trend of the InSAR coherence and the spatial average of the exponential of the InSAR phase in each PS. Both interferometric products are expected to have high and stable values in the PSs; therefore, anomalous decreases may indicate that floodwater is present in an urban area. The stack of Sentinel-1 data has been divided into two subsets. The first one has been used as a calibration set to identify the PSs and determine, for each PS, reference values of the coherence and the spatial average of the exponential of the interferometric phase under standard non-flooded conditions. The other subset has been used for validation purposes. Flood maps produced by UNOSAT, analyzing very-high-resolution optical images of the floods that occurred in Beletweyne in April–May 2018, October–November 2019, and April–May 2020, have been used as reference data. In particular, the map of the April–May 2018 flood has been used for training purposes together with the subset of Sentinel-1 calibration data, whilst the other two maps have been used to validate the products generated by applying the proposed method. The main product is a binary map of flooded PSs that complements the floodwater map of rural/suburban areas produced by applying a well-consolidated algorithm based on intensity data. In addition, a flood severity map that labels the different districts of Beletweyne, as not, partially, or totally flooded has been generated to consolidate the validation. The results have confirmed the effectiveness of the proposed method.
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29

Lazecky, M., F. Canaslan Comut, E. Nikolaeva, M. Bakon, J. Papco, A. M. Ruiz-Armenteros, Y. Qin, J. J. M. de Sousa, and P. Ondrejka. "POTENTIAL OF SENTINEL-1A FOR NATION-WIDE ROUTINE UPDATES OF ACTIVE LANDSLIDE MAPS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 22, 2016): 775–81. http://dx.doi.org/10.5194/isprs-archives-xli-b7-775-2016.

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Slope deformation is one of the typical geohazards that causes an extensive economic damage in mountainous regions. As such, they are usually intensively monitored by means of modern expertise commonly by national geological or emergency services. Resulting landslide susceptibility maps, or landslide inventories, offer an overview of areas affected by previously activated landslides as well as slopes known to be unstable currently. Current slope instabilities easily transform into a landslide after various triggering factors, such as an intensive rainfall or a melting snow cover. In these inventories, the majority of the existing landslide-affected slopes are marked as either stable or active, after a continuous investigative work of the experts in geology. In this paper we demonstrate the applicability of Sentinel-1A satellite SAR interferometry (InSAR) to assist by identifying slope movement activity and use the information to update national landslide inventories. This can be done reliably in cases of semi-arid regions or low vegetated slopes. We perform several analyses based on multitemporal InSAR techniques of Sentinel-1A data over selected areas prone to landslides.
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30

Lazecky, M., F. Canaslan Comut, E. Nikolaeva, M. Bakon, J. Papco, A. M. Ruiz-Armenteros, Y. Qin, J. J. M. de Sousa, and P. Ondrejka. "POTENTIAL OF SENTINEL-1A FOR NATION-WIDE ROUTINE UPDATES OF ACTIVE LANDSLIDE MAPS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 22, 2016): 775–81. http://dx.doi.org/10.5194/isprsarchives-xli-b7-775-2016.

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Slope deformation is one of the typical geohazards that causes an extensive economic damage in mountainous regions. As such, they are usually intensively monitored by means of modern expertise commonly by national geological or emergency services. Resulting landslide susceptibility maps, or landslide inventories, offer an overview of areas affected by previously activated landslides as well as slopes known to be unstable currently. Current slope instabilities easily transform into a landslide after various triggering factors, such as an intensive rainfall or a melting snow cover. In these inventories, the majority of the existing landslide-affected slopes are marked as either stable or active, after a continuous investigative work of the experts in geology. In this paper we demonstrate the applicability of Sentinel-1A satellite SAR interferometry (InSAR) to assist by identifying slope movement activity and use the information to update national landslide inventories. This can be done reliably in cases of semi-arid regions or low vegetated slopes. We perform several analyses based on multitemporal InSAR techniques of Sentinel-1A data over selected areas prone to landslides.
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31

Yao, Jiaming, Xin Yao, Zuoqi Wu, and Xinghong Liu. "Research on Surface Deformation of Ordos Coal Mining Area by Integrating Multitemporal D-InSAR and Offset Tracking Technology." Journal of Sensors 2021 (April 1, 2021): 1–14. http://dx.doi.org/10.1155/2021/6660922.

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Underground mining in coal mining areas will induce large-scale, large-gradient surface deformation, threatening the safety of people’s lives and property in nearby areas. Due to mining-related subsidence is characterized by fast displacement and high nonlinearity, monitoring this process by using traditional and single interferometric synthetic aperture radar (InSAR) technology is very challenging, and it cannot accurately and quantitatively calculate the deformation of the mining area. In this paper, we proposed a new method that combines both multitemporal consecutive D-InSAR and offset tracking technology to construct a complete deformation field of the coal mining area. Taking into account the accuracy of multitemporal consecutive D-InSAR in calculating small deformation areas and the ability of offset tracking to measure large deformation areas, we utilized their respective advantages to extract the surface influence range and applied an adaptive spatial filtering method to integrate their respective results for inversion of the deformation field. 12 ascending high-resolution TerraSAR-X images (2 m) from September 3, 2018, to October 26, 2019, and 39 descending Sentinel-1 TOPS SAR images from August 5, 2018, to November 4, 2019, in the Ordos Coalfield located at Inner Mongolia, China, were utilized to obtain the whole subsidence field of the working faces F6211 and F6207 during the 454-day mining period. The GPS monitoring station located in the direction of the mining surface is used to verify the accuracy of the above method; at the same time, to a certain extent, the difference between the unmanned aerial vehicle’s DSM data acquired after coal mining and the Shuttle Radar Topography Mission (STRM) DEM can qualitatively verify the accuracy of the results. Our results show that the results of TerraSAR are basically consistent with the deformation trend of GPS data, and that of Sentinel-1 have large errors compared with GPS. The maximum central subsidence reaches ~12 m in the working face F6211 and ~4 m in the working face F6207. In the working face F6207, the good agreement between GPS and TerraSAR results indicated that the method above using high-resolution SAR data could be reliable for monitoring the large deformation area in the mining field.
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32

Beccaro, Lisa, Cristiano Tolomei, Roberto Gianardi, Vincenzo Sepe, Marina Bisson, Laura Colini, Riccardo De Ritis, and Claudia Spinetti. "Multitemporal and Multisensor InSAR Analysis for Ground Displacement Field Assessment at Ischia Volcanic Island (Italy)." Remote Sensing 13, no. 21 (October 22, 2021): 4253. http://dx.doi.org/10.3390/rs13214253.

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Volcanic islands are often affected by ground displacement such as slope instability, due to their peculiar morphology. This is the case of Ischia Island (Naples, Italy) dominated by the Mt. Epomeo (787 m a.s.l.), a volcano-tectonic horst located in the central portion of the island. This study aims to follow a long temporal evolution of ground deformations on the island through the interferometric analysis of satellite SAR data. Different datasets, acquired during Envisat, COSMO-SkyMed and Sentinel-1 satellite missions, are for the first time processed in order to obtain the island ground deformations during a time interval spanning 17 years, from November 2002 to December 2019. In detail, the multitemporal differential interferometry technique, named small baseline subset, is applied to produce the ground displacement maps and the associated displacement time series. The results, validated through the analysis and the comparison with a set of GPS measurements, show that the northwestern side of Mt. Epomeo is the sector of the island characterized by the highest subsidence movements (maximum vertical displacement of 218 mm) with velocities ranging from 10 to 20 mm/yr. Finally, the displacement time series allow us to correlate the measured ground deformations with the seismic swarm started with the Mw 3.9 earthquake that occurred on 21 August 2017. Such correlations highlight an acceleration of the ground, following the mainshock, characterized by a subsidence displacement rate of 0.12 mm/day that returned to pre-earthquake levels (0.03 mm/day) after 6 months from the event.
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33

Engdahl, Marcus E., Jouni Pulliainen, and Martti Hallikainen. "Segment-based stem volume retrieval in boreal forests using multitemporal ERS-1/2 InSAR data." Canadian Journal of Remote Sensing 34, no. 1 (January 2008): 46–55. http://dx.doi.org/10.5589/m08-009.

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34

Aimaiti, Yusupujiang, Fumio Yamazaki, and Wen Liu. "Multi-Sensor InSAR Analysis of Progressive Land Subsidence over the Coastal City of Urayasu, Japan." Remote Sensing 10, no. 8 (August 18, 2018): 1304. http://dx.doi.org/10.3390/rs10081304.

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In earthquake-prone areas, identifying patterns of ground deformation is important before they become latent risk factors. As one of the severely damaged areas due to the 2011 Tohoku earthquake in Japan, Urayasu City in Chiba Prefecture has been suffering from land subsidence as a part of its land was built by a massive land-fill project. To investigate the long-term land deformation patterns in Urayasu City, three sets of synthetic aperture radar (SAR) data acquired during 1993–2006 from European Remote Sensing satellites (ERS-1/-2 (C-band)), during 2006–2010 from the Phased Array L-band Synthetic Aperture Radar onboard the Advanced Land Observation Satellite (ALOS PALSAR (L-band)) and from 2014–2017 from the ALOS-2 PALSAR-2 (L-band) were processed by using multitemporal interferometric SAR (InSAR) techniques. Leveling survey data were also used to verify the accuracy of the InSAR-derived results. The results from the ERS-1/-2, ALOS PALSAR and ALOS-2 PALSAR-2 data processing showed continuing subsidence in several reclaimed areas of Urayasu City due to the integrated effects of numerous natural and anthropogenic processes. The maximum subsidence rate of the period from 1993 to 2006 was approximately 27 mm/year, while the periods from 2006 to 2010 and from 2014 to 2017 were approximately 30 and 18 mm/year, respectively. The quantitative validation results of the InSAR-derived deformation trend during the three observation periods are consistent with the leveling survey data measured from 1993 to 2017. Our results further demonstrate the advantages of InSAR measurements as an alternative to ground-based measurements for land subsidence monitoring in coastal reclaimed areas.
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Xu, Hang, Fulong Chen, Wei Zhou, and Cheng Wang. "Reducing the Residual Topography Phase for the Robust Landscape Deformation Monitoring of Architectural Heritage Sites in Mountain Areas: The Pseudo-Combination SBAS Method." Remote Sensing 14, no. 5 (February 27, 2022): 1178. http://dx.doi.org/10.3390/rs14051178.

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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.
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Palamà, Riccardo, Michele Crosetto, Jacek Rapinski, Anna Barra, María Cuevas-González, Oriol Monserrat, Bruno Crippa, Natalia Kotulak, Marek Mróz, and Magdalena Mleczko. "A Multi-Temporal Small Baseline Interferometry Procedure Applied to Mining-Induced Deformation Monitoring." Remote Sensing 14, no. 9 (May 2, 2022): 2182. http://dx.doi.org/10.3390/rs14092182.

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This work addresses a methodology based on the interferometric synthetic aperture radar (InSAR) applied to analyze and monitor ground-motion phenomena induced by underground mining activities in the Legnica-Glogow copper district, south-western Poland. The adopted technique employs an InSAR processing chain that exploits a stack of Sentinel-1 synthetic aperture radar (SAR) images using a small baseline multitemporal approach. Interferograms with small temporal baselines are first selected, then their network is optimized and reduced to eliminate noisy data, in order to mitigate the effect of decorrelation sources related to seasonal phenomena, i.e., snow and vegetation growth, and to the radar acquisition geometry. The atmospheric disturbance is mitigated using a spatio-temporal filter based on the nonequispaced fast Fourier transform. The estimated displacement maps and time series show the effect of both linear and impulsive ground motion and are validated against global navigation satellite system (GNSS) measurements. In this context, a significant threat to the built environment is represented by seismic tremors triggered by underground mining activities, which are analyzed using the proposed method to integrate the information gathered by in situ seismometer devices.
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Zhang, Bochen, Songbo Wu, Xiaoli Ding, Chisheng Wang, Jiasong Zhu, and Qingquan Li. "Use of Multiplatform SAR Imagery in Mining Deformation Monitoring with Dense Vegetation Coverage: A Case Study in the Fengfeng Mining Area, China." Remote Sensing 13, no. 16 (August 5, 2021): 3091. http://dx.doi.org/10.3390/rs13163091.

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Ground deformation related to mining activities may occur immediately or many years later, leading to a series of mine geological disasters, such as ground fissures, collapses, and even mining earthquakes. Deformation monitoring has been carried out with techniques, such as multitemporal interferometric synthetic aperture radar (MTInSAR). Over the past decade, MTInSAR has been widely used in monitoring mining deformation, and it is still difficult to retrieve mining deformation over dense vegetation areas. In this study, we use multiple-platform SAR images to retrieve mining deformation over dense vegetation areas. The high-quality interferograms are selected by the coherence map, and the mining deformation is retrieved by the MSBAS-InSAR technique. SAR images from TerraSAR-X, Sentinel-1A, Radarsat-2, and PALSAR-2 over the Fengfeng mining area, Heibei, China, are used to retrieve the deformation of mining activities covered with dense vegetation. The results show that the subsidence in the Fengfeng mining area reaches up to 90 cm over the period from July 2015 to April 2016. The root-mean-square error (RMSE) between the results from InSAR and leveling is 83.5 mm/yr at two mining sites, i.e., Wannian and Jiulong Mines.
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Richter, Nicole, and Jean-Luc Froger. "The role of Interferometric Synthetic Aperture Radar in Detecting, Mapping, Monitoring, and Modelling the Volcanic Activity of Piton de la Fournaise, La Réunion: A Review." Remote Sensing 12, no. 6 (March 22, 2020): 1019. http://dx.doi.org/10.3390/rs12061019.

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Synthetic Aperture Radar (SAR) remote sensing plays a significant role in volcano monitoring despite the measurements’ non real-time nature. The technique’s capability of imaging the spatial extent of ground motion has especially helped to shed light on the location, shape, and dynamics of subsurface magmatic storage and transport as well as the overall state of activity of volcanoes worldwide. A variety of different deformation phenomena are observed at exceptionally active and frequently erupting volcanoes, like Piton de la Fournaise on La Réunion Island. Those offer a powerful means of investigating related geophysical source processes and offer new insights into an active volcano’s edifice architecture, stability, and eruptive behavior. Since 1998, Interferometric Synthetic Aperture Radar (InSAR) has been playing an increasingly important role in developing our present understanding of the Piton de la Fournaise volcanic system. We here collect the most significant scientific results, identify limitations, and summarize the lessons learned from exploring the rich Piton de la Fournaise SAR data archive over the past ~20 years. For instance, the technique has delivered first evidence of the previously long suspected mobility of the volcano’s unsupported eastern flank, and it is especially useful for detecting displacements related to eruptions that occur far away from the central cone, where Global Navigation Satellite System (GNSS) stations are sparse. However, superimposed deformation processes, dense vegetation along the volcano’s lower eastern flank, and turbulent atmospheric phase contributions make Piton de la Fournaise a challenging target for applying InSAR. Multitemporal InSAR approaches that have the potential to overcome some of these limitations suffer from frequent eruptions that cause the replacement of scatterers. With increasing data acquisition rates, multisensor complementarity, and advanced processing techniques that resourcefully handle large data repositories, InSAR is progressively evolving into a near-real-time, complementary, operational volcano monitoring tool. We therefore emphasize the importance of InSAR at highly active and well-monitored volcanoes such as Mount Etna, Italy, Kīlauea Volcano, Hawai’i, and Piton de la Fournaise, La Réunion.
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Murgia, Bignami, Brunori, Tolomei, and Pizzimenti. "Ground Deformations Controlled by Hidden Faults: Multi-Frequency and Multitemporal InSAR Techniques for Urban Hazard Monitoring." Remote Sensing 11, no. 19 (September 26, 2019): 2246. http://dx.doi.org/10.3390/rs11192246.

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This work focuses on the study of land subsidence processes by means of multi-temporal and multi-frequency InSAR techniques. Specifically, we retrieve the long-term evolution (2003–2018) of the creeping phenomenon producing ground fissuring in the Ciudad Guzmán (Jalisco state, Mexico) urban area. The city is located on the northern side of the Volcan de Colima area, one of the most active Mexican volcanoes. On September 21 2012, Ciudad Guzmán was struck by ground fissures of about 1.5 km of length, causing the deformation of the roads and the propagation of fissures in adjacent buildings. The field surveys showed that fissures follow the escarpments produced during the central Mexico September 19 1985 Mw 8.1 earthquake. We extended the SAR (Synthetic Aperture Radar) interferometric monitoring starting with the multi-temporal analysis of ENVISAT and COSMO-SkyMed datasets, allowing the monitoring of the observed subsidence phenomena affecting the Mexican city. We processed a new stack of Sentinel-1 TOPSAR acquisition mode images along both descending and ascending paths and spanning the 2016–2018 temporal period. The resulting long-term trend observed by satellites, together with data from volcanic bulletin and in situ surveys, seems to suggest that the subsidence is due to the exploitation of the aquifers and that the spatial arrangement of ground deformation is controlled by the position of buried faults.
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Luo, Hui, Zhenhong Li, Zhen Dong, Peng Liu, Chisheng Wang, and Jun Song. "A New Baseline Linear Combination Algorithm for Generating Urban Digital Elevation Models With Multitemporal InSAR Observations." IEEE Transactions on Geoscience and Remote Sensing 58, no. 2 (February 2020): 1120–33. http://dx.doi.org/10.1109/tgrs.2019.2943919.

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Papoutsis, Ioannis, Charalampos Kontoes, Stavroula Alatza, Alexis Apostolakis, and Constantinos Loupasakis. "InSAR Greece with Parallelized Persistent Scatterer Interferometry: A National Ground Motion Service for Big Copernicus Sentinel-1 Data." Remote Sensing 12, no. 19 (October 1, 2020): 3207. http://dx.doi.org/10.3390/rs12193207.

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Advances in synthetic aperture radar (SAR) interferometry have enabled the seamless monitoring of the Earth’s crust deformation. The dense archive of the Sentinel-1 Copernicus mission provides unprecedented spatial and temporal coverage; however, time-series analysis of such big data volumes requires high computational efficiency. We present a parallelized-PSI (P-PSI), a novel, parallelized, and end-to-end processing chain for the fully automated assessment of line-of-sight ground velocities through persistent scatterer interferometry (PSI), tailored to scale to the vast multitemporal archive of Sentinel-1 data. P-PSI is designed to transparently access different and complementary Sentinel-1 repositories, and download the appropriate datasets for PSI. To make it efficient for large-scale applications, we re-engineered and parallelized interferogram creation and multitemporal interferometric processing, and introduced distributed implementations to best use computing cores and provide resourceful storage management. We propose a new algorithm to further enhance the processing efficiency, which establishes a non-uniform patch grid considering land use, based on the expected number of persistent scatterers. P-PSI achieves an overall speed-up by a factor of five for a full Sentinel-1 frame for processing in a 20-core server. The processing chain is tested on a large-scale project to calculate and monitor deformation patterns over the entire extent of the Greek territory—our own Interferometric SAR (InSAR) Greece project. Time-series InSAR analysis was performed on volumes of about 12 TB input data corresponding to more than 760 Single Look Complex Sentinel-1A and B images mostly covering mainland Greece in the period of 2015–2019. InSAR Greece provides detailed ground motion information on more than 12 million distinct locations, providing completely new insights into the impact of geophysical and anthropogenic activities at this geographic scale. This new information is critical to enhancing our understanding of the underlying mechanisms, providing valuable input into risk assessment models. We showcase this through the identification of various characteristic geohazard locations in Greece and discuss their criticality. The selected geohazard locations, among a thousand, cover a wide range of catastrophic events including landslides, land subsidence, and structural failures of various scales, ranging from a few hundredths of square meters up to the basin scale. The study enriches the large catalog of geophysical related phenomena maintained by the GeObservatory portal of the Center of Earth Observation Research and Satellite Remote Sensing BEYOND of the National Observatory of Athens for the opening of new knowledge to the wider scientific community.
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Kumar, Praveen, and Akhouri Pramod Krishna. "InSAR-Based Tree Height Estimation of Hilly Forest Using Multitemporal Radarsat-1 and Sentinel-1 SAR Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, no. 12 (December 2019): 5147–52. http://dx.doi.org/10.1109/jstars.2019.2963443.

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43

Daout, Simon, Henriette Sudhaus, Thore Kausch, Andreas Steinberg, and Benedetta Dini. "Interseismic and Postseismic Shallow Creep of the North Qaidam Thrust Faults Detected with a Multitemporal InSAR Analysis." Journal of Geophysical Research: Solid Earth 124, no. 7 (July 2019): 7259–79. http://dx.doi.org/10.1029/2019jb017692.

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Chen, Yueqing, Sijia Qiao, Guangxin Zhang, Y. Jun Xu, Liwen Chen, and Lili Wu. "Investigating the potential use of Sentinel-1 data for monitoring wetland water level changes in China’s Momoge National Nature Reserve." PeerJ 8 (February 17, 2020): e8616. http://dx.doi.org/10.7717/peerj.8616.

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Background Interferometric Synthetic Aperture Radar (InSAR) has become a promising technique for monitoring wetland water levels. However, its capability in monitoring wetland water level changes with Sentine-1 data has not yet been thoroughly investigated. Methods In this study, we produced a multitemporal Sentinel-1 C-band VV-polarized SAR backscatter images and generated a total of 28 interferometric coherence maps for marsh wetlands of China’s Momoge National Nature Reserve to investigate the interferometric coherence level of Sentinel-1 C-VV data as a function of perpendicular and temporal baseline, water depth, and SAR backscattering intensity. We also selected six interferogram pairs acquired within 24 days for quantitative analysis of the accuracy of water level changes monitored by Sentinel-1 InSAR. The accuracy of water level changes determined through the Sentinel-1 InSAR technique was calibrated by the values of six field water level loggers. Results Our study showed that (1) the coherence was mainly dependent on the temporal baseline and was little affected by the perpendicular baseline for Sentinel-1 C-VV data in marsh wetlands; (2) in the early stage of a growing season, a clear negative correlation was found between Sentinel-1 coherence and water depth; (3) there was an almost linear negative correlation between Sentinel-1 C-VV coherence and backscatter for the marsh wetlands; (4) once the coherence exceeds a threshold of 0.3, the stage during the growing season, rather than the coherence, appeared to be the primary factor determining the quality of the interferogram for the marsh wetlands, even though the quality of the interferogram largely depends on the coherence; (5) the results of water level changes from InSAR processing show no agreement with in-situ measurements during most growth stages. Based on the findings, we can conclude that although the interferometric coherence of the Sentinel-1 C-VV data is high enough, the data is generally unsuitable for monitoring water level changes in marsh wetlands of China’s Momoge National Nature Reserve.
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Zhao, Changjun, Yunyun Dong, Wenhao Wu, Bangsen Tian, Jianmin Zhou, Ping Zhang, Shuo Gao, Yuechi Yu, and Lei Huang. "A Modification to Phase Estimation for Distributed Scatterers in InSAR Data Stacks." Remote Sensing 15, no. 3 (January 20, 2023): 613. http://dx.doi.org/10.3390/rs15030613.

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To improve the spatial density and quality of measurement points in multitemporal interferometric synthetic aperture radar, distributed scatterers (DSs) should be processed. An essential procedure in DS interferometry is phase estimation, which reconstructs a consistent phase series from all available interferograms. Influenced by the well-known suboptimality of coherence estimation, the performance of the state-of-the-art phase estimation algorithms is severely degraded. Previous research has addressed this problem by introducing the coherence bias correction technique. However, the precision of phase estimation is still insufficient because of the limited correction capabilities. In this paper, a modified phase estimation approach is proposed. Particularly, by incorporating the information on both interferometric coherence and the number of looks, a significant bias correction to each element of the coherence magnitude matrix is achieved. The bias-corrected coherence matrix is combined with advanced statistically homogeneous pixel selection and time series phase optimization algorithms to obtain the optimal phase series. Both the simulated and Sentinel-1 real data sets are used to demonstrate the superiority of this proposed approach over the traditional phase estimation algorithms. Specifically, the coherence bias can be corrected with considerable accuracy by the proposed scheme. The mean bias of coherence magnitude is reduced by more than 29%, and the standard deviation is reduced by more than 18% over the existing bias correction method. The proposed approach achieves higher accuracy than the current methods over the reconstructed phase series, including smoother interferometric phases and fewer outliers.
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46

Blackwell, Em, Manoochehr Shirzaei, Chandrakanta Ojha, and Susanna Werth. "Tracking California’s sinking coast from space: Implications for relative sea-level rise." Science Advances 6, no. 31 (July 2020): eaba4551. http://dx.doi.org/10.1126/sciadv.aba4551.

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Coastal vertical land motion affects projections of sea-level rise, and subsidence exacerbates flooding hazards. Along the ~1350-km California coastline, records of high-resolution vertical land motion rates are scarce due to sparse instrumentation, and hazards to coastal communities are underestimated. Here, we considered a ~100-km-wide swath of land along California’s coast and performed a multitemporal interferometric synthetic aperture radar (InSAR) analysis of large datasets, obtaining estimates of vertical land motion rates for California’s entire coast at ~100-m dimensions—a ~1000-fold resolution improvement to the previous record. We estimate between 4.3 million and 8.7 million people in California’s coastal communities, including 460,000 to 805,000 in San Francisco, 8000 to 2,300,00 in Los Angeles, and 2,000,000 to 2,300,000 in San Diego, are exposed to subsidence. The unprecedented detail and submillimeter accuracy resolved in our vertical land motion dataset can transform the analysis of natural and anthropogenic changes in relative sea-level and associated hazards.
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Di Traglia, Federico, Claudio De Luca, Mariarosaria Manzo, Teresa Nolesini, Nicola Casagli, Riccardo Lanari, and Francesco Casu. "Joint exploitation of space-borne and ground-based multitemporal InSAR measurements for volcano monitoring: The Stromboli volcano case study." Remote Sensing of Environment 260 (July 2021): 112441. http://dx.doi.org/10.1016/j.rse.2021.112441.

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48

Necsoiu, Marius, Ronald N. McGinnis, and Donald M. Hooper. "New insights on the Salmon Falls Creek Canyon landslide complex based on geomorphological analysis and multitemporal satellite InSAR techniques." Landslides 11, no. 6 (October 10, 2014): 1141–53. http://dx.doi.org/10.1007/s10346-014-0523-8.

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Lazecký, Milan, Emma Hatton, Pablo J. González, Ivana Hlaváčová, Eva Jiránková, František Dvořák, Zdeněk Šustr, and Jan Martinovič. "Displacements Monitoring over Czechia by IT4S1 System for Automatised Interferometric Measurements Using Sentinel-1 Data." Remote Sensing 12, no. 18 (September 11, 2020): 2960. http://dx.doi.org/10.3390/rs12182960.

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The Sentinel-1 satellite system continuously observes European countries at a relatively high revisit frequency of six days per orbital track. Given the Sentinel-1 configuration, most areas in Czechia are observed every 1–2 days by different tracks in a moderate resolution. This is attractive for various types of analyses by various research groups. The starting point for interferometric (InSAR) processing is an original data provided in a Single Look Complex (SLC) level. This work represents advantages of storing data augmented to a specifically corrected level of data, SLC-C. The presented database contains Czech nationwide Sentinel-1 data stored in burst units that have been pre-processed to the state of a consistent well-coregistered dataset of SLC-C. These are resampled SLC data with their phase values reduced by a topographic phase signature, ready for fast interferometric analyses (an interferogram is generated by a complex conjugate between two stored SLC-C files). The data can be used directly into multitemporal interferometry techniques, e.g., Persistent Scatterers (PS) or Small Baseline (SB) techniques applied here. A further development of the nationwide system utilising SLC-C data would lead into a dynamic state where every new pre-processed burst triggers a processing update to detect unexpected changes from InSAR time series and therefore provides a signal for early warning against a potential dangerous displacement, e.g., a landslide, instability of an engineering structure or a formation of a sinkhole. An update of the processing chain would also allow use of cross-polarised Sentinel-1 data, needed for polarimetric analyses. The current system is running at a national supercomputing centre IT4Innovations in interconnection to the Czech Copernicus Collaborative Ground Segment (CESNET), providing fast on-demand InSAR results over Czech territories. A full nationwide PS processing using data over Czechia was performed in 2017, discovering several areas of land deformation. Its downsampled version and basic findings are demonstrated within the article.
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Cando-Jácome, Marcelo, Antonio Martínez-Graña, and Virginia Valdés. "Prevention of Disasters Related to Extreme Natural Ground Deformation Events by Applying Spatial Modeling in Urban Areas (Quito, Ecuador)." International Journal of Environmental Research and Public Health 17, no. 3 (January 24, 2020): 753. http://dx.doi.org/10.3390/ijerph17030753.

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Synthetic Aperture Radar Interferometry (InSAR) is a spatial technique based on obtaining the phase differences of two radar images, acquired by a satellite from separate orbits and at different times, to obtain a ground displacement image of a study area, This image is called interferogram. On the other hand, space syntax is a technique within architecture that is applied to quantify and describe the level of ease of population movement through any urban space in a city. It analyzes the flow, transit, displacement, accessibility and concentration of the population in areas of basic services, health, security, commerce and entertainment. What would happen if an earthquake greater than 6 or 7 Moment Magnitude-Mw occurs in these areas of intense concentration of the population that are in buildings constructed on intense deformations of the land? With respect to the seismic risk in the city of Quito, many studies related to seismic risks have been published, but there are no studies that relate the deformation of the land (INSAR) with the space syntax, so this article presents a new vision in the joint application of these tools, a useful vision for urban planners and designers, considering the occurrence of a major earthquake in areas of buildings that are located on intense land deformations and have high population concentrations. This study has been prepared in two phases: in the first phase, the built-up areas concentrated in the greatest terrain deformations by accumulated displacement obtained using the APS estimation & multitemporal analysis by PSI-InSAR time series analysis methodology and Sentinel 1A and 1B satellite images were categorized. In the second phase, through the space syntax’s theory and the use of DepthmapX, the movement patterns and traffic flows of the population were determined by means of graphs of spaces interconnected by streets (axial maps), to predict the spatial behavior of humans and its concentration in the mentioned sites. Finally, the results were integrated, determining the degree of exposure of the population found in built areas with high to very high displacement and an intense population concentration.
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