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Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones.
Research & information: general --- wildfire --- satellite vegetation indices --- live fuel moisture --- empirical model function --- Southern California --- chaparral ecosystem --- forest fire --- forest recovery --- satellite remote sensing --- vegetation index --- burn index --- gross primary production --- South Korea --- land subsidence --- PS-InSAR --- uneven settlement --- building construction --- Beijing urban area --- floodplain delineation --- inaccessible region --- machine learning --- flash flood --- risk --- LSSVM --- China --- Himawari-8 --- threshold-based algorithm --- remote sensing --- dryness monitoring --- soil moisture --- NIR-Red spectral space --- Landsat-8 --- MODIS --- Xinjiang province of China --- SDE --- PE --- groundwater level --- compressible sediment layer --- tropical cyclone formation --- WindSat --- disaster monitoring --- wireless sensor network --- debris flow --- anomaly detection --- deep learning --- accelerometer sensor --- total precipitable water --- Himawari-8 AHI --- random forest --- deep neural network --- XGBoost
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Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones.
Research & information: general --- wildfire --- satellite vegetation indices --- live fuel moisture --- empirical model function --- Southern California --- chaparral ecosystem --- forest fire --- forest recovery --- satellite remote sensing --- vegetation index --- burn index --- gross primary production --- South Korea --- land subsidence --- PS-InSAR --- uneven settlement --- building construction --- Beijing urban area --- floodplain delineation --- inaccessible region --- machine learning --- flash flood --- risk --- LSSVM --- China --- Himawari-8 --- threshold-based algorithm --- remote sensing --- dryness monitoring --- soil moisture --- NIR–Red spectral space --- Landsat-8 --- MODIS --- Xinjiang province of China --- SDE --- PE --- groundwater level --- compressible sediment layer --- tropical cyclone formation --- WindSat --- disaster monitoring --- wireless sensor network --- debris flow --- anomaly detection --- deep learning --- accelerometer sensor --- total precipitable water --- Himawari-8 AHI --- random forest --- deep neural network --- XGBoost --- n/a --- NIR-Red spectral space
Choose an application
Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones.
wildfire --- satellite vegetation indices --- live fuel moisture --- empirical model function --- Southern California --- chaparral ecosystem --- forest fire --- forest recovery --- satellite remote sensing --- vegetation index --- burn index --- gross primary production --- South Korea --- land subsidence --- PS-InSAR --- uneven settlement --- building construction --- Beijing urban area --- floodplain delineation --- inaccessible region --- machine learning --- flash flood --- risk --- LSSVM --- China --- Himawari-8 --- threshold-based algorithm --- remote sensing --- dryness monitoring --- soil moisture --- NIR–Red spectral space --- Landsat-8 --- MODIS --- Xinjiang province of China --- SDE --- PE --- groundwater level --- compressible sediment layer --- tropical cyclone formation --- WindSat --- disaster monitoring --- wireless sensor network --- debris flow --- anomaly detection --- deep learning --- accelerometer sensor --- total precipitable water --- Himawari-8 AHI --- random forest --- deep neural network --- XGBoost --- n/a --- NIR-Red spectral space
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This book focuses on remote sensing for urban deformation monitoring. In particular, it highlights how deformation monitoring in urban areas can be carried out using Persistent Scatterer Interferometry (PSI) and Synthetic Aperture Radar (SAR) Tomography (TomoSAR). Several contributions show the capabilities of Interferometric SAR (InSAR) and PSI techniques for urban deformation monitoring. Some of them show the advantages of TomoSAR in un-mixing multiple scatterers for urban mapping and monitoring. This book is dedicated to the technical and scientific community interested in urban applications. It is useful for choosing the appropriate technique and gaining an assessment of the expected performance. The book will also be useful to researchers, as it provides information on the state-of-the-art and new trends in this field
surface subsidence --- PS --- permanent scatterers --- land subsidence --- PS-InSAR --- thermal dilation --- SBAS-InSAR --- Sepulveda Transit Corridor --- deformation --- differential SAR interferometry --- reclaimed land --- Istanbul --- deformation monitoring --- skyscrapers --- generalized likelihood ratio test --- validation --- uplift --- displacement monitoring --- pursuit monostatic --- radar interferometry --- Sentinel-1A --- urbanization --- synthetic aperture radar --- Turkey --- terraSAR-X --- geological and geomorphological mapping --- London --- differential compaction --- expansive soils --- health monitoring --- Copernicus Sentinel-1 --- displacement mapping --- PALSAR --- land reclamation --- tomography --- Venetian-Friulian Plain --- ALOS PALSAR --- multi-temporal DInSAR --- SAR interferometry --- InSAR --- persistent scatterers --- carbonate karstification --- ENVISAT ASAR --- multiple PS detection --- sparse signals --- urban subsidence --- time series InSAR analysis --- time series analysis --- Persistent Scatterer Interferometry (PSI) --- engineering construction --- Rome --- persistent scatterer interferometry --- subsidence --- persistent scatterer interferometry (PSI) --- SNAP-StaMPS --- Lingang New City --- dewatering --- atmospheric component --- urban deformation monitoring --- Sentinel-1 --- differential interferometry --- Late-Quaternary deposits --- modelling --- Generalized Likelihood Ratio Test --- Persistent Scatterer Interferometry --- synthetic aperture radar (SAR) --- Capon estimation --- differential tomography --- deformation time series --- groundwater level variation --- radar detection --- multi-look SAR tomography --- spaceborne SAR --- SAR --- ERS-1/-2 --- reclamation settlements --- Wuhan --- subsidence monitoring --- water level changes --- polarimetry --- asymmetric subsidence --- urban monitoring --- urban areas --- landslide --- SAR tomography --- Urayasu City --- risk --- Los Angeles --- PALSAR-2
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