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Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography

Authors: --- ---
ISBN: 9783039211265 / 9783039211272 Year: Pages: 308 DOI: 10.3390/books978-3-03921-127-2 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-06-26 08:44:06
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Abstract

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

Keywords

SBAS-InSAR --- surface subsidence --- Sentinel-1A --- Wuhan --- engineering construction --- carbonate karstification --- water level changes --- reclamation settlements --- Lingang New City --- time series InSAR analysis --- terraSAR-X --- ENVISAT ASAR --- ALOS PALSAR --- time series analysis --- InSAR --- PS --- landslide --- subsidence --- land reclamation --- urbanization --- risk --- Istanbul --- Turkey --- Persistent Scatterer Interferometry (PSI) --- Sentinel-1 --- uplift --- expansive soils --- dewatering --- London --- synthetic aperture radar (SAR) --- SAR tomography --- deformation monitoring --- persistent scatterer interferometry (PSI) --- urban deformation monitoring --- radar interferometry --- displacement mapping --- spaceborne SAR --- differential interferometry --- differential tomography --- ERS-1/-2 --- PALSAR --- PALSAR-2 --- InSAR --- land subsidence --- reclaimed land --- Urayasu City --- SAR interferometry --- displacement monitoring --- Sentinel-1 --- permanent scatterers --- thermal dilation --- health monitoring --- SAR --- Sentinel-1 --- differential SAR interferometry --- atmospheric component --- modelling --- deformation time series --- validation --- multi-look SAR tomography --- multiple PS detection --- Capon estimation --- Generalized Likelihood Ratio Test --- synthetic aperture radar --- persistent scatterers --- differential interferometry --- tomography --- radar detection --- generalized likelihood ratio test --- sparse signals --- pursuit monostatic --- PS-InSAR --- urban monitoring --- skyscrapers --- urban subsidence --- Copernicus Sentinel-1 --- Persistent Scatterer Interferometry --- SNAP-StaMPS --- Rome --- synthetic aperture radar --- tomography --- polarimetry --- radar detection --- generalized likelihood ratio test --- sparse signals --- geological and geomorphological mapping --- Late-Quaternary deposits --- differential compaction --- multi-temporal DInSAR --- Venetian-Friulian Plain --- subsidence monitoring --- persistent scatterer interferometry --- asymmetric subsidence --- groundwater level variation --- Sepulveda Transit Corridor --- Los Angeles --- synthetic aperture radar --- persistent scatterers --- tomography --- differential interferometry --- polarimetry --- radar detection --- urban areas --- deformation

Google Earth Engine Applications

Authors: ---
ISBN: 9783038978848 9783038978855 Year: Pages: 420 DOI: 10.3390/books978-3-03897-885-5 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- Environmental Technology
Added to DOAB on : 2019-04-25 16:37:17
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In a rapidly changing world, there is an ever-increasing need to monitor the Earth’s resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth’s surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales.

Keywords

Google Earth Engine --- NDVI --- vegetation index --- Landsat --- remote sensing --- phenology --- surface reflectance --- cropland mapping --- cropland areas --- 30-m --- Landsat-8 --- Sentinel-2 --- Random Forest --- Support Vector Machines --- segmentation --- RHSeg --- Google Earth Engine --- Africa --- remote sensing --- semi-arid --- ecosystem assessment --- land use change --- image classification --- seasonal vegetation --- carbon cycle --- Google Earth Engine --- crop yield --- gross primary productivity (GPP) --- data fusion --- Landsat --- MODIS --- MODIS --- Random Forest --- pasture mapping --- Brazilian pasturelands dynamics --- Google Earth Engine --- crop classification --- multi-classifier --- cloud computing --- time series --- high spatial resolution --- BACI --- Enhanced Vegetation Index --- Google Earth Engine --- cloud-based geo-processing --- satellite-derived bathymetry --- image composition --- pseudo-invariant features --- sun glint correction --- empirical --- spatial error --- Google Earth Engine --- low cost in situ --- Sentinel-2 --- Mediterranean --- burn severity --- change detection --- Landsat --- dNBR --- RdNBR --- RBR --- composite burn index (CBI) --- MTBS --- lower mekong basin --- landsat collection --- suspended sediment concentration --- online application --- google earth engine --- Landsat --- Google Earth Engine --- protected area --- forest and land use mapping --- machine learning classification --- China --- temporal compositing --- image time series --- multitemporal analysis --- change detection --- cloud masking --- Landsat-8 --- Google Earth Engine (GEE) --- Google Earth Engine --- LAI --- FVC --- FAPAR --- CWC --- plant traits --- random forests --- PROSAIL --- small-scale mining --- industrial mining --- google engine --- image classification --- land-use cover change --- seagrass --- habitat mapping --- image composition --- machine learning --- support vector machines --- Google Earth Engine --- Sentinel-2 --- Aegean --- Ionian --- global scale --- soil moisture --- Soil Moisture Ocean Salinity --- Soil Moisture Active Passive --- Google Earth Engine --- drought --- cloud computing --- remote sensing --- snow hydrology --- water resources --- Google Earth Engine --- user assessment --- MODIS --- snow cover --- flood --- disaster prevention --- emergency response --- decision making --- Google Earth Engine --- land cover --- deforestation --- Brazilian Amazon --- Bayesian statistics --- BULC-U --- Mato Grosso --- spatial resolution --- Landsat --- GlobCover --- SDG --- surface urban heat island --- Geo Big Data --- Google Earth Engine --- global monitoring service --- Google Earth Engine --- web portal --- satellite imagery --- trends --- earth observation --- wetland --- Google Earth Engine --- Sentinel-1 --- Sentinel-2 --- random forest --- cloud computing --- geo-big data --- cloud computing --- big data analytics --- long term monitoring --- data archival --- early warning systems

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MDPI - Multidisciplinary Digital Publishing Institute (2)


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eng (2)


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2019 (2)