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Due to the considerable impacts of hydrological hazards on water resources, natural environments, and human activities, as well as on human health and safety, climate variability and climate change have become key issues for the research community. In fact, a warmer climate, with its heightened climate variability, will increase the risk of hydrological extreme phenomena, such as droughts and floods. The Special Issue “Hydrological Hazard: Analysis and Prevention” presents a collection of scientific contributions that provides a sample of the state-of-the-art and forefront research in this field. In particular, innovative modelling methods for flood hazards, regional flood, and drought analysis and the use of satellite and climate data for drought analysis were the main research and practice targets that the papers published in this Special Issue aimed to address.

catchment --- climate --- drought --- flood --- forecast --- hazards --- landslide --- modelling --- precipitation --- temperature

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Earth Observations (EO) encompasses different types of sensors (e.g., SAR, LiDAR, Optical and multispectral) and platforms (e.g., satellites, aircraft, and Unmanned Aerial Vehicles) and enables us to monitor and model geohazards over regions at different scales in which ground observations may not be possible due to physical and/or political constraints. EO can provide high spatial, temporal and spectral resolution, stereo-mapping and all-weather-imaging capabilities, but not by a single satellite at a time. Improved satellite and sensor technologies, increased frequency of satellite measurements, and easier access and interpretation of EO information have all contributed to the increased demand for satellite EO data. EO, combined with complementary terrestrial observations and with physical models, have been widely used to monitor geohazards, revolutionizing our understanding of how the Earth system works.

earth observation --- geohazards --- landslide --- land subsidence --- earthquake --- remote sensing --- InSAR --- LiDAR --- optical --- UAV --- surface displacement --- deformation --- damage assessment

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As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.

landslide --- bagging ensemble --- Logistic Model Trees --- GIS --- Vietnam --- colorization --- random forest regression --- grayscale aerial image --- change detection --- gully erosion --- environmental variables --- data mining techniques --- SCAI --- GIS --- mapping --- single-class data descriptors --- materia medica resource --- Panax notoginseng --- one-class classifiers --- geoherb --- change detection --- convolutional network --- deep learning --- panchromatic --- remote sensing --- remote sensing image segmentation --- convolutional neural networks --- Gaofen-2 --- hybrid structure convolutional neural networks --- winter wheat spatial distribution --- classification-based learning --- real-time precise point positioning --- convergence time --- ionospheric delay constraints --- precise weighting --- landslide --- weights of evidence --- logistic regression --- random forest --- hybrid model --- traffic CO --- traffic CO prediction --- neural networks --- GIS --- land use/land cover (LULC) --- unmanned aerial vehicle --- texture --- gray-level co-occurrence matrix --- machine learning --- crop --- landslide susceptibility --- random forest --- boosted regression tree --- information gain --- landslide susceptibility map --- ALS point cloud --- multi-scale --- classification --- large scene --- coarse particle --- particulate matter 10 (PM10) --- landsat image --- machine learning --- support vector machine --- high-resolution --- optical remote sensing --- object detection --- deep learning --- transfer learning --- land subsidence --- Bayes net --- naïve Bayes --- logistic --- multilayer perceptron --- logit boost --- change detection --- convolutional network --- deep learning --- panchromatic --- remote sensing --- leaf area index (LAI) --- machine learning --- Sentinel-2 --- sensitivity analysis --- training sample size --- spectral bands --- spatial sparse recovery --- constrained spatial smoothing --- spatial spline regression --- alternating direction method of multipliers --- landslide prediction --- machine learning --- neural networks --- model switching --- spatial predictive models --- predictive accuracy --- model assessment --- variable selection --- feature selection --- model validation --- spatial predictions --- reproducible research --- Qaidam Basin --- remote sensing --- TRMM --- artificial neural network --- n/a

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Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing.

road extraction --- very high-resolution image --- fast marching method --- semiautomatic --- edge constraint --- beaver mimicry --- beaver dam analogue --- QuickBird --- riparian --- stream restoration --- Worldview --- benthic mapping --- seagrass --- airborne hypespectral imagery --- Worldview-2 --- atmospheric correction --- sunglint correction --- water column correction --- dimensionality reduction techniques --- SVM classification --- linear unmixing --- building detection --- built-up areas extraction --- local feature points --- saliency index --- morphological building index --- Deformable CNN --- Faster R-CNN --- data augmentation --- occluded object detection --- very high-resolution Pléiades imagery --- canopy height model --- acquisition geometry --- forested mountain --- accuracy assessment --- remote sensing imagery --- super-resolution --- ultra-dense connection --- feature distillation --- video satellite --- compensation unit --- urban water mapping --- water index --- shadow detection --- threshold stability --- agriculture parcel segmentation --- superpixels --- consensus --- texture analysis --- multi-resolution segmentation (MRS) --- greenhouse extraction --- over-segmentation index (OSI) --- under-segmentation index (USI) --- error index of total area (ETA) --- composite error index (CEI) --- GaoFen-2 (GF-2) --- synthetic aperture radar --- landslide monitoring --- sub-pixel offset tracking --- Slumgullion landslide --- natural hazards --- large displacements --- remote sensing --- scene classification --- CNN --- capsule --- PrimaryCaps --- CapsNet --- High-resolution satellite imagery --- submesoscale --- spiral eddy --- cyanobacteria --- surface convergence --- western Baltic Sea

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This Special Issue is a collection of papers addressing the scientific use of data acquired in the course of the TerraSAR-X mission 10 years after launch. The articles deal with the mission itself, the accuracy of the products, with differential interferometry, and with applications in the domains cryosphere, oceans, wetlands, and urban areas.

synthetic aperture radar --- TerraSAR-X --- geolocation --- absolute localization accuracy --- stereo sar --- imaging geodesy --- TerraSAR-X --- internal calibration --- geometric and radiometric calibration --- antenna model verification --- antenna pointing determination --- radiometric accuracy --- calibration targets --- long term performance monitoring --- TerraSAR-X --- TanDEM-X --- LEO --- POD --- SLR --- SAR --- Satellite Laser Ranging --- radar ranging --- satellite orbit --- validation --- InSAR coherence --- NDVI --- damage assessment --- density map --- tsunami --- earthquake --- GIS --- TSX Staring spotlight --- high resolution InSAR --- small-scale movements --- atmospheric phase --- layover --- DSM blending --- SAR --- internal waves --- Andaman Sea --- radar --- satellite --- remote sensing --- SAR --- TerraSAR-X --- operations --- ground segment --- orbit --- mission --- global --- urban footprint --- processing --- validation --- community survey --- sustainability --- synthetic aperture radar --- X-band --- marine --- estuarine --- lacustrine --- riverine --- palustrine --- time-series --- SAR applications --- vegetation --- remote sensing data --- DInSAR --- landslide monitoring --- PSI --- super high-spatial resolution TerraSAR-X images --- pixel selection --- measurement pixels’ density --- synthetic aperture radar --- PolSAR --- TerraSAR-X --- surface water monitoring --- flooded vegetation --- classification --- segmentation --- InSAR --- landslide --- phase unwrapping --- phase demodulation --- TerraSAR-X --- RADARSAT-2 --- ALOS-1 --- ERS --- synthetic aperture radar --- TerraSAR-X --- habitat mapping --- monitoring --- remote sensing --- Wadden Sea --- mussel beds --- intertidal bedforms --- tidal gullies --- remote sensing --- film slicks on the sea surface --- dual co-polarized microwave radar --- surface wind waves --- wave breaking --- Snow Cover Extent (SCE) --- TerraSAR-X --- Landsat --- wet snow --- small Arctic catchments --- satellite time series --- TerraSAR-X --- synthetic aperture radar (SAR), radar mission --- remote sensing --- land subsidence --- TerraSAR-X --- SAR interferometry --- coastal environments --- Venice lagoon --- multi-baseline --- multi-pass --- PS --- DS --- geodetic --- TomoSAR --- D-TomoSAR --- PSI --- robust estimation --- covariance matrix --- InSAR --- SAR --- review --- SAR --- SAR interferometry --- atmospheric propagation delay --- persistent scatterer interferometry --- numerical weather prediction --- stratified atmospheric delay --- zenith path delay --- slant path delay --- interferometry --- surface movement monitoring --- ground control points --- radargrammetry --- automated target recognition --- convolutional neural networks (CNN), deep CNN --- support vector machine --- SVM --- synthetic aperture radar --- TerraSAR-X --- SAR interferometry --- land subsidence --- precise orbit determination --- geometric and radiometric calibration --- PSI