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Geo-spatial analysis has become an essential component of hydrological studies to process and examine geo-spatial data such as hydrological variables (e.g., precipitation and discharge) and basin characteristics (e.g., DEM and land use land cover). The advancement of the data acquisition technique helps accumulate geo-spatial data with more extensive spatial coverage than traditional in-situ observations. The development of geo-spatial analytic methods is beneficial for the processing and analysis of multi-source data in a more efficient and reliable way for a variety of research and practical issues in hydrology. This book is a collection of the articles of a published Special Issue Geo-Spatial Analysis in Hydrology in the journal ISPRS International Journal of Geo-Information. The topics of the articles range from the improvement of geo-spatial analytic methods to the applications of geo-spatial analysis in emerging hydrological issues. The results of these articles show that traditional hydrological/hydraulic models coupled with geo-spatial techniques are a way to make streamflow simulations more efficient and reliable for flood-related decision making. Geo-spatial analysis based on more advanced methods and data is a reliable resolution to obtain high-resolution information for hydrological studies at fine spatial scale.

Research & information: general --- Geography --- Canadian Hydrographic Service --- Satellite-Derived Bathymetry --- empirical --- classification --- photogrammetry --- level of confidence --- spatio-temporal GIS --- hydrodynamic model --- spatio-temporal computation framework --- flood risk --- 3D simulation --- watershed division --- Sentinel-2A --- Google Earth Engine (GEE) --- Taihu Basin --- hydrology --- plains area --- RapidEye --- water quality --- red edge --- remote sensing --- flash flood --- PCSWMM --- curve number --- rainfall-runoff model --- HEC-RAS --- Pakistan --- crop water requirement --- reflectance-based crop coefficients --- normalized difference vegetation index --- evapotranspiration --- geo-spatial analysis --- scaling issue --- basin characteristic extraction --- hydrological modelling --- Canadian Hydrographic Service --- Satellite-Derived Bathymetry --- empirical --- classification --- photogrammetry --- level of confidence --- spatio-temporal GIS --- hydrodynamic model --- spatio-temporal computation framework --- flood risk --- 3D simulation --- watershed division --- Sentinel-2A --- Google Earth Engine (GEE) --- Taihu Basin --- hydrology --- plains area --- RapidEye --- water quality --- red edge --- remote sensing --- flash flood --- PCSWMM --- curve number --- rainfall-runoff model --- HEC-RAS --- Pakistan --- crop water requirement --- reflectance-based crop coefficients --- normalized difference vegetation index --- evapotranspiration --- geo-spatial analysis --- scaling issue --- basin characteristic extraction --- hydrological modelling

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Geo-spatial analysis has become an essential component of hydrological studies to process and examine geo-spatial data such as hydrological variables (e.g., precipitation and discharge) and basin characteristics (e.g., DEM and land use land cover). The advancement of the data acquisition technique helps accumulate geo-spatial data with more extensive spatial coverage than traditional in-situ observations. The development of geo-spatial analytic methods is beneficial for the processing and analysis of multi-source data in a more efficient and reliable way for a variety of research and practical issues in hydrology. This book is a collection of the articles of a published Special Issue Geo-Spatial Analysis in Hydrology in the journal ISPRS International Journal of Geo-Information. The topics of the articles range from the improvement of geo-spatial analytic methods to the applications of geo-spatial analysis in emerging hydrological issues. The results of these articles show that traditional hydrological/hydraulic models coupled with geo-spatial techniques are a way to make streamflow simulations more efficient and reliable for flood-related decision making. Geo-spatial analysis based on more advanced methods and data is a reliable resolution to obtain high-resolution information for hydrological studies at fine spatial scale.

Canadian Hydrographic Service --- Satellite-Derived Bathymetry --- empirical --- classification --- photogrammetry --- level of confidence --- spatio-temporal GIS --- hydrodynamic model --- spatio-temporal computation framework --- flood risk --- 3D simulation --- watershed division --- Sentinel-2A --- Google Earth Engine (GEE) --- Taihu Basin --- hydrology --- plains area --- RapidEye --- water quality --- red edge --- remote sensing --- flash flood --- PCSWMM --- curve number --- rainfall-runoff model --- HEC-RAS --- Pakistan --- crop water requirement --- reflectance-based crop coefficients --- normalized difference vegetation index --- evapotranspiration --- geo-spatial analysis --- scaling issue --- basin characteristic extraction --- hydrological modelling

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This book was inspired by the Hydrology–H030 Session of the 2019 AGU (America Geophysical Union) Fall Meeting. In recent years, simulating potential future vulnerability and sustainability of water resources due to climate change are mainly focused on global and regional scale watersheds by using climate change scenarios. These scenarios may have low resolution and may not be accurate for local watersheds. This book addresses the impacts of climate change upon water quantity and quality at small scale watersheds. Emphases are on climate-induced water resource vulnerabilities (e.g., flood, drought, groundwater depletion, evapotranspiration, and water pollution) and methodologies (e.g., computer modeling, field measurement, and management practice) employed to mitigation and adapt climate change impacts on water resources. Application implications to local water resource management are also discussed in this book.

Research & information: general --- Environmental economics --- GCM --- bias correction methods --- hydrological simulation --- climate change --- IRES --- OM --- DOC --- POC --- ephemeral stream --- event sampling --- headwaters --- tailwater recovery ditch --- AnnAGNPS --- BMP --- asymmetric warming --- normalized difference vegetation index --- second-order partial correlation analysis --- day and nighttime warming --- diurnal temperature range --- cottonwood --- sap flux --- STELLA --- vapor pressure deficit --- water year type --- hydrological drought --- adaptive strategies --- Central Valley --- streamflow --- SWAT model --- CNRM-CM5 --- CESM1-BGC.1 --- HADGEM2-AO.1 --- Alabama River Basin --- GRACE --- GGDI --- drought --- wavelet coherence --- teleconnections --- water model --- energy model --- climate scenario --- Nile River Basin --- perception --- adaptation --- irrigation water quality --- agriculture --- smallholder farmers --- Ethiopia Rift Valley --- vulnerability assessment --- Indian Himalayas --- springs --- springshed management --- water security --- n/a

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Remote image capture systems are a key element in efficient and sustainable agriculture nowadays. They are increasingly being used to obtain information of interest from the crops, the soil and the environment. It includes different types of capturing devices: from satellites and drones, to in-field devices; different types of spectral information, from visible RGB images, to multispectral images; different types of applications; and different types of techniques in the areas of image processing, computer vision, pattern recognition and machine learning. This book covers all these aspects, through a series of chapters that describe specific recent applications of these techniques in interesting problems of agricultural engineering.

History of engineering & technology --- SVM --- budding rate --- UAV --- geometric consistency --- radiometric consistency --- point clouds --- ICP --- reflectance maps --- vegetation indices --- Parrot Sequoia --- artificial intelligence --- precision agriculture --- agricultural robot --- optimization algorithm --- online operation --- segmentation --- coffee leaf rust --- machine learning --- deep learning --- remote sensing --- Fourth Industrial Revolution --- Agriculture 4.0 --- failure strain --- sandstone --- digital image correlation --- Hill-Tsai failure criterion --- finite element method --- reference evapotranspiration --- moisture sensors --- machine learning regression --- frequency-domain reflectometry --- randomizable filtered classifier --- convolutional neural network --- U-Net --- land use --- banana plantation --- Panama TR4 --- aerial photography --- remote images --- systematic mapping study --- agriculture --- applications --- total leaf area --- mixed pixels --- Cabernet Sauvignon --- NDVI --- Normalized Difference Vegetation Index --- precision viticulture --- 3D model --- spatial vision --- fertirrigation --- teaching-learning --- spectrometry --- Sentinel-2 --- pasture quality index --- normalized difference vegetation index --- normalized difference water index --- supplementation --- decision making --- digital agriculture --- grape yield estimate --- berries counting --- Dilated CNN --- machine learning algorithms --- classification performance --- winter wheat mapping --- large-scale --- water stress --- Prunus avium L. --- stem water potential --- low-cost thermography --- thermal indexes --- canopy temperature --- non-water-stressed baselines --- non-transpiration baseline --- soil moisture --- andosols --- image processing --- greenhouse --- automatic tomato harvesting --- SVM --- budding rate --- UAV --- geometric consistency --- radiometric consistency --- point clouds --- ICP --- reflectance maps --- vegetation indices --- Parrot Sequoia --- artificial intelligence --- precision agriculture --- agricultural robot --- optimization algorithm --- online operation --- segmentation --- coffee leaf rust --- machine learning --- deep learning --- remote sensing --- Fourth Industrial Revolution --- Agriculture 4.0 --- failure strain --- sandstone --- digital image correlation --- Hill-Tsai failure criterion --- finite element method --- reference evapotranspiration --- moisture sensors --- machine learning regression --- frequency-domain reflectometry --- randomizable filtered classifier --- convolutional neural network --- U-Net --- land use --- banana plantation --- Panama TR4 --- aerial photography --- remote images --- systematic mapping study --- agriculture --- applications --- total leaf area --- mixed pixels --- Cabernet Sauvignon --- NDVI --- Normalized Difference Vegetation Index --- precision viticulture --- 3D model --- spatial vision --- fertirrigation --- teaching-learning --- spectrometry --- Sentinel-2 --- pasture quality index --- normalized difference vegetation index --- normalized difference water index --- supplementation --- decision making --- digital agriculture --- grape yield estimate --- berries counting --- Dilated CNN --- machine learning algorithms --- classification performance --- winter wheat mapping --- large-scale --- water stress --- Prunus avium L. --- stem water potential --- low-cost thermography --- thermal indexes --- canopy temperature --- non-water-stressed baselines --- non-transpiration baseline --- soil moisture --- andosols --- image processing --- greenhouse --- automatic tomato harvesting

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Remote image capture systems are a key element in efficient and sustainable agriculture nowadays. They are increasingly being used to obtain information of interest from the crops, the soil and the environment. It includes different types of capturing devices: from satellites and drones, to in-field devices; different types of spectral information, from visible RGB images, to multispectral images; different types of applications; and different types of techniques in the areas of image processing, computer vision, pattern recognition and machine learning. This book covers all these aspects, through a series of chapters that describe specific recent applications of these techniques in interesting problems of agricultural engineering.

SVM --- budding rate --- UAV --- geometric consistency --- radiometric consistency --- point clouds --- ICP --- reflectance maps --- vegetation indices --- Parrot Sequoia --- artificial intelligence --- precision agriculture --- agricultural robot --- optimization algorithm --- online operation --- segmentation --- coffee leaf rust --- machine learning --- deep learning --- remote sensing --- Fourth Industrial Revolution --- Agriculture 4.0 --- failure strain --- sandstone --- digital image correlation --- Hill–Tsai failure criterion --- finite element method --- reference evapotranspiration --- moisture sensors --- machine learning regression --- frequency-domain reflectometry --- randomizable filtered classifier --- convolutional neural network --- U-Net --- land use --- banana plantation --- Panama TR4 --- aerial photography --- remote images --- systematic mapping study --- agriculture --- applications --- total leaf area --- mixed pixels --- Cabernet Sauvignon --- NDVI --- Normalized Difference Vegetation Index --- precision viticulture --- 3D model --- spatial vision --- fertirrigation --- teaching–learning --- spectrometry --- Sentinel-2 --- pasture quality index --- normalized difference vegetation index --- normalized difference water index --- supplementation --- decision making --- digital agriculture --- grape yield estimate --- berries counting --- Dilated CNN --- machine learning algorithms --- classification performance --- winter wheat mapping --- large-scale --- water stress --- Prunus avium L. --- stem water potential --- low-cost thermography --- thermal indexes --- canopy temperature --- non-water-stressed baselines --- non-transpiration baseline --- soil moisture --- andosols --- image processing --- greenhouse --- automatic tomato harvesting --- n/a --- Hill-Tsai failure criterion --- teaching-learning