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Coastal regions are susceptible to rapid changes, as they constitute the boundary between the land and the sea. The resilience of a particular segment of coast depends on many factors, including climate change, sea-level changes, natural and technological hazards, extraction of natural resources, population growth, and tourism. Recent research highlights the strong capabilities for remote sensing applications to monitor, inventory, and analyze the coastal environment. This book contains 12 high-quality and innovative scientific papers that explore, evaluate, and implement the use of remote sensing sensors within both natural and built coastal environments.

Research & information: general --- big data applications --- data processing --- data visualization --- neural networks --- reduction --- coastal waters --- urban expansion --- remote sensing and GIS --- expansion types and rates --- major explanatory factors --- Miami metropolitan area --- cliff coastlines --- cliff retreat --- time-series analysis --- airborne laser scanner --- warm upwelling --- sea surface temperature --- numerical modelling --- winter --- southern Baltic Sea --- beach monitoring --- mobile terrestrial LiDAR --- intensity calibration --- beach surface moisture --- Baltic coast --- Poland --- CORINE Land Cover --- land cover flow --- urbanisation --- afforestation --- deforestation --- spatial analysis --- SDGs --- coastal habitats --- ecosystem monitoring --- land cover mapping --- random forest algorithm --- Sentinel-2 --- modified soil-adjusted vegetation index 2-MSAVI2 --- normalized difference water index 2-NDWI2 --- brightness index 2-BI2 --- oil spill --- remote sensing --- review --- machine learning --- deep learning --- trajectory modeling --- vulnerability assessment --- coastal geomorphology --- shoreline change --- coastal process --- monitoring --- geomatic techniques --- Po River Delta --- archival multi-temporal data --- coastline changes --- emerged/submerged surfaces --- land subsidence --- relative sea level rise 2100 --- land cover --- dune coast --- air photograph --- South Baltic Sea --- coastal monitoring --- estuaries --- IoT --- lidar --- big data applications --- data processing --- data visualization --- neural networks --- reduction --- coastal waters --- urban expansion --- remote sensing and GIS --- expansion types and rates --- major explanatory factors --- Miami metropolitan area --- cliff coastlines --- cliff retreat --- time-series analysis --- airborne laser scanner --- warm upwelling --- sea surface temperature --- numerical modelling --- winter --- southern Baltic Sea --- beach monitoring --- mobile terrestrial LiDAR --- intensity calibration --- beach surface moisture --- Baltic coast --- Poland --- CORINE Land Cover --- land cover flow --- urbanisation --- afforestation --- deforestation --- spatial analysis --- SDGs --- coastal habitats --- ecosystem monitoring --- land cover mapping --- random forest algorithm --- Sentinel-2 --- modified soil-adjusted vegetation index 2-MSAVI2 --- normalized difference water index 2-NDWI2 --- brightness index 2-BI2 --- oil spill --- remote sensing --- review --- machine learning --- deep learning --- trajectory modeling --- vulnerability assessment --- coastal geomorphology --- shoreline change --- coastal process --- monitoring --- geomatic techniques --- Po River Delta --- archival multi-temporal data --- coastline changes --- emerged/submerged surfaces --- land subsidence --- relative sea level rise 2100 --- land cover --- dune coast --- air photograph --- South Baltic Sea --- coastal monitoring --- estuaries --- IoT --- lidar

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• Reference Manager
• EndNote
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Coastal regions are susceptible to rapid changes, as they constitute the boundary between the land and the sea. The resilience of a particular segment of coast depends on many factors, including climate change, sea-level changes, natural and technological hazards, extraction of natural resources, population growth, and tourism. Recent research highlights the strong capabilities for remote sensing applications to monitor, inventory, and analyze the coastal environment. This book contains 12 high-quality and innovative scientific papers that explore, evaluate, and implement the use of remote sensing sensors within both natural and built coastal environments.

big data applications --- data processing --- data visualization --- neural networks --- reduction --- coastal waters --- urban expansion --- remote sensing and GIS --- expansion types and rates --- major explanatory factors --- Miami metropolitan area --- cliff coastlines --- cliff retreat --- time-series analysis --- airborne laser scanner --- warm upwelling --- sea surface temperature --- numerical modelling --- winter --- southern Baltic Sea --- beach monitoring --- mobile terrestrial LiDAR --- intensity calibration --- beach surface moisture --- Baltic coast --- Poland --- CORINE Land Cover --- land cover flow --- urbanisation --- afforestation --- deforestation --- spatial analysis --- SDGs --- coastal habitats --- ecosystem monitoring --- land cover mapping --- random forest algorithm --- Sentinel-2 --- modified soil-adjusted vegetation index 2–MSAVI2 --- normalized difference water index 2–NDWI2 --- brightness index 2–BI2 --- oil spill --- remote sensing --- review --- machine learning --- deep learning --- trajectory modeling --- vulnerability assessment --- coastal geomorphology --- shoreline change --- coastal process --- monitoring --- geomatic techniques --- Po River Delta --- archival multi-temporal data --- coastline changes --- emerged/submerged surfaces --- land subsidence --- relative sea level rise 2100 --- land cover --- dune coast --- air photograph --- South Baltic Sea --- coastal monitoring --- estuaries --- IoT --- lidar --- n/a --- modified soil-adjusted vegetation index 2-MSAVI2 --- normalized difference water index 2-NDWI2 --- brightness index 2-BI2

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Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others.

Research & information: general --- Geography --- geographic information system (GIS) --- pocket beaches --- coastal management --- Interreg --- climate change --- remote sensing --- drone --- Sicily --- Malta --- Gozo --- Comino --- systematic literature review --- anomaly intrusion detection --- deep learning --- IoT --- resource constraint --- IDS --- evapotranspiration --- penman-monteith equation --- artificial neural network --- canopy conductance --- Ziz basin --- water quality --- satellite image analysis --- modeling approach --- nitrate --- dissolved oxygen --- chlorophyll a --- time series analysis --- environmental monitoring --- water extraction --- modified normalized difference water index (MNDWI) --- machine learning algorithm --- hyperspectral --- proximal sensing --- panicle initiation --- normalized difference vegetation index (NDVI) --- green ring --- internode-elongation --- Sentinel 1 and 2 --- Copernicus Sentinels --- crop classification --- food security --- agricultural monitoring --- data analysis --- SAR --- random forest --- 3D bale wrapping method --- equal bale dimensions --- mathematical model --- minimal film consumption --- optimal bale dimensions --- round bales --- Sentinel-2 --- SVM --- RF --- Boufakrane River watershed --- irrigation requirements --- water resources --- sustainable land use --- agriculture --- invasive plants --- precision agriculture --- rice farming --- site-specific weed management --- nitrogen prediction --- 1D convolution neural networks --- cucumber --- crop yield improvement --- mango leaf --- CCA --- vein pattern --- leaf disease --- cubic SVM --- chlorophyll-a concentration --- transfer learning --- overfitting --- data augmentation --- guava disease --- plant disease detection --- crops diseases --- entropy --- features fusion --- machine learning --- object-based classification --- density estimation --- histogram --- land use --- crop fields --- soil tillage --- data fusion --- multispectral --- sensor --- probe --- temperature profile --- forest roads --- simulation --- autonomous robots --- smart agriculture --- environmental protection --- photogrammetry --- path planning --- internet of things --- modeling --- convolutional neural networks --- machine vision --- computer vision --- modular robot --- selective spraying --- vision-based crop and weed detection --- Faster R-CNN --- YOLOv5 --- band selection --- CNN --- NDVI --- hyperspectral imaging --- crops --- urban flood --- Sentinel-1a --- Synthetic Aperture Radar (SAR) --- 3D Convolutional Neural Network --- multi-temporal data --- land use classification --- GIS --- Coatzacoalcos --- algorithms --- clustering --- pest control --- site-specific --- virtual pests --- rice plant --- weed --- hyperspectral imagery --- sustainable agriculture --- green technologies --- Internet of Things --- natural resources --- sustainable environment --- IoT ecosystem --- hyperspectral remoting sensing --- crop mapping --- image classification --- deep transfer learning --- hyperparameter optimization --- metaheuristic --- soil attribute --- ordinary Kriging --- rational sampling numbers --- spatial heterogeneity --- sampling --- soil pH --- spatial variation --- ordinary kriging --- Land Use/Land Cover --- LISS-III --- Landsat --- Vision Transformer --- Bidirectional long-short term memory --- Google Earth Engine --- Explainable Artificial Intelligence