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In recent decades, there has been an increase in the development of strategies for water ecosystem mapping and monitoring. Overall, this is primarily due to legislative efforts to improve the quality of water bodies and oceans. Remote sensing has played a key role in the development of such approaches—from the use of drones for vegetation mapping to autonomous vessels for water quality monitoring. Within the specific context of vegetation characterization, the wide range of available observations—from satellite imagery to high-resolution drone aerial imagery—has enabled the development of monitoring and mapping strategies at multiple scales (e.g., micro- and mesoscales). This Special Issue, entitled “Novel Advances in Aquatic Vegetation Monitoring in Ocean, Lakes and Rivers”, collates recent advances in remote sensing-based methods applied to ocean, river, and lake vegetation characterization, including seaweed, kelp, submerged and emergent vegetation, and floating-leaf and free-floating plants. A total of six manuscripts have been compiled in this Special Issue, ranging from area mapping substrates in riverine environments to the identification of macroalgae in marine environments. The work presented leverages current state-of-the-art methods for aquatic vegetation monitoring and will spark further research within this field.

bottom reflectance --- aquatic vegetation --- normalized difference vegetation index (NDVI) --- Lake Ulansuhai --- concave–convex decision function --- radiative transfer --- methodological comparison --- remote sensing extraction --- invasive plants --- CAS S. alterniflora --- spectroscopy --- China --- nuclear power station --- floating algae index (FAI) --- Landsat OLI --- Spartina alterniflora --- substrate --- unmanned aerial vehicle --- Lake Baikal --- reflectance --- 1st derivative --- seaweed --- remote sensing --- WorldView-2 --- species discrimination --- WorldView-3 --- water-column correction --- Selenga River Delta --- macroalgae --- object-based image analysis --- seaweed enhancing index (SEI) --- freshwater wetland --- GF-1 satellite --- river

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With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

metadata --- image classification --- sensitivity analysis --- ROI detection --- residual learning --- image alignment --- adaptive convolutional kernels --- Hough transform --- class imbalance --- land surface temperature --- inundation mapping --- multiscale representation --- object-based --- convolutional neural networks --- scene classification --- morphological profiles --- hyperedge weight estimation --- hyperparameter sparse representation --- semantic segmentation --- vehicle classification --- flood --- Landsat imagery --- target detection --- multi-sensor --- building damage detection --- optimized kernel minimum noise fraction (OKMNF) --- sea-land segmentation --- nonlinear classification --- land use --- SAR imagery --- anti-noise transfer network --- sub-pixel change detection --- Radon transform --- segmentation --- remote sensing image retrieval --- TensorFlow --- convolutional neural network --- particle swarm optimization --- optical sensors --- machine learning --- mixed pixel --- optical remotely sensed images --- object-based image analysis --- very high resolution images --- single stream optimization --- ship detection --- ice concentration --- online learning --- manifold ranking --- dictionary learning --- urban surface water extraction --- saliency detection --- spatial attraction model (SAM) --- quality assessment --- Fuzzy-GA decision making system --- land cover change --- multi-view canonical correlation analysis ensemble --- land cover --- semantic labeling --- sparse representation --- dimensionality expansion --- speckle filters --- hyperspectral imagery --- fully convolutional network --- infrared image --- Siamese neural network --- Random Forests (RF) --- feature matching --- color matching --- geostationary satellite remote sensing image --- change feature analysis --- road detection --- deep learning --- aerial images --- image segmentation --- aerial image --- multi-sensor image matching --- HJ-1A/B CCD --- endmember extraction --- high resolution --- multi-scale clustering --- heterogeneous domain adaptation --- hard classification --- regional land cover --- hypergraph learning --- automatic cluster number determination --- dilated convolution --- MSER --- semi-supervised learning --- gate --- Synthetic Aperture Radar (SAR) --- downscaling --- conditional random fields --- urban heat island --- hyperspectral image --- remote sensing image correction --- skip connection --- ISPRS --- spatial distribution --- geo-referencing --- Support Vector Machine (SVM) --- very high resolution (VHR) satellite image --- classification --- ensemble learning --- synthetic aperture radar --- conservation --- convolutional neural network (CNN) --- THEOS --- visible light and infrared integrated camera --- vehicle localization --- structured sparsity --- texture analysis --- DSFATN --- CNN --- image registration --- UAV --- unsupervised classification --- SVMs --- SAR image --- fuzzy neural network --- dimensionality reduction --- GeoEye-1 --- feature extraction --- sub-pixel --- energy distribution optimizing --- saliency analysis --- deep convolutional neural networks --- sparse and low-rank graph --- hyperspectral remote sensing --- tensor low-rank approximation --- optimal transport --- SELF --- spatiotemporal context learning --- Modest AdaBoost --- topic modelling --- multi-seasonal --- Segment-Tree Filtering --- locality information --- GF-4 PMS --- image fusion --- wavelet transform --- hashing --- machine learning techniques --- satellite images --- climate change --- road segmentation --- remote sensing --- tensor sparse decomposition --- Convolutional Neural Network (CNN) --- multi-task learning --- deep salient feature --- speckle --- canonical correlation weighted voting --- fully convolutional network (FCN) --- despeckling --- multispectral imagery --- ratio images --- linear spectral unmixing --- hyperspectral image classification --- multispectral images --- high resolution image --- multi-objective --- convolution neural network --- transfer learning --- 1-dimensional (1-D) --- threshold stability --- Landsat --- kernel method --- phase congruency --- subpixel mapping (SPM) --- tensor --- MODIS --- GSHHG database --- compressive sensing

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Geomorphometry is the science of quantitative terrain characterization and analysis, and has traditionally focused on the investigation of terrestrial and planetary landscapes. However, applications of marine geomorphometry have now moved beyond the simple adoption of techniques developed for terrestrial studies, driven by the rise in the acquisition of high-resolution seafloor data and by the availability of user-friendly spatial analytical tools. Considering that the seafloor represents 71% of the surface of our planet, this is an important step towards understanding the Earth in its entirety.This volume is the first one dedicated to marine applications of geomorphometry. It showcases studies addressing the five steps of geomorphometry: sampling a surface (e.g., the seafloor), generating a Digital Terrain Model (DTM) from samples, preprocessing the DTM for subsequent analyses (e.g., correcting for errors and artifacts), deriving terrain attributes and/or extracting terrain features from the DTM, and using and explaining those terrain attributes and features in a given context. Throughout these studies, authors address a range of challenges and issues associated with applying geomorphometric techniques to the complex marine environment, including issues related to spatial scale, data quality, and linking seafloor topography with physical, geological, biological, and ecological processes. As marine geomorphometry becomes increasingly recognized as a sub-discipline of geomorphometry, this volume brings together a collection of research articles that reflect the types of studies that are helping to chart the course for the future of marine geomorphometry.

geomorphology --- simulation --- accuracy --- spatial scale --- marine geomorphology --- surface roughness --- forage fish --- satellite imagery --- thalwegs --- digital elevation models (DEMs) --- Seabed 2030 --- Pacific sand lance --- Acoustic applications --- python --- Nippon Foundation/GEBCO --- Oceanic Shoals Australian Marine Park --- submarine topography --- multi beam echosounder --- sedimentation --- bedforms --- carbonate banks --- polychaete --- cold-water coral --- multiscale --- automated-mapping --- semi-automated mapping --- sediment habitats --- Atlantic Ocean --- Northwestern Australia --- random forest --- benthic habitat mapping --- paleoclimate --- submerged glacial bedforms --- seafloor --- currents --- Cenomanian–Turonian --- Multibeam bathymetry --- geomorphometry --- ArcGIS --- filter --- seabed mapping --- coral reefs --- eastern Brazilian shelf --- digital terrain analysis --- multibeam spatial resolution --- multibeam --- multibeam sonar --- Timor Sea --- seafloor geomorphometry --- shelf-slope-rise --- terrain analysis --- seafloor mapping technologies --- spatial analysis --- Canary Basin --- paleobathymetry --- Bonaparte Basin --- pockmarks --- benthic habitats --- Malin Basin --- geographic object-based image analysis --- seafloor mapping standards and protocols --- GIS --- Bering Sea --- object segmentation --- Barents Sea --- bathymetry --- carbonate mound --- underwater acoustics --- integration artefacts --- multibeam echosounder --- domes --- global bathymetry --- Random Forests --- North Sea --- spatial prediction --- Glaciated Margin --- marine geology --- image segmentation --- shelf morphology --- Alaska --- paleoceanography --- confidence --- swath geometry --- volcanoes --- deglaciation --- Cretaceous --- DEM --- habitat mapping --- marine remote sensing --- reconstruction --- acoustic-seismic profiling --- canyons