Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

Integrated Applications of Geo-Information in Environmental Monitoring

This book focuses on fundamental and applied research on geo-information technology, notably optical and radar remote sensing and algorithm improvements, and their applications in environmental monitoring. This Special Issue presents ten high-quality research papers covering up-to-date research in land cover change and desertification analyses, geo-disaster risk and damage evaluation, mining area restoration assessments, the improvement and development of algorithms, and coastal environmental monitoring and object targeting. The purpose of this Special Issue is to promote exchanges, communications and share the research outcomes of scientists worldwide and to bridge the gap between scientific research and its applications for advancing and improving society

Algoritmos de segmentação de imagem baseados em deep learning para fotografia aérea de drones da zona costeira portuguesa

As human-induced pressures continue to rise in the coastal zone, there is an increasing need to resourcefully predict, detect and monitor environmental patterns to support large scale conservation strategies. The Portuguese coastal zone is the home to profuse biological communities, including mussels, which are a key ecological species for the biodiversity of seashore ecosystems, supporting and shielding a vast amount of invertebrate species. Additionally, the improvement of unmanned aerial devices and high-resolution aerial photography have provided the possibility to produce large temporal and spatial datasets while subsiding both biological and physical disturbances in the ecosystems. On this basis, a low-altitude and high resolution aerial image set was captured by a research team from the Biology Department of the University of Aveiro to measure the coverage, size and density of mussels along the Portuguese shoreline. With this newly-gathered dataset, a group from the Department of Electronics, Telecommunications and Informatics, from the same institution, took the initiative to create computer vision algorithms through deep learning in order to assist the analysis of the collected data and verify the viability of the data-gathering methods. This work presents all the thorough procedures executed to answer the proposed challenge, from the development of a functional pixel-wise image segmentation dataset, to the development of predicting models using renowned architectures in the deep learning community, capable of achieving good results to enable the understanding of the dynamics of the ecosystem and predict the mussel abundance under distinct environmental scenarios. Furthermore, the solution has the potential to grow and be improved further. By exploring a new dataset that may open new doors for understanding and classification of coastal zones, with models that could potentially be re-trained in the future for different kinds of shores and intertidal zones with more and other animal communities, this work also proves the possibility of using deep learning models to analyze image data acquired from drones and hopes to allow further research on the subject and on different types of areas and vegetation.À medida que as pressões induzidas pelo homem continuam a aumentar na zona costeira, há uma necessidade crescente de prever, detetar e monitorizar padrões ambientais para apoiar estratégias de conservação em grande escala. A zona costeira portuguesa é o lar de comunidades biológicas abundantes, incluindo mexilhões, que são uma espécie ecológica chave para a biodiversidade dos ecossistemas costeiros, apoiando e protegendo uma vasta quantidade de espécies invertebradas. Adicionalmente, o aperfeiçoamento dos dispositivos aéreos não tripulados e da fotografia aérea de alta resolução proporcionaram a possibilidade de produzir grandes conjuntos de dados temporais e espaciais, reduzindo ao mesmo tempo tanto perturbações biológicas como físicas nos ecossistemas. Nesta base, um conjunto de imagens aéreas de baixa altitude e alta resolução foi capturado por uma equipa de investigação do Departamento de Biologia da Universidade de Aveiro para medir a cobertura, tamanho e densidade dos mexilhões ao longo da costa portuguesa. Com este conjunto de dados reunido, um grupo do Departamento de Eletrónica, Telecomunicações e Informática, da mesma instituição, tomou a iniciativa de criar algoritmos de visão computacional através de deep learning, com o objetivo de auxiliar a análise das imagens recolhidas e verificar a viabilidade dos métodos de recolha de dados. Este trabalho apresenta todos os procedimentos exaustivos efetuados para responder ao desafio proposto, desde o desenvolvimento de um conjunto de dados funcional para segmentação de imagens ao nível do pixel, até ao desenvolvimento de modelos preditivos utilizando arquiteturas de renome na comunidade de deep learning, capazes de alcançar bons resultados para permitir a compreensão da dinâmica do ecossistema e prever a abundância dos mexilhões em cenários ambientais distintos. Além disso, a solução apresenta potencial para crescer e ser futuramente aperfeiçoada. Ao explorar um novo conjunto de dados que poderá abrir novas portas para a compreensão e classificação das zonas costeiras, com modelos que poderão ser potencialmente re-treinados no futuro para diferentes tipos de costas e zonas intertidais com mais e outras comunidades animais, este trabalho prova também a possibilidade de utilizar modelos de deep learning para analisar dados adquiridos através de drones e espera possibilitar uma investigação mais aprofundada no tema e em diferentes tipos de áreas e vegetação.Mestrado em Engenharia Informátic

Remote Sensing of the Aquatic Environments

The book highlights recent research efforts in the monitoring of aquatic districts with remote sensing observations and proximal sensing technology integrated with laboratory measurements. Optical satellite imagery gathered at spatial resolutions down to few meters has been used for quantitative estimations of harmful algal bloom extent and Chl-a mapping, as well as winds and currents from SAR acquisitions. The knowledge and understanding gained from this book can be used for the sustainable management of bodies of water across our planet

Remote Sensing in Applications of Geoinformation

Remote sensing, especially from satellites, is a source of invaluable data which can be used to generate synoptic information for virtually all parts of the Earth, including the atmosphere, land, and ocean. In the last few decades, such data have evolved as a basis for accurate information about the Earth, leading to a wealth of geoscientific analysis focusing on diverse applications. Geoinformation systems based on remote sensing are increasingly becoming an integral part of the current information and communication society. The integration of remote sensing and geoinformation essentially involves combining data provided from both, in a consistent and sensible manner. This process has been accelerated by technologically advanced tools and methods for remote sensing data access and integration, paving the way for scientific advances in a broadening range of remote sensing exploitations in applications of geoinformation. This volume hosts original research focusing on the exploitation of remote sensing in applications of geoinformation. The emphasis is on a wide range of applications, such as the mapping of soil nutrients, detection of plastic litter in oceans, urban microclimate, seafloor morphology, urban forest ecosystems, real estate appraisal, inundation mapping, and solar potential analysis

BoatNet: automated small boat composition detection using deep learning on satellite imagery

Tracking and measuring national carbon footprints is key to achieving the ambitious goals set by the Paris Agreement on carbon emissions. According to statistics, more than 10% of global transportation carbon emissions result from shipping. However, accurate tracking of the emissions of the small boat segment is not well established. Past research looked into the role played by small boat fleets in terms of greenhouse gases, but this has relied either on high-level technological and operational assumptions or the installation of global navigation satellite system sensors to understand how this vessel class behaves. This research is undertaken mainly in relation to fishing and recreational boats. With the advent of open-access satellite imagery and its ever-increasing resolution, it can support innovative methodologies that could eventually lead to the quantification of greenhouse gas emissions. Our work used deep learning algorithms to detect small boats in three cities in the Gulf of California in Mexico. The work produced a methodology named BoatNet that can detect, measure and classify small boats with leisure boats and fishing boats even under low-resolution and blurry satellite images, achieving an accuracy of 93.9% with a precision of 74.0%. Future work should focus on attributing a boat activity to fuel consumption and operational profile to estimate small boat greenhouse gas emissions in any given region

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

Remote Sensing Applications in Coastal Environment

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