Remote Sensing of Ocean and Coastal Environments

Remote Sensing of Ocean and Coastal Environments advances the scientific understanding and application of technologies to address a variety of areas relating to sustainable development, including environmental systems analysis, environmental management, clean processes, green chemistry and green engineering. Through each contributed chapter, the book covers ocean remote sensing, ocean color monitoring, modeling biomass and the carbon of oceanic ecosystems, sea surface temperature (SST) and sea surface salinity, ocean monitoring for oil spills and pollutions, coastal erosion and accretion measurement.

This book is aimed at those with a common interest in oceanography techniques, sustainable development and other diverse backgrounds within earth and ocean science fields. This book is ideal for academicians, scientists, environmentalists, meteorologists, environmental consultants and computing experts working in the areas of earth and ocean sciences.

• Provides a comprehensive assessment of various ocean processes and their relative phenomena
• Includes graphical abstract and photosets in each chapter
• Presents literature reviews, case studies and applications

• Language: English
• Publisher: Elsevier Science
• Release date: Sep 27, 2020
• ISBN: 9780128231609

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Remote Sensing of Ocean and Coastal Environments

Edited by

Meenu Rani

Department of Geography, Kumaun University, Nainital, Uttarakhand, India

Kaliraj Seenipandi

Central Geomatics Laboratory (CGL), National Center for Earth Science Studies (NCESS), Thiruvananthapuram, Kerala, India

Sufia Rehman

Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, Delhi, India

Pavan Kumar

College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi, Uttar Pradesh, India

Haroon Sajjad

Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, Delhi, India

Table of Contents

Cover image

Title page

Copyright

Contributors

Biographies

Foreword

1. Remote sensing of Ocean and Coastal Environment – Overview

1. Introduction

2. Satellite remote sensing

3. Geographical information system

4. Fundamental techniques

2. Ocean and coastal remote sensing: platforms, sensors, instruments, data products, tools, and techniques

1. Introduction

2. Platforms and sensors

3. Data products

4. Tools and techniques

3. Ocean remote sensing for seasonal predictability of phytoplankton (chl-a) biomass in the Southern Indian coastal water region using Landsat 8 OLI images

1. Introduction

2. Study area profile

3. Materials and methods

4. Results and discussion

5. Conclusions

4. Ocean remote sensing for spatiotemporal variability of wave energy density and littoral current velocity in the Southern Indian offshore

1. Introduction

2. Study area

3. Materials and methods

4. Result and discussion

5. Conclusions

5. Ocean remote sensing of seawater salinity and its seasonal variability: a case study of Southern Indian coastal water using Landsat 8 OLI images

1. Introduction

2. Study area

3. Materials and methods

4. Results and discussion

5. Conclusions

6. Impacts of placer mining on groundwater and air quality in the Chavara coastal stretch of Kerala: remote sensing and GIS-based approach

1. Introduction

2. Materials and methods

3. Results and discussion

4. Conclusion

7. Evaluation of coastal sediments: an appraisal of geochemistry using ED-XRF and GIS techniques

1. Introduction

2. Study area

3. Materials and method

4. Results and discussion

5. Conclusion

8. Assessing the impact of aquafarming on landscape dynamics of coastal West Bengal, India using remotely sensed data and spatial metrics

1. Introduction

2. Methodology

3. Results

4. Discussion

5. Conclusions

Supplementary data

9. Applications of geostationary satellite data in the study of ocean and coastal short-term processes: two cases in the East China Sea

1. Introduction

2. Geostationary satellite data

3. Applications: two cases in the East China Sea short-term cross-shelf process

4. Conclusion

10. Evaluation of heavy metals in coastal aquifers and seawater: an appraisal of geochemistry using ICPMS and remote sensing

1. Introduction

2. Study area

3. Materials and methods

4. Results and discussion

5. Conclusion

11. Remote sensing for exploring heavy mineral deposits: a case study of Chavara and Manavalakurichi deposits, southwest coast of India

1. Introduction

2. Study area

3. Materials and methods

4. Results and discussion

5. Conclusion

12. Simulation studies about the role of Lakshadweep-Maldives ridge in determining tsunami characteristics along the Kerala coast of India

1. Introduction

2. Methodology

3. Results and discussion

4. The influence of LMR for 2004 Sumatra tsunami

5. The effect of LMR for 1945 Makran-like tsunami

6. Conclusions

13. Measuring the vulnerability of coastal ecosystems in a densely populated west coast landscape, India: a remote sensing perspective

1. Introduction

2. Study area

3. Methodology

4. Analysis and discussions

5. Results

6. Conclusion

14. Modeling of coastal environmental vulnerability in South India: a multiple parametric approach using remote sensing and GIS

1. Introduction

2. The geographical profile of the study area

3. Materials and methods

4. Results and discussions

5. Conclusions

15. Evaluation of suspended sediment concentration and heavy metal distribution in Ashtamudi Lake, a Ramsar site in the southwest coast of India using remote sensing and GIS techniques

1. Introduction

2. Study area

3. Materials and methods

4. Results and discussion

5. Conclusions

16. Seasonal variability of sea surface temperature in Southern Indian coastal water using Landsat 8 OLI/TIRS images

1. Introduction

2. Study area

3. Materials and methods

4. Results and discussion

5. Conclusions

17. Ocean remote sensing of suspended sediment variability in Southern Indian coastal water region using Landsat 8 OLI images

1. Introduction

2. Profile of the study area

3. Materials and methods

4. Results and discussion

5. Conclusions

18. Modeling of coastal vulnerability to sea-level rise and shoreline erosion using modified CVI model

1. Introduction

2. Study area

3. Materials and methods

4. Results and discussion

5. Conclusion

19. An investigation of a credible strategy for coral reef bleaching and its management using a geospatial approach for the Gulf of Kutch

1. Introduction

2. Study area

3. Materials and methodology

4. Results and discussion

20. Assessment of shoreline vulnerability in parts of the coastline of Kasaragod district, Kerala, India

1. Introduction

2. History

3. Study area

4. Materials and methods

5. Results and discussion

6. Conclusion

21. Insight to the spatial-temporal extent of mangrove forests in the northern coast of Kerala

1. Introduction

2. Study area

3. Methodology

4. Result and discussion

5. Conclusion

Index

Copyright

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Notices

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Contributors

S.K. Aditya,     National Centre for Earth Science Studies (NCESS), Thiruvananthapuram, Kerala, India

R. Aneesh Kumar,     Environmental Technology Division, Council of Scientific & Industrial Research National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram, Kerala, India

K. Anoop Krishnan,     National Centre for Earth Science Studies (NCESS), Thiruvananthapuram, Kerala, India

J. Ansari

Environmental Technology Division, Council of Scientific & Industrial Research National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram, Kerala, India

Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India

Trisha Chakraborty,     Department of Geography, Jadavpur University, Kolkata, West Bengal, India

N. Chandrasekar,     Centre for GeoTechnology, Manonmaniam Sundaranar University & Francis Xavier Engineering College, Tirunelveli, Tamil Nadu, India

B.S. Chaudhary,     Department of Geophysics, Kurukshetra University, Kurukshetra, Haryana, India

Debajit Datta,     Department of Geography, Jadavpur University, Kolkata, West Bengal, India

Prashant Ghadei,     Department of Geography, Central University of Tamil Nadu, Thiruvarur, Tamil Nadu, India

Kalita Himangshu,     Haryana Space Applications Centre (HARSAC), (Department of Science & Technology, Haryana) CCS HAU Campus, HISAR, Haryana, India

Daji Huang,     State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China

K. Ibrahim-Bathis,     Department of Engineering, Politeknik Negeri Pontianak, Pontianak, West Kalimantan, Indonesia

Jeenu Jose,     National Centre for Earth Science Studies (NCESS), Thiruvananthapuram, Kerala, India

V. Kashyap

Environmental Technology Division, Council of Scientific & Industrial Research National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram, Kerala, India

Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India

A. Krishnakumar,     National Centre for Earth Science Studies (NCESS), Thiruvananthapuram, Kerala, India

Pavan Kumar,     College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi, Uttar Pradesh, India

Md. Masroor,     Department of Geography, Jamia Millia Islamia, New Delhi, Delhi, India

Shafique Matin,     Environment Protection Agency (EPA), Wexford, Ireland

M.A. Mohammed-Aslam,     Department of Geology, School of Earth Sciences, Central University of Karnataka, Gulbarga, Karnataka, India

Mrinmoyee Naskar

Department of Geography, Jadavpur University, Kolkata, West Bengal, India

Department of Geography, Baruipur College, Baruipur, West Bengal, India

Sohini Neogy,     Department of Geography, Jadavpur University, Kolkata, West Bengal, India

M.K. Rafeeque,     National Centre for Earth Science Studies, Thiruvananthapuram, Kerala, India

K.K. Ramachandran,     National Centre for Earth Science Studies (NCESS), Thiruvananthapuram, Kerala, India

M. Rameshan,     National Centre for Earth Science Studies, Thiruvananthapuram, Kerala, India

Meenu Rani,     Department of Geography, Kumaun University, Nainital, Uttarakhand, India

Sufia Rehman,     Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, Delhi, India

R.G. Rejith

Minerals Section, Materials Science and Technology Division, National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Council of Scientific & Industrial Research, Thiruvananthapuram, Kerala, India

Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India

R.A. Renjith,     Minerals Section, Materials Science and Technology Division, National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Council of Scientific & Industrial Research, Thiruvananthapuram, Kerala, India

Asit Kumar Roy,     Department of Geography, Jadavpur University, Kolkata, West Bengal, India

P.M. Saharuba,     Environmental Technology Division, Council of Scientific & Industrial Research National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram, Kerala, India

Haroon Sajjad,     Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, Delhi, India

Praveen Sathee Sankar,     Department of Physics, St. Thomas College, Kozhencherry, Kerala, India

Sakhre Saurabh

Environmental Technology Division, Council of Scientific & Industrial Research National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram, Kerala, India

Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India

Kaliraj Seenipandi,     National Centre for Earth Science Studies (NCESS), Thiruvananthapuram, Kerala, India

Sulochana Shekhar,     Department of Geography, Central University of Tamil Nadu, Thiruvarur, Tamil Nadu, India

K. Shravanraj,     Minerals Section, Materials Science and Technology Division, National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Council of Scientific & Industrial Research, Thiruvananthapuram, Kerala, India

Ram Kumar Singh,     Department of Natural Resources, TERI School of Advanced Studies, New Delhi, India

M.K. Sreeraj,     National Centre for Earth Science Studies, Thiruvananthapuram, Kerala, India

M. Sundararajan

Minerals Section, Materials Science and Technology Division, National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Council of Scientific & Industrial Research, Thiruvananthapuram, Kerala, India

Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India

S. Venkatesan,     Department of Geology, National College, Trichy, Tamil Nadu, India

M. Vrinda,     Department of Geology, Govt. College, Kasaragod, Kerala, India

Wenbin Yin,     State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China

Biographies

Dr. Meenu Rani received her MTech degree in remote sensing from the Birla Institute of Technology, Ranchi, India. She is currently affiliated with the Department of Geography, Kumaun University, Nainital, Uttarakhand, India. She has worked on remote-sensing applications as a junior research fellow at the Haryana Space Applications Centre, as a research associate on the Indian Council of Agricultural Research, and at the G.B. Pant National Institute of Himalayan Environment and Sustainable Development. Dr. Rani has authored and coauthored several peer-reviewed scientific research papers and presented work at many national and international conferences, including in the United States, Italy, and China. She has been awarded various fellowships from the International Association for Ecology, Future Earth Coasts, and the Scientific Committee on Antarctic Research Scientific Research Programme. She was awarded an Early Career Scientist achievement in 2017 from Columbia University, New York, New York, USA.

Dr. Kaliraj Seenipandi is a scientist at the Central Geomatics Laboratory, National Centre for Earth Science Studies, Thiruvananthapuram. He received his MSc in remote sensing and geoinformation technology with a first from Madurai Kamaraj University, Madurai, and his MTech in geomatics from the Indian Institute of Surveying and Mapping, Survey of India, Hyderabad. He was awarded the DST-INSPIRE Fellowship (both JRF and SRF) for his PhD in remote sensing–geotechnology from the Centre for GeoTechnology, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India. He has specialized in remote sensing, geoinformatics, and GIS modeling of earth and environmental processes. He has published over 30 research papers in the earth and environment fields and more than 25 proceedings in national and international conferences. He was awarded the Young Scientist of the Year 2016 award by the International Foundation for Environment and Ecology, Kolkata, in association with the Confederation of Indian Universities, New Delhi, and the Green Technologist of the Year 2017 award by the Scientific and Environmental Research Institute, New Delhi, in association with the Indian Institute of Ecology and Environment, New Delhi. His research interests are in the fields of remote sensing, geoinformatics, GIS modeling, earth and environmental dynamics, coastal vulnerability assessment, and natural resource monitoring and management.

Ms. Sufia Rehman is a doctoral candidate in the Department of Geography, Jamia Millia Islamia, New Delhi, India. She has completed her bachelor’s in geography and subsequently obtained her master’s degree in geography from Jamia Millia Islamia. She is the recipient of a Gold Medal in Master of Arts. She specializes in remote sensing and GIS and hydrological studies. Her areas of interest include coastal ecosystem conservation and management, climate change, and disaster management. She has made a remarkable contribution to water-related research in areas such as coastal landscape vulnerability and flood vulnerability. She has presented her research in national and international conferences. She has many research papers in journals of international repute and book chapters to her credit. Ms. Rehman has been awarded many scholarships from various agencies.

Dr. Pavan Kumar is a Faculty Member at the College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi, U.P., India. He obtained his PhD from the Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi. He obtained his BSc (botany) and MSc (environmental science) from Banaras Hindu University, Varanasi, India, and subsequently obtained a master's degree in remote sensing (MTech) from the Birla Institute of Technology, Mesra Ranchi, India. His current research interests include climate change and coastal studies. He is the recipient of an Innovation China National Academy Award for Remote Sensing. Dr. Kumar has published 50 research papers in international journals and authored a number of books. He has visited countries such as the United States, France, the Netherlands, Italy, China, Indonesia, Brazil, and Malaysia for various academic and scientific assignments, workshops, and conferences. Dr. Kumar is a member of the International Association for Vegetation Science (France) and the Institution of Geospatial and Remote Sensing Malaysia.

Haroon Sajjad is Professor in the Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India. He obtained his B.Sc, M.Sc, M.Phil and Ph.D degrees all from Aligarh Muslim University, Aligarh, India. His present research interests include environmental management, sustainable development, watershed management and applications of remote sensing and GIS. He has four books to his credit. He has published more than hundred research papers in journals of repute. Prof. Sajjad has presented fifty research papers at national and international conferences including at Sapienza University of Rome, Italy, University of British Columbia, Canada, University of Western Cape, Bellville, South Africa, and University of Brighton, U.K. He has delivered invited talks at various universities. Ten doctoral degrees to the scholars have been awarded under his supervision. He has chaired academic sessions at various conferences. He is the reviewer of many scientific research journals and member of scientific bodies.

Foreword

It has been 60 years since the first low-earth orbital weather satellite, TIROS-1, was launched. To perform remote sensing, the satellite used just a simple TV camera pointed down from space to observe the earth. Despite the rudimentary data obtained from the satellite's slow-scan camera, the field of meteorology was revolutionized by the use of remote sensing data.

In the following years, remote sensing has increased in its breadth of applications, with satellites now being used to guide decisions and inform stakeholders in subjects that range from coastal ecosystem monitoring to hazard mitigation and land use and management. The data provided by remote-sensing platforms will continue to serve a critical role in guiding scientists, consultants, engineers, environmental managers, and policy makers as we navigate the challenges presented by the changing climate.

Remote Sensing of Ocean and Coastal Environments provides a robust foundation for all who strive to understand the theory and processes behind remote sensing, and familiarizes the reader with the current state-of-the-art methodologies in the application of remote sensing to ecological, economic, and risk management problems. This work was made possible, in part, by the strong support of CLIVAR (Climate and Ocean: Variability, Predictability, and Change), one of the four core projects of the World Climate Research Programme (WCRP). Through CLIVAR's commitment to knowledge transfer, education, capacity building, and outreach—notably through the establishment of international conferences and workshops, bringing together senior scientists and early career researchers—the organization has facilitated collaboration and positively influenced many careers, including my own.

Understanding the humble origins of our field, and looking forward to a bright future guided by technological innovations, we scientists in the remote sensing community take up the mantle of responsibility handed to us by past generations. I believe that it is the duty of all of those who work in the earth sciences to understand how to maximize the tools at their disposal for the well-being of our planet. And there are few tools in our scientific arsenal as powerful as the fleets of watchful eyes in the sky.

Dr. Noel C. Baker,     ALTIUS and PICASSO satellite missions, Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium

1: Remote sensing of Ocean and Coastal Environment – Overview

Meenu Rani ¹ , Md. Masroor ² , and Pavan Kumar ³       ¹ Department of Geography, Kumaun University, Nainital, Uttarakhand, India      ² Department of Geography, Jamia Millia Islamia, New Delhi, Delhi, India      ³ College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi, Uttar Pradesh, India

Abstract

Remote sensing is logically termed as an Eye in the Sky that never tells lie. Nowadays, Remote sensing is a vital technology for exploring ocean and coastal system dynamics. A variety of satellites and sensors are proving Spatio-temporal data for monitoring and assessment of day-to-day changes in the ocean and coastal environments. Ocean and coastal areas as integrated parts of the earth’s ecosystem are immensely important biologically and socially. These areas are under constant threat due to anthropogenic activities unprecedented resource extraction and changing climatic behaviour. The oceans have varied and complex geometry and physiography and thus, cognizance of its varied characteristics is essential for identifying any implication over these ecosystems. Remote sensing and geographical information system (GIS) techniques have not only proved effective in analyzing the surface characteristics of the coastal areas but also adheres importance in identifying the characteristics of the ocean floor, mapping the coastal details and hydrodynamic modelling. The present work provides a brief interpretation of the varied remote sensing techniques, sensors, platforms, and data products in the ocean and coastal assessment. Several challenges and their solutions were also discussed for addressing the effectiveness of geospatial techniques.

Keywords

Data products; Geographical information systems; Ocean and coast; Platforms; Remote Sensing; Sensors

1. Introduction

2. Satellite remote sensing

2.1 Oil spill

2.2 Current velocity extraction and mapping

2.3 Sea surface temperature

2.4 Sea surface salinity

2.5 Coastal currents

3. Geographical information system

4. Fundamental techniques

4.1 Eulerian technique

4.2 Lagrangian technique

References

1. Introduction

Satellite imagery has greatly contributed to the mapping of coastal ecosystems and provided estimates of land coverage and alteration in the coastal ecosystem (Tripathy et al., 2018; Burke et al., 2001; Hochberg et al., 2003). Advances in the design of sensors and data analysis techniques make remote sensing systems more practical and desirable for use in coastal ecosystem management, such as estuaries, wetlands, and coral reefs (Held et al., 2003; Conchedda et al., 2008; Lyons et al., 2012; Rani et al., 2018). Multispectral and hyperspectral sensors are used to monitor coastal land cover, coastal water dissolved substances, and biotic/abiotic suspended particle concentrations. Coastal ecosystems are highly complex in terms of geography. To validate and accurately measure the remotely sensed information, an effective field data collection sampling technique is required using ships, navigation marks, and field instruments. Ocean remote sensing is mainly concerned with collecting and interpreting information from a remote point of view on coast, sea, land, and atmosphere. The remote sensing platforms can range from towers above Earth's surface to aircraft at low and medium altitudes and satellites in space, depending on the requirements of resolution and cost limitations (Klemas, 2015; Colomina and de la Tecnologia, 2008; Watts et al., 2012). Previously, satellites provided wide spatial coverage, consistent revisits, and multispectral images for coastal environment transformation analyses, but lacked high spatial resolution required for different uses. High-resolution satellite images are now available, but the high resolution and regular, flexible overflights provided by aerial sensors are more appropriate for a variety of applications, such as coastline delineation, land use–land cover change analyses, wetland mapping, and oil slick tracking (Klemas et al., 1993; Klemas, 2015). Using large-scale mapping and simulation of applications, satellite images can be combined with global positioning system (GPS) location and used as layers in geographic information systems (GIS). Unmanned aerial platforms are cheaper in comparison to manned aircraft platforms. GPS-controlled unmanned aerial vehicles (UAVs) are capable of obtaining very high-resolution images of particular landscape features with revisit times set by the pilot. In coastal environmental studies, UAVs like drones, balloons, blimps, and quadcopters are now being used effectively (Eisenbeiss and Sauerbier, 2011). Table 1.1 summarizes the specifications of all known past, current, and future satellites that have been widely used in marine applications.

Coastal regions will be affected by changing atmospheric and ocean temperatures, weather patterns, sea-level rise, ocean chemistry, and rising demands from a rapidly increasing global population over the next few decades. Without appropriate resource management plans, these changes can lead to increased threats to ecosystem services, human health, and land and economic prosperity. The aim of this chapter is to familiarize the reader with different techniques, advantages, and challenges related to coastal remote sensing applications.

2. Satellite remote sensing

Satellite observations of thermal infrared ocean currents help scientists and other users obtain real time data of current in the oceans (Schwab and Bedford, 1994; Robinson, 2004). High-resolution satellite measurements of sea surface temperature (SST) are suitable for analyzing western boundary currents such as the Kuroshio and the Gulf Stream with broad temporal and spatial displacements. Global change, accurate, precise, and long-term observations of SST are also relevant for large-scale studies (Kwon et al., 2010; Lee and Cornillon, 1995; Schmitz and Holland, 1986). Fish and wildlife communities used SST data to monitor marine habitats in many parts of the world. Thermal infrared remote sensing was first used by oceanography and meteorological societies to gain widespread acceptance (Dzwonkowski et al., 2014; Stammer et al., 2002). Thermal infrared sensors have been mounted on fully operational meteorological satellites for over 40   years to provide cloud-high temperature images; when no clouds occur, they observe SST variation. Thermal infrared instruments used to derive SST include Advanced Very High-Resolution Radiometer (AVHRR) of National Oceanic and Atmospheric Administration (NOAA) Polar-orbiting Environmental Operational Satellites, moderate resolution imaging spectro-radiometer aboard National Aeronautics and Space Administration (NASA) Earth Observing System Terra and Aqua satellites, the geostationary operational environmental satellite imagery and long-range scanning radiometer on the European Remote Sensing Satellite (ERS-2) (Gentemann et al., 2003; Purkis and Klemas, 2011; Samberg, 2007; Cracknell and Hayes, 2007; Klemas, 2011 ). Table 1.2 shows the oceanographic satellite database and which frequencies are used for transmitting data for microwave active or passive remote sensing.

Table 1.1

2.1. Oil spill

Large scale oil spills could destroy wetland, marine life, and estuarine animal ecosystems. In order to mitigate the damage caused by a spill and enhance prevention and clean-up efforts, shipping carriers, oil companies, and other accountable authorities must immediately obtain information on the source of the leak, size, and nature of the spill; speed and direction of oil movement; current, wave, and wind information to predict future oil flow and dispersion (Klemas, 2010; Nwilo and Badejo, 2006; Kennish, 2002). Most large oil spills in the oceans were caused due to tanker landings, crashes, and break-ups resulting in a high proportion of oil floating over the ocean surface and endangering the aquatic and coastal ecosystem. Many times, remote sensors observed data to track and forecast oil direction and possible movement (Blumer et al., 1971). These observed data helped direct recovery and preventive measures, including protective booms and shipments of skimming vessels. Users of remote sensing oil spill tracking data include the Coast Guard, oil companies, environmental agencies, and shipping/fishing/insurance and defense agencies (Klemas, 2010). The key operating data requirements for oil spill incidents include regular site images to track the spill dynamics. Sensors of satellites and aircrafts meet these requirements and provide multitemporal images on different resolutions at high frequency intervals to monitor oil spills. These sensors also provide key inputs for modeling of drift prediction and controlling activities. Most of these sensors use electromagnetic waves. Oil fluoresces in the ultraviolet field seem to have substantially higher reflectance. However, ultraviolet light is easily absorbed in the atmosphere and can only be used on low-altitude aircraft to avoid this scattering. Sun glint, hydrothermal materials, and wind slicks can also confuse ultraviolet sensors. To reduce this uncertainty, sensors are used in conjunction with other thermal infrared sensors and radar. The development of low-cost digital cameras on aircraft and multispectral sensors on satellites are widely used and their visible wavelengths also have a reasonable atmospheric transmission window. Oil has higher reflectivity than water in the visible region, and can be observed even more easily by using horizontally spaced filters.

Table 1.2

2.2. Current velocity extraction and mapping

Feature-tracking sequential satellite images are used to track the displacement of specified ocean features such as areas of different water temperatures, surface slicks, and chlorophyll plumes over time between consecutive images to quantify surface flow. AVHRR thermal infrared images, Synthetic Aperture Radar (SAR) radar images, and sea-viewing large field-of-view (SeaWiFS) ocean-color images were all used for current velocity (Kuo and Yan, 1994; Liu et al., 2006). Satellite feature-tracking techniques were used to measure water currents in areas like the Gulf Stream, the Gulf of Mexico, Ireland's west coast, California Current, Kuroshio Current, and New Zealand's coastline. A major drawback of this technique is that the cloud cover also obscures the surface features of visible and thermal infrared oceans (Breaker et al., 1994; Romeiser and Runge, 2007). A more contemporary technique, using interferometric SAR (InSAR) tracking, allows improved spatial resolution and efficiency to receive data from satellites anywhere in the world. This enables the imaging of line-of-sight surface velocity fields with SARs spatial resolution of the order of meters for satellites within a wide range of tens to hundreds of kilometers (Monaldo et al., 2003; Tarquis Alfonso et al., 2014).

2.3. Sea surface temperature

For a wide range of oceanography research, accurate large-scale, long-term SST measurements are significant. Satellite-derived high-resolution SST observations are suitable for monitoring boundary currents such as the Canary and the Gulf Stream, which exhibit displacements on a large scale. Long-term series of reliable, global SSTs are required to assess the health of coral reefs, supporting a wide range of marine diversity. The thermal infrared red radiance measured over all the oceans varies mainly with SST, which makes it easy to accurately determine the SST if certain atmospheric corrections are included. After the launch of AVHRR on NOAA- 7, infrared satellite SST observations have continued for almost 3 decades and have contributed to global climate studies, weather forecasting, physical oceanography research, and regional support of ship routing and fishing (Nagaraja Rao et al., 1989; Llewellyn-Jones et al., 1984; Riegl and Purkis, 2012). Spatiotemporal studies include variation in SST pattern related to interannual climate phenomena such as El Niño and La Niña cycles in the equatorial region of the Pacific and Atlantic Oceans (Espinoza Villar et al., 2009; Liu et al., 2005). In coastal upwelling studies, where increasing cold water carries nutrients to the sea surface, encouraging phytoplankton and zooplankton to attract and grow large concentrations of fish is another important application of SST sensing (Bell et al., 2004; Tozzi et al., 2004; Yan, 1993).

2.4. Sea surface salinity

Sea surface salinity (SSS) is crucial to estimate global water balance and evaporation rates, and to understand currents. Furthermore, low-salinity water is indicative sources of fresh water, such as rivers and streams that are feeding the ocean. Such rivers often carry natural and anthropogenic contamination from the land to the sea and can directly impact marine ecosystems with a higher level of salinity (Schroeder et al., 2012; Burrage et al., 2008). Airborne microwave radiometers are capable of measuring sea surface salinity and are widely used for various applications. The power receiving radiometer antennae in microwave radiometry is proportional to emissivity of microwave and ocean surface temperature. Salinity is measured as parts per 1000 (ppt) and average salinity of the ocean is 35   ppt (Droppleman et al., 1970). This means that the dissolved salt occurs at a concentration of 35   ppt or 3.5% with the remaining 96.5% water molecules. The salinity of the sea surface was the most important oceanic parameter that was not measured from satellites. However, newly advanced instruments designed to help SSS from satellites are available now. For example, a fixed two-dimensional interferometric antenna is used by the European Soil Moisture and Ocean Salinity satellite. The satellite sensor can detect salinity with high accuracy up to 50   km spatial resolution (Bell et al., 2004; Glenn et al., 2004; Yan, 1993).

2.5. Coastal currents

Ocean flow is determined by various physical factors such as wind friction, tides, and ocean density while atmospheric circulation, including winds, are caused by convection due to latitudinal temperature variation and the influence of Earth's Coriolis effect (Catry et al., 2009). Oceanic currents are considered as water movement from one place to the next and measured in knots or meters per second (Brill et al., 1993). Air movement primarily induces surface currents as the wind passes over the water. Currents have speed of three–four knots in the influence of generating wind speed because the main ocean currents cover a large distance. The Coriolis movement deflects the currents to turn right in the northern hemisphere, forcing them to pass in circular and gyres patterns in the clockwise direction while counterclockwise in the south. Major ocean currents of world includes the Humboldt, Kuroshio, Oyashio, Alaska, and California currents in the Pacific Ocean; Brazil, Benguela, Canary, Gulf Stream, and Labrador currents in the Atlantic Ocean; and Agulhas, Muzambique (Cox, 1975; Carton et al., 2000; Lebreton et al., 2012), and Somali currents in the Indian Ocean (Shankar et al., 2002; McCreary et al., 1993; Wyrtki, 1973). The most significant offshore and ocean currents are wind generated, wave-driven, tidal currents and buoyant channel plumes. These local currents may be short-term (daily) or long-term (seasonal) according to duration. Difference in ocean density depends on temperature and salinity. Warm water is less dense and steps up to the surface whereas colder, salt-laden water goes down (Vachon et al., 1995). Deep ocean currents cannot be detected using airborne or satellite remote sensors and are considered a limitation of remote sensing in the studies of coastal and ocean remote sensing (Tralli et al., 2005).

3. Geographical information system

Although digital elevation model (DEM) visualizations are helpful for data interpretation and analysis, a key issue remains how to provide complete DEM to the coastal zone user in a portable digital form (useable in GIS) that maximizes available data resolutions (Yin et al., 2012; Marfai and King, 2008; Mills et al., 2005). This is particularly important as recent data are usually available in the form of high spatial resolution than the previous data (NOAA and US Geological Survey) used to create the basic DEM. However, these new datasets must not be forced to be gridded down to lower resolutions just to fit in with the DEM. The vertical accuracy of the DEM varies spatially due to the wide range of temporal datasets and data collection techniques used in the acquisition of source data (Parker, 2002). Merged uniformly spaced grid cell models are useful to handle such complexity.

4. Fundamental techniques

Oceanographers and coastal experts recognize two fundamental techniques for coastal and offshore recent observations, Eulerian and Lagrangian methods. Eulerian methods calculate the rate of water flow in the ocean past a point while the Lagrangian method measures the shifting of water parcels in the ocean by tracking the location of chemical tracers or subsurface drifters (Morang and Gorman, 2005).

4.1. Eulerian technique

The Eulerian technique generally includes current meters on buoy moorings set to the ocean bottom and record currents on different depths at a particular location. Arrays of these moorings with current meters on different depths are used in marine water for a day to a month to track currents at particular locations such as harbor channel entrances and tidal inlets (Gorman et al., 1998; Morang et al., 1997a,b). The impeller current meters directed by vane are used to track ocean currents, calculating existing wind speed in the current. Thus, vane rotation rate is related to the ocean current speed. Current speed and direction calculations are stored in the memory of a computer chip (Webb, 1996; Bleck et al., 1995). A sound pulse can be used to recover the current meter that triggers