uk geos coastal erosion and accretion project · that expected eo data cannot be used to detect...

UK GEOS Coastal Erosion and Accretion Project

© Ordnance Survey 2018

UK GEOS Coastal Erosion

and Accretion Project

Final Report

Version 1.0

April 2018


© Ordnance Survey 2018 2

This report was produced by Ordnance Survey in collaboration with additional Subject Matter Experts

from Satellite Applications Catapult, Earth-I, and Environment Agency for the UK Space Agency as part of

an initiative by UK Government Earth Observation Service (UKGEOS),

Ordnance Survey wish to extend our thanks specifically to Kyle Brown and Alison Matthews (EA), Daniel

Wicks and Terri Freemantle (Satellite Application Catapult), and Tom Waddington and Xu Teo (Earth-i),

who were major contributors to this report.

Copyright Notice

This document incorporates material from a variety of third party sources. Sources of copyright material

are acknowledged within the text as appropriate.

Version History

Version Date Description Authors

1.0 26/04/2018 First Issue Mark Tabor

(

For further information on this report

Please contact: Mark Tabor

Telephone: +44 (0) 2380055488

Mobile: +44 (0) 7798870181

Email: [email protected]



1. Executive summary ..................................................................................................... 8

1.1 Summary of the project and its aims ............................................................................................. 8

1.2 Key findings ..................................................................................................................................... 9

1.3 Key recommendations ..................................................................................................................10

1.4 Glossary and abbreviations ...........................................................................................................11

2 Introduction ............................................................................................................... 15

2.1 Background ....................................................................................................................................16

2.1.1 Coastal morphological change..............................................................................................16

2.1.2 Tides .......................................................................................................................................16

2.1.3 Tidal levels ..............................................................................................................................17

2.1.4 Deriving tidal levels for coastal mapping ..............................................................................18

2.1.5 Limitations with deriving tidal levels from elevation data. .................................................20

2.1.6 Accuracy issues with mapping tidal levels directly from aerial imagery .............................21

2.1.7 Summary ................................................................................................................................23

2.1.8 Satellite Earth observations for capturing MHW/MLW .........................................................23

2.1.9 An introduction to Satellite Earth Observation (EO) ............................................................23

2.1.10 Active and Passive EOS sensing ............................................................................................25

3 Manual Capture Trial ................................................................................................. 30

3.1 Mapping tidal change at Ordnance Survey ..................................................................................30

3.2 Data Source options previously examined by Ordnance Survey ................................................32

3.3 Ordnance Survey approach to use of satellite imagery for this project ......................................33

3.4 Satellite imagery used ...................................................................................................................36

3.4.1 Options for KOMPSAT-3 and SPOT 7 imagery .......................................................................36

3.4.2 Delivered formats of satellite imagery ..................................................................................36

3.4.3 Capture Remit ........................................................................................................................37

3.4.4 Workflow ................................................................................................................................38

3.4.5 Lessons Learned.....................................................................................................................40

4 Accuracy assessment and quality assurance ........................................................... 42

4.1 Data and tides ................................................................................................................................42

4.2 Analysis areas ................................................................................................................................44

4.3 Tidal boundaries and analysis ......................................................................................................45



4.4 Slope...............................................................................................................................................56

4.5 Errors in mapping water/land boundaries ...................................................................................57

4.6 Summary ........................................................................................................................................60

5 Operationalisation and cost comparison assessment ............................................ 63

5.1 Operationalisation .........................................................................................................................63

5.2 Costs ...............................................................................................................................................64

5.3 Prohibitive Data Capture Costs .....................................................................................................64

5.4 Additional product options ...........................................................................................................65

5.4.1 NDVI Map for vegetation habitat ...........................................................................................65

5.4.2 Known areas of coastal erosion database ............................................................................65

5.4.3 Asset register ..........................................................................................................................65

5.4.4 Risk map .................................................................................................................................65

5.5 Value of vulnerable national assets ..............................................................................................66

5.5.1 English Environment Agency findings ...................................................................................66

5.5.2 Scottish Natural Heritage ......................................................................................................66

5.5.3 Natural Resources Wales .......................................................................................................67

6 Recommendations for future phases of work .......................................................... 69

6.1 Identification of the challenges of using optical satellite imagery ..............................................69

6.2 Further investigation in complex tidal changes in small geographic extents .............................69

6.3 Using satellite data to map the water/land boundary at a specified tidal level .........................70

6.4 Using elevation data to satisfy the requirements of multiple users ............................................70

6.5 Technology choices .......................................................................................................................71

6.6 Investigation into creation of NDVI and NDWI maintained data for habitat ...............................71

1 Annex A - Literature Review - Use of EO for Coastal Change Mapping .................... 73

1.1 Optical Satellite imagery for mapping MHW/MLW .......................................................................73

1.1.1 SAR imagery for mapping MHW/MLW ...................................................................................78

1.2 EOS for coastal monitoring – Current readiness ..........................................................................85

1.3 Examples of utilisation of EO for monitoring of Mean High Water and Mean Low Water ...........87

1.3.1 European Space Agency COASTCHART .................................................................................87

1.3.2 Coastline extraction using high resolution Worldview-2 satellite imagery .........................88

1.3.3 Reconstruction of time-varying tidal flat topography using optical remote sensing

imageries ................................................................................................................................89



1.3.4 Conclusions ............................................................................................................................90

1.4 EOS data processing ......................................................................................................................91

1.5 Associated EOS downstream technologies ..................................................................................91

1.5.1 Computer Vision .....................................................................................................................92

1.5.2 IT Infrastructure .....................................................................................................................92

1.5.3 Business Models .....................................................................................................................92

1.6 Current EO Technology Drivers .....................................................................................................93

1.6.1 Improved availability and variability of data sources: .........................................................93

1.6.2 Interoperability of datasets ...................................................................................................93

1.6.3 Accessibility of datasets .........................................................................................................94

1.6.4 Maturing Non-traditional EO Approaches ............................................................................94

1.7 Current EO Scientific Developments .............................................................................................95

1.7.1 Improved Application of Traditional Coastline Monitoring Methods ..................................95

1.7.2 Evolving Change Detection Approaches ...............................................................................95

1.8 Future Satellite Innovations relevant to coastal monitoring ......................................................95

1.8.1 Small Satellite Constellation .................................................................................................96

1.8.2 High Altitude Pseudo-Satellite (HAPS) ..................................................................................96

1.8.3 Real Time Data .......................................................................................................................97

1.8.4 Video from Space ...................................................................................................................97

1.8.5 Better Technical Specifications .............................................................................................98

1.8.6 On-board Processing .............................................................................................................98

1.9 References ......................................................................................................................................99

1 Annex B - Existing Ordnance Survey Capture Specification .................................. 106

1.1 Tidal Water ...................................................................................................................................106

1.2 Foreshore Depiction ....................................................................................................................106

1.3 Extent of the Realm .....................................................................................................................106

1.4 Land Surface in Intertidal Areas ..................................................................................................107

1.5 Landform: Cliff and Coastal Slopes .............................................................................................107

1.6 Coastal Slope: An area of steep natural slope along the coast..................................................108

1.7 Nautical Berthing Sites ................................................................................................................108

1.8 Slipways .......................................................................................................................................108

1.9 Spreads ........................................................................................................................................108



1.10 Boundaries Merged to Tidelines .................................................................................................108

1.11 Coastal Protection, e.g., Groynes, Sea Walls, Boulders .............................................................109

1.12 Calculating Tide Values ...............................................................................................................110

1.12.1 Existing lidar data ................................................................................................................110

1.12.2 Boulders ...............................................................................................................................110

1.12.3 Estuaries ...............................................................................................................................110

1.13 England and Wales ......................................................................................................................111

1.14 Scotland .......................................................................................................................................112



01

Executive Summary



1. Executive summary

1.1 Summary of the project and its aims

The focus of this study is to determine:

1

The extent Earth Observations (EO) data can be used, now or in the near future to update

coastal change, specifically Mean High Water Mark, and Mean Low Water Mark (MHWS and

MLWS for Scotland)

2 The high-level issues of using EO data as part of existing processes (within Ordnance Survey

and wider government

3 The financial suitability of using EO data now or in the near future

Specifically, this is explored in this project using satellite imagery (Kompsat and Spot) and SAR (N/A),

whether:

• Derive Mean High-Water Mark (MHWM) and Mean Low Water Mark (MLWM) to the appropriate

accuracy,

• Identify additional value regular EO imagery can provide;

• Identify viability of coastal "maps" provided pre-and post-winter storms for analysis of

erosion/accretion;

• Assess the processing cost points comparative to existing processes.

This report will cover the following:

• A literature review of source material - (previous reference studies / academic literature)

including review of how MHW / MLW is currently captured within Ordnance Survey)

• Methodology used for the trial

• Results from the trial including the comparison with truth data

• Operationalisation review options

• Additional product options including Habitat assessment

• Recommendations for future phases of work

• Cost comparisons of technical options

• Value of land / assets that could be affected by flood (This is a basic cost/benefit – comparative

value of ‘at risk’ assets)

• Visual depiction of results

• Conclusions



The project undertaken in a discreet, time and cost bound period utilising resource from Ordnance

Survey (OS), Earth-I, Satellite Application Catapult (SAC), and Environment Agency (EA). The report, trial,

results and recommendations were achieved within twelve weeks.

1.2 Key findings

KF1

The accuracy assessment of the capture of tidal change captured from high and medium

resolution optical satellite imagery did not meet the accuracy requirement, meaning

that expected EO data cannot be used to detect change in coastal erosion and accretion

with sufficient certainty.

Assuming that the accuracy of the KOMPSAT-3 derived tidal level for the most accurate

area could be matched every time, it is unlikely that areas of erosion and/or accretion

would be detected with confidence unless they were greater than two to three times the

maximum error (30 m – 55 m). For this technique to be operational we would have no

prior knowledge of spatial variations in error and so the confidence in an

erosion/accretion map is likely to be lower, especially in areas where the tidal range is

larger or that are relatively flat.

KF2

The time in which remotely sensed data is captured is crucial when measuring high / low

tide and can very challenging in small geographic areas which have large variations in

tide times

KF3 There is no single tide level that represents the coastline that is appropriate for all users

KF4

Some of the key benefits of using EO for this purpose which can cover large areas of

coastal change is mitigated by the availability of cloud free imagery due to prevailing

weather conditions

KF5 There is a lack of access to sufficient ground control points.

KF6 Tidal influence and air pressure can provide inaccuracies.

KF7

Deriving tidal boundaries from the water/land boundary using satellite images is totally

unsuited to estuarine areas where the tidal regime is complex and tide times can vary a

great deal over short distances.

KF8

The key to understanding if a technique will be suitable for this kind of mapping will

require further detailed analysis that accounts for the major factors that influence the

accuracy. This would provide an indication of the point at which change is likely to be

detected



1.3 Key recommendations

R1 Further investigation into the potential of SAR is required as this project was not able

to source appropriate SAR data of the appropriate time stamp

R2

A future trial which targets coincident capture of lidar, SAR, aerial imagery, Satellite

imagery, and ground truth data measured by surveyors using GPS should be

commissioned

R3 The opportunity to share data across Government entities who have responsibility for

coastal change should urgently be investigated. To include the option of cost sharing

R4

Investigate the potential of creating a national open dataset of coastal lidar data

(England, Scotland, and Wales). This national dataset would be a valuable source for

further research for the identification and prediction of change.

R5 Differencing surface models created from lidar data as a method of predicting areas of

more significant change should be investigated.

R6 Investigate the use of archived topographic data to assist in predicting likely change in

the future

R7 Investigate International case studies

R8

Further investigation in understanding how best to efficiently and accurately map

complex tidal changes (large variances in relatively small geographies) in small

geographic extents

R9

Consider using meteorological information to determine when cloud free conditions

are likely to occur with a view to maximising conducive conditions to capture larger

areas of coastal change



1.4 Glossary and abbreviations

Acronym/abbreviation Text in full

AIS Automated Identification system

ARD Analysis Ready Data. Standardised, processed EO data suitable for analysis.

ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer (Terra

sensor)

AVHRR Advanced Very High-Resolution Radiometer

AWS Amazon Web Services – cloud-based processing and storage

Catapult Satellite Applications Catapult

DEM Digital Elevation Model

DMU Digital Map Unit (Ordnance Survey)

DTM Digital Terrain Model

EDRS European Data Relay Satellite System

EM Electromagnetic

EO Earth observation

EOS Earth observation satellites

ESA European Space Agency

ETM+ Enhanced Thematic Mapper (Landsat -7 sensor)

EVI Enhanced Vegetation Index

FME Feature Manipulation Engine

fPAR Fraction of Photosynthetically Active Radiation

GCP Ground Control Point




GNSS Global Navigation Satellite System

GPS Global Positioning System

GRD Ground Range Detected Processing Level

HAPS High Altitude Pseudo Satellite

HAT Highest Astronomical Tide

IoT Internet of Things

JRC Joint Research Council

LAI Leaf area index

LAT Lowest astronomical tide

Lidar Light Detection and Ranging

MHW Mean High Water

MHWN Mean High Water Neaps

MHWS Mean high Water Springs

MLW Mean Low Water

MLWS Mean Low Water Springs

MODIS Moderate Resolution Imaging Spectroradiometer (Terra/Aqua sensor)

MSI Multi-Spectral Instrument

MSL Mean Sea Level

NASA National Aeronautics and Space Administration (USA)

NDVI Normalised Difference Vegetation Index

NIR Near-Infrared




NOAA National Oceanic and Atmospheric Administration (USA)

NOPSEMA National Offshore Petroleum Safety and Environmental Authority

OE Object Editor (Ordnance Survey)

RADAR Radio Detection and Ranging. By usage: data from radar systems.

SAR Synthetic Aperture Radar

Satcom Satellite Communication Systems

SLC Single Look Complex Processing Level

SLMS Satellite Land Monitoring System

SWIR Shortwave Infrared

TIR Thermal Infrared Sensor

UAV Unmanned Aerial Vehicle

UK United Kingdom of Great Britain and Northern Ireland

USGS United States Geological Survey

VNIR Visible Near-Infrared

VORF Vertical Offshore Reference Frames



02

Introduction



2 Introduction

As a collection of islands, Great Britain has a coastline that is approximately 31,000kms in length. Much

of the coastline is susceptible to erosion and accretion. According to British Geological Survey, climate

change forecasts predict an increase in global temperatures; over the past 25 years an increase of 0.2°C

per decade has been observed [46].

This is likely to cause global sea levels to rise yet further — they are currently rising around 3 mm per year

[46] — with an additional increase in the frequency and magnitude of storm events.

When these two factors are combined it will have the effect of focusing wave energy closer to the shore

and cliff faces, leading to increased rates of coastal erosion. [46].

Such coastal erosion events are often newsworthy events:

Erosion risk to a fifth of Scots coastline, scientists say (BBC news 4th August 2017)

http://www.bbc.co.uk/news/uk-scotland-40827093

Hemsby coastal erosion leaves cliff-top homes uninhabitable (BBC news 19th March 2018)

http://www.bbc.co.uk/news/av/uk-england-norfolk-43465731/hemsby-coastal-erosion-leaves-cliff-top-

homes-uninhabitable

Figure 1 – Image of recent erosion at Hemsby, Norfolk courtesy of BBC

http://www.bbc.co.uk/news/uk-scotland-40827093

http://www.bbc.co.uk/news/av/uk-england-norfolk-43465731/hemsby-coastal-erosion-leaves-cliff-top-homes-uninhabitable

http://www.bbc.co.uk/news/av/uk-england-norfolk-43465731/hemsby-coastal-erosion-leaves-cliff-top-homes-uninhabitable



Susceptibility to coastal erosion or accretion exposes valuable national assets. Scotland’s National

Coastal Change Assessment programme recently estimated the total value of assets, including buildings,

roads, railways, and runways within 50 metres of MHWS is more than £13 billion.

To ensure that a depiction of the coast that reflects current coastal position is captured in the most

efficient way, and is therefore readily available to key decision makers, this report evaluates current

approaches and compares them with potential new technology that could enable a more efficient

method of capturing change, and to assess if alternatives could be implemented. The examination of

medium and high-resolution satellite imagery in comparison to the traditional use of aerial imagery has

traditionally suggested that it is unsuitable for the collection of tidal data.

This report evaluates the viability of satellite imagery for this purpose and provides an accuracy

assessment of the results.

2.1 Background

2.1.1 Coastal morphological change

When measuring coastal morphological change in the UK there are the following main approaches:

• Measuring the movement of a morphological feature, such as the edge of a cliff top or the

position of a sand bar. This is a local ground-based approach that can successfully show change

for a limited area but is not suitable for a national program as it would not be scalable from a

cost or resource perspective.

• Measuring height or volume changes within the coastal zone. Traditionally this form of

mapping was carried out using ground survey at transects perpendicular to the coast from low

water. Modern programmes, as carried out by organisations such as Environment Agency,

Natural England, Scottish Natural Heritage, and Natural Resource Wales tend to use lidar (Light

Detection and Ranging) elevation data to model change over time.

• Measuring the position of a tidal level, such as Mean High Water, and monitoring its movement

to determine whether change is taking place and in what form.

2.1.2 Tides

Tides are caused by the interaction of the gravitational effects of moon and the sun and are affected by

the position and distance of both the Moon and the Sun relative to each other and to Earth. In the UK

there are on average two high waters and two low waters during a 24-hour 50-minute period.

The tidal range - the difference between high water and low water - is greatest at spring tides. Spring

tides occur just after full moons and new moons. There are two periods of spring tides every 29.5 days, a

lunar month. Neap tides occur just after half-moon and have a relatively small tidal range.



Again, there are two sets of neap tides a lunar month. The tidal range in the UK varies hugely, with the

range at Avonmouth in the Bristol Channel regularly over 14 m, while Port Ellen, Islay on the west coast

of Scotland generally has a tidal range less than 1 m (http://www.ntslf.org/tides/hilo) .

2.1.3 Tidal levels

Within the tidal cycle there are various tidal levels that are of interest to organisations in the UK and can

be used as a proxy for the coastline in erosion/accretion studies.

• Highest Astronomical Tide

• Mean High Water Springs

• Mean High Water

• Mean High Water Neaps

• Mean Sea Level

• Mean Low Water Neaps

• Mean Low Water

• Mean Low Water Springs

• Lowest Astronomical Tide

• Chart Datum

Figure 2 - Different tidal levels on coastline

http://www.ntslf.org/tides/hilo



Highest and Lowest Astronomical Tides (HAT and LAT) are the highest and lowest tidal heights that are

probable under average meteorological conditions and under any combination of astronomical

conditions. Tidal levels can be outside this range due to conditions such as extreme atmospheric

pressure variations or due to a storm surge that pushes water towards or away from a particular

coastline. Values used in the UK have been derived from 19 years’ worth of predictions for tidal gauges

(http://www.ntslf.org/tgi/definitions accessed 18/01/2018)

Mean High Water Springs (MHWS) is the average spring tide high water and is measured throughout the

year from the two highest successive tides when the tidal range is largest. Mean Low Water Springs

(MLWS) is the average height of the two lowest successive tides when the tidal range is largest.

Mean High Water (MHW) is the average height of all high tides. Mean Low Water (MLW) is the average

height of all low tides. Mean High Water Neaps (MHWN) is the average neap tide high water and is

measured from the two high water levels during neap tides. Mean Low Water Neaps (MLWN) is the

average height of the two lowest successive tides when the tidal range is largest.

Mean Sea Level is the average height of the sea.

Chart Datum is the datum used on navigational charts. In the UK it approximates LAT, though there are

small variations around the coast.

UK organisations use various tidal levels for different purposes. For example, MHW and MLW are used by

Ordnance Survey for mapping in England and Wales, MHWS is used in Scotland (In Scotland, Ordnance

Survey data represents a high and low water mark for the average spring tide). The Environment Agency

use the following tidal levels:

• Estuary and intertidal squeeze prediction (HAT, MSL, MLWN)

• Flood asset vulnerability assessments (HAT, MHWS)

• Saltmarsh zonation mapping (MHWS)

• Intertidal habitat mapping (HAT)

2.1.4 Deriving tidal levels for coastal mapping

In the UK both Ordnance Survey and Environment Agency use elevation data derived from airborne lidar

to derive to derive tidal levels as described in Section 3. To determine the height of the relevant tidal levels

it is possible to use UK Hydrographic Office tidal level data converted to Ordnance Datum Newlyn. There

is also the Vertical Offshore Reference Frames (VORF) project run by University College London and the UK

Hydrographic Office which has provided surfaces representing the spatial variation in vertical height of the

following tidal references:

• Highest astronomical tide

• Lowest astronomical tide

• Mean high water springs

• Mean low water springs

• Mean Sea Level

http://www.ntslf.org/tgi/definitions



VORF data can be combined directly with lidar DEM data to provide tide level lines (Figure 3). Other tidal

levels can be interpolated from the VORF data.

Figure 3 - Tidal levels overlaid on lidar used to derive them



2.1.5 Limitations with deriving tidal levels from elevation data.

The accuracy of tidal levels derived using elevation data will be a function of the vertical and horizontal

accuracy of the Digital Elevation Model (DEM) and the topography. For this reason, accuracy of the DEM is

a critical component in providing accurate tidal level data and in turn coastal change information. The

positional (horizontal) accuracy of the derived tidal level as a function of the vertical accuracy of the DEM

and the slope is shown in Figure 4.

Figure 4 - Impact of vertical errors and vertical differences on horizontal error as a function of slope.

For example, if the DEM error is 0.2 m in an area where the slope is 6 degrees, the horizontal error of the

derived tide level will be 2 m. This graph is also relevant for mapping tidal levels directly from imagery. In

the example above the imagery would be acquired when the actual tidal height was 0.2 m from the

expected level (for example MLW) the horizontal error will be 2 m in an area with a slope of 6 degrees.



2.1.6 Accuracy issues with mapping tidal levels directly from aerial imagery

There are a variety of variables that will impact on the accuracy of tidal levels derived directly from

imagery. Tide times can vary a great deal over small distances.

An example of this is shown in Figure 5. Particularly in areas where the tidal range is large this can mean

that the height difference from the expected tidal level can be large. This will have an impact on the

horizontal accuracy of the derived tidal levels (Figure 4) which has a significant impact on the height

difference that data are acquired. For a 5m tidal range, acquiring satellite or aerial imagery with a 30

minutes difference from high or low water will result in approximately 0.1 m variation from the

predicted. A 60-minute delay will result in approximately a 0.5 m variation.

Figure 5 - Spring high water tide times for Swanage to Portsmouth on 9th January 2018. Times in UT. High water times

vary between 0927 UT (Swanage) and 1250 UT (Portsmouth). Derived from the UKHO Admiralty EasyTide tidal

predication service.: http://www.ukho.gov.uk/Easytide/easytide/

In addition, sea level is affected by air pressure, wind and storm surges

(http://noc.ac.uk/files/documents/business/Tides-and-Meteorological-Effects.pdf). A 1mb pressure

difference can mean approximately 1 cm height difference. Generally, the height difference caused by

atmospheric variations is less than 0.3 m.

http://www.ukho.gov.uk/Easytide/easytide/

http://noc.ac.uk/files/documents/business/Tides-and-Meteorological-Effects.pdf



Wind increases sea height on coasts where the wind is blowing towards the coast, this has the opposite

effect on coasts where the wind is blowing away from the coastline. A strong westerly wind will tend to

increase sea height on west coast of Scotland but will reduce sea height on the East Anglian coast. Storm

surges caused by atmospheric pressure differences and wind can occur several times a year with

between 0.6 m and 0.9 m height variation not infrequent (see Figure 6). These sea height variations can

have an impact on the horizontal accuracy of the derived sea level.

Figure 6 - Storm surge predictions versus observed 23rd – 25th January 2018 for Aberdeen and Liverpool during strong

wind conditions (http://www.ntslf.org/storm-surges/latest-surge-forecast).

http://www.ntslf.org/storm-surges/latest-surge-forecast?port=Liverpool&chrt=3



2.1.7 Summary

From a UK perspective there is no single tidal level that represents the coastline. In England and

Wales, the OS use MHW and MLW as tidal levels represented on mapping. In Scotland MHWS and MLWS

are used by the OS. The EA use HAT and MHWS as terrestrial/intertidal boundaries in different

applications.

Morphological changes in the coastal zone can manifest themselves differently at different tidal levels.

For example, accretion high up the intertidal zone may be a result of sediment eroding in the lower

intertidal zone.

Accretion at the MLWS can be caused by landslips at or above the HAT, increasing sediment input to the

lower intertidal zone. For this reason, ideally, there will be multiple representations of the coastline for

change mapping accounting for changes in the terrestrial, intertidal and benthic zones. Ideally all the

tidal levels would be derived, though the lower the position in the tidal cycle the less likely it can be

derived using the elevation model approach, as it will be more likely to be covered in water reducing

data acquisition opportunities.

2.1.8 Satellite Earth observations for capturing MHW/MLW

Effective coastal management relies on an integrated monitoring campaign. Satellite observations can

provide valuable data from local to global scales and represent an important counterpart to existing in-

situ and remote observing systems such as tide gauges and aerial photography.

The availability of multispectral and multitemporal images from satellite, as well as advances in digital

processing and analysis have enabled research to be conducted on the spatial and temporal evolution,

and sensitivity of changes in coastal environments due to both natural and anthropogenic events [29].

This section introduces satellite Earth observation technology and an overview of the suitability of

satellite data for measuring and monitoring Mean High Water (MHW) and Mean Low Water (MLW) as

complimentary technology to existing methods of capture. For the purposes of this report MHW and MLW

will be abbreviated as ‘shoreline’ or ‘coastline’ as both have been used interchangeably in the supporting

literature.

2.1.9 An introduction to Satellite Earth Observation (EO)

Satellite EO is a form of remote sensing focused on obtaining information about the Earth’s surface and

atmosphere from platforms flying at altitudes up to 36,000 km in space. The derived information does

not originate from a single satellite mission, but a range of satellites with different instrumentation and

mission objectives. The target application determines the choice of EOS mission and instrumentation.

EOS have been in orbit since the early 1970’s, with the launch of the Landsat mission in 1972. Currently,

state of the art spatial resolution – the level at which surface details may be depicted - is as little as 0.31



metres (Digitalglobe Worldview 3 and 4 satellites), obtained by very high resolution (VHR) commercial

satellites.

Comparatively, publicly owned passive satellites provide Open Data available imagery down to 10-metre

spatial resolution. Higher spatial resolution is typically associated with smaller area coverage and on-

demand data acquisition (i.e. typically data is not systematically collected in a background mission).

Figure 7 highlights the trend of increasing spatial resolution attainable over the last 30 years.

There are a wide variety of both commercial and open source satellite data available for different

spatial/temporal resolution and associated costs. A summary of the most relevant and commonly used

optical/radar satellites and their characteristics is provided within Annex A, whilst Figure 7 provides a

comparison of spatial versus temporal resolution for both commercial and open source optical EOS

sensors.

Another benefit of utilising satellite imagery is the Increased temporal resolution (capture frequency).

More frequent data capture provides more opportunities to update the shoreline. The increased

frequency of capture also increases the chance of obtaining a cloud free image, which is a challenge in

Great Britain.

The Increasing number of earth observation satellites also enables a greater variety of data capture

times. This will significantly increase the likelihood of capturing a cloud free image at suitable tide state

(MLW) for coastal update.

Figure 7 - Trends of increasing spatial resolution of EOS



Figure 8 - Spatial resolution versus temporal resolution for both commercial and open source optical EOS sensors

2.1.10 Active and Passive EOS sensing

All EOS sensors use measurements of electromagnetic (EM) radiation to understand the target of

interest, including crops, habitats or man-made structures. These sensors can be classified into two

categories based upon the source of EM radiation: passive and active (Figure 9).

Passive EOS sensors, such as optical instruments, use the light emitted from the sun as the source of EM

radiation and can take measurements at various wavelengths within the visible and infrared regions of

the electromagnetic spectrum to create multi- or hyperspectral datasets.

All Earth surface materials have characteristic spectral responses resulting from their distinct interaction

with certain portions of the EM spectrum. These unique spectral interactions allow an automated

classification and therefore, an efficient identification, and extraction of features of interest. Table 1 and

Table 2 provides an overview of the characteristics and considerations to be aware of related to active

and passive EOS options currently available.

Active EOS sensors, such as radar and lidar, emit their own EM radiation which is then reflected, or

scattered back to the sensor (Figure 10). The most common type of active EOS sensor is Radar,

specifically Synthetic Aperture Radar (SAR).



Unlike optical imagery, radar systems rely on microwaves, which are not sensitive to colour. Instead,

microwave-target interaction is dependent on the geometric and dielectric properties of the surface, as

well as the parameters of the radar system such as wavelength, polarisation, incidence angle and look

direction.

Acquisitions made by SAR satellites are generally unaffected by cloud cover, obtaining useable imagery

regardless of weather conditions and time of day. Characteristics of the radiation returning to the SAR

sensor enables, for example, the detection of Earth’s surface changes, estimation of biomass, and very

precise (sub-millimetre) ground subsidence and uplift in the satellite’s line of sight.

Figure 9 - Fundamental differences between passive (Left) and active sensors (Right).

Figure 10 - Radar signal transmission, reflection and backscatter



Sensor Spatial Resolution Spectral Range Temporal Revisit Other considerations

Passive

(Commercial)

Between 0.31 (Worldview-4)

and 6 metres (Planet).

Higher spatial resolution is

typically associated with

smaller area coverage per

image and increased costs.

Typically, 4-5 spectral bands across

the VNIR part of the EM spectrum

(450-900).

Worldview-3 offers 16 bands

including bands in the SWIR

spectrum (1195-2636nm).

Larger satellites will generally have

better sensors and therefore

increased spectral data quality –

signal to noise ratio.

No commercial EOS sensors offering

TIR capabilities.


typically associated with on

demand data acquisition of a

site rather than routine

acquisition.

Lower resolution data (<5m)

acquired through CubeSats

(e.g. Planet Scope) are now

offering routine daily global

revisit.

Imagery provided by commercial vendors

will often require calibration to remove

radiometric, topographic, geometric or

atmospheric distortions. This is starting to

change with the emergence of ‘Analysis

Ready Data’ products which account for

these corrections.

Image providers are now offering value

added solutions to their data through data

platforms enabling users to build, access

and run advanced workflows and tools that

extract actionable information from cloud-

based satellite image libraries.

Passive

(Open Data)

Between 10 (Sentinel 2) and

15-30 metres (Landsat 8) in

the VNIR, and 30-100 metres

in the SWIR and TIR.

Open Data sources have a

much larger image footprint

-up to 290km in width.

These satellites typically carry a

single multi-spectral instrument (MSI)

with 8-13 spectral channels in the

VNIR and SWIR part of the EM

spectrum.

Datasets are very uniform and of a

very high calibrated quality which

increases the ability to make

consistent and repeatable

measurements.

These satellites cannot be

tasked for acquisition and are

designed for global and

consistent repeat coverage.

Revisits are made between 10

(Sentinel-2) and 28 days

(Landat-8).

Open data sources enable data acquisition

over the entire globe without requiring the

permission of a government or a provider.

These sensors are designed within a

context of scientific research and are

therefore, highly stable and well calibrated

instruments.

Table 1 - Characteristics related to currently available commercial and open source passive EOS



Sensor Spatial Resolution Spectral Range Temporal Revisit Other considerations

Active

(Commercial)

Varies with imaging mode

o Staring Spotlight

(0.25m)

o Spotlight (1m)

o Stripmap (3-5m)

o ScanSAR (16m, 30m,

100m)


typically associated with smaller

area coverage/swath width per

image and increased costs.

X-band (8.0 – 12.0 GHz)

C-band (4.0 – 8.0 GHz)

L-band (1.0 – 2.0 GHz)

S-band (2.0 – 4.0 GHz)

Longer wavelengths are

associated with deeper canopy

penetration, for example, L-

band will scatter off a tree trunk

and branches, however shorter

X-band wavelengths will result

in top of canopy scattering,

useful for measuring top of

canopy leaf area.

Whilst they operate

background missions defined

by their operator, commercial

SAR satellites typically need to

be tasked to acquire new data.

Revisit varies by satellite, from

daily revisit (CosmoSkymed),

11 days (TerraSAR-X) to 24

days (Radarsat-2).

Advancements in technology in recent

years have seen a dramatic increase in the

number of operational commercial SAR

satellites.

The UK will soon be launching NovaSAR,

the first S-band small SAR satellite.

UrtheCast have announced plans to create

a constellation of 8 optical, and 8 SAR

satellites, due for launch from 2019

providing optical and SAR data in the same

acquisition.

Whilst VHR Commercial SAR data can be

expensive, operators often offer bulk

discounts and R&D pricing rates.

Active

(Open Data)

5 m by 20 m (10 m GRD) for

Sentinel-1 operating in

Interferometric Wide Swath (IW)

mode. It also operates Extra Wide

swath (EW) mode (20m by 40m),

Stripmap (SM) mode (5m by 5m)

and Wave Mode (20 km by 20km).

Sentinel-1A and 1B have C-Band

SAR (5.405 GHz) payloads,

supporting operation in dual

polarisations (HH+HV, VV+VH).

Dual polarisation data is useful

for land cover classification and

sea-ice applications.

Sentinel-1 (S1) cannot be

tasked for acquisition, its

background mission is

determined by ESA, where its

primary mission is data

acquisition over Europe and

tectonically active regions.

S1 revisit over Europe is 6 days

and typically 12 days for the

rest of the world (between

Sentinel-1A and Sentinel-1B).

Historic SAR data is also Open data from

Envisat, ERS-1, ERS-2 (C-band) and ALOS

PALSAR-1 (L-band). However, these

satellites are no longer operational. Data is

available for these missions between 1991

and 2011.

Table 2 - Characteristics related to currently available commercial and open source active EOS



03

Manual capture trial



3 Manual Capture Trial

3.1 Mapping tidal change at Ordnance Survey

Ordnance Survey has a remit to ensure that all tidal (MHW, MLW) and foreshore updates are updated

within 12 months of the identification of the known change. Such changes are identified by several

sources, including changes identified by Ordnance Survey photogrammetric operators who identify

change from aerial imagery, emails from customers, and information gained from Environment Agency.

Annex. B describes the current Ordnance Survey capture specification in more detail.

In England and Wales Ordnance Survey data represents a high and low water mark for the average tide.

The average tide is defined as that which occurs mid-way between the Spring and Neap tides.

In Scotland Ordnance Survey data represents a high and low water mark for the average spring tide.

A further mark indicated within Ordnance Survey’s Geobase-04 data is the Normal Tidal Limit. This is an

indication of the point along tidal rivers where mean tides (England and Wales) or spring tides (Scotland)

affect the level of the water in the river.

The accuracy of OS coastal data is important to a number of key users of this information who are listed

in the table below:

Customer Use for Data Example Consequences of Data Inaccuracy

Scottish National

Coastal Change

Assessment

(NCCA)

Building a national ‘coastal

erosion susceptibility model’.

NCCA cannot develop a national picture of

susceptibility and resilience – government money

wasted, and reputation damaged amongst

stakeholders (Scottish National and Local

Government, SNH, SEPA, universities and interest

groups).

Land Registry (and

its customers)

MHW and MLW used to define

foreshore.

Viewing change in time of the

foreshore is important in

current registration and

dispute resolution (requires

regular coastal update).

Large landowners are increasingly registering

foreshore, where patently out-of-date tide lines

will require case-by-case updates such as that

which occurred in the Wash in 2011.

Local Councils

Assessment of beach area for

maintenance (e.g. refuse

collection in summer).

Inaccurate mapping of beach area leads to errors

in budget and management plans.



Planning coastal defences. Mistakes made in coastal defence plans or costly

additional surveys required.

OS Leisure Users

Ensuring that a planned

activity will not be affected by

tides.

Inconvenience, evacuation or death.

Environmental

Monitoring (e.g.

EA, JNCC, SEPA,

NRW)

Monitoring of habitat loss /

creation. Flood risk and

coastal management.

Incorrect analyses due to out-of-date data could

have high profile consequences during a period of

flooding.

Rural Payments

Agency

Mean high water used as a

parcel (field) boundary.

If erosion / landslip reduced the possible payment

but was not mapped by OS quickly enough, EU

Auditors would take a dim view.

Deposition may increase field size – farmers

would be unhappy if they couldn’t claim on it.

The Crown Estate

The Crown Estate owns

around half of the foreshore

and almost all the UK’s

seabed. OS mapping

therefore identifies

boundaries of these assets.

The Crown Estate are increasingly registering title

to land for offshore renewables, inaccurate tide

lines would become apparent during planning

and require resurvey.

Estate

Management (e.g.

Church

Commissioners,

National Trust)

Budgeting and asset

management (as for the

Crown Estate).

Financial losses and need for ad hoc resurvey.

Water Companies

(e.g. South West

Water)

Managing bathing beaches. Inaccurate mapping of beach area leads to errors

in budget and management plans.

Maritime and

Coastguard

Agency

Coordinating search and

rescue. Difficulties in search and rescue planning.



3.2 Data Source options previously examined by Ordnance Survey

Current accuracy requirements state that a tide line captured by contouring should be measured to an

accuracy of 0.3m – i.e. data sources should be able to offer a minimum accuracy in the height of 0.3m.

The following table provides a list of current options that Ordnance Survey have considered. The

potential of EO imagery has also been considered but is described in more detail as part of this trial.

England Wales Scotland

3rd Party Open Source lidar

Data

Ortho rectified imagery

and lidar derived DSMs

and DTM of the coastal

fringes and foreshore

available under an Open

Government License.

Having run trials on 50km of

coast, the Wales Coastal

Monitoring Centre are trying to

secure funding from their new

Minister to produce

bathymetric lidar of ⅓ of the

Welsh coast every year

(resulting in a total revision

cycle for the entire Welsh coast

every 3 years). This would be

released as open data.

lidar Data for part of the

coast exists, some will be

available under a

commercial data license,

some will be open data.

UKHO

The UK Hydrographic Office has a remit to chart the position of tide lines, albeit to a

different specification to that of OS. They therefore hold data (e.g. from bathymetry

studies) which could be useful to OS to create tide lines. A visit in June confirmed that

they were open to the possibility of sharing data – they will visit OS in October to

continue discussions.

OS DSM

By reprocessing current imagery, a DSM could be produced to fit with the accuracy

requirements needed to produce tide lines. This would be suitable for generating high

tide lines (unless captured at a spring high tide) but generally unsuitable for low tide

generation as the mean low water line will generally be covered by water.

UAVs

Visible line of sight – roughly 250 m – is required by current legislation, meaning that

realistically only a 0.5 – 1 km stretch could be captured while the tide is at an

appropriate level.

This means conventional UAVs may potentially be suitable for imagery capture in

small, isolated areas where rapid coastal change has occurred, but are currently

unsuitable for operation over large stretches of coastline.

Ultralight

Aircraft

Ultralight aircraft (e.g. www.e-goaeroplanes.com) have a greater range and speed than

UAV’s yet remain highly deployable. They would therefore be very suitable for tidal

update work and merit further research.



Flying Programme

To capture MLW(S) as part of the flying programme, the tide must be lower than the

level of the tide line which is being captured. To allow for variation in sea level due to

atmospheric pressure variation (especially considering that cloud-free flying

conditions are typically accompanied by a high-pressure weather system), it is sensible

to fly only when the predicted tide is 0.3m below the level of MLW(S) (this is roughly the

sea level change resulting from a 34 millibar pressure difference). The following

examples illustrate the difficulties this poses.

Plymouth (Devonport): MLW is 1.5m above chart datum. Between April and

September 2013, 46% of days contained a low tide below 1.2m during daylight hours

(over two hours after sunrise / before sunset).

Holyhead: A slightly different tide cycle means that only 28% of days were suitable

between April and September 2013.

Lowestoft: Because the tidal range on the east coast is typically smaller than the west,

allowing for a 0.3m variation in tide level is impractical. Even by looking for days with

low tides 0.15m below MLW, only 25% of days between April and September 2013 were

suitable.

Aberdeen: In Scotland, as MLWS must be mapped, it is even harder to find suitable

days for flying. Reducing the tolerance further to 0.1m below MLWS still only results in

11% of days between April and September 2013 being suitable for flying. Factoring in

poor weather, and the fact that many of the suitable low tides in that period are very

early in the day reduces further the chance of a suitable day’s flying.

It is clear, therefore, that adding tidal targets to the flying programme is impractical for

Scotland, and probably for the east coast. If spare capacity were available in the flying

programme, it could be considered for Wales where lidar data is currently unavailable.

Surveyor

Where safe and practical – i.e. not rocks or extensive soft mud flats, but solid sand

(conveniently coinciding, in general, with areas of higher customer interest) – a

surveyor could capture the high and low tide lines by capturing a series of points at the

height of the tide line being determined. This could be useful to fill small but important

gaps in the main information source.

Contract

Out

Ordnance Survey have recently contracted out the flying of coastal targets using lidar

and imagery. This could provide additional resource, particularly when inclement

weather conditions prevent flying areas of known coastal change.

3.3 Ordnance Survey approach to use of satellite imagery for this project

The remit of this task was to capture MLW change using medium resolution and high-resolution satellite

imagery. The challenge of the project team, which included representatives from Ordnance Survey,

Earth-I, Environment Agency, and Satellite Application Catapult, was to identify an area of coastal

geography that had the following data captured at as close as possible to the time at MLW:

• Lidar (truth data)



• Ordnance Survey Aerial Imagery

• Medium Resolution Satellite Imagery

• High Resolution Satellite imagery

• Synthetic Aperture Radar

Due to the contained nature of this study it proved impossible to obtain all datasets for one geographic

extent, however Bournemouth and Poole Harbour was eventually chosen as it contained all appropriate

data with the exception of SAR and provided a variety of geography including sandy beaches containing

groynes, a large natural harbour, a small island within the harbour, and cliffs along the western extent of

the area.

For this study area, there was no stereo data available in the Pleaides or Digitalglobe Worldview archives

and only half of the area of interest was covered by GeoEye in 2013.

Therefore, the most appropriate satellite imagery available with the same time epoch as the other

datasets was KOMPSAT-3 high resolution (- 0.7 m for Pan band at nadir, - 2.8 m for MS bands at nadir),

and Spot 7 medium resolution satellite imagery (Panchromatic - 1.5m, Multispectral - 6.0m (B,G,R, NIR))

Ordnance Survey Very High resolution (0.15m) stereo, colour imagery was also used as part of the

comparison. (See figure 11 for coverage)



Figure 11 - Shows availability and coverage of Ordnance Survey existing aerial imagery in Bournemouth area flown on

29th May 2016



3.4 Satellite imagery used

3.4.1 Options for KOMPSAT-3 and SPOT 7 imagery

Satellite

Imagery Product Type

Product Levels

(which apply to either product type)

Options for

KOMPSAT-3

imagery from SI

Imaging Services

• Bundle – Panchromatic and

Multispectral channels co-registered and delivered as separate files.

• 4-band Pansharpened

• 1R – Image has been radiometrically

corrected.

• 1G – Same as 1R plus, basic geometric correction. Image is projected using a coarse DEM and no GCPs.

• Earth-i can offer higher level

orthorectification using reference data

supplied by OS or EA.

Options for SPOT 7 from Airbus

• Bundle – as above. 8/16 bit GeoTIFF

• 4-band Pansharpened

• Primary – Basic radiometric corrections applied.

• Projected – Data is projected to a

coordinate system of choice using a

coarse DEM and without GCPs.

• Ortho – Orthorectified by Airbus using

the ‘best available’ DEM.

http://www.si-imaging.com/ and http://www.airbus.com/space/earth-observation.html

3.4.2 Delivered formats of satellite imagery

The KOMPSAT-3 data was delivered as a 16-bit GeoTIFF, but the sensor captures the data as 14 bit.

Similarly, for SPOT 7, the data was delivered as an 8/16-bit GeoTIFF with the sensor capturing the data as

12 bit. (See Figure 12 for extent of Spot 7 imagery)

For the purposes of this study, the highest possible processing levels were required. The datasets are

considered to be analysis ready at this processing level but that does depend on the positional accuracy

requirements of the end user. Airbus quote that they will use the ‘best available’ DEM for orthorectification,

but that is unlikely to be of a higher resolution than the lidar data in this project.

The KOMPSAT-3 imagery was orthorectified using the EA lidar data and aerial data provided by OS. The

same reference data was used for the SPOT 7 data.

http://www.si-imaging.com/

http://www.airbus.com/space/earth-observation.html



Figure 12 - Extent of Spot 7 imagery

3.4.3 Capture Remit

The capture comparisons undertaken by Ordnance Survey fulfilled the following requirements:

• Manual and automated capture of coastal change from VHR (Kompsat-3) satellite imagery (MHW

– MLW)

• Manual and automated capture of coastal change from MHR Spot 7) satellite imagery (MHW –

MLW)



3.4.4 Workflow

Ordnance Survey have used Microsoft SharePoint for sharing documentation across the project team for

this project however the movement of satellite imagery between project teams was more problematic,

due inevitably to the large file sizes. The download of the data took over eight hours using an alternative

cloud-based data share called Sync (https://www.sync.com).

The satellite data was sent in WGS84. Transforming to OSGB36 was performed by FME. To load the

images into Ordnance Survey editing systems mosaics were created from the tiff images.

Lidar data received needed converting into a Tiff format, so that it could be imported into Ordnance

Survey’s editing system.

Average tidal jobs at Ordnance Survey tend to be 3km in length as this makes the project manageable

from a data volume (Ordnance Survey Topographic data) perspective so with this task there was a need

to cut it down into small, manageable blocks. A total of 9 jobs were created.

https://www.sync.com/



Figure 13 - Analysis areas for study.

Poole lidar was the better quality lidar but didn’t cover the whole extent of the job so a secondary lidar

needed to be used on several occasions.

Discussions with EA about best way to supply edits meant that each job file would be too large (as it

contains all our Topographic data). MLW marks were therefore converted to shapefiles and delivered to

EA for accuracy assessment.



3.4.5 Lessons Learned

As Ordnance Survey Operators are used to capturing from high resolution 15cm processed colour

balanced aerial imagery, fully utilising the multi-colour bands on the satellite imagery can make a huge

difference when manually plotting a line. Ordnance Survey initially used band 1 of the available spectral

bands within the satellite imagery, however when switching to using all 4 bands, the difference between

the lines plotted was up to 200m. For this reason, some of the capture was redone using the re-processed

satellite imagery.

This exercise proved the theory that it is particularly difficult to capture tidal change using 0.7m- 2.8m

satellite imagery (particularly in gently sloping beach extents) to within the prescribed 0.3m height

accuracy. The satellite imagery could be used to identify where significant changes had occurred to the

tidal landscape, but it did prove a challenge to accurately capture that change.



04

Accuracy Assessment of

Results



4 Accuracy assessment and quality assurance

To test how accurately the satellite derived water/land boundaries matched the tidal lines, lidar data

acquired on the 11th February 2016 was used. MLWN tidal lines were derived from the lidar data using

VORF tidal height information modified to account for the difference with MLWS.

Data were tested by determining the distance between the tidal lines every 10 m. Slope information was

derived from the lidar data to assess whether there was a relationship between slope and error.

4.1 Data and tides

KOMPSAT-3 data was acquired on the 6th January 2016 at 1310 UT. Close to Bournemouth and at the

entrance to Poole Harbour this was within 10 minutes of Low Water (Table 3; Figure 14). Within Poole

Harbour this was within 30 minutes of LW, apart from at Pottery Pier where LW was at 1430. The tide

height of the LW at Poole Entrance was 1.2 m above Chart Datum, which is Mean Low Water Neaps (Table

4).

SPOT-7 data was acquired on 17th March 1106 UT. Close to Bournemouth and at the entrance to Poole

Harbour this was within 20 minutes of LW (Table 3). Within Poole Harbour this is within 45 minutes of LW.

The tide height of the LW at Poole Entrance was 1.1 m above Chart Datum, which is within 0.1 of Mean

Low Water Neaps (Table 4).

Times 6th January 2016 (UT) Times 17th March 2016 (UT)

Satellite acquisition 1310 (KOMPSAT-3) 1106 (SPOT-7)

Swanage LW 1246 1053

Bournemouth LW 1302 1048

Poole (entrance) LW 1307 1101

Poole Harbour LW 1330 1119

Cleavel Point 1336 1132

Pottery Pier LW 1430 1149

Table 3: Times of satellite data acquisition and Low Water at sites in study area on dates of satellite acquisition. Positions of

reference tidal points are in Figure 14.



Figure 14 - Positions of tidal reference points.

Tides on 6 Jan 2016

Tides on 17 Mar 2016

Mean Neap Tide

Mean Spring Tide

Mean Tide

Low water height (m above CD)

1.2 1.1 1.2 0.6 0.9

Range (m) 0.7 0.7 0.5 1.6 1.05

Table 4: Low water heights and range at Poole Harbour Entrance



The Swanage tide gauge data was examined on the dates and times of the satellite data acquisition to

assess the difference between the predicted and measured tide levels.

On 6th January 2016 the measured tide level at low water was approximately 0.15 m higher than

predicted (Figure 15). On 17th March 2016 the measured tide level at low water was approximately 0.15 m

lower than predicted (Figure 15).

This variation from the predicted value means that the Low Water tidal height difference between the

dates of the satellite acquisition was 0.4 m, rather than predicted 0.1 m. This highlights the difficulties in

acquiring data at a specific tidal level, as the impacts of wind, atmospheric pressure and storm surges

can influence tide height increasing the uncertainty associated with derived tidal levels.

a) 6th January 2016: KOMPSAT-3 satellite acquisition b) 17th March 2016: SPOT-7 satellite

acquisition

Figure 15 - Actual and predicted tide levels at Swanage around LW on dates of satellite data acquisition. Actual tidal

information from Swanage Pier Tide Gauge.

4.2 Analysis areas

The analysis was carried out in nine areas (Figure 16). Though the water/land boundary was derived from

satellite data for other areas, some of these did not have lidar acquired at a similar time to the satellite

data, and in others there were issues with the methodology (as described in the data capture section of

this report) used to extract the boundaries from the satellite data.

Five of the areas (Areas 1 – 5) were outside Poole Harbour and may be considered Coastal areas. There

were four areas inside Poole Harbour (Areas 5 – 9), which may be considered Estuarine. Of the areas,

Area 8 had the greatest distance (up to 1.5 km) between the MHW and MLW marks. Area 3 had the

shortest horizontal distance between MHW and MLW marks, in some cases these lines were overlapped.



Figure 16 - Analysis areas for study.

4.3 Tidal boundaries and analysis

The water/land boundaries derived from satellite data are provided in Figure 17 to Figure 21. Based on

the tidal information at the times and on the dates when the satellite data were acquired it would be

expected that the water/land boundary derived would match the Mean Low Water Spring boundary.



To test the accuracy of the satellite derived boundaries lidar data acquired on the 11th February 2016 was

used to derive MLWN tidal contours. The heights of the tide levels were derived using VORF Ordnance

Datum Newlyn MLWS data and adjusted to MLWN.

There were 71 days between the satellite data acquisitions. lidar data used to derive the baseline MLWN

were acquired 36 and 35 days from the KOMPSAT-3 and SPOT-7 data respectively. There would have

been changes in the position of the tidal boundaries in time between each if the datasets being acquired,

though the magnitude of those changes is unknown. It is likely that the Coastal areas would have

experienced the greatest change due to wave action. Large changes are less likely in areas away from the

strong current experienced in the main channels in Poole Harbour.

Points every 10 m along the satellite derived water/land boundaries were analysed and compared to the

lidar derived tidal boundaries. The error at each of the points derived from the water/land boundary is

displayed in Figure 22 and Figure 23. The error statistics were derived individually for each of the areas in

Figure 16 and as an overall figure (table 5). The two main statistics derived were the Root Mean Square

Error (RMSE) which indicates the overall accuracy. Approximately 66% of errors will be within one RMSE.

Approximately 99% of errors will be within three times the RMSE. The maximum error was also used to

provide an indication of large errors in the boundaries that are not indicated by the RMSE, as it is

possible to have a low RMSE (i.e. low overall error), but a limited number of extremely inaccurate areas.

The overall results indicate that the SPOT-7 derived water/land boundary was inaccurate at deriving the

MLWN with an overall RMSE of 59.9 m (Table 5Table ). When the SPOT-7 water/land boundary results for

the individual areas are examined the RMSE varied between 9.7 m and 115.4 m and the maximum error

varied between 20.7 m and 297.0 m. There were 4 areas where the SPOT-7 RMSE was greater than 25 m,

two Coastal and two Estuarine. The largest RMSE and maximum error between the water/land boundary

from SPOT-7 and the MLWN occurred in Area 8, where the SPOT-7 water/land boundary was hugely

seaward of the lidar derived MLWN (Figure 21)

The KOMPOSAT derived water/land boundary much more accurately mapped the MLWN than the SPOT-

7 data, with an overall RMSE of 13.8 (Table 5). When the KOMPSAT-3 water/land boundary results for the

individual areas are examined the RMSE varied between 3.6 m and 25.1 m and the maximum error varied

between 10.8 m and 87.7 m. There was 1 area where the SPOT-7 RMSE was greater than 25 m, Area 4, a

Coastal area. There were two areas where large maximum errors occurred, Area 4 (83.4 m) and Area 8

(87.7 m).

As the KOMPSAT-3 and SPOT-7 data were acquired at very similar tidal levels it would be expected that

the derived water/land boundaries would match. However, Table 6 shows that the difference in

positioning was large (greater than 30 m) in four of the nine areas and Figure 24 shows spatial variation

in those errors. The results indicate that the approach is not consistent or that there were substantial

changes in the MLWN mark between the acquisitions.



While it is possible that there were environmental changes in the time between data acquisition it would

be expected that the more protected areas in Poole harbour would not vary greatly over two months.

Areas

1 2 3 4 5 6 7 8 9 Overall

RMSE (m) SPOT MLWN 13.6 11.5 10.6 28.9 30.3 10.7 9.7 115.4 32.9 59.9

KOMPSAT MLWN 8.1 8.6 3.6 25.1 9.7 16.2 7.3 15.5 7.8 13.8

Max. error (m) SPOT MLWN 20.7 30.4 28.0 64.1 42.7 49.3 32.0 297.0 67.9 297.0

KOMPSAT MLWN 18.2 16.5 10.8 83.4 37.6 48.6 12.0 87.7 21.1 87.7

Table 5 - Error table showing RMSE and Maximum error between satellite derived tidal boundaries and lidar derived

MLWN. Areas in Figure 16.

Areas

1 2 3 4 5 6 7 8 9 Overall

RMSE (m) 11.2 8.2 13.4 54.1 32.1 19.6 8.0 131.2 41.8 69.0

Max. error (m) 20.7 17.9 33.9 118.0 59.9 54.6 25.3 306.7 90.1 306.7

Table 6 - Error table showing RMSE and Maximum error between SPOT-7 and KOMPSAT-3 satellite derived tidal

boundaries. Areas in Figure 16.



Figure 17 -Satellite derived water/land boundaries and lidar derived MLWN boundary for whole of study site



Figure 18 - Satellite derived water/land boundaries and lidar derived MLWN boundary for NE of study site, Sandbanks

and Bournemouth.



Figure 19 - Satellite derived water/land boundaries and lidar derived MLWN boundary for SE of study site, Studland

Bay and SE of Poole Harbour.



Figure 20 -Satellite derived water/land boundaries and lidar derived MLWN boundary for NW of study site, Brownsea

Island.



Figure 21 - Satellite derived water/land boundaries and lidar derived MLWN boundary for SW of study site, Brands Bay

in SE of Poole Harbour.



Figure 22 - SPOT-7 NIR channel with spatial error in derived MLWN overlaid.



Figure 23 - KOMPSAT-3 NIR channel with spatial error in derived MLWN overlaid.



Figure 24 - KOMPSAT-3 NIR channel with spatial difference between SPOT-7 and KOMPSAT-3 water/land boundaries

overlaid.



4.4 Slope

As predicted by Figure 25 slope has an impact on error (Figure 25) and (Figure 26). The error component

associated with slope will be difficult to isolate, but generally in flatter areas the potential for error in

mapping the MLW (or any of the other tidal lines) will tend to increase. This is true of all the methods of

mapping the tidal lines.

It is highly that there will be a multiplicatory effect on this relationship due to tidal range. As the tidal

range increases it will be more likely that larger errors will occur. Using Figure 26 the potential for vertical

error will increase moving from the blue to the red line.

Figure 25 - Relationship between slope and spatial error for MLWN line derived from SPOT-7 data for coastline outside

Poole Harbour. Power regression line and r2 overlaid.



Figure 26 - Relationship between slope and spatial error for MLWN line derived from KOMPSAT-3 data for coastline

outside Poole Harbour. Power regression line and r2 overlaid.

4.5 Errors in mapping water/land boundaries

There were initially problems with mapping the water/land boundary due to the using less suitable band

combinations of the satellite data (Figures 27 and 28). Shorter wavelengths more readily penetrate

water.

This means that in blue and green wavelengths shallow water can appear similar to areas of land. To

accurately map the water/land boundary manually the most suitable approach is to use a single channel

in the near-infrared or an index that maximises the visual differences between water and land surfaces.

When using a single near-infrared channel, water generally appears much darker than most other

surface types. Thematic Mapper

Band Location in Electromagnetic Spectrum (in nm) Principal Use

1 Blue – 450 to 520

Water body penetration, useful for coastal mapping. Soil

differentiation

2 Green – 530 to 590 Vegetation health Cultural features

3 Red – 625 to 695 Chlorophyll absorption Cultural features

4 NIR – 760 to 890

Peak vegetation response, useful for biomass estimation

Vegetation health

Table 7 summarises the sensitivity and primary use of each band on Spot 7 imagery



Figure 27 - SPOT-7 satellite data and water/land boundaries derived from SPOT-7 true colour and greyscale NIR.



Figure 28 - KOMPSAT-3 satellite data and water/land boundaries derived from SPOT-7 true colour and greyscale NIR.



4.6 Summary

Of the satellite derived water/land boundaries the KOMPSAT-3 derived data was more accurate than the

SPOT-7. This is unlikely to have been solely due to inherent greater positional accuracy of the data from

the KOMPSAT-3 satellite and is more likely to have been due to acquisition time matching the MLWN

more consistently.

Noise (can arise from many electrical systems onboard the satellite, including the power supply, the

detector circuitry, and every electrical system in between. All satellites have some noise sources that

appear at a certain frequency and magnitude. These noise sources are often discovered before launch,

but they may change over the life of an instrument. If significant changes occur, or if new CN patterns

appear that were previously unseen, they may be cause for concern and further analysis.) levels created

by in the SPOT-7 data seemed to be higher than the KOMPSAT-3 data, making it harder to map the

water/land boundary, particularly in shallow water areas.

The results highlight some of the problems in deriving the water/land boundary using satellite imagery

and using this to map a specified tidal boundary. Particular problems are the issues around tidal

consistency, with the water height varying from the predicted levels when an acquisition is planned as

with this study, when the difference between lower water heights on the days of the satellite acquisition

should have been 0.1m but was actually 0.4m (Figure 15). Factors that can influence the actual water

levels compared with predicted include:

• Effects on water height from atmospheric pressure, wind and storm surges.

• Local geographic variations in tide times.

• Waves can result in the water/land boundary moving a great deal over short time periods. This

effect has the potential to be exaggerated where the beach is flat.

Poole Harbour has one of the smallest tidal ranges in the UK, with a Mean Spring range of 1.6 m. This can

be compared to Avonmouth where the Mean Spring range is 12.2 m. Average of the Spring ranges at UK

the Standard Ports is 4.6 m. On the days when the satellite data were acquired for this study the range

was only 0.7 m. This very small range should result in a very accurate representation of the MLWN, as the

water level should vary slowly, but this was not the case, especially in Area 8 where there were errors

that were several hundreds of metres.

In an area like the Wash where the Spring range is more than 6 metres and the intertidal zone stretches

several kilometres seawards it will be much more difficult. Deriving tidal boundaries from the water/land

boundary using satellite images is totally unsuited to estuarine areas where the tidal regime is complex

and tide times can vary a great deal over short distances.

If the purpose of the tidal lines is to identify areas where erosion/accretion have taken place, then the

errors seen in this study are likely to be magnified when attempting to detect change.

The key to understanding if a technique will be suitable for this kind of mapping will be a sensitivity

analysis that accounts for the major factors that influence the accuracy. This would provide an indication

of the point at which change is likely to be detected.



Assuming that the accuracy of the KOMPSAT-3 derived tidal level for the most accurate area could be

matched every time, it is unlikely that areas of erosion and/or accretion would be detected with

confidence unless they were greater than two to three times the maximum error (30 m – 55 m). For this

technique to be operational we would have no prior knowledge of spatial variations in error and so the

confidence in an erosion/accretion map is likely to be lower, especially in areas where the tidal range is

larger or that are relatively flat.

This study used lidar data as the standard. Factors such as air pressure, on-shore / off-shore wind, the

effect of slope, local significant variations of tide times were not known. Such factors are likely to affect

the results of the comparisons with the different sources of EO data.

The differences between lidar and the higher resolution satellite imagery demonstrates that further

investigation between lidar and EO data would be merited if these variables were known or could be

restricted.



05 Operationalisation and

cost comparison

assessment



5 Operationalisation and cost comparison assessment

5.1 Operationalisation

The potential of operationalising any of the findings of this report are dependent on whether it would

either provide efficiencies, and therefore reduce operating costs, or would increase the value of the

product to the customer by improving the accuracy or the currency.

As Ordnance Survey currently uses aerial imagery for its primary method of updating topographic

mapping, the use of additional satellite imagery would therefore be an additional cost.

Options could be as follows:

A

Satellite imagery is used as a change intelligence method which identifies areas of coastal

change. That change is automatically identified by comparing different epochs of imagery,

which then drive aerial survey tasks to these areas of change.

In this scenario Ordnance Survey could contract a satellite imagery provider to provide a

change only file that has already automatically identified the change. This would be a

service provided to Ordnance Survey that would mean that no additional storage space

would be required, and no additional processing of multi band satellite imagery would be

required.

Cost estimates of this type of service would heavily depend on how current the imagery

would need to be (i.e. would it need to be tasked or could archived imagery of 12-18

months currency be suitable), and would be dependent on what cloud free satellite is

available

B

As above, but with time specific tasking – in this scenario the tasking of satellite imagery

could be commissioned.

This option is not favoured as tasking is traditionally more expensive and again relies on

cloud free weather conditions. A potential model of reducing cost could be to share the

cost of time specific targeting across different interested Government entities

C

Ordnance Survey works with other agencies to utilise lidar data to update coastal change.

Ordnance Survey could also help to facilitate a database of appropriate high-water marks

over time so that relevant bodies could determine where known coastal accretion or

erosion occurs which could be shared by all government stakeholders. This option would

require Ordnance Survey to store lidar data but assumes that the data will have already

been processed in advance.

D

More access to lidar data means that the improved accuracy of the lidar method for

deriving tide lines (compared to traditional aerial imagery methods) will mean that tide

lines change noticeably even in the areas of low real-world change.



OS has recently come across examples of where lidar has captured areas where the coast

is hard rock, and little real-world change has occurred, however significant changes in

existing tide lines become apparent as the lidar is inherently more accurate; this effect is

particularly pronounced with the low tide line.

OS will need to engage with key customers to agree priorities. A sweep of the country to

assign priorities for the coast would be a sensible first step.

E

Partnerships for Data Capture - Due to the high cost of data capture, it would be worth

investigating whether this cost could be shared across relevant government bodies across

GB.

5.2 Costs

Extrapolation of results from a recent Ordnance Survey capture trial indicate that updating tide lines

together with coastal features could cost up to £1.8m for the entire coast of Great Britain. In addition to

this, costs may be incurred for lidar capture in Wales and Scotland if open-source lidar data is not

captured and / or made available. Streamlining the update procedure and targeting only high priority

areas could significantly lower the total cost.

The following (Table 8) provide a high-level estimate of the internal cost to update the national coastline.

These costs are worse case as they assume that the whole of the coastline would require update,

whereas only a small percentage of the coastline suffers from erosion and accretion.

England Wales Scotland Total

Length* 8,360 km 1,990 km 12,200 km 22,600 km

Cost £660k £160k £960k £1,800k

Table 8 – Indicative capture costs from Ordnance Survey (* extents of coastline are generalised and therefore

approximate)

5.3 Prohibitive Data Capture Costs

Although coastal lidar data for England is Open Data, lidar for the rest of Great Britain is not guaranteed

to be captured or made available. If this is the case, Ordnance Survey may be forced to undertake a

costly data capture programme to complete the project.

Conversations are in progress with UKHO to discuss the sharing of data in the coastal region, and this

may also help resolve some of the problems associated with low tide line capture.



5.4 Additional product options

5.4.1 NDVI Map for vegetation habitat

To assist with the creation of a more advanced understanding of vegetation habitat around the coastal

zone which has a significant effect on erosion and accretion, the production and maintenance of a

Normalised Difference Vegetation Index (NDVI) map, together with a Normalised Difference Water Index

(NDWI) covering the coastal zones of Great Britain could be produced using the spectral bands available

within multi band satellite imagery.

Accurate mapping of a coastal NDVI is best achieved using satellite imagery captured by sensors that

have a high number of spectral bands, a narrow width within those bands (higher spectral resolution),

high spatial resolution, and greater radiometric resolution. (Digitalglobe Worldview 2 is a good example).

The fact that the NIR bands are narrower, allows the user to precisely distinguish the vegetation

reflectance value.

The information from the NDVI this could significantly automate the maintenance of coastal habitat

change.

5.4.2 Known areas of coastal erosion database

As previously mentioned, it would be of significant national interest if a data base of known areas of

coastal erosion and accretion could be created. This would require the collation of information from

England, Scotland and Wales, and would have the benefit of ensuring that such high-risk areas were

targeted for frequent update. The usage of EO data to prioritise and identify more frequent update of

such areas is the recommended solution.

5.4.3 Asset register

The creation of an asset register or feature risk heat map which identifies buildings, golf courses, roads,

railways, utility assets, residential properties by running spatial and attribution queries in areas of significant

known change so that the highest risk areas can be managed as part of a national database

5.4.4 Risk map

By using the inputs from an asset register the areas of known erosion and accretion, a coastal change

risk map could be created to predict change. This would also require data from The British Geological

Survey so that vulnerable soft rock geology areas, that intersect with EA/OS DTM data to find low lying

areas. Intersect with Ordnance Survey Topographic Data to find location of valuable features which

might be at risk (Roads, Rail, Buildings). Useful for councils, government, insurance companies, ...



5.5 Value of vulnerable national assets

5.5.1 English Environment Agency findings

A report “Understanding the risks’ empowering communities, building resilience – The national

flood and coastal erosion risk management strategy for England” commissioned by Department for

Environment Food and Rural Affairs and Environment Agency in 2011 stated that approximately 1,800

kms English coastline was at risk of coastal erosion (approximately 340 km of which is defended). It is

estimated that approximately 200 properties were vulnerable to coastal erosion but by 2029, up to 2,000

residential properties, and 15 km of major road and railway may become vulnerable.

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/228898/978010851036

6.pdf

5.5.2 Scottish Natural Heritage

The following table from Scottish Government suggests that the total value of buildings, roads, railways,

and runways that are within 50 metres of MHWS is more than £13 billion.

Figure 29 - Source – Scottish Government current value of assets on soft coast 2017

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/228898/9780108510366.pdf

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/228898/9780108510366.pdf



5.5.3 Natural Resources Wales

It has been estimated by Natural Resources Wales, that over 200,000 properties in Wales are currently at

risk of flooding from rivers and the sea. Thousands more properties are at risk of surface water flooding.

It is estimated that coastal erosion is occurring along around 346km (23%) of the Welsh coastline.

The Welsh coastline is protected by 415kms of constructed coastal defence. Welsh Government are

currently investing over £256 million in flood and coastal erosion risk management over the life of this

Government (2016-2021).



06

Recommendations for

future phases of work



6 Recommendations for future phases of work

6.1 Identification of the challenges of using optical satellite imagery

The literature review has revealed a significant gap when it comes to the capture of Mean High Water and

Mean Low Water using optical satellite imagery but has highlighted some challenges that may be faced

by anyone attempting this. The first of these is that the usefulness of optical satellite imagery in a

specific location is tied to prevailing weather conditions. This, coupled with the difficulty of capturing

data to coincide with high and low water, could result in the time taken to complete capture of a survey

area being longer than practical to capture meaningful real-world change. Similar issues need to be

considered when flying lidar.

A second factor is to account for the availability and quality of ground control data. For the output to be

of a suitable quality, the positional accuracy of the satellite data needs to be high.

From the review, the most successful methods of automatically extracting the coastline have involved

Normalised Difference Water Index (NDWI) analysis, using the Short-Wave Infrared band available in 8

band multispectral imagery. As 8-band data is not being used as part of the UKGEOS study, new

methodology may have to be developed to successfully extract the coastline automatically.

6.2 Further investigation in complex tidal changes in small geographic extents

It is difficult to determine from a limited study area whether a technique is suitable for a national

monitoring program. The results within the estuarine environment in Poole Harbour are inaccurate for

both satellites of medium and high resolution. This error is partially a function of the complex tidal

regime resulting in relatively large differences in the predicted and actual tide levels, which equally can

affect the accuracy of lidar data.

The results in the coastal facing areas are more accurate, but there were still differences between the

derived tide lines. Highest volume change, and most catastrophic (therefore most important to be

quickly and accurately mapped) will occur in Autumn/Winter months. These are also the cloudiest

months - so are hardest to acquire imagery.

There are several variables that will impact on the accuracy of the technique used in this study to map a

pre-determined tidal line.

These include:



• Accuracy of spatial referencing of the satellite imagery.

• Accuracy in discriminating the water/land boundary. While in some cases this will be relatively

easy, in areas where there is very shallow water and/or saturated sediment it will be more

difficult. As has been shown in the study, use of the NIR is essential to map this boundary. In

addition, the spatial resolution and signal-to-noise ratio of the satellite data are components in

the accurate determination of the water/land boundary.

• Local topography. Areas where the intertidal zone is steeply sloping will be mapped more

accurately than areas where the slope is shallow.

• The difference between the actual water level and the required tide level (for example the MLW)

is a critical component and is most likely the limiting factor.

6.3 Using satellite data to map the water/land boundary at a specified tidal level

This approach, which is equally valid for aerial and lidar flying programs, is limited for the following reasons:

• It will be difficult to use a consistent tide level (e.g. MLW) throughout the UK, and even regionally,

due to satellite overpass times and tidal levels.

• The approach only maps a single tide line, which can mask the true impact of coastal

erosion/accretion. A change in MLW may not be reflected by a change in HAT and vice-versa.

• Different organisations in the UK will prioritise different tidal levels, as coastal erosion is

considered differently by organisations and even within organisations.

• In a given month there will be a very limited number of theoretical opportunities to acquire data

at the correct tidal height and for this technique to work the scene needs to be cloud free over

the coast, further reducing potential acquisitions.

• The technique relies on satellite data being acquired at specific water level, which may be

difficult to achieve due to local tidal variations, atmospheric pressure variations, wind and storm

surges, as well as the impact of waves varying the water/land boundary.

• Unless there is independent data to verify the tidal level (for example a local tide gauge) it would

be very difficult to determine likely errors, and this will result in a lower sensitivity of the

technique. Tidal level information at a single point may not be enough to account for local

variations in tidal level.

6.4 Using elevation data to satisfy the requirements of multiple users

An approach that uses an elevation dataset to map coastal change is probably much more suitable, as it

may be used to derive whichever tidal lines are required, satisfying the requirements of multiple

organisations and to map coastal height and volume changes. There are challenges to this approach, but

the technique is already being used in the UK to map tide levels, coastal height changes and volumetric

changes using data from non-satellite platforms, such as lidar and bathymetry.



6.5 Technology choices

Given the inherent noise in SAR data. The issues of layover and shadow could be mitigated by targeting

SAR capture to coastal areas of low relief but known aggressive erosion or accretion, thus avoiding cliffs

where layover could become an issue.

The terrain model should be representative of the coastline at the time of observation and can be

derived from SAR using single pass SAR interferometry. For the UK there is limited availability of this

data, and therefore it was not investigated further in this study.

Furthermore, when considering these techniques, the limitations of shadowing and layover must be

accounted for in the identification of a suitable location, which further limits the opportunity for

identifying suitable data historically. Without considerable testing across multiple locations this effect

further propagate uncertainty into any experimentation results.

For these reasons, notably a lack of available data, it was deemed that meaningful experimentation was

not possible.

A thorough overview of possible approaches with SAR has been provided in the literature review.

A proposed future project would be to commission the acquisition of Tandem x-SAR, high resolution

aerial imagery, and lidar at a synchronous point of a coastal area at as close as possible the point of low

tide

For this study area, there is no stereo data available in the KOMPSAT, Pleaides or Worldview archives and

only half of the AOI is covered by GeoEye in 2013.

6.6 Investigation into creation of NDVI and NDWI maintained data for habitat

To assist with the creation of a more advanced understanding of vegetation habitat around the coastal

zone, the production and maintenance of a Normalised Difference Vegetation Index (NDVI) map,

together with a Normalised Difference Water Index (NDWI) covering the coastal zones of Great Britain

could be produced using the spectral bands available within multi band satellite imagery. The

information from this could significantly automate the maintenance of coastal change.



Annex A Literature Review



1 Annex A - Literature Review - Use of EO for Coastal

Change Mapping

This literature review presents several research studies that have analysed the detection and monitoring

of the shoreline by means of EOS. Most applications involve delineation of the waterline.

This problem has been tackled with a range of data acquisition approaches and analysis methods,

ranging from density slicing or manual digitisation of optical imagery bands, through to edge detection

of SAR imagery.

This section will discuss current methods for both optical and SAR based delineation of shoreline,

including manual, automated and semi-automated approaches, whilst discussing key technical

considerations for both optical and SAR based approaches.

1.1 Optical Satellite imagery for mapping MHW/MLW

Remote sensing techniques can yield huge benefits for coastline monitoring over conventional field

methods via facilitating rapid and more frequent data acquisition, faster and more automated

processing and a greater sampling intensity.

To allow sufficient delineation of shoreline from optical data, it is necessary to consider the spectral

characteristics of the typical environments present in the coastal zone. As different materials / water

constituents reflect and absorb electromagnetic radiation at different wavelengths, target elements of

interest can be differentiated by their spectral reflectance signatures in remotely sensed imagery.

These can include coastal water, open water, beach zones, tidal flats, vegetation and bare earth, for

example (Figure 30). In addition, optically active water constituents such as minerals (Coloured

Dissolved Organic Matter – CDOM) and phytoplankton (chlorophyll-a – Chla) also have an impact on the

optical response of water in the visible wavelengths [37].

The spectral response of water (water curve) is characterised by high absorption in the NIR portion of the

EM spectrum. This unique absorption property means water bodies can easily be detected, located and

extracted from remotely sensed data, particularly where NIR or thermal wavelengths are present. Water

that contains suspended sediments – ‘turbid’ water has a higher reflectance in the visible portion of the

EM spectrum.



Figure 30 - Reflectance spectra for 5 surface materials. For vegetation and water, note the difference between the red

and NIR bands. Vegetation: low reflectance in the visible, but some reflectance in the green, and then strong

reflectance in the NIR. Water: low reflectance in the visible, but no reflectance in the NIR.

The optical properties of shoreline indicators should be considered when choosing a data source for a

coastal study, as the choice of data source will inform methods which could be applied successfully.

Optical data should be sufficiently cloud free, of a resolution adequate to distinguish relevant coastal

indicators and have a spectral resolution capable of exploiting the spectral reflectance properties of

typical coastal environments. Fortunately, the majority of current EO sensors have a standard 4-band

multispectral capability (Red, Green, Blue, NIR) therefore for land and water extraction it is possible to

exploit the NIR band and the optical properties of high reflectance in NIR for vegetation, and high

absorption in NIR for water, using techniques such as band ratios.

Due to the dynamic nature of shorelines, the use of shoreline indicators is frequently adopted. A

shoreline indicator is defined as ‘a feature that is used as a proxy to represent the “true” shoreline

position’ [37]. Boak and Turner categorised shoreline indicators into three groups (i) visible discernible

features; (ii) tidal datum-based indicators; and (iii) indicators based on the processing technique to

extract the shoreline [40].

One of the most common techniques for mapping shorelines has typically been manual visual

interpretation, using aerial or satellite imagery – this however has its drawbacks, manual techniques are

often resource intensive and subject to the use of expensive aerial survey imagery. It is therefore

necessary to consider digital processing techniques as a viable low-cost alternative to the use of aerial

surveys. As such, the purpose of this study is to evaluate the use of satellite remote sensing-based

techniques; therefore, a discussion into the use of aerial imagery will not be included.



A paper by Gens presented a comprehensive overview of the use of remote sensing for shoreline

delineation and coastline monitoring [38], whilst the review has been written in a US context most of the

research discussed can be applied globally.

Optical satellite imagery has been widely used to map shoreline and its change over time, offering the

potential for systematic map updates over large areas. Both manual, automated and semi-automated

approaches have previously been adopted. Typically, this is done by exploiting the optical contrast

between sea and the land, using image processing techniques such as band ratios for example, the

Normalized Difference Water Index (NDWI) for the extraction of water features from multispectral

satellite data. Careful consideration in performing this type of analysis must be undertaken due to the

influence of environmental factors such as oceanographic conditions, water levels, tides, wave set-up

and wave run-up.

The literature presents many examples of using remote sensing to manually delineate shoreline, using a

variety of different techniques. Pradhan et al. sought to acquire high resolution satellite imagery

coincident with Mean High Water to map shoreline, however because of the weather constraints that

exist for optical imagery this could not always be achieved. When working with 2.5 metre resolution

imagery, accuracies of 25 metres were achieved. A key limitation of accurate shoreline extraction was

noted to be access to sufficient Ground Control Points (GCPs) to orthorectify the data to a suitable

standard [34].

Guariglia et al investigated a multi-sensor approach for coastline mapping, applying band ratios and

ISODATA classification techniques. They concluded that satellite imagery can be affected by tidal

variation depending on image resolution, however when using Landsat data (30m) the error introduced

by tidal variation was removed, meaning that tidal influence is likely to be a considerable factor for

evaluation when delineating coastlines from VHR imagery [41].

Maiti and Bhattacharya combined data from Landsat and ASTER to extract shoreline positions using the

NIR bands and grey level thresholding and segmentation using edge detection and enhancement

techniques [42].

Maglione et al. consider the extraction of instantaneous coastline, as identified in high resolution

WorldView-2 imagery using two techniques, Normalised Difference Vegetation Index (NDVI) and

Normalised Difference Wetness Index (NDWI). For NDWI analysis they used the Coastal band in the

Worldview-2 data which is not available for this project. NDVI analysis we can conduct with 4-band data.

Both NDVI and NDWI permit to easily delineate the coastline, guaranteeing horizontal accuracies

compatible with studies on a large scale of sub 1 metre [35].

Tseng et al. use a combination of optical Landsat data and tide model data to produce a DEM over an

area of tidal flats in the intertidal zone. NDWI was employed to extract water line. Using a large sample of

Landsat images over time, they produced a probability of inundation map and then, coupled with a tide

model, produced a DEM. Due to using Landsat, 30m pixel, the study was able to ignore the differences in

water level created by wave heights. This would likely have to be considered for a VHR study, where wave

action can cover several pixels.



The study successfully created a DEM for the tidal flat with a horizontal accuracy reaching 48cm when

compared indirectly with tide gauge data. This technique could potentially be employed to find MHW

and MLW by taking the boundaries of the intertidal DEM to be MHW and MLW [36].

Several papers explore the extraction and monitoring of shoreline from EO imagery using automated and

semi-automated approaches. Kuleli et al applied automated image analysis techniques to extract

shoreline change in Turkey. They applied histogram-based segmentation of land and water based on

using automated thresholding algorithm applied to Landsat data [43].

Pardo-Pascual et al presented a high precision geometric approach for automated detection. The

algorithm presented is based on the principle of sub-pixel shoreline extraction, this works on the

assumption that the separation point between land and water will occur when the infrared intensity

gradient around the pixel-level shoreline is at its maximum. The results concluded that when applying

this technique to moderate resolution data, such as Landsat, that the results were comparable in

accuracy to high resolution techniques [44]

Syaifulnizam Abd Manaf et al, validated the most effective and efficient machine learning technique for

the extraction of shoreline based on Landsat OLI satellite imagery. Eleven machine-learning classifiers

were used to perform a series of validation assessments on extracted shorelines. To achieve a precise

shoreline, the researchers had to perform a change analysis on the extracted shoreline and the reference

shoreline.

Predictably, this resulted in differences in the distance between those shorelines of the order of

magnitude of 30 metres, due to the spatial resolution of Landsat 8, which was 30 m. The most effective

classifier could discriminate mean distance of only 0.25 m as compared to other classifiers [9].

R. Dewi et al, apply fuzzy c-means (FCM) classification on Landsat images from three different sensors to

monitor shoreline change. It focuses on: (1) inherent uncertainty due to continuous variation of a

shoreline over time; and (2) uncertainty as it propagates from extraction and implementation of the

shoreline change detection method.

This research presents two methods to identify shoreline positions: as a line and as a margin, including a

measure of change uncertainty at different epochs. Both methods used FCM classification to determine

partial membership of water and non-water. While shoreline changes can be detected by both methods,

the shoreline as a margin provides a more detailed estimation of change area than the shoreline as a line

[10].

Y.Choung and M.Jo compared two different methods for mapping shorelines using high-resolution

satellite imagery. The first shoreline was generated using normalized-difference water index (NDWI), and

the second shoreline was generated using a machine-learning-based method, support vector machine

(SVM). Figure 31 illustrates a flowchart showing the procedure for mapping shorelines using the two

different methods.



Both shorelines had high accuracy in the well-identified coastal zones while the second shoreline

generated by the machine-learning-based method had better accuracy than the first shoreline generated

by the water index- based method in the coastal zones with irregular shapes, light shades, and so forth.

Both methods, however, showed inefficient performance for mapping the shorelines in the coastal zones

with significant shades that were not identified in the NDWI image or the coastal-surface classification

map [11].

Figure 31 - Flowchart showing the procedure for mapping shorelines using the two different methods.

I. Sekovski et al explored the potential of using a semi-automatic approach in delineating a proxy-based

shoreline by processing high-resolution multispectral WorldView-2 satellite imagery. The wet/dry

shorelines were delineated between the classes of wet and dry sand, resulting from different supervised

and unsupervised image classification techniques.

The average median distance between reference shorelines and those resulting from the classification

methods (horizonal accuracy) was less than 5.6 m for the unsupervised techniques (Maximum likelihood)

and a distance of just 2.2 m was detected from the supervised techniques (ISODATA and Mahalanobis)

[14].

Aedla et al. used an automatic shoreline extraction method using clipped histogram equalization-based

contrast improvement for enhancing coastal pixels and thresholding techniques for segment water and

land regions. This technique improved significant contrast enrichment of coastal edges and coastal

objects for clear recognition and delineation [15].

H. Liu et al explore automation of shoreline extraction from different data sources: panchromatic or single

band imagery, colour or multi-spectral image, and lidar elevation data. The algorithms and software

routines presented in this paper are object-based in the sense that shoreline features are treated as

boundary lines between land objects and water objects. Horizontal positional accuracies achieved were

in the order of 4 – 5 metres [20].



The literature review has revealed a significant gap when it comes to the capture of Mean High Water and

Mean Low Water using optical satellite imagery but has highlighted some challenges that may be faced by

anyone attempting this. The first of these is that the usefulness of optical satellite imagery in a specific

location is tied to prevailing weather conditions. This, coupled with the difficulty of capturing data to

coincide with high and low water, could result in the time taken to complete capture of a survey area being

longer than practical to capture meaningful real-world change.


of a suitable quality, the positional accuracy of the satellite data needs to be high. This requires high

quality reference data, which is often not available, particularly in remote areas.

The most successful methods of automatically extracting the coastline have involved Normalised

Difference Water Index (NDWI) analysis, using the Short-Wave Infrared band available. Manual

techniques are deemed most effective although several automated procedures, notably learning

techniques, have yielded good results. However, the most common techniques currently used include

manual identification, image enhancement, image classification (supervised and unsupervised), image

segmentation and density slicing using single, or multiple bands [37].

1.1.1 SAR imagery for mapping MHW/MLW

The use of Synthetic Aperture Radar (SAR) is an ideal data source for monitoring coastal changes,

particularly in the UK. The cloud penetration ability and the day/night imaging capabilities allow

valuable information to be extracted when data acquired by conventional optical remote sensing

imagery are restricted. The difference of SAR return from land versus water is primarily a function of

contrast of the dielectric properties and surface roughness, providing a striking interface which also

facilitates coastal delineation and related mapping studies [33].

For a given SAR satellite sensor, the frequency of the transmitted and received electromagnetic wave is

an important parameter. Frequencies are classified in bands. The most typical SAR bands are the X-band,

C-band and L-band. SAR backscatter strongly depends on the bands, especially for natural targets such

as forests and agricultural fields. In general, the smaller the wavelength, the lower the ‘scattering phase

centre’. Indeed, the scattering phase centre, i.e. the reflection point, is not at the surface top but is within

the volume of the natural target.

To provide some examples, reflections from agricultural fields are close to plants top for X-band,

between soil and plants top for C-band and at the soil level for L-band. A different behaviour, i.e. a

different scattering centre, is also expected for the wave polarization.



Figure 32 - SAR imaging over water.

Water generally yields a very low backscatter and appears ‘black’ in the SAR amplitude. This is due to the

specular reflection of the transmitted wave. Indeed, water surfaces behave like a ‘mirror’ and scatter the

incident wave in the opposite direction of the SAR sensor.

Figure 32. depicts the considered scenario. Notably, strong waves may yield significant returns to the

sensor, showing a wave pattern in the SAR image. 11 implies an important consideration for mapping of

water line with SAR: the radar sensor is a side looking one.

This geometrical constraint impacts in the SAR imagery with a loss of quality depending on the terrain

slope and the incidence angle of the transmitted wave. The loss of quality is also called ‘geometrical

decorrelation’ and is specifically driven by two phenomena: layover and shadow [1]. Layover occurs

when multiple targets are mapped within the resolution cell. An example over urban area in shown in

Figure 33.

Figure 33 - Layover example in SAR imagery. In the resolution cell (violet strip), multiple scatterers from different

targets contribute to the signal.



Over coastal zones, layover can occur because of cliffs. The impacts in the SAR image over coastal areas

are summarised as follows:

• The return is bright since it is the sum of several scattering contributions

• It is impossible to define the location of the coastline, i.e. the last water pixel is the last pixel

unaffected by layover and not the coastline.

An example of SAR imagery over cliffs in displayed in Figure 34. Figure 35 shows an example over flat

topography.

Figure 34 - Example of layover in coastal areas. (Left) picture (right) SAR image.

Figure 35 - Example of SAR imaging over smooth coastal areas. (Left) Bay (right) SAR image.



Shadow is a complementary phenomenon and occurs for areas not imaged by the sensor. For coastal

zones, this happens again in areas of high topography, but when imaged in the other direction, i.e. not

subject to layover.

The viewing angle is defined by the orbit direction and it may be ‘ascending’ (looking towards east) or

‘descending’ (looking towards west). A visual example of an area not imaged by the sensor is depicted in

Figure 37. The yellow area in Figure 37 is not represented in the SAR image, therefore the coastline

cannot be precisely estimated.

Coastline extraction from SAR data is a standard operation nowadays [2-5]. Although there are many

algorithms, the main technique is based on filtering operations. Roughly, a simple threshold may be

sufficient to distinguish between calm surface water and land, thus deriving the coastline. In practice,

higher reflectivity from waves may create many false alarms in this simple procedure and yield

inaccurate coastline derivation. The algorithms described in [2-5] deal specifically with this issue.

Once the coastline is derived, the estimation of water height from multi-temporal data with low and high

tides is a straightforward geometrical problem. Given two coastline estimations and a height model of

the underlying terrain, the water height difference is simply derivable from the projection of the coastline

into the terrain model.

More specifically, let’s assume having low tide at time 0, t(0), and high-tide at time 1, t(1). At these two

times we have two coastline estimations from SAR amplitudes. These coastlines need to be projected in

geographic coordinates, or, alternatively, the terrain model needs to be re-projected in SAR coordinates

[6]. Once this step is performed, the height estimation, dH, is simply the evaluation of the terrain model

at the dX boundaries (Figure 38).

A final remark shall be made on the resolution. SAR data must have a higher resolution than dX.

Moreover, the Digital Terrain Model (DTM) used in this process must be accurate enough to represent

both tidal situations (ideally it is then generated for low tides). Finally, the coast must not be in

geometrical decorrelation areas, as previously explained.

Figure 36 - TERRSAR X Satellite Imagery - Straight Kerch, Russia, © CNES 2015, Distribution Airbus DS



Figure 37 - Example of region not illuminated by the radar due to shadowing.

Figure 38 - Geometry for the derivation of water height from multi-temporal data

Several papers explore the extraction and monitoring of shoreline from SAR imagery using automated

and semi-automated approaches.



A study undertaken by N.Demir et al utilises Sentinel-1 C band SAR imagery to extract the shoreline with

a fuzzy logic approach. that has been applied to distinguish the coastal pixels from the land surface

pixels.

Quality was assessed by comparison to a lidar data set, achieving a horizontal accuracy in the order of 6

metres [16]. In another study by N. Demir et al, RASAT pansharpened and SENTINEL-1A SAR images have

been used to implement shoreline extraction methods. This study combines the land/water body

segmentation results of both RASAT MS and SENTINEL-1A SAR images to improve the quality of the

results. The proposed approach is shown in 8.

Figure 39. Flow chart of the shoreline detection from SAR image.

RASAT images were segmented using a Random Forest method and resulting land/waterbody binary

segmented images were used for estimating the parameters used in a fuzzy approach to extract the

shorelines from the SENTINEL-1A imagery.

The horizontal accuracy assessment has been performed by calculating perpendicular distances

between reference data and extracted shoreline by proposed method. As a result, the mean difference

has been calculated around 8 metres. [19].

Additionally, E. Vandebroek et al investigate whether shorelines detected in high resolution TerraSAR-X

imagery are accurate enough to monitor the shoreline dynamics. The paper proposes a semi-automated

4-step method that extracts the shoreline from a series of images (9).

The images are classified into land and water using a two-step semi-automated process: through

clustering and reclassification. K-means clustering (Land - Water classification), Region growing

(Shoreline extraction) and georeferencing. It was concluded that TerraSAR-X imagery is a valid option for

assessing coastal dynamics on the order of tens of meters at approximately monthly intervals in the

horizontal axis. [17].



Figure 40 - General workflow to process synthetic aperture radar (SAR) imagery and extract shoreline

M. Bruno et al study the coastal evolution through high resolution CosmoSkyMed SAR satellite imagery in

order to:

(i) perform automatic selection of all archive radar data suited for precise shoreline identification

(based on weather and sea conditions in the investigated area);

(ii) extract the coastline;

(iii) classify the shoreline according to the coastal morphology and (iv) correct the shoreline

position in case of tidal fluctuations.

Accuracy assessment against a GPS survey showed an overall horizontal mapping accuracy of only 2

meters [18].

All methods described above have the need to attribute a known tide height or elevation to the data sets

used, most often to attribute multiple waterlines for construction of a Digital Elevation Model (DEM).

S. Sagar et al introduce a novel approach based on the idea that the analysis of tidal extents can be

reframed from time of acquisition of the imagery into the tidal height domain using an ancillary tidal

model [21]. It leverages a full-time series of Landsat observations from 1987 to 2015 managed in the

Australian Geoscience Data Cube (AGDC).

They combine this reframed imagery using a pixel compositing approach of derived Normalised

Difference Water Index (NDWI) stacks; examining medians to robustly estimate the intertidal extent. This

new framework aids to create a continental-scale intertidal extents model across the full tidal range,

detailing intertidal zone topography at a 25m spatial scale. Validation against elevation data collected

from Real Time Kinematic (RTK) GPS surveys show close agreement.



1.2 EOS for coastal monitoring – Current readiness

The literature has presented a wide variety of EO techniques which all provide viable results for shoreline

delineation, however with varying degree of accuracy. As such it is necessary to explore multiple

methods where possible over a test area to determine which may be the most successful, however the

ability to do so is dependent on a variety of factors.

It is clear from the literature that for optical shoreline delineation, a combination of band ratio

techniques (NDWI, NDVI) coupled with supervised classification to discriminate between land and water

will yields results, with varying accuracies. The high absorption of water in the NIR / Thermal bands is a

useful property to exploit, allowing for easy delineation of water presence using band ratios such as

NDWI, contrast enhancements and thresholding.

A comprehensive overview provided by Gens [38] concluded that the accuracies of different remote

sensing techniques are directly related to the spatial resolution of the data chosen for the study.

Horizontal accuracy, which is directly relevant for shoreline delineation, is typically of the same order of

magnitude as the spatial resolution of the source data [38]. Therefore, it is implied that the resolution of

the input data can have a significant impact on the output accuracies (e.g. error is of order of magnitude

of pixel resolution, e.g. 30m when using Landsat data).

This leads to the assumption that VHR optical data would always yield better results than moderate

resolution data, however this may not be the case, as lower resolution data is in fact capable of ignoring

error introduced because of the influence of wave height. For regions with high tidal variability, or high

wave activity, it may therefore become problematic to use VHR data.

In regions with high tidal variability, the time that the data is acquired is critical, which introduces

another level of uncertainty – if low or high tide is not coincident with the time of image capture then it

will be more difficult to extract an accurate shoreline.

The vast majority of current EO payloads carry sensors capable of imaging in the NIR, therefore there are

many options to consider when acquiring optical data. Of more importance is that the optical data is

acquired at a time of day comparable to the optimum tidal conditions – this of course will naturally

limit the number of suitable scenes available. Tasking new imagery may help alleviate this however

satellites are in fixed orbits therefore typically overpass a target location at the same time of day each

day, which is generally fixed, therefore in some circumstances it may not be possible to acquire optical

data coincident with the optimum tidal conditions.

When considering image resolution, data with resolution in the region of 1m – 5m could potentially

provide the best results, however could be associated with a cost to acquire the data. Open Data

alternatives to consider include Sentinel-2 (10m) and Landsat-8 (30m) – both sensors carry a NIR

capability, Landsat also has a Thermal band which can be used to extract the presence of water.

A key consideration when using SAR is the potential for geometric decorrelation – particularly when the

orientation of the shoreline in question in relation the viewing angle of the SAR satellite results in layover



and shadow – therefore when considering the use of SAR data, the shoreline orientation must be taken

into consideration to alleviate the effects caused by these geometric phenomena. The extraction of

shorelines from SAR data typically presents variable mapping accuracy due to issues of shadow and

layover as previously outlined and issues of inherent noise in the data.

The issues of layover and shadow could be mitigated by deliberately targeting SAR capture to coastal

areas of low relief but known aggressive erosion or accretion Higher resolution data sets yielded the best

results which were enhanced by combining additional data sets into the processing methodology such

as optical and lidar. SAR readily overcomes issues of data capture during bad weather conditions

experienced by other data sets.

There are currently several SAR missions producing data with varying degrees of resolution which can be

exploited for shoreline mapping. Open data from Sentinel-1, with a typical resolution in the order of

magnitude of 20m (Interferometric Wide Swath Mode) provides a low-cost option, however the

resolution may result in loss of detail along the shoreline of interest, in addition 20m resolution data may

not be suitable for shorelines with a narrow tidal range (i.e. the distance between high tide and low tide

water marks).

High resolution alternatives to consider include Radarsat-2, TerraSAR-X and CosmoSkyMed. Each of

these missions can generate data in up to 25cm resolution (Spotlight mode), however the most

commonly exploited modes for commercial SAR data is Stripmap, with a typical resolution of 3-5m – this

option offers the best trade off in image resolution against area coverage – as generally speaking the

higher resolution the data the smaller ground coverage is achieved in a single acquisition. As the

resolution of the SAR data used should be higher than the distance between the high and low tide water

marks, it is critical to have a prior understanding of the tidal dynamics present in the study area of

interest.

The vast majority of techniques investigated in the literature present one-off manual processing, as

opposed to the generation of an operational service. However, should an operational service be

required, recent technological advancements are facilitating improved access to cloud based

computational capability, for example Amazon Web Services, Google Search Engine, Digital Globe’s

GBDX and the Open Data Cube.

Therefore, it is safe to conclude that advancements in technology have facilitated ease of processing of

data on a large scale. Should an algorithm be developed suitable for accurate shoreline mapping,

reprocessing data at scale is feasibly possible with current technology.

An alternative approach described in literature from the Earth Observation Centre at DLR in Germany

would be to combine sensor information.



The SAR Oceanography Team at the "Maritime Safety and Security Lab" in Bremen develops algorithms

that extract and make available to users in near-real-time information on the condition of the oceans.

This information is derived from radar images collected by a variety of satellites.

This information includes:

• Meteorological parameters like wind and wave action

• Position and size of icebergs drifting in open water, and the type and movement of sea ice in

polar regions

• Topographic changes in coastlines and river deltas, and the shifting of narrow channels,

sandbanks and shellfish stocks in the Wadden Sea

• Estimation of underwater topography

• Position and extent of oil slicks

• Position and route of ships

To obtain situation overviews that are as up-to-date as possible, data from radar satellites such as

TerraSAR-X, Sentinel-1 and RADARSAT-2 are combined. Reports from ships as well as data collected in

situ are also procured and processed in near-real-time. [45]

1.3 Examples of utilisation of EO for monitoring of Mean High Water and Mean Low

Water

There is very little, publicly available work, that has been done with a specific focus on Mean High Water

and Mean Low Water mapping, using optical satellite imagery. There have been, however, several studies

conducted on coastline mapping and automated coastline extraction. Three of which have elements that

can be applied to the UKGEOS project, these are summarised below.

1.3.1 European Space Agency COASTCHART

Completed in 2006. The section of this study that used optical satellite imagery, used SPOT5 optical

imagery, 2.5m resolution PAN and 10m MS, and cites this resolution as ‘Very High Resolution’ which

would no longer be the case. Acquisition of the imagery was initially supposed to be coincident with High

Water so that the HW line could be captured, however because of the weather constraints that exist for

optical imagery this could not always be ensured.

Choice of location for the study site proved to be important as there were insufficient Ground Control

Points to orthorectify the data to a suitable standard.

Identification and capture of the coastline was completed manually in PCI Geomatica. All the available

orthorectified data was displayed simultaneously and the coastline was drawn in as individual vectors in

a separate layer.



Mean High Water could not be captured because weather conditions in the study site prevented the

capture of enough optical data coincident with high water within the study period. The data was of

insufficient quality to be used to produce a coastline for use on navigation charts (see Figure 41, below).

http://due.esrin.esa.int/files/192-171-5-73_2006112315560.pdf (link to full report)

Figure.41 - Example of anomalous features captured. Badagri area. Centred on approx. 6°24.6’ N, 3°22.0’ E. Coastline

anomalies all attributed with a confidence level of 1.

1.3.2 Coastline extraction using high resolution Worldview-2 satellite imagery

Pasquale Maglione, Claudio Parente & Andrea Vallario (2014) Coastline extraction using high resolution

WorldView-2 satellite imagery, European Journal of Remote Sensing, 47:1, 685-699, DOI:

10.5721/EuJRS20144739

‘In this paper the attention was focused on the extraction of instantaneous coastline, as identified in the

WorldView-2 imagery without considering tide’.

Even though the tide was not considered, the techniques employed in this study could be applied to the

UK-GEOS study to help automate the extraction. Automated coastline extraction was conducted using

two techniques, NDVI and NDWI extraction with some smoothing subsequently applied. For NDWI

analysis they used the Coastal band in the Worldview-2 data which is not available for this project. NDVI

analysis we can conduct with 4-band data. Their NDVI and NDWI results are shown in Figure 42, below.

http://due.esrin.esa.int/files/192-171-5-73_2006112315560.pdf



Figure 42 - comparison of the results of the automated feature extraction against a visual inspection of the pan-

sharpened images showed a shift of less than 1m.

1.3.3 Reconstruction of time-varying tidal flat topography using optical remote sensing

imageries

The study used a combination of optical Landsat data and tide model data to produce a DEM over an

area of tidal flats in the intertidal zone. They used a similar technique to the Italian study to

automatically extract the water line from the imagery, employing NDWI evaluation.

Once again, this was done using the Short-Wave InfraRed band which is unavailable to the UKGEOS

study. Then, using a large sample of Landsat images over time, they produced a probability of

inundation map and then, coupled with a tide model, produced a DEM.Due to using Landsat, 30m pixel,

the study was able to ignore the differences in water level created by wave heights.



This would likely have to be considered for a VHR study, where wave action can cover several pixels The

study successfully created a DEM for the tidal flat with an accuracy reaching 48cm when compared

indirectly with tide gauge data (see Figure 43).This technique could potentially be employed to find MHW

and MLW by taking the boundaries of the intertidal DEM to be MHW and MLW.

Kuo-Hsin Tseng, Chung-Yen Kuo, Tang-Huang Lin, Zhi-Cheng Huang, Yu-Ching Lin, Wen-Hung Liao, Chi-Farn Chen https://www.sciencedirect.com/science/article/pii/S0924271617300217

Figure 43 – Example of merged DEM in the Hsiang-Shan Wetland in the Northwest of Taiwan using a tidal flats-DEM

and SRTM

1.3.4 Conclusions

The literature review has revealed a significant gap in being able to find existing tangible examples when

it comes to the capture of Mean High Water and Mean Low Water using optical satellite imagery but has

highlighted some challenges that may be faced by anyone attempting this.

The first of these is that the usefulness of optical satellite imagery in a specific location is tied to

prevailing weather conditions. This, coupled with the difficulty of capturing data to coincide with high

https://www.sciencedirect.com/science/article/pii/S0924271617300217



and low water, could result in the time taken to complete capture of a survey area being longer than

practical to capture meaningful real-world change.


of a suitable quality, the positional accuracy of the satellite data needs to be high. This requires high

quality reference data, which is often not available, particularly in remote areas.

From the review, the most successful methods of automatically extracting the coastline have involved

Normalised Difference Water Index (NDWI) analysis, using the Short-Wave Infrared band available in 8

band multispectral imagery. As 8-band data is not being used as part of the UKGEOS study, new

methodology may have to be developed to successfully extract the coastline automatically.

1.4 EOS data processing

To access the valuable information contained within EOS data, users are required to undertake a series

of complex pre-processing steps to turn the data from a ‘raw’ unprocessed format into a state that can

be analysed and interpreted. Failure to do so will result in inaccuracies within the delivered products and

services. Unless the user has the expertise, software and infrastructure to handle and process this

information, an efficient exploitation of EOS data cannot be realised.

A solution to this, in part, is through the systematic and regular provision of Analysis Ready Data (ARD).

ARD can be defined as satellite data that have been processed to a minimum set of requirements and

organised into a form that allows immediate analysis without additional user effort and interoperability

with other datasets both through time and space.

The main steps in the production of ARD include:

• Orthorectification - removes the geometric distortions introduced during image capture (i.e.

satellite’s view angle) and inherent topography of the area. The result is a product that has

planimetric geometry- like a traditional map.

• Radiometric correction – converts raw digital numbers recorded at the satellite sensor into a

physical quantity radiance, Top-of-Atmosphere (ToA) reflectance or brightness temperatures for

thermal imagery.

• As with optical image processing, radar data, and in this case SAR data, requires pre-processing

steps before a value-added product analysis could be undertaken. Typically, these include a

series of steps to relate the raw data measurements into energy scattered back to the sensor,

considering geographical location and topographic effects.

1.5 Associated EOS downstream technologies

Alongside EOS technologies operational in space, downstream technological developments are enabling

routine exploitation of EOS data and the creation of new products and services. Downstream innovations



are referring to developments towards the latter stages of the industrial process. In the case of EOS, this

is referring to the value-added services based on satellite data and potential business models.

1.5.1 Computer Vision

Computer vision (CV) refers to the ability to teach a computer how to recognise objects, surfaces,

elements of an image or a video, and associate a semantic concept to each. Artificial Intelligence is

disrupting several industries and computer vision is at the heart of this revolution. There is an enormous

potential in leveraging these techniques for the EO industry alongside companies that provide value

added services. The space industry has now reached a point where it is possible to implement the latest

computer vision technologies. This type of technology is facilitating the automatic recognition of

important dynamic assets in and around the natural environment.

Computer Vision approaches of relevance to this domain include traditional statistical techniques of

Machine Learning and increasingly mainstream Neural Network techniques associated with the field of

Deep Learning. These approaches enable novel and highly efficient insights (e.g. Finer spatial-temporal

modelling or automated identification of features) to be derived from the vast amounts of geospatial

data available.

1.5.2 IT Infrastructure

The latest generation of EO instruments carried on-board constellations of small satellites are beginning

to produce a near continual stream of data intelligence. This unprecedented explosion of data is rapidly

introducing new processing challenges, speeding the development and advancement in contemporary

computing infrastructures, technologies, and data architectures that overcome data management and

analysis challenges.

One such example is Data Cube (DC) technology, which considers a scalable architecture of spatially

aligned time series stack of pixels for ease of data interrogation and analysis. Such a solution has

enormous potential to streamline data distribution and management for providers while simultaneously

lowering the technical barriers for users to access and exploit EOS data. It allows all EOS image data of

30 or more years over an area to be compared and analysed, essential for identifying image trends and

change over time.

Implications of this technology for coastal applications is the increased ease of access to EO data at

scale, allowing for rapid analysis of shoreline change over long-time series’, with the potential to deliver

robust mapping products through both space and time, giving an insight into coastline evolution over

years, and even decades, where data is available. Additionally, the use of existing computer

infrastructure reduces the need for in-house computer capability, reducing operational costs.

1.5.3 Business Models

Current commercial data provider business models tend to be inflexible being predominantly based on

“pay-per-image”, irrespective of how much of the image data is of use to the customer.



This, together with the relatively high cost of imagery, has led to a scenario where market needs are not

being met and there is an underutilisation of available EO data. We are now seeing a diversification of

these business models to allow a more customer-oriented experience, with a decrease in emphasis on

minimum area orders and the development of “pay-per-pixels” models.

These new business models will dramatically decrease the cost of accessing commercial EOS data and

therefore the costs of products and services delivered to the end user. For coastal applications, this

adaption by commercial data providers will facilitate improved access to commercial data at a

dramatically reduced cost, meaning analysis of commercial data can be undertaken in a cost-effective

manner, which is particularly useful for organisations who may have limited funding available for such

studies

1.6 Current EO Technology Drivers

Exposure to global technological advancements is allowing the EO community to take unprecedented

steps towards automation in various applications. Here we tie these to some of the fundamental

requirements of automated mapping of coastal change.

1.6.1 Improved availability and variability of data sources:

Automated mapping of coastal change requires image datasets to be acquired and useable at a relevant

date and time. Improved availability and variability of EO data sources enables denser temporal

coverage and therefore greater likelihood of a cloud free image being available for analysis.

Routine monitoring of change over the same area of interest allows for the improved detection of clouds

due to greater understanding of baseline conditions. Exploitation of contemporary geospatial analysis

infrastructures enables implementation of these localised cloud detection algorithms at the required

temporal scales to be able to identify cloud-free pixel clusters throughout a dense time series of multi-

sensor datasets, maximising the available useful input data to algorithms for detecting coastal changes.

1.6.2 Interoperability of datasets

An essential consideration for the exploitation of multi-datasets for manual, semi-automated, and

automated processing is interoperability across sensors through space and time.

There is a significant challenge of obtaining consistent surface reflectance values due to inherent

geographical variability of aerosol coefficients that must first be addressed. However, there are currently

several international activities which are aiming to overcome these challenges, these include the Data

Cube technology, developed by Geoscience Australia, and the provision of Analysis Ready Data (ARD).



A critical part of both these activities is the initiative to define internationally recognised standards for

data, for example INSPIRE. Consistency with data formats and pre-processing greatly improves the

interoperability of usually disparate EO data sets acquired from an array of difference sensors, and thus

facilitating new techniques such as data fusion.

For coastal shoreline mapping, the ability to work with data from multiple sensors (optical, EO, lidar) will

lead to improved analysis techniques and greater accuracy for end products. Research into multi-sensor

approaches yields successful and can contribute to removing the issue of poor data availability over any

one given area, for example due to frequent dense cloud cover or minimal datasets being available over

a specific area of interest.

1.6.3 Accessibility of datasets

Local processing and data distribution methods currently exploited for managing EO data are not

suitable to address the challenge of scalability, increases in data volumes, and the growing complexities

in preparation, handling, storage, and analysis.

There is increasing precedent to make data available alongside state of the art computing

infrastructures, leveraging datasets in platform environments such as the Open Data Cube, Google Earth

Engine, Amazon Web Services (AWS) etc.

The Deployment of complex technical approaches at unprecedented spatial and temporal scales for

multiple datasets, allows efficient incorporation of artificial intelligence and other automated

approaches. The increased abundance of compute capability allows these approaches to be applied at

ever decreasing costs.

1.6.4 Maturing Non-traditional EO Approaches

Generally, conventional geo-information analyses are still very labour intensive and therefore not well

suited to the growing volumes of Earth observation imagery.

Artificial intelligence, including machine learning and computer vision, present non-traditional

technological approaches to efficiently exploit valuable information from such large volumes of imagery

and other geospatial datasets. Technical applications that may be of relevance to coast line monitoring

include:

• Convolutional neural networks (CNNs) are demonstrably more effective than humans at detecting

and identifying features at spatial and temporal scales associated with contemporary satellite

archives. Intrinsic to this process is the robust representation of lower level features (i.e. edges) [7].

• Recurrent neural networks (RNNs) are proven in effective exploitation of dense timeseries analysis

and increasingly applied to the dense multi-temporal archives of satellite imagery [8].



1.7 Current EO Scientific Developments

Technical developments in both research and commercial domains are greatly accelerated by the

technology drivers outlined above.

In direct relation to moving toward automated coastline monitoring in the immediate to short term are

three key scientific development areas: (i) the improved offering of traditional coastline monitoring

methods, (ii) the evolving considerations associated with remote sensing change detection approaches

and, perhaps most importantly, (iii) the maturing of non-traditional EO approaches to this type of

monitoring challenge.

1.7.1 Improved Application of Traditional Coastline Monitoring Methods

Technological drivers of contemporary EO capabilities have helped to overcome many of the dataset characteristics that until recently have been blockers to delivering automated monitoring EO applications.

These characteristics improve the ability for traditional coastline monitoring approaches to be more

effectively applied in a less manual manner, for example edge-detection, brightness thresholding and

land masking.

1.7.2 Evolving Change Detection Approaches

There has been a genesis of pixel-based change detection approaches, focused on maximising the

exploitation of coarse pixel size data.

This is driven by VHR imagery demonstrating the value of incorporating geometric characteristics

associated with features into the analysis. Leveraging dense timeseries of VHR images allows novel

exploration of feature representation within multi-temporal datasets; primarily in defining the unit for which change shall be monitored.

1.8 Future Satellite Innovations relevant to coastal monitoring

The EO satellite industry is only going to improve and mature going forward, the Copernicus programme

has been extended until at least 2030 with the provision of the 3rd and 4th generation Sentinel satellites

(Sentinel-1C, 1D and Sentinel 2C, 2D etc) ensuring continued monitoring for decades to come.

Whilst the commercial satellite industry is set to grow through the launch of new satellites, both regular

and small, as the associated costs of satellite launch steadily decrease, improving access to space.

Reusable launch vehicles, such as those recently flown successfully by Space-X, will contribute to driving

the cost of launch down.



The provision of additional satellite platforms paves the way for new innovative payload technologies,

which include multi-sensor approaches (e.g. constellation of optical, SAR, and AIS – OptiSAR mission,

Urthecast). In addition, more companies are investigating the use of hyperspectral payloads, such as

that proposed for the upcoming EnMAP mission. Hyperspectral remote sensing has been outlined as a

critical technology which could greatly improve coastal monitoring [37, 38].

Upstream innovations are those, which refer to a change in physical satellite capability, size, quantity or

type. These innovations are going to be mostly driven by miniaturisation of electronics and improved

communications.

1.8.1 Small Satellite Constellation

The next 5 years will see a substantial change in the EO landscape with the continued launch and

deployment of distributed small satellite systems (mass in the range 1 to 100kg). Constellations, such as

UrtheCast’s proposed 16 satellite dual optical and SAR constellation will facilitate daily revisit over

Earth’s surface. Additionally, Planet continue to improve on and develop their fleet of ‘Doves’ –

CubeSat’s capable of daily revisit with producing moderate resolution imagery.

The high temporal resolution satellite imagery that will be available can reveal patterns in and monitor

rapidly changing environmental phenomena. This increased temporal resolution may facilitate image

acquisition at multiple times of day, meaning it would be possible to capture data during various points

during a tidal cycle, and contribute to greatly improving the accuracy of shoreline mapping, as well as

begin to facilitate the development of operational services for coastline monitoring.

1.8.2 High Altitude Pseudo-Satellite (HAPS)

A complimentary potential technology which is likely to benefit from the miniaturisation of electronics

and communications on the back of small satellite advances is the development of High Altitude Pseudo

Satellite (HAPS). There are strong indications that this technology will mature in the next 2-3 years.

Airbus are developing a platform called Zephyr that is quoted as having the following potential

advantages:

• Autonomous, high reliability platform

• Exclusively solar powered – no fuel limitation on flight endurance

• Operating at altitudes above the weather and conventional air traffic

• Low vibration and structural loads to allow high efficiency, lightweight payloads

• Operable globally as a “Constellation” – markedly reducing operating costs

• World’s most advanced and only flight proven HAPS

• Over 14 continuous days of flight – longer than any other High-Altitude UAV

• Not constrained by the flying hours limitation of manned flying vehicles

• Designed and tested to allow routine flight clearance by military and civil authorities



http://www.airbus.com/content/dam/corporate-

topics/publications/brochures/0612_17_zephyr_datasheet_e_horizontal_a4_lowres.pdf

This platform is currently scheduled to be available from 2020 and has the capability of having options

on payloads which include 8 band optical imagery, RADAR, and lidar. The potential of HAPS to operate as

a manoeuvrable persistent, low cost platform could drastically reduce the capture costs for updating

tidal change.

1.8.3 Real Time Data

Greater demand for near real-time applications, especially from the commercial and defence markets,

has led to the development of Geostationary data relay satellites, such as ESA’s European Data Relay

Satellite System (EDRS), the first of which was launched in 2016.

Such satellites will enable EO satellites in low Earth orbits to have almost continual communication with

a controlling ground station. This will facilitate near real-time transfer of data from the satellite to the

ground, thus reducing data latency and allowing near-real time response for product generation, such as

for as shoreline mapping.

1.8.4 Video from Space

Video from space is a relatively innovation, first demonstrated by Terrabella and Urthecast through the

installation of payloads on the International Space Station.

However, these types of data offerings are currently limited and very expensive. The current payloads

can provide imagery for up to 90 seconds at a time over an area of interest covering approximately 1 x 2

km². The key is that this type of technology and data offering will continue to become more prolific.

Video from space will allow true situational awareness over an area of interest, however current

capability may have limited application for the purposes of shoreline detection, where changes tend to

take place slowly. Some value may exist in being able to record the dynamic changes taking place as the

tide changes throughout the day, however whether the temporal resolution of these existing payloads

can meet that requirement is yet to be seen.

However, when considering future innovation, the British company Earth-i has recently launched a proof

of Concept satellite for a new constellation called Vivid-i, which will be the first of its kind to provide full-

colour video; and the first European-owned constellation able to provide both video and still images.

The multiple satellites within the Vivid-i Constellation will significantly increase the ability of companies

and institutions to monitor, track and analyse activities, patterns of life and changes at any location on

earth. The satellite weighs 100kg and will orbit at 505km above the earth travelling at approximately 7km

a second. At the heart of the new satellite is an Ultra High Definition (UHD) camera which will capture

high-resolution images for any location on Earth – and film up to two minutes at a time of video which

can show moving objects such as vehicles, vessels and aircraft.

http://www.airbus.com/content/dam/corporate-topics/publications/brochures/0612_17_zephyr_datasheet_e_horizontal_a4_lowres.pdf

http://www.airbus.com/content/dam/corporate-topics/publications/brochures/0612_17_zephyr_datasheet_e_horizontal_a4_lowres.pdf



Earth-i’s Vivid-i Constellation will be a major leap forward for the Earth Observation industry providing

several innovative capabilities including:

• The provision of high-frame rate images with resolutions better than one metre for any location

on Earth.

• The ability to film moving objects such as vehicles, vessels and aircraft in Ultra High Definition

colour video.

• Revisiting the same location multiple times per day with agile satellites that can be pointed to

image specific areas of interest.

• Rapid tasking of satellites to take images or video, and fast data download within minutes of

acquisition.

• It is envisaged that this technology will enable capture of high accuracy height information,

meaning that it is likely to be suitable for capturing coastal change, whilst also potentially

providing the opportunity to monitor shorelines through both stages of MHW and MLW

1.8.5 Better Technical Specifications

As the industry continues to carryout advances in the miniaturisation of electronics, better optics

systems, power capture and storage and communications systems, EO satellites continue to improve

their technical specifications.

These advancements are concentrated in five key areas: spatial resolution, temporal resolution,

radiometric resolution, real time data access and greater capacity. These advances also facilitate the

development of cheaper satellites, which enables individual companies and institutions to buy their own

dedicated spaceborne assets.

Furthermore, increased engagement with the user community in advance of developing new sensors

means that the opportunity exists to tailor new missions to be able to provide specific data requirements

aimed at addressing current gaps.

1.8.6 On-board Processing

Satellites are producing increasing amounts of data, for example, Sentinel-1 generates 1.6TB/Day, which

needs to be downlinked to a ground station. This puts a huge strain on the downlink in the service chain,

especially when the end use might only use a fraction of this data to extract the desired useful

information. By carrying out satellite outboard board processing it vastly reduces that amount of data,

which needs to be downlinked to the ground, an early example of this is the ESA Wavemill project.



1.9 References

[1] Kropatsch, W. G., & Strobl, D. (1990). The generation of SAR layover and shadow maps from digital

elevation models. IEEE Transactions on Geoscience and Remote Sensing, 28(1), 98-107.

[2] Dellepiane, S., De Laurentiis, R., & Giordano, F. (2004). Coastline extraction from SAR images and a

method for the evaluation of the coastline precision. Pattern Recognition Letters, 25(13), 1461-1470.

[3] Descombes, X., Moctezuma, M., Maître, H., & Rudant, J. P. (1996). Coastline detection by a Markovian

segmentation on SAR images. Signal Processing, 55(1), 123-132.

[4] Liu, H., & Jezek, K. C. (2004). Automated extraction of coastline from satellite imagery by integrating

Canny edge detection and locally adaptive thresholding methods. International Journal of remote

sensing, 25(5), 937-958.

[5] Niedermeier, A., Romaneessen, E., & Lehner, S. (2000). Detection of coastlines in SAR images using

wavelet methods. IEEE Transactions on Geoscience and Remote Sensing, 38(5), 2270-2281.

[6] Bamler, R., & Schättler, B. (1993). SAR data acquisition and image formation.

[7] CNN REF: Emmanuel Maggiori, Yuliya Tarabalka, Guillaume Charpiat, Pierre Alliez. Convolutional

Neural Networks for Large-Scale Remote Sensing Image Classification. IEEE Transactions on Geoscience

and Remote Sensing, Institute of Electrical and Electronics Engineers, 2017.

[8] Emmanuel Maggiori, Guillaume Charpiat, Yuliya Tarabalka, Pierre Alliez. Recurrent Neural Networks

to Correct Satellite Image Classification Maps. IEEE Transactions on Geoscience and Remote Sensing,

Institute of Electrical and Electronics Engineers, 2017

[9] Syaifulnizam Abd Manaf, Norwati Mustapha, Md Nasir Sulaiman, Nor Azura Husin, and Helmi Zulhaidi

Mohd Shafri, “Validation Assessment of Shoreline Extraction on Medium Resolution Satellite Image”, The

2nd International Conference on Applied Science and Technology 2017 (ICAST’17), 05 October 2017.

[10] Yun-Jae Choung and Myung-Hee Jo,” Comparison between a Machine-Learning-Based Method and a

Water-Index-Based Method for Shoreline Mapping Using a High-Resolution Satellite Image Acquired in

Hwado Island, South Korea”, Hindawi Journal of Sensors, Volume 2017.

[11] Tarig A. Ali, Serter Atabay, Maruf Mortula, and Ruijin Ma, “Semiautomatic Extraction of the Shoreline

from High-Resolution Satellite Imagery and Coastal Terrain Model”, Surveying and Land Information

Science, Vol. 74, No. 1, 2015, pp. 15-21.

[12] Ratna Sari Dewi, Wietske Bijker, Alfred Stein, and Muh Aris Marfai, “Fuzzy Classification for Shoreline

Change Monitoring in a Part of the Northern Coastal Area of Java, Indonesia”, Remote Sensing in Coastal

Environments, 2016, 8, 190.



[13] Kaichang Di, Ruijin Ma, Jue Wang, Ron Li, “Coastal Mapping and Change Detection Using High-

Resolution IKONOS Satellite Imagery”, Conference: Proceedings of the 2003 annual national conference

on Digital government research, January 2003.

[14] Ivan Sekovski, Francesco Stecchi, Francesco Mancini and Laura Del Rio, “Image classification

methods applied to shoreline extraction on very high-resolution multispectral imagery”, International

Journal of Remote Sensing, Volume 35, 2014 - Issue 10, Pages 3556-3578.

[15] Raju Aedla, G. S. Dwarakish and D. Venkat Reddy, “Automatic Shoreline Detection and Change

Detection Analysis of Netravati-Gurpur Rivermouth Using Histogram Equalization and Adaptive

Thresholding Techniques”, Aquatic Procedia, Volume 4, 2015, Pages 563-570.

[16] N.Demir , M.Kaynarca, S.Oya, “Extraction Of Coastlines with Fuzzy Approach using Sentinel-1 SAR

image”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information

Sciences, Volume XLI-B7, 2016.

[17] Elena Vandebroek, Roderik Lindenbergh, Freek van Leijen, Matthieu de Schipper, Sierd de Vries and

Ramon Hanssen, “Semi-Automated Monitoring of a Mega-Scale Beach Nourishment Using High-

Resolution TerraSAR-X Satellite Data”, Remote Sens, 2017, 9, 653.

[18] Maria Francesca Bruno, Matteo Gianluca Molfetta, Michele Mossa, Raffaele Nutricato, Alberto Morea

and Maria Teresa Chiaradia, “Coastal Observation through Cosmo-SkyMed High-Resolution SAR Images”,

Journal of Coastal Research Part 2(Special issue 75), March 2016, 795-799.

[19] N. Demir, S. Oy, F. Erdem, D. Z. Şeker, B. Bayram, “Integrated shoreline extraction approach with use

of RASAT MS and Sentinel-1A SAR images”, ISPRS Annals of the Photogrammetry, Remote Sensing and

Spatial Information Sciences, Volume IV-2/W4, 2017 ISPRS Geospatial Week 2017, 18–22 September 2017,

Wuhan, China.

[20] Hongxing Liu, Lei Wang, Douglas J. Sherman, Qiusheng Wu, Haibin Su, “Algorithmic Foundation and

Software Tools for Extracting Shoreline Features from Remote Sensing Imagery and lidar Data”, Journal

of Geographic Information System, 2011, 3, 99-119.

[21] Stephen Sagar, Dale Roberts, Biswajit Bala, Leo Lymburner, “Extracting the intertidal extent and

topography of the Australian coastline from a 28-year time series of Landsat observations”, Remote

Sensing of Environment 195 (2017) 153–169.

[22] Marine and Coastal Ecosystems and Human Well-Being, UNEP, 2006.

[23] UNEP (2006). Marine and coastal ecosystems and human wellbeing: A synthesis report based on the

findings of the Millennium Ecosystem Assessment. UNEP. 76pp.

[24] UNEP, European Environment Agency (1999). State and pressures of the marine and coastal

Mediterranean environment.



[25] Lisa Cowart, D. Reide Corbett and J.P. Walsh (2011). Shoreline Change Along Sheltered Coastlines:

Insights from the Neuse River Estuary, NC, USA. Remote Sens. 2011, 3, 1516-1534; doi:10.3390/rs3071516.

[26] Thomas R. Allen (2011). Remote Sensing in Coastal Ecosystem. A special issue of Remote Sensing

(ISSN 2072-4292).

[27] Michael Ablain, Mélanie Becker, Jérôme Benveniste, Anny Cazenave, Nicolas Champollion, Silvia

Ciccarelli, Svetlana Jevrejeva, Gonéri Le Cozannet, Nicoletta Leornardi, Hubert Loisel, Nathalie Long,

Philippe Maisongrande, Cyril Mallet, Marta Marcos, Melisa Menéndez, Benoît Meyssignac, Andrew Plater,

Daniel Raucoules, Andrea Taramelli, Stefano Vignudelli, Emiliana Valentini, Philip Woodworth and Guy

Wöppelmann (2016). Monitoring the evolution of coastal zones under various forcing factors using space-

based observing systems. International Space Science Institute (ISSI) Forum.

[28] Fatih Temiz, S. Savaş Durduran (2016). Monitoring Coastline Change Using Remote Sensing and GIS

Technology: A case study of Acıgöl Lake, Turkey. IOP Conf. Series: Earth and Environmental Science

44,042033, doi:10.1088/1755-1315/44/4/042033.

[29] Dean B. Gesch (2009). Analysis of lidar elevation data for improved identification and delineation of

lands vulnerable to sea-level rise. Journal of Coastal Research, SI (53), 49–58.

[30] Mason, D.C., Gurney, C., Kennett, M. (2000). Beach topography mapping – a comparison of

techniques. Journal of Coastal Conservation 6: 113-124, 2000.

[31] Jamie Carter, Keil Schmid, Kirk Waters, Lindy Betzhold, Brian Hadley, Rebecca Mataosky, and

Jennifer Halleran (2012). Lidar 101: An Introduction to lidar Technology, Data, and Applications. National

Oceanic and Atmospheric Administration (NOAA) Coastal Services Centre. Revised. Charleston, SC: NOAA

Coastal Services Center.

[32] PEDRO W.M. SOUZA-FILHO, WALDIR R. PARADELLA (2003). Use of synthetic aperture radar for

recognition of Coastal Geomorphological Features, land-use assessment and shoreline changes in

Bragança coast, Pará, Northern Brazil. Anais da Academia Brasileira de Ciências, 75(3): 341-356.

[33] S. Dellepiane, R. De Laurentiis, F. Giordano (2004). Coastline extraction from SAR images and a

method for the evaluation of the coastline precision. Pattern Recognition Letters 25 (2004) 1461–1470.

[34] http://due.esrin.esa.int/files/192-171-5-73_2006112315560.pdf

[35] Pasquale Maglione, Claudio Parente & Andrea Vallario (2014) Coastline extraction using high

resolution WorldView-2 satellite imagery, European Journal of Remote Sensing, 47:1, 685-699, DOI:

10.5721/EuJRS20144739

[36] Kuo-Hsin Tseng, Chung-Yen Kuo, Tang-Huang Lin, Zhi-Cheng Huang, Yu-Ching Lin, Wen-Hung Liao, Chi-

Farn Chen https://www.sciencedirect.com/science/article/pii/S0924271617300217

http://due.esrin.esa.int/files/192-171-5-73_2006112315560.pdf

https://www.sciencedirect.com/science/article/pii/S0924271617300217



[37] Teodoro, A.C. (2016) Optical Satellite Remote Sensing of the Coastal Zone Environment – An

Overview’ in Environmental Applications of Remote Sensing. Intech Publications.

[38] Gens R: Remote sensing of coastlines: detection, extraction and monitoring. Int J Remote

Sens. 2010;31(7): 1819-1836.

[39] SEOS – Spectral Reflectance Signature - Clark, R.N., Swayze, G.A., Wise, R., Livo, E., Hoefen, T.,

Kokaly, R., Sutley, S.J., 2007, USGS digital spectral library splib06a: U.S. Geological Survey, Digital Data

Series 231.

[40] Boak EH, Turner IL: Shoreline definition and detection: a review. J Coastal Res. 2005;

21:688-703.

[41] Guariglia A, Buonamassa A, Losurdo A, Saladino R, Trivigno ML, Zaccagnino A, Colangelo

A: A multisource approach for coastline mapping and identification of shoreline

changes. Ann Geophys. 2006;49:295-304

[42] Maiti S, Bhattacharya AK: Shoreline change analysis and its application to prediction:

A remote sensing and statistics-based approach. Mar Geol. 2009; 257:11-23.

[43] Kuleli T, Guneroglu A, Karsli F, Dihkan M: Automatic detection of shoreline change

on coastal Ramsar wetlands of Turkey. Ocean Engin. 2011;38:1141-1149.

[44] Pardo-Pascual JE, Almonacid-Caballer J, Ruiz LA, Palomar-Vázquez J: Automatic extraction

of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel

precision. Remote Sens Environ. 2012;123:1-11.

[45] http://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-5426/10518_read-23261/

[46] Jenkins, G J, Perry, M C and Prior, M J O. 2008. The climate of the United Kingdom and recent trends.

Met Office Hadley Centre, Exeter, UK

http://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-5426/10518_read-23261/



Annex A Table - Earth Observation Satellites

Mission Operator Commercial/

academic/pubic

Spatial

resolution

(m)

Spectral

(bands)

Average

temporal

resolution

(mid

latitudes)

Operational

WorldView 3 DigitalGlobe Commercial 0.31** 28 1 - 4.5 days As of 2014

WorldView 4 DigitalGlobe Commercial 0.31** 4 1 - 4.5 days As of 2016

WorldView 2 DigitalGlobe Commercial 0.46 8 1 - 4 days As of 2009

Pleiades 1a/

1b

Airbus Commercial 0.50 4 1 - 2 days As of 2011;

2012

Deimos 2 Elecnor

Deimos

Commercial 1 4 2 days As of 2014

UK-DMC3 Earth-i Commercial 1 4 1-5 days As of 2015

SPOT 6/7 Airbus Commercial 1.5 4 1 - 3 days As of 2012;

2014

SPOT 5 Airbus Commercial 2.5 4 2-5 days As of 2002

Planet Scope Planet Commercial 4 4 1 As of 2017

RapidEye Planet Commercial 5 6.5 1 - 6 days As of 2008

Sentinel-2 ESA/EC Publicly

Available

10 7 6-12 days As of 2015

Landsat 8 USGS publicly

Available

15 8 16 days As of 2013


Available

15 7 16 days As of 1999


Available

30 7 16 days As of 1984

** Pansharpened data product

Table 9 - Commonly used optical EO satellites ordered by spatial resolution.



Mission Operato

r

Commercial

/

academic/

pubic

Frequenc

y

Waveban

d

Polarisatio

n

Average

temporal

resolutio

n (mid-

latitudes)

Operationa

l

Radarsat

- 2 MDA Commercial 5.4 GHz C-band

Multimode

single, dual

or quad

24 Days As of 2007

TerraSAR

- X DLR Commercial 9.65 GHz X-band

Multimode

single, dual 11 Days As of 2007

Cosmo –

SkyMed ASI Italy Commercial 9.60 GHz X-band

Multimode

single or

dual

Daily As of 2007

Sentinel-

1 ESA

Publicly

Available 5.4 GHz C-band

Dual

selectable 5 Days As of 2014

ALOS - 2 JAXA Commercial 1.2 GHz L-band

Multimode

single, dual

or quad

14 Days As of 2014

RISAT - 1 ISRO Academic 5.35 GHz C-band

Multimode

single, dual,

circular

(hybrid)

polarimetry

or quad

25 Days As of 2012

RISAT - 2 ISRO Academic 9.59 GHz X-band

Multimodal,

polarisation

selectable

among: HH,

HV, VH, VV

14 Days As of 2008

KOMPSA

T - 5 KARI Commercial 9.66 GHz X-band

Multimodal,

polarisation

selectable

among: HH,

HV, VH, VV

25 Days As of 2014

Table 10 - Characteristics of the most commonly used SAR satellites.



Annex B

Existing Ordnance Survey

Capture Specification



1 Annex B - Existing Ordnance Survey Capture

Specification

1.1 Tidal Water

When Ordnance Survey capture a tide line by measurement at the correct state of the tide the actual

height of the tide that was measured must be within 0.3m of the predicted value.

When capturing a tide line by identifying the appropriate contour (through direct capture or

interpolation from a Digital Terrain Model) the contour is also measured to an accuracy of 0.3m.

1.2 Foreshore Depiction

Foreshore and permanent tidal water includes all areas that are affected by the action of normal tides.

The limit of tidal water at high and low tides is represented by a Topographic Line feature in

OS MasterMap®.

Where the tide line is not coincident with a manmade feature the Topographic Line is given the Form

'Tidal Mark', the description of the tide line (for example Mean Low Water) as the Function attribute.

Where the tide line is coincident with a manmade feature (such as a sea wall) the Topographic Line is

given the appropriate Form e.g. 'Built Obstruction' or 'Lock Gate' with the description of the tide line

recorded as the Function attribute 'Tidal Mark'. Where high and low water marks are coincident a

combined Function attribute is recorded. For example, 'Mean High Water Mark and Mean Low Water

Mark'. Watercourses depicted as single Topographic Line features across the foreshore are always

described with the Real-World Term 'Tidal Water'.

1.3 Extent of the Realm

The limit of the mainland and islands of Great Britain that exist above the Mean High-Water mark in

England and Wales; or the Mean High-Water mark (Springs) in Scotland;

The foreshore extending from the mainland and islands of Great Britain as defined above out to the

Mean Low Water mark in England and Wales; or the Mean Low Water mark (Springs) in Scotland.

Offshore rock outcrops, sand and mud banks or similar are not included in the Extent of the Realm

Features within the extent of the realm are captured plus any features outside the realm as detailed

below:

• All rock features meeting the size criteria for offshore rocks (10m² at high water or 20m² at low

water)

• All structures meeting specification (8 sq. m).

• Areas of sand, mud or shingle whether inter tidal or not, that exceed 0.1 hectares



Features in the open sea and in estuaries, lochs and tidal rivers seaward of the lowest bridge or boundary

feature crossing the river or loch. Capture:

• All rock features meeting the size criteria for offshore rocks (10m² at high water or 20m² at low water)

• Structures meeting standard minimum size criteria for an area (8 sq. m)

Areas of sand, mud or shingle above high water and exceeding 0.1 ha. Areas of sand, mud or shingle

below high water where:

• they contain a structure that warrants capture or

• they are connected to or contain a non-tidal area or

• they are crossed by a right of way.

1.4 Land Surface in Intertidal Areas

The intertidal zone is that part of the land surface that lies between the high and low water marks.

To describe the nature of this area multiple Form values are given for all Topographic Area features that

represent this zone.

For example, 'Inter Tidal + Slipway' or 'Inter Tidal + Sand'

Vegetation is never captured on the foreshore (with the exception of Saltmarsh).

1.5 Landform: Cliff and Coastal Slopes

Cliffs are not shown below MHW. The basic depiction of cliffs is as follows:

• Cliffs are depicted as area features using Landform features.

• Coastal cliffs are additionally depicted with Topographic area or line features.

• MHW alignment is always captured in its true position (this is important for customers).

Where features vary between >2m and <2m wide, changes in depiction between topographic area and

line should not be made for short sections (approx. < 20m). One approach should be taken which gives

the most representative view for the user (usually area depiction) and used, even though this may result

in having to exaggerate the width of the cliff and topographic area for short sections.

• For an area of coastal cliff generally over 2m wide, capture a topographic area in addition to the Cartographic Cliff landform feature

• For an area of coastal cliff generally under 2m wide capture a topographic line in addition to the Cartographic Cliff landform feature.



There are often areas of land between the base of a cliff and MHW, for example some long and thin strips

of beach, and rocky ledges. As a guide these should be captured where over 2m wide (10m in 4.0m RMSE

areas) and over 0.1 Ha area (1ha in 4.0m RMSE areas). If under these sizes, then the cliff features should

be extended to the MHW alignment.

1.6 Coastal Slope: An area of steep natural slope along the coast.

Coastal slopes are normally shown only in the immediate area of the coast when extensive and are

terminated where they merge into the general surface topography.

The basic depiction of coastal slopes is as follows:

• Coastal slopes are depicted as area features using Landform Area feature captured with a Real-

World Term of Coastal Slope.

• The limits of the coastal slope are indicated by use of a Landform Line feature.

1.7 Nautical Berthing Sites

Always capture, except those within a single private residence or a site in which they can be expected.

1.8 Slipways

Always capture.

If below 1m wide in Urban areas (2m elsewhere) capture as a line feature.

Collect the Real-World Features of the slipway including the changes in ground surface i.e. shingle

Collect the position of the Mean High Water on the slip

1.9 Spreads

Where a water feature ‘Spreads’ or ‘Collects’, capture of water links are not required coincident with the

topographic line features classified as spreads or collects are not required.

1.10 Boundaries Merged to Tidelines

Where an administrative boundary has been merged to a high or low water mark it will normally move

with any change in position of the feature representing the high or low water mark regardless of the

cause of the change.



A boundary merged to a feature that represents both a high or low water mark and a physical real-world

feature - for example the edge of a quay or wharf - may remain merged to the alignment of the original

feature even if the high or low water mark. An example would be the creation of a barrage that made a

body of water non-tidal.

Whilst updating a coastal area or river estuary there may be positional changes to low water channels

with attached boundaries. The Boundaries team should be consulted and notified of the changes, they

will advise on best course of action.

1.11 Coastal Protection, e.g., Groynes, Sea Walls, Boulders

Collect the Real-World Features of the Groyne/Sea Wall.

Collect Mean High-Water line.

They are captured to the point where they no longer project above MLW.

The following work instructions explain the process that Ordnance Survey currently follows to update

tidal change in Great Britain. This explains the process of updating tide lines from their creation using

lidar data through to the editing stage.

Tidal Surveys are a unique area of data capture and require knowledge of this area of the specification.

The relevant sections of Ordnance Survey’s Data Capture and Edit Guide provide context for the

topographic themes which include the following:

• Tidal Definitions, including Accuracy Requirements and Normal Tidal Limit (NTL)

• Tidal Water

• Foreshore Depiction

• Extent of the Realm

• Land Surface in Intertidal Areas

• Landform: Cliff and Coastal Slopes

• Nautical Berthing Sites

• Slipways

• Spreads

• Boundaries Merged to Tidelines

• Coastal Protection, e.g., Groynes, Sea Walls, Boulders

• Worked Examples: Harbour, Coastal Features



1.12 Calculating Tide Values

Tidal contour values are calculated by referring to Admiralty Tide Tables.

Figure 44 – Example of Tide table information that is used by Ordnance Survey

1.12.1 Existing lidar data

There are different types of lidar datasets available to Ordnance Survey which vary depending on the

geography of the location:

• Lidar for Wales

• Channel Coastal Observatory (CCO) DSM Scotland, Coast Surfzone

• EA Composite and LAZ (England)

1.12.2 Boulders

Boulders below the MLW are deleted except boulders below the MLW line that protrude higher than it

and are greater than 20m in area can be kept. Boulders in the inter tidal range must be greater than 10m

in area to be kept.

1.12.3 Estuaries

In some cases, the results for lidar imagery in docks, harbours and estuaries is not always accurate so the

use of current imagery to capture new channels or keep the original lines is recommended.



Figure 45 - The above example showed the original MLW (purple line) which is now in incorrect position.

Using the imagery (flown at nearly low tide) and setting up the cursor to predicted MLW height it is

possible to plot the channel by hand into correct position.

The above example showed the lidar data (purple line) did not cover some of the smaller estuaries, but

they must be retained

1.13 England and Wales

In England and Wales, Ordnance Survey data represents a high and low water mark for the average tide.

The average tide is defined as that which occurs midway between the Spring and Neap tides.

• The line reflecting the alignment of the mean high tide is attributed with a Function of ‘Mean High

Water Mark’.

• The line reflecting the alignment of the mean low tide is attributed with a Function of ‘Mean Low

Water Mark’.

• If the alignments are coincident then the line is attributed with a function of ‘Mean High Water

Mark and Mean Low Water Mark’.

Mean High Water (MHW) and Mean Low Water (MLW) is calculated by finding the mean between the High

and Neap tides for that location.



1.14 Scotland

In Scotland Ordnance Survey data represents a high and low water mark for the average spring tide.

• The line reflecting the alignment of the mean spring high tide is attributed with a Function of

‘Mean High Water Spring Mark’.

• The line reflecting the alignment of the average mean spring low tide is attributed with a Function

of ‘Mean Low Water Spring Mark’.

• If the alignments are coincident then the line is attributed with a function of ‘Mean High Water

Spring Mark and Mean Low Water Spring Mark’.

These figures give you the predicted values at which to fix the heights (z values) and plot tide line

contours within Dat/EM and Object Editor.

Contact us

Ordnance Survey, Explorer House,

Adanac Drive, Southampton SO16 0AS

+44 (0)345 605 0505

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