Monitoring, Delineating and Analysing Shorelines
- Arpit Shah
- Jun 18, 2024
- 14 min read
Updated: Feb 9
1. INTRODUCTION
Where the Land meets the Sea...
This phrase has a distinct poetic connotation - it invoked the memory of a pristine beach for me - perhaps for you too, just as well.
Where does the Land actually meet the Sea, though?
The exact spot is called the Shoreline and its position and shape is in a continuous state of flux -influenced by natural factors such as Waves, Winds and Currents as well as anthropogenic factors such as Coastal Infrastructure Development, Sand Mining and other human causes. A way to categorize the natural influencers is based on their temporal (time) characteristics - for example, Tides occur daily and are cyclical in nature, a Storm Surge is a sporadic phenomenon, and the effects of Transgression (rise in sea level due to melting glaciers etc.) becomes evident only over a much longer time horizon. Together, these contribute towards making the coast dynamic in nature.
The photo of Gibson Beach (Figure 1) was captured during Low Tide - the illustration below depicts a basic anatomy of a coastal environment.
Some 37 per cent of the world's population lives within 100 kilometers of the coast, at a population density twice the global average - United Nations Environment Programme.
Coupled with the growing threat from Coastal Erosion (loss of coastal sediments to the sea) and Marine Transgression (rise in sea level due to climate change among other factors), population density is what triggers the need for regular and accurate monitoring of the shoreline position as well as its evolution through the passage of time.
India, by virtue of having 7000+ kilometers of coastline (18th longest in the world), also conducts extensive research in this field spearheaded by the National Centre for Coastal Research under Ministry of Earth Sciences. Here's a snippet-
"(Based on Shoreline Change Surveys done from 1990-2018), it is observed that 33.6% of the Indian Coastline was vulnerable to erosion, 26.9% was under accretion (growing) and 39.6% was in stable state" - Press Information Bureau (PIB) Media Release in December 2023
Besides sharing India-specific data through this release, I also wanted to highlight an interesting aspect which the readers of this post should become aware of - the terms Shoreline and Coastline are often used interchangeably. Are they one and the same?
No, they aren't.
It took me a while to figure this out but here's what I've gathered - while the exact definition could vary from country to country, there are two distinct interpretations of the word Coastline - one is the geopolitical version and the other is the geological version.
The geopolitical coastline is the predetermined boundary of a nation / region located adjacent to a water body (not to be confused with a nation's maritime boundary). The Indian Ministry's use of the word coastline in the media release above is in the geopolitical context. I presume that the precise location of this geopolitical coastline is demarcated from the boundary of the Littoral Zone i.e. the edge of the Nearshore/the end of the Continental Plate - labelled as Low-tide breaker line in Figure 1.
The geological interpretation of coastline is distinctly different: refer the depiction below-
An easy way to grasp what is being depicted is that while a Shoreline is where the Sea meets the Shore, the Coastline is, geologically speaking, where the Shore meets the Land. Just in case you are wondering, distinguishing Shore from Land is straightforward - a shore is made of sand (formed from the deterioration of rocky structures upon millennia of action by waves and winds) and/or other accumulated sediments (deposited by the sea via its currents and tides - the difference between these two is lucidly explained here).
What should strike you, however, is that in neither of these two interpretations - geopolitical or geological - does the definition of Coastline match the definition of Shoreline. This delineation is important to understand - because while both are casually used interchangeably, all scientific references pertaining to the precise location where the land meets the sea (technically described as the physical interface of land and water - Dolan et al., 1980) that I've come across involves the use of the word Shoreline and not of Coastline (re-read the media release above once again if you'd like...😉).
Hence, to be technically accurate, this post concerns itself with Shoreline Delineation & Monitoring.
SECTION HYPERLINKED TABLE OF CONTENTS
1.     Introduction
2. Scope of this Study
2. SCOPE OF THIS STUDY
As it turns out, there are multiple ways to detect / estimate the location of Shoreline - from photographs, drone images, beach surveys, vehicle-mounted GPS, remote sensing, and others. Some of the aspects to consider while selecting an appropriate method are - desired spatial and temporal resolution, data frequency and accuracy requirements, and the cost of acquiring and processing data.
In this post, I'll present seven video walkthroughs on some of the ways in which Satellite-based Remote Sensing can be, and is being used, to detect Shorelines and monitor its evolution through time. The techniques range from rudimentary monitoring using Google Earth to more technical extraction methods such as Radar Reflectance Thresholding, NDVI+Tasseled Cap transformation, SAET and EPR4Q algorithms. While this is not an exhaustive list, it is diverse in terms of the methodology utilized. I've used two sources of Satellite imagery in my demonstrations- Landsat (NASA, USA) & Sentinel (ESA, Europe) - both have a spatial and temporal resolution suitable for Earth Observation purposes and are commonly used by researchers for a variety of related workflows.
Besides knowledge-sharing with enthusiasts, I hope that my work encourages the creation of Open-Source Intelligence. Given the diversity and magnitude of Climate Change-related threats that we face today, it is imperative that there are aware, informed and trained personnel who can keep an eye on the developments and pre-empt disasters.
2.1 Viewing Shoreline Evolution Using Google Earth
Study Area: Someswar - Batapady Beach (South-West India)
This technique entails using Google Earth - Alphabet's Satellite Imagery rendering platform - to visually monitor the Shoreline through time using the Historical Imagery feature (optical) available on its platform. Additionally, with the Measurement tool in the software, one can obtain an estimate about the rate of erosion / accretion too.
While this technique is rudimentary, it does give a quick, macro-level perspective about the state and evolution of shoreline for a given study area. For example, I was able to ascertain which stretches of the Someswar - Batapady coast alongside the Arabian Sea were ravaged by erosion over a period of time in a matter of a couple of minutes. Alphabet also has an advanced Satellite Imagery processing platform - Google Earth Engine - but I've not demonstrated it in this post.
TIMESTAMPS:
00:00Â - Video #.1 Headline
00:04Â - 1.1 Exploring the Area of Interest on Google Maps
02:30Â - 1.2 Visually monitoring the Shoreline using Google Earth Pro
2.2 Viewing Shoreline Evolution Using EO Browser
Study Area:Â Marina Beach, Chennai - Tamil Nadu (South-East India)
EO Browser surpasses Google Earth for Shoreline Monitoring purposes on a few counts-
a) it has a steady stream of Historical Satellite Imagery from a variety of sources to choose from,
b) it allows for imagery processing on-the-fly (cloud) with preset and custom Band Combinations,
c) it has tools such as Histogram and Time-Lapse video creation which allows for accurate thresholding and enhanced visualization.
These aspects helped me to distinctly identify when the exit route of Adyar river into the Bay of Bengal was being blockaded by Marina beach due to accretion (this is a cause of concern as water pollution in the city is unable to dilute into the sea through Tidal Flushing).
TIMESTAMPS:
00:00Â - Video #.2 Headline
00:04Â - 2.1 Exploring Area of Interest and Band Combinations on EO Browser
02:48Â - 2.2 Creating a Time-Lapse Video of the Area of Interest using EO Browser
2.3 Extracting Shoreline by Thresholding Radar Reflectances
Study Area: Visakhapatnam / Vizag - Andhra Pradesh (South-East India)
While the previous two workflows allowed me to visually monitor Shorelines from Satellite Imagery accessible online and observe its evolution through time, this is the first workflow where I've demonstrated the actual extraction/delineation of a shoreline from downloaded raw Satellite Imagery. The Study Area - buzzing tourism destination of Visakhapatnam - is home to several beaches that have undergone rampant erosion in the recent past thereby impacting visitor footfall.
Radar Satellite Imagery (SAR) has a couple of notable advantages over Optical/Multispectral Imagery - it has an active illumination source - an electromagnetic emitter which allows a) the satellite to obtain readings even in zero-light conditions such as during night and b) the longer wavelengths it emits bypasses clouds and rain, thereby allowing the reflectances (backscatter) to remain unaffected by weather conditions aiding in the accurate assessment of surface features. That being said, using Radar Remote Sensing to delineate Shorelines has a few limitations as well - a) the low spatial resolution of the Imagery itself, as well as b) outdated DEMs which make it difficult to mask out Water pixels. Nonetheless, this method can be combined with other Shoreline extraction techniques that involve the use of Optical Imagery which would allow for better delineation.
TIMESTAMPS:
00:00Â - Video #.3 Headline
00:04Â - 3.1 Exploring Area of Interest using Google Maps
03:08Â - 3.2 Downloading SAR (Synthetic Aperture Radar) Imagery using Copernicus Browser
08:48Â - 3.3 Post-Processing the Raw Radar Imagery using SNAP Software
25:25Â - 3.4 Post-Processing the SNAP-processed Radar Imagery using ArcGIS Pro
2.4 Extracting Shoreline by using Water Radiometric Index
Study Area: Anjuna - Goa (South-West India)
Out of the 41 beaches surveyed by National Centre for Sustainable Coastal Management (NCSCM) in the popular beach state of Goa, 21, including my study area - Anjuna Beach, were found to be suffering from sand erosion.
How Solar Radiation, particularly the electromagnetic rays in the Visible Light portion of the spectrum, interacts with surface features is fundamental to being able to distinguish one type from the other using Multispectral Remote Sensing, and it is no different when it comes to Shoreline delineation - Water has a low reflectance rate in Visible Light range and zero reflectance in Near Infrared range (NIR), for example.
This is in stark contrast to Vegetation & Soil - both of which have a very high reflectance to NIR. Hence, a particular type of Water Index - Modified Normalized Difference Water Index (MNDWI) in this instance - can be used to demarcate Land features from Sea features, thereby making it possible to extract the Shoreline from Multispectral Imagery.
Because Multispectral Imagery relies on a passive source of illumination - Sunlight - it is important to select Cloud-free Satellite Images for the analysis, especially over the Study Area, in order to perform the delineation accurately.
TIMESTAMPS:
00:00Â - Video #.4 Headline
00:04Â - 4.1 Exploring Area of Interest and Band Combinations on EO Browser
06:48Â - 4.2 Downloading Optical Imagery from Copernicus Browser
10:13Â - 4.3 Post-Processing the Raw Optical Imagery using SNAP Software
24:15Â - 4.4 Post-Processing the SNAP-processed Optical Imagery on ArcGIS Pro
35:59Â - 4.5 Determining Land-Sea Threshold using EO Browser
2.5 Extracting Shoreline by using Vegetation Index + Tasseled Cap transformation technique
Study Area: Anjuna - Goa (South-West India)
Just as Water features were first identified and then subsequently the edge where it met non-Water features was demarcated as the Shoreline in the previous workflow, a similar logic albeit with an contrasting method has been demonstrated in this workflow. Here, a Vegetation Index - Normalized Difference Vegetation Index (NDVI) - has been used to identify non-Water features and subsequently its edge has been demarcated as the Shoreline.
I've deliberately kept the Study Area - Anjuna and its surroundings in the state of Goa, India, and the timeline of Imagery datasets the same as the previous workflow as this would allow me to compare the output derived from both the methods (Water Index and Vegetation Index) and draw interesting insights! Check out the demonstration to know more-
TIMESTAMPS
00:00Â - Video #.5 Headline
00:04Â - 5.1 Landsat 8 vs Sentinel-2 Optical Imagery
01:44Â - 5.2 Downloading Landsat 8 Imagery from USGS Earth Explorer
07:20Â - 5.3 Deploying NDVI+Tasseled Cap technique using Landsat toolbox on ArcGIS Pro
27:28Â - 5.4 Visualizing Landsat 8-derived shoreline on Google Earth and comparing it with the Sentinel 2-derived shoreline
2.6 Automatic Extraction of Shoreline using SAET algorithm and performing Change Analysis
Study Area: Satabhaya - Odisha (East India)
If you've seen the previous workflow videos, you'll notice how tedious it could be sometimes to extract a shoreline if the coast is not linear. Moreover, the landscape around the coast is ever-evolving - a complex interplay of factors such as tides, waves, floods and winds which does make it difficult to extract a contiguous cluster of edge pixels/line segments which one can demarcate as Shoreline.
The Shoreline Analysis and Extraction tool or SAET - developed by European Coastal Flood Awareness System (ECFAS) and released only recently in July 2023 is a blessing in this regard. Not only is the method of Multispectral Imagery search, download and processing automated but also the resulting Shoreline extracted is contiguous over the Study Area as it should be. The erosion-ravaged region around Satabhaya in the eastern Indian state of Odisha has a tricky terrain - a rugged coast with floods and/or tiny water bodies interspersed with the landmass. But the SAET algorithm proved to be a boon and was able to process the Imagery and extract a contiguous Shoreline in a few minutes.
While setting up SAET can be complicated, you'll surely find using it to be a breeze! Come, have a look at the video workflow below-
TIMESTAMPS
00:00Â - Video #.6 Headline
00:04Â - 6.1 Getting to know and preparing the SAET tool
10:54Â - 6.2 Downloading Landsat 8 Optical Imagery for Area of Interest using SAET tool
16:02Â - 6.3 Extracting Shoreline from Downloaded Landsat 8 Imagery using SAET
19:26Â - 6.4 Observing the extracted shoreline on ArcGIS Pro
20:41Â - 6.5 Using Google Earth Pro to validate the accuracy of the extracted shoreline
23:39Â - 6.6 Using the Raw Landsat 8 Imagery itself to validate the accuracy of the extracted shoreline 28:16Â - 6.7 Quantitative Analysis of Temporal Shoreline Movement using QGIS
49:51Â - 6.8 Symbolizing Results on ArcGIS Pro
This workflow is also the first time where I have demonstrated Shoreline Change Analysis - I used QGIS software to determine the rate of Erosion/Accretion between two Satellite Imagery datasets - the process involved drawing a landward baseline/reference line parallel to the shoreline, splitting it into equally-spaced sectors/hubs and then computing the average distance between equally-spaced points on the shoreline and the sector/hub nearest to it.
2.7 Granular Shoreline Change Analysis and Forecasting using EPR4Q Model
Study Area: Satabhaya - Odisha (East India) and Juhu Beach, Mumbai - Maharashtra (West India)
The End Point Rate Tool for QGIS (EPR4Q) has been developed by Dr. Lucas Terres de Lima and his research colleagues at CESAM, University of Aveiro in Portugal. It has been technically validated and stacks favorably when compared to DSAS and AMBUR - two highly technical and popular methods for performing research-grade Shoreline Change Analysis. As informed to me by Dr. Lima himself, he did not get a chance to develop this proprietary model further after performing his research - hence it does not work well on the latest QGIS software versions - not to worry, I've covered the preparatory aspects for effective analysis in the video demonstration.
While I had performed Quantitative analysis on the Satabhaya shoreline using SAET in my previous workflow, I was left highly impressed by the granularity of the output over the same Area of Interest using the EPR4Q model which is based on casting numerous evenly-spaced transects over the selected region and subsequently measuring the distance between the Shoreline and the Baseline.
This model, just like the other popular research-grade Shoreline Change Analysis tools, has trouble generating high-quality Shorelines for embayed/curved Shores - there are workarounds though which I've elaborated in the latter half of the video demonstration using the popular Juhu Beach shore in Mumbai, India to demonstrate it (I had grappled with this delineation myself for several days which eventually led me to contact Dr. Lima who was kind enough to lend his valuable advice).
Go give EPR4Q a try!
TIMESTAMPS
00:00Â - Video #.7 Headline
00:04Â - 7.1 Rewinding the previous six workflows
05:31Â - 7.2 Overview of some of the Advanced Tools for Quantitative Analysis of Temporal Shoreline Movement
08:10Â - 7.3 Introducing EPR4Q tool for Temporal and Predictive Shoreline Analysis and Prerequisites to run the tool without errors
18:31Â - 7.4 Running the EPR4Q model on QGIS and Interpreting the results over the Area of Interest on ArcGIS Pro
36:18Â - 7.5 Challenges faced while running EPR4Q tool over Embayed Shorelines and how to address it
3. Concluding Remarks
What eventually shaped up to be an elaborate video series on Shoreline Detection and Monitoring actually began as a learning endeavor a couple of years ago. Having set out to write the post then, I put the assignment on hold due to lack of meaningful output and storyline, other projects taking centerstage, among a myriad of other reasons. Only around four months ago (~February 2024), after weeks of scouring learning resources, did I finally feel confident in my ability to weave a compelling narrative on the topic of Shoreline delineation and monitoring.
That being said, I regret not being able to ensure that the erosion/accretion analysis across multiple timelines is directly comparable - for that to have happened, I would need to factor in Tidal trends i.e. select datasets from different timelines which are undergoing the same/similar tidal activity - this is an aspect that I've ignored in my demonstrations - have just randomly selected Satellite Imagery six months or a year apart across the workflows.
Also, I do realize that it is taking me long (six months!) to come up with new content, but it is definitely richer and more in-depth qualitatively. That being said, it has just dawned on me that I have added ONE MORE article related to Water Research😮- it was not exactly intentional as my thoughts were devoted to only adding a post in the Remote Sensing category of this website as it was long since I did my last OSINT work in this space - but I guess it was destiny that I ended up interfacing with Water😊, just like a Shoreline does!
I am happy that my work is being accessed and appreciated by students, researchers and enthusiasts around the world and I do try to address all queries on email, YouTube comments as soon as possible/as soon as it comes to my attention. Climate Change and Earth Observation are macro-level Operations problems that are close to my heart and I'll be happy to collaborate with individuals and institutes who are trying to remedy human dealings with nature before it is too late.
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Article & Video Credits: RUS Copernicus, NASA USGS, GeographyRealm, Esri ArcGIS Pro, QGIS, ESA SNAP, ECFAS, Dr. Lucas Terres De Lima besides several other individual researchers, organizations and institutes who have contributed towards development of Shoreline monitoring., delineation and change analysis.