Monitoring, Delineating and Analysing Shorelines using Remote Sensing
- Arpit Shah
- Jun 18, 2024
- 14 min read
Updated: 2 days ago
1. INTRODUCTION
Where 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, sand mining and other causes. A way to categorize the natural influencers is based on their temporal (time) characteristics. For example, while 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. And all these contribute towards making the coast dynamic in nature.
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 necessitates the need for regular and accurate monitoring of 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 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. 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 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 a Coastline match that of a Shoreline. This delineation is important to understand because while both are casually used interchangeably, all scientific references pertaining to the precise location of where 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, this post concerns itself with Shoreline Monitoring, Delineation and Analysis.
HYPERLINKS TO SECTIONS
1. Introduction
2. Scope of this Study
All the seven workflows are demonstrated in this video compilation-
2. SCOPE OF THIS STUDY
As it turns out, there are multiple ways to detect/estimate the location of Shoreline - 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, accuracy requirements and the cost of acquiring and processing data.
In this post, I'll present video demonstrations on seven ways in which Satellite-based Remote Sensing can be, and is being used to monitor Shorelines and study 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. While the techniques covered in this post are not exhaustive of the entire body of knowledge, they are diverse/wide-ranging in terms of the methodology utilized.
I've used two sources of Satellite Imagery in my demonstrations - Landsat (NASA, USA) and Sentinel (ESA, Europe). Both have a Spatial and Temporal resolution that is 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 more Open-Source Intelligence of geospatial nature. 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 at all times and preempt disasters.
2.1 Viewing Shoreline Evolution Using Google Earth
Study Area: Someswar - Batapady Beach (South-West India)
This technique entails using Google Earth - the Satellite Imagery rendering platform of Alphabet Inc. - to visually monitor the Shoreline through time using the (optical) Historical Imagery tool (Alphabet Inc. also has an advanced Satellite Imagery processing platform - Google Earth Engine, the use of which I have not demonstrated it in this post). Additionally, with the Measurement tool in the software, one can obtain an estimate about the rate of erosion/accretion as well.
While this technique is elementary, 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 sections of the Someswar-Batapady coast along the Arabian Sea were ravaged by erosion over a period of time in just a couple of minutes.
TIMESTAMPS
00:00 - Video #.1 Headline
00:04 - 1.1 Exploring the Area of Interest on Google Maps
02:30 - 1.2 Visually monitoring the Shoreline using Google Earth Pro
2.2 Viewing Shoreline Evolution Using EO Browser
Study Area: Marina Beach, Chennai - Tamil Nadu (South-East India)
EO Browser surpasses Google Earth for Shoreline Monitoring purposes on a few counts-
it has a steady stream of Historical Satellite Imagery from a variety of sources to choose from
it allows for Imagery processing on-the-fly (Cloud) with preset and custom Band Combinations
it has tools such as Histogram and Time-Lapse video creation which allows for accurate thresholding and enhanced visualization
These functionalities helped me to distinctly identify where and 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 it negates Tidal Flushing from occurring).
TIMESTAMPS
00:00 - Video #.2 Headline
00:04 - 2.1 Exploring Area of Interest and Band Combinations on EO Browser
02:48 - 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 rendered Satellite Imagery and observe its evolution through time, this is the first workflow where I will demonstrate the actual extraction/delineation of a Shoreline from a downloaded raw Satellite Imagery dataset. The Study Area - the buzzing tourism destination 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 Multispectral Satellite Imagery - the former has an active illumination source, a microwave emitter, which allows-
the satellite to obtain readings even in zero-light conditions such as during night-time, and
the longer wavelengths of the microwaves are unaffected by atmospheric constituents such as clouds and aerosols, thereby aiding in the accurate assessment of surface features from the reflected energy (backscatter)
TIMESTAMPS:
00:00 - Video #.3 Headline
00:04 - 3.1 Exploring Area of Interest using Google Maps
03:08 - 3.2 Downloading SAR (Synthetic Aperture Radar) Imagery using Copernicus Browser
08:48 - 3.3 Post-Processing the Raw Radar Imagery using SNAP Software
25:25 - 3.4 Post-Processing the SNAP-processed Radar Imagery using ArcGIS Pro
This method to delineate Shorelines suffers from a couple of limitations-
the Spatial resolution of SAR Imagery is not high enough
outdated Global Digital Elevation Model makes it difficult to isolate the water pixels
Nonetheless, this method can be clubbed/validated with other approaches that make use of Multispectral Imagery which would enable more accurate Shoreline delineation.
2.4 Extracting Shoreline by using Water Radiometric Index
Study Area: Anjuna - Goa (South-West India)
Out of the 41 beaches surveyed by NCSCM in the popular beach state of Goa, 21 (including my Study Area - Anjuna Beach) were found to be suffering from sand erosion.
How the constituents of Solar Radiation - visible light, infrared and ultraviolet wavelengths of the electromagnetic spectrum - interact with surface features is fundamental to being able to distinguish one type of surface from the other using Multispectral Remote Sensing, and the same principle is applicable for Shoreline delineation workflow as well - Water has a low reflectance to Visible Light and zero reflectance to Near Infrared waves (NIR), a significant contrast to the reflective properties of other common-surface features such as Soil and Vegetation (both reflect NIR in much larger quantities). Hence, a particular type of Water Index - Modified Normalized Difference Water Index (MNDWI) - can be used to demarcate land from water, thereby making it possible for us to extract Shorelines from Multispectral Imagery.
Because Multispectral Imagery relies on a passive source of illumination - Sunlight - it is important to select cloud-free Multispectral Satellite Imagery datasets for accurate Shoreline extraction.
TIMESTAMPS
00:00 - Video #.4 Headline
00:04 - 4.1 Exploring Area of Interest and Band Combinations on EO Browser
06:48 - 4.2 Downloading Optical Imagery from Copernicus Browser
10:13 - 4.3 Post-Processing the Raw Optical Imagery using SNAP Software
24:15 - 4.4 Post-Processing the SNAP-processed Optical Imagery on ArcGIS Pro
35:59 - 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 subsequently, the edge where it met non-Water features was demarcated to isolate the Shoreline in the previous workflow, a similar logic albeit using a 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 where it meets Water features has been used to isolate the Shoreline.
I've deliberately kept the Study Area and the timeline of Imagery acquisitions the same as the previous workflow as this would allow me to compare the Shoreline output derived from both the methods - Water Index and Vegetation Index - and draw interesting insights. For this workflow however, I've used a different source of Multispectral Satellite Imagery - Landsat 8.
TIMESTAMPS
00:00 - Video #.5 Headline
00:04 - 5.1 Landsat 8 vs Sentinel-2 Optical Imagery
01:44 - 5.2 Downloading Landsat 8 Imagery from USGS Earth Explorer
07:20 - 5.3 Deploying NDVI+Tasseled Cap technique using Landsat toolbox on ArcGIS Pro
27:28 - 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 demonstrations, you'll notice how tedious it could be sometimes to extract a Shoreline if the coast is not linear in shape. Moreover, the landscape around the coast is constantly shifting - 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 to search, download and process Multispectral Imagery automated but also the Shoreline extracted is contiguous over the Study Area (as it should be ideally). The erosion-ravaged Study Area - Satabhaya and the surrounding areas in the eastern Indian state of Odisha - has a tricky terrain - a rugged coast with flooded/ tiny water bodies interspersed with the landmass, but the SAET algorithm proved to be a boon and I was able to process the Landsat 8 Imagery and extract a contiguous Shoreline in just a few minutes. While setting up SAET can be complicated, you are sure to find using it to be a breeze.
This is also the first workflow where I have demonstrated Shoreline Change Analysis - I used QGIS software to determine the rate of erosion/accretion between the two selected 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.
Have a look at the demonstration of this workflow below-
TIMESTAMPS
00:00 - Video #.6 Headline
00:04 - 6.1 Getting to know and preparing the SAET tool
10:54 - 6.2 Downloading Landsat 8 Optical Imagery for Area of Interest using SAET tool
16:02 - 6.3 Extracting Shoreline from Downloaded Landsat 8 Imagery using SAET
19:26 - 6.4 Observing the extracted shoreline on ArcGIS Pro
20:41 - 6.5 Using Google Earth Pro to validate the accuracy of the extracted shoreline
23:39 - 6.6 Using the Raw Landsat 8 Imagery itself to validate the accuracy of the extracted shoreline 28:16 - 6.7 Quantitative Analysis of Temporal Shoreline Movement using QGIS
49:51 - 6.8 Symbolizing Results on ArcGIS Pro
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 his research stint at university - hence this tool does not work well on recent QGIS software versions. This is not a reason to worry as I've covered the preparatory aspects for effective analysis in my 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 tool - the processing involves 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 in Mumbai as my Study Area (I had grappled with doing this delineation for several days which eventually led me to contact Dr. Lima! - he was kind enough to lend his valuable advice).
Go give EPR4Q a try!
TIMESTAMPS
00:00 - Video #.7 Headline
00:04 - 7.1 Rewinding the previous six workflows
05:31 - 7.2 Overview of some of the Advanced Tools for Quantitative Analysis of Temporal Shoreline Movement
08:10 - 7.3 Introducing EPR4Q tool for Temporal and Predictive Shoreline Analysis and Prerequisites to run the tool without errors
18:31 - 7.4 Running the EPR4Q model on QGIS and Interpreting the results over the Area of Interest on ArcGIS Pro
36:18 - 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 this 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 online, did I finally feel confident in my ability to weave a compelling narrative on the topic of Shoreline monitoring, delineation and analysis.
That being said, I regret not being able to make the erosion/accretion analysis across two or more timelines directly comparable - to make this possible, I would have had to factor in Tidal trends i.e. select datasets from different timelines that are undergoing the same/similar type of tidal activity during the moment of acquisition. This is an aspect that I've ignored in my demonstrations - I have just randomly selected Satellite Imagery datasets six months or a year apart across the workflows.
Also, I do realize that it is taking me longer (six months!) to come up with new content, but I certainly find the output to be richer and more in-depth, qualitatively speaking. Also, It has just dawned on me that I have added ONE MORE post related to Water research😮- this was not exactly intentional as my thoughts were only devoted to adding a post in the Remote Sensing category of this website as it had been quite sometime 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 also feel happy and fulfilled that my work is being accessed and appreciated by students, researchers and enthusiasts around the world and I do try to address all queries shared with me over email, YouTube 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 delighted to collaborate with individuals and institutes who are trying to remedy human interactions 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.
