Ariel orthophoto from National Agricultural Inventory Project (NAIP), taken in 2012

Analysis of Shoreline Change along Cape

Hatteras, North Carolina

GEOG 362 Final Project

Devin Boyer

December 17, 2013

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According to the Merriam-Webster dictionary1, a cape is “a point or extension of

land jutting out into water as a peninsula or as a projecting point.” Because they are points

which protrude into the water father than surrounding land, they are places of constant

change, especially with respect to coastline change. Because they share the features of

peninsulas (if they are not exactly peninsulas), their beaches can either be eroded or built

up from multiple directions at once. Often, both of these processes may be occurring at the

same time. Strong storms, both tropical and “extra-tropical” (such as Nor’easters) can

significantly impact these two processes.

Arguably one of the most well-known capes in the United States is Cape Hatteras in

the Outer Banks of North America. Cape Hatteras has a significant prominence in the

geography of North Carolina: it is nearly its easternmost point. That, combined with its

unique shape as a cape and its close proximity to a bend in the Gulf Stream ocean current2,

makes it a prime ground for impacts from many weather systems. And indeed, according to

Wikipedia, the Outer Banks are “the most hurricane-prone area north of Florida, for both

landfalling storms and brushing storms offshore.”3

Because of the interesting and surely dynamic environment, I decided to study

coastline change along Cape Hatteras for this project. This project will analyze the change

in shoreline over a period of about a decade – from early 2000 to mid-2011. Even before

any analysis is performed, it is apparent that the shoreline changes fairly rapidly at this

location, from the perspective of the Cape Hatteras Light. Just before the start time of this

1 - "Cape." Def. 1. Merriam Webster Online, Merriam-Webster, n.d. Web. 17 Dec 2013.

2 - This can be observed in diagrams available in The Watchers blog entry “Will the Gulf Stream slow down?” -

http://thewatchers.adorraeli.com/2011/03/30/will-the-gulf-stream-slow-down/ 3 - Wikipedia contributors. "Outer Banks." Wikipedia, The Free Encyc lopedia. Wikipedia, The Free Encyclopedia, 11

Nov. 2013. Web. 17 Dec. 2013.

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project, in 1999, the iconic lighthouse was moved over half a mile inland, as beach erosion

had left the historic structure dangerously close to the ocean ’s edge.4

This project makes use of GIS classification techniques to analyze the change in land

cover in the area near Cape Hatteras, as well as a model of potential water flow across the

selected part of Hatteras Island.

Overview Map

Figure 1 – Cape Hatteras’ location within eastern North Carolina, with an inset map of the project extent at Cape Hatteras.

My project focused on Cape Hatteras, North Carolina, on the east coast of the United

States. Cape Hatteras, part of the Outer Banks, protrudes into the Atlantic Ocean where

4 - Wikipedia contributors. "Cape Hatteras Light." Wikipedia, The Free Encyclopedia. Wikipedia, The Free

Encyclopedia, 14 Dec. 2013. Web. 17 Dec. 2013.

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Hatteras Island bends. The area where I observed changes in the shoreline are those right

along the point at the edge of the cape; this makes up the beachfront near the Cape Hatteras

seashore.

Data Used

The following table outlines the four data sources that were used in this project.

Image/Description Source Acquisition Date

Sensor Type

GSD Resolution

Projection

2000 Landsat Image

USGS EarthExplorer

21 Jan. 2000 LANDSAT 5 Thematic Mapper

30m UTM 18N

2011 Landsat Image

USGS EarthExplorer

28 Jun 2011 LANDSAT 5 Thematic Mapper

30m UTM 18N

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Methodology

The first process necessary in this project was to ensure all data was properly

projected and aligned in ArcMap. This project used

the Universal Transverse Mercator projected

coordinate system, specifically zone 18N. After all

data was properly aligned, a project boundary was

Image/Description Source Acquisition Date

Sensor Type

GSD Resolution

Projection

2012 Dare County NAIP Mosaic Imagery

USDA Geospatial Data Gateway

28 Jun 2012 Intergraph Digital Mapping Camera

1m UTM 18N

National Elevation Dataset DEM

USDA Geospatial Data Gateway

Current as of 12 Dec 2013

Unknown, likely LiDAR

10m UTM 18N

Table 1 – The four pieces of data used in this project.

Figure 2 – Establishing a project boundary using the NAIP orthophoto.

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established that would capture a large enough portion of Cape Hatteras for useful study, as

seen in Figure 2.

With this boundary established, the proper DEM files could be brought in to be

mosaicked into a single DEM image using Arc’s “Mosaic to New Raster” tool. After this was

done, all four data sets were clipped to only the domain of the project boundary, to make all

future calculations less needlessly computationally-expensive. These clipped images

replaced the full datasets in the project.

After this step was complete, I used the DEM to establish a “stream” network within

the project boundary. The term “stream” is used loosely here, as there is not much in the

way of traditional streams in a small subset of a barrier island. The small streams that do

exist within my project boundary were illustrated decently, as shown in Figure 3 below.

Figure 3 – Alignment of the “calculated” streams (light blue) with respect to the actual stream paths in the 2012 orthophoto. The area near the stream outlet appears to be like a flat floodplain, which may explain the path error in that area.

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To establish this stream network, a series of steps were performed. First, the Spatial

Analyst Fill tool was used to remove “sinks” from the DEM, creating a filled, depressionless

DEM. This filled DEM was then used to create a Flow Direction map, from which a flow

accumulation raster was subsequently made. By applying grid algebra, it was possible to

define streams as only areas which had a sufficient amount of flow (>26,000 cells “flowing

into” that point). The streams delineated in raster form were then converted to vector

features using the Stream to Feature tool. Figure 4 below outlines the process used to

create the stream features.

Figure 4 – The process used to create stream features in ArcGIS.

A few other products were created using the original (not filled) DEM. These include

a hillshade, a slope map, and an aspect map. All were created using the standard tools in

ArcMap’s Spatial Analyst toolbox (see Table 2 on the next page).

Create Depressionless DEM

Flow Direction Raster

Flow Accumulation

Raster

Streams Raster (input flow > 26,000 cells)

Stream feature (vector

shapefile)

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(a) Hillshade

(b) Slope Map

(c) Aspect Map

Table 2 – The three elevation products created for this project.

This project also involved using unsupervised classification to actually perform

analysis of the coastal change. Two different tools were used to perform this analys is, ENVI

Tools and ISO Cluster Unsupervised Classification provided in ArcMap. They yielded

slightly different results, as shown in Figure 5 below.

Figure 5 – The results of using the two different classification schemes on the 2011 Landsat scene – ISO Cluster on the left and ENVI Tools on the right, Note that coloration of each class is not particularly transferrable between classification jobs.

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Both of these analyses were performed on the 2000 and 2011 Landsat images, using

the visible, true-color bands. As such, the first required step was to determine which of the

bands in each Landsat scene the visible bands were, a process that required experimenting

with different band combination until finding the one that roughly matched the NAIP

orthophoto, which only displayed visible bands. Perhaps strangely, these band

combinations were different for the two Landsat scenes.

More experimentation was done to determine the number of classes which would

adequately represent the different land coverage visible within the project boundary, as

well as to determine which classification method seemed to do a “better” job classifying the

scene. In this sense, a “better” classification means that more common features were

correctly grouped in the same class and that fewer uncommon features were not placed in

the same class. Through this experimentation, it was determined that a 10-class

unsupervised classification using ENVI Tools produced an adequate result set.

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Figure 6 – A handy visualization technique used in this project involved placing the partially transparent NAIP orthophoto on top of the classification layers.

In the process of performing this analysis, I created invaluable visualization which

was first used to rate performance of the two classification schemes and was then used to

identify what type of land cover each class represented. It involved setting the NAIP

orthophoto partially transparent and above all of the Landsat classification layers in the

ArcMap table of contents. By doing this, it became very easy to peer “below” the

classification colors (although the orthophoto was technically on top) and gauge what was

actually present on the ground at a given spot. An example of this visualization is provided

above in Figure 6.

After I finished identifying each of the classes for the two Landsat scenes, I collapsed

the 10 classes into common classes based on land cover type by changing colors and

grouping the classes in the symbology of the layer (see Figure 7 on the next page). I

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assigned common colors to the land cover types

for both Landsat scenes so that they could easily

be compared side-by-side using the Swipe tool in

ArcMap.

The final step in this analysis was to use

the ENVI Tools “Calculate Thematic Change” tool

to provide another way to compare the coastline

change between the 2000 and 2011 images.

Results and Discussion

Figure 8 – The “collapsed” land cover maps for (a) 2000 at left and (b) 2011 at right. Costal differences become quite apparent when the two images are overlaid and compared using the Swipe tool in Arc.

As is probably apparent from the two collapsed land cover maps in

Figure 8 above, there is quite a bit of change in land coverage between the

earlier (left) and later (right) scenes. Some of this can be attributed to

differences in the unsupervised classification, but there appears to be more

variabliity than could simply attributed to that. This especially applies to

Figure 7 – The combined classes in a “collapsed” Landsat land cover raster.

Figure 8c – Legend for the land cover types depicted in Figures 8a and 8b above.

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the area of interest in this project – coastline changes. Using the Swipe tool in ArcMap, it becomes

clear that in the 11-year period, the south-facing shoreline grows, while the eastern shore shrinks.

A ribbon of sand visible on the 2000 image near the Cape Hatteras Light is no longer visible in the

2011 image, and closer analysis reveals that near the original location of the lighthouse, the beach

has retreated by more than 100 meters, as measured by the land coverage (this difference was

established by measuring the difference between the westernmost blue “water” pixel in the 2000

image compared to the 2011 image. While this figure is surely high because of error due both to

Landsat spatial resolution and the ability for the untrained classifyer to correctly identify pixels, it

does likely represent the proper trend.

The fact that one side of the cape is growing while the other is being eroded is not an

untested idea. Indeed, along eroding beaches, man-made protrusions into the water called “groins”

are often built to attempt to prevent sand loss to the deep sea. Usually, these groins are effective in

preventing sand loss on the “upstream” side of the groin but cause additional erision on the

“downstream” side.5 Groins are shaped similarly to the tip of Cape Hatteras, thus it is not

unreasonable to predict that depending on which the dominate longwave flow is, over a long time

average, one side of the cape will experience growth while the other erodes.

Another feature visible in the 2000 Landsat

classification results but not the 2011 results is the

shoals just offshore the point of the cape. These

sandbars are highly variable in shape and height

(they are often submerged). Although not always

picked up by the land cover classifier, they are a bit

5 - Alley, Richard. “Textbook 8.2: Acadia.” GEOSC 10: Geology of the National Parks. The Pennsylvania State

University. n.d. Web. 12 Dec 2013.

Figure 9 – Comparing the visibility of offshore

shoals past the point of the Cape in the visible Landsat bands.

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more noticeable in the 2011 visible Landsat scene (see Figure 9 on the previous page),

though even there it is apparent that they are much smaller in size (or at least they are

deeper and not visible to the Thematic Mapper sensor).

Figure 10 – The resulting raster from use of the Calculate Thematic Change tool.

As stated at the beginning of this section, even by simply comparing the two

collapsed land cover maps side by side (or with the swipe tool), it is apparent that much

change occurred across all land coverage types, including the beach area. This can be seen

very clearly in the resulting raster from the Calculate Thematic Change tool in Figure 10

above. Unfortunately, there is so much going on in the way of classes changing, even along

the shorelines. As such, it is difficult to get a sense of what exactly is going on in terms of

growth or erosion of the coastline at a specific point. The biggest difficulty is that even if a

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class changed from, say, one “water” class in the 2000 image to a different water class in

the 2011 image, this change is picked up on this map. This appears to be a common

occurrence in this analysis. The unfortunate circumstance surely leads to an overly-

complicated map. Despite the drawbacks, one can still pick out a pink stripe on the

southern shore but not the eastern shore, likely indicating growth of that shore. Ironically,

the shoals off the point of the Cape are quite apparent in the Calculate Thematic Change

raster and are perhaps the most apparent shore change feature visible on that map.

Conclusion

In completing this project, I learned a lot about image classification and some

techniques that work well, some techniques that do not work well and some techniques

that work only moderately well. I’m very proud of my visualization I was able to create in

Figure 6, as it made it much easier to reliably assign information classes to the

unsupervised spectral classes that were created with either ENVI Tools or the ArcMap

image classification tool. It did clearly demonstrate the importance of ancillary data, as the

high-resolution orthophoto is what made that visualization possible in the first place.

The fact that it was necessary to attempt to create reliable information classes using

my visualization technique does show a downfall of using unsupervised classification. By

using supervised classification, especially on the rapidly changing shoreline, it ’s likely that

more reliable differences in coastline change could be found.

In completing this project, I also learned that the Calculate Thematic Change tool

does not quite work as I expected. I had already created my collapsed land cover maps in

Figure 8 before running the tool and I expected it to nicely generate changes between those

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grouped classes (the collapsed map simply groups the generated spectral classes that I

assigned the same information class and gives them all the same color). Instead, it ignored

the fact that they were grouped and generated 120 different change possibilities, which

means for this project it was quite difficult to then determine what sort of change was

occurring, even just focusing along the seashore. Perhaps in the future I would be better off

making use of some other type of tool, if it exists, that would respect grouped classes.