Questions, methods, and analysis of aerial imagery and topographic surveys of Alsea Spit in Bayshore, Oregon

Background

Foredunes are important features on Oregon’s coast; they provide essential habitat, recreational opportunities, and protection from coastal hazards. Managing foredunes, both for human benefit in developed areas and for habitat restoration, has proven to be a difficult task, as there is not a common goal that homeowners, planners, and managers agree on. The dunes we see today were built by the introduction of invasive beach grass over a century ago that changed how sand accumulated. The invasive European beach grass promotes vertical growth and the establishment of tall ridges of stable foredunes, while the original ecosystem was composed of lower, more variable hummocked dunes. While these taller, heavily vegetated dunes provide coastal protection to developed areas, homeowners often flatten (grade) the dunes to restore or establish a view of the ocean. This also removes any established vegetation. After an area is graded, homeowners are required to revegetate the dune to promote sand stabilization, but some homeowners purposely remove or kill the new vegetation. The biophysical feedbacks between vegetation, sand supply, and dune growth are well-studied, but there is lacking research on the morphology of managed dunes (i.e., dunes that have been graded and replanted). I am interested in using vegetation cover change as a proxy for dune grading events, and then exploring how these changes in vegetation cover affect future dune morphology.

Questions and Hypotheses

Question 1: How can I detect vegetation change with aerial imagery on coastal dunes?

Hypothesis: I was looking for changes in vegetation cover using two methods of classification (NDVI change and unsupervised classification). I expected to see distinct “rectangles” of vegetation loss.

Question 2: How are vegetation change and elevation change along one shore-normal transect related?

Hypothesis: I predicted that an increase in NDVI would be correlated with an increase in elevation, i.e., where NDVI change is high, elevation change is high.

Question 3: How have the dunes on Alsea Spit changed over time?

Hypothesis: I expected to see general seaward progradation of the dunes and more variability in the Alsea1 dune crest elevation and position, since it is in a more variable and managed area.

Datasets

1. NAIP imagery from 2014 and 2016 (1-m pixels, 4 bands [B,G,R,NIR]), clipped to study area (approx. 1 km², imagery clipped to bounding rectangle).
2. 18 topographic surveys of 3 shore-normal transects on Alsea Spit. 1-m resolution along transects and transects are ~400 m long (map not representative of actual transect length).

Approaches

Exercise 1: NDVI and vegetation cover change. All analysis performed in ArcGIS Pro using raster tools and image analysis

1. Calculated NDVI for two clipped images (2014 and 2016)
2. Subtracted 2014 NDVI raster from 2016 NDVI raster to get the NDVI change
3. Classified NDVI change > 0 as vegetation increase and NDVI change < 0 as vegetation decrease
4. Ran unsupervised image classification (ISOcluster algorithm) to classify both years of imagery
   1. Reclassified the images into three classes (vegetation, sand, other/structure/shadow).
   2. Set the “other” class to null
   3. Subtracted 2014 reclassified raster from 2016 reclassified raster to get the cover change

Exercise 2: Cross-correlation function in Matlab (crosscorr) with elevation change and NDVI change along a transect.

1. Extracted NDVI change values (from result from Exercise 1) to points spaced 1-m along the “Alsea3” transect in ArcGIS Pro
2. Calculated elevation difference by subtracting the 2013 topo survey data from the 2016 topo survey data in Matlab
3. Loaded the NDVI change data into Matlab and ran the crosscorr function with NDVI change and elevation change
4. Created a bivariate symbology along the transect in Matlab to visually inspect the NDVI change and elevation change

Exercise 3: I used various Matlab scripts to plot the timeseries and compute the following dune morphometrics: shoreline location, dune toe elevation, dune toe location, dune crest elevation, dune crest position, dune heel elevation, dune heel position, backshore slope, dune face slope, dune back slope, beach width, dune width, dune small volume, and dune big volume.

1. Modified an existing script to read the topography data provided by DOGAMI
2. Picked the shoreline, dune toe, dune crest, and dune heel locations on each profile
3. Wrote a new script to calculate and plot the between-survey change in dune crest elevation and dune crest cross-shore position

Results

Exercise 1: I produced a map to show the vegetation change from both techniques.

Exercise 2: I produced two plots and a map that show NDVI change and elevation change.

Exercise 3: I produced time series plots of profiles and dune crest metric changes.

This was probably the most useful result (that isn’t just a learning opportunity); in the dune crest and position change plots, we can actually see the offshore movement of the dune crest for transect Alsea3, which is likely the formation of a new incipient foredune seen in the time series profiles. Additionally, in Alsea1, we can see seaward progradation and the formation of a slightly more flat top, which could indicate that the area was graded or sand was moved mechanically.

Lessons Learned

Exercise 1: NDVI change is hard to use! The range of NDVI values was different between images; for example, areas that were open sand in both images had different NDVI values. In order to make NDVI useful, I need to correct the images or determine the appropriate adjustment to align the NDVI values. I also learned that ISO classification works well for what I’m looking for—it picked up the areas of vegetation pretty accurately so I could see the areas of vegetation that we removed when a dune area was graded.

Exercise 2: I tried to use NDVI again, but I learned that I still need to find a better way to measure vegetation change. There are discrepancies in the 2014 and 2016 imagery, so if I can normalize those, or find a better way to measure NDVI change, I could likely get better results. Once I do that, the cross-correlation might be more useful in determining the effect that vegetation change has on elevation change.

Exercise 3: This was extremely useful for me! Being able to see the evolution of the different metrics over time is really important and helped me start to pick out some patterns. However, I need to change some of my “rules” for picking out the dune toe, crest, and heel. To make accurate comparisons within one profile’s timeseries, I need to establish a static “dune compartment”. For example, I could determine that this compartment is from the 4-meter contour to a set cross-shore position near the back of the dune. That way, only one variable is moving along the cross-shore direction (the 4-meter contour), so I can more accurately measure accretion/erosion on the dune.

Significance

Understanding how dunes change after grading/replanting events is important and very relevant to current management problems. Coastal foredunes, especially in developed areas, provide the first line of defense against rising sea levels, storms, and even tsunamis. However, homeowners like to see the ocean, which is why view grading is a common practice. So, exploring how dunes respond to grading events will allow us to understand how dunes will look in the future if grading continues. Managers can use this information to guide dune management implementations, and potentially change policies to further prohibit dune grading (if coastal protection is seen as the important ecosystem service to manage for).

Software Learning

In exercise 1, I got more practice with the suite of image classification and processing tools in ArcGIS Pro. In exercise 2, I was able to perform statistical tests in Matlab for the first time, and I explored bivariate symbology in ArcGIS Pro. Finally, in exercise 3, I got more comfortable with the scripts I already had in Matlab and was able to modify them for the DOGAMI data.

Statistics Learning

I learned a lot more about autocorrelation and cross-correlation and how to interpret spatial lags, which is still something I’m trying to grasp. I learned the most from hearing my classmates’ tutorials and working through questions together. Additionally, I learned more about image classification techniques and experienced how one parameter can really affect the results. Understanding what the algorithm is doing, at least at a basic level, is crucial. I read some literature about change detection, specifically using LandTrendr, which is something I hope to try in the future.

Evolving Question

My first question was how does vegetation change affect coastal foredune morphology?

Over the course of this term, I learned more about the data I was using and how I can better analyze it to answer my question. I thought that NDVI change would be a good indicator of vegetation change in the context of my research, but it’s not! I care more about larger “chunks” of areas that can more easily be detected by a classic image classification technique.

I still have the same fundamental question, but plan to approach it differently. I want to be able to pick out dune grading events from vegetation change, and then use that to build a “management history” or “grading history” of the area. Then, I can observe the changes in dune topography at different time steps after a distinct grading event. So, my new questions are:

• What is the dune management history of Alsea Spit?
• How have dunes changed 1 year, 5 years, and 10 years after being graded?
• What role is vegetation playing in dune morphology at this site?

Future Techniques

I want to fine-tune the image classification I used to optimize it for my question and data. I might try to do it in Matlab since my topography data is already in Matlab. I would like to perform more statistical tests on my topography data after I calculate various metrics—this might have a spatial component if I look at metrics across the area. Overall, I think I have a much better handle on the data I’m using, and more refined research questions, so my analyses can be more focused going forward.