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Contents Copyright 2010, Michael E. Davias Google Earth Imagery presented under the Fair Use Doctrine of the US Copyright Act (section 107 of title 17)

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My thanks to those in attendance here, and to the GSA, for the opportunity to share some of the LiDAR techniques and resources we have applied in researching Carolina bay landforms. We will also be demonstrating the integration of LiDAR imagery with the Google Earth Virtual Globe, along with sharing a few preliminary results.

So, What is a Carolina bay? Until 80 years ago, they were simply a scattered collection of swamps and lakes that represented challenges for local farmers and road builders.

This photograph displays numerous bays under different land uses. Due to their water retention characteristics, the bays’ outlines are often easy to see.

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Since the bays were first visualized in aerial photography of Myrtle Beach in the 1930s, their presence on the landscape has generated controversy as to their geomorphology. Differing from simple parabolic dunes, these landforms universally exhibit a closed circumpheral rim. What would generate shallow ellipsoidal basins, clustered together with a commonly oriented major axis?

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Here we take an original Fairchild Aerial Survey photograph and overlay it on the Virtual Globe, and as we fade out, the current satellite imagery becomes visible. Our goal is to capture multiple planform metrics using remote sensing.

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USGS elevation data is of no use here: the best DEM data offered for Myrtle Beach is 1/3 arc-second, and it looks like this. The original survey seems to be the best! &

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Here is another example of satellite imagery. While bays and their planforms are visible, it is difficult and imprecise to trace the rims.

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The same are using LiDAR elevation map overlay. We suggest that visual imagery reveals only a small part of their unique planforms.

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In a dense urban landscape, a bay in a park might be noticed

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– but that would be overlooking the big elephant in the room. LiDAR imagery to the rescue !

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In our opinion, Dr. Johnson’s challenge is as valid today as it was 70 years ago!

So, we are inventing a novel explanation, where the bays are not nearly as important as the sand strata they are embedded within.

We posit that those sands represent a thin veneer ( up to 10 meters) of pulverized ejecta from a remote cosmic impact. The bays are proposed to be voids in that blanket,

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Here is the spatial distribution LiDAR-derived data in the areas of interest. We eagerly await similar data for other regions.

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… many options are presented. We are interested in Elevation data, in ArcGrid format, 1/9 arc second where available.&

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Here, we have loaded eight 24K Quad segments (supplied as zip files) into Global Mapper’s interface.

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One of Global Mapper’s many tools is an elevation profile capability, The 2 kilometer-wide bay has only 5 meters of rim relief… FL AT

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We have used the profile tool to show the diverse elevations that bays occur at, even over relatively short distances. Our proposed blanket would have merely draped over antecedent terrain. Note the bay planforms on the valley floor, lower left.

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William Zanner identified ancestral basins in Nebraska similar to the Carolina bays. He proposed these to be the controlling structure underlying many meters of late Wisconsinan loess. In the LiDAR, the basins jump out. We have identified hundreds of these in the Midwest.

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Basins visualized with LiDAR vs Satellite imagery

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Basins visualized with LiDAR vs Satellite imagery

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Once we have a scope of LiDAR to define a “field” of bays, we proceed with the export of data in a form digestible by Google Earth: - KML

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The exported fie is opened in Google Earth, with the Image automatically positioned on the virtual globe. The tree of increasingly detailed image tiles is shown on the left.

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This LiDAR imagery covers 600 square km surrounding Rex, NC.

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Here is the same landscape seen in Google Earth imagery. Flipping back and fourth, it is easy to see the value of LiDAR..

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Now, let’s look at some of the interesting planform features we have visualized using these LiDAR maps. This juxtaposition of adjacent bays is what might be termed “daughter bubbles”: small bays at the southeastern end of a large bay.…

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Douglas Johnson comfortably dismissed direct impact theories using numerous observations. Among them was that antecedent drainage channels clearly survived across the planform of many bays. As such, it was impossible for the bay to have been carved out by an excavating event. But a blanketing event would allow those channels to map through.

Note that one channel exits at the thickest section of the rim, another of Johnson’s observations. Daughter bubbles are again present along the Southeast end.

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Occasionally, we find the bays in a paired arrangements. Sometimes (as noted by Johnson), there is no intervening rim. We suggest this is mimicking the common bubble wall network geometry. The walls should ideally meet at 120º angles, if these were indeed “bubbles”.

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It would be expected that larger basins would host bodies of water. Occasionally we see artifacts considered by us to be “bathtub rings”- concentric rings within a bay rim, formed as the enclosed lake level receded. Correctly dating this assemblage would require discriminating between the two regimes of deposition.

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Here are LiDAR representations of bays in Maryland, Delware and New Jersey. We propose these continue to be flowing in from the upper left (north-west), but are visualized as ‘squashed” and abruptly halted. Challenging for our triangulation network, no doubt.

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LiDAR imagery has been generated for our catalogue of 250 “fields”, each typically containing dozens to hundreds of bays. The catalogue is available for you to view on line using a web browser with a Google Earth “Plug-in”, although the same imagery is available for you in the Google Earth Application, if you prefer. See >MOD__C"6857J59<_!"#$%&

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To capture the orientation of an entire field of bays, we position a transparent overlay on the virtual globe, and using the edit controls, size it and rotate it to best represent the prevailing orientation. This example, from the area around Edgar, Nebraska, also demonstrates how linear dunes are slowly filling the basins from the northwest.

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An individual bay’s orientation can similarly be captured - here using an overlay representing the archetype central Carolina bay planform. Default orientation is due north.

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The overlay is rotated and sized to the outline of the bay’s actual rim.

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The overlay is rotated and sized to the outline of the bay’s actual rim.

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Orientation of bays is tightly constrained in any give area.

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We note that there are tens of thousands of bays which are crisply represented by this particular overlay. “Cookie Cutter” geomorphology.

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Spatial sizes vary considerably. Length/width ratio tightly constrained.

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With a bit of fine tuning, a very snug fit is obtained! We give the overlay a name, and it appears in the object directory. That object can be copied, as it is comprised of a series of meta data elements in the kml text format.

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Placing this in a text editor, we see the overlay carries information which can be extracted: from the bounding box latitudes and longitudes we can calculate the length of the major and minor axis as well as the general bay surface area. The rotation angle from due north is given directly.

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A survey has been undertaken to attempt the identification and planform capture from all identified Carolina bays.

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Name Axis Axis Ratio Area Latitude Longitude Bearing

bay_A00031 0.0771 0.0651 1.1843 0.3938 34.6149 -79.5588 138.15

bay_A00075 0.1278 0.0949 1.3470 0.9521 34.5950 -79.5457 143.48

bay_A00024 0.1279 0.1046 1.2226 1.0506 34.6041 -79.5803 141.25 bay_A00023 0.1380 0.1122 1.2300 1.2160 34.6057 -79.5814 138.57

bay_A00068 0.1559 0.0999 1.5599 1.2238 34.5640 -79.5532 143.48

bay_A00069 0.1559 0.0999 1.5598 1.2238 34.5616 -79.5408 143.48

bay_A00076 0.1572 0.1043 1.5076 1.2880 34.5971 -79.5390 143.48

bay_B0081 0.1647 0.1037 1.5887 1.3416 34.7264 -79.6825 141.09

bay_A00057 0.1636 0.1053 1.5542 1.3532 34.5620 -79.5183 142.44

bay_B0296 0.1515 0.1177 1.2872 1.4001 34.6431 -79.6113 134.66

bay_A00054 0.1672 0.1086 1.5394 1.4272 34.5804 -79.5223 142.44

bay_B0113 0.1578 0.1250 1.2617 1.5491 34.6799 -79.5285 136.58

A Java program is used to parse a folder of bay overlays and generate metrics in tab delimited format for use in a spreadsheet. Here is a range of bays sorted by surface area

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Here is a graph generated using results from two regions– representing a total of about 900 bays. Clearly the Major/minor axis ratio is tightly constrained, even across the full range of bay sizes from 1 hectares out over 300

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This histogram shows the quantity of bays by surface area.

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Graphic displays geospatial extent of bays in survey, and a graph of flight distances from our proposed impact site.

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Our work gives us to a better sense of the hypothesis’s merit, but accomplishing the necessary ground-proofing will require skills and efforts well beyond our capabilities. We encourage others to consider evaluation of the bay rim sand in the context of our proposal.

Thank you for your attention!

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Abstract Geological Image category winner, Meeting’s Photography Exhibition.