Abstract
Carolina Bays are oriented and sandy-rimmed depressions that are ubiquitous throughout the Atlantic Coastal Plain (ACP). Their origin has been a highly debated topic since the 1800s and remains unsolved. Past population estimates of Carolina Bays have varied vastly, ranging between as few as 10,000 to as many as 500,000. With such a large uncertainty around the actual population size, mapping these enigmatic features is a problem that requires an automated detection scheme. Using publicly available LiDAR-derived digital elevation models (DEMs) of the ACP as training images, various types of convolutional neural networks (CNNs) were trained to detect Carolina bays. The detection results were assessed for accuracy and scalability, as well as analyzed for various morphologic, land-use and land cover, and hydrologic characteristics. Overall, the detector found over 23,000 Carolina Bays from southern New Jersey to northern Florida, with highest densities along interfluves. Carolina Bays in Delmarva were found to be smaller and shallower than Bays in the southeastern ACP. At least a third of Carolina Bays have been converted to agricultural lands and almost half of all Carolina Bays are forested. Few Carolina Bays are classified as open water basins, yet almost all of the detected Bays were within 2 km of a water body. In addition, field investigations based upon detection results were performed to describe the sedimentology of Carolina Bays. Sedimentological investigations showed that Bays typically have 1.5 m to 2.5 m thick sand rims that show a gradient in texture, with coarser sand at the bottom and finer sand and silt towards the top. Their basins were found to be 0.5 m to 2 m thick and showed a mix of clayey, silty, and sandy deposits. Last, the results compiled during this study were compared to similar depressional features (i.e., playa-lunette systems) to pinpoint any similarities in origin processes. Altogether, this study shows that CNNs are valuable tools for automated geomorphic feature detection and can lead to new insights when coupled with various forms of remotely sensed and field-based datasets.
Highlights
IntroductionCarolina Bays are enigmatic and widespread geomorphic features with an unknown origin
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We demonstrated the development of a Carolina Bay detector using convolutional neural networks and publicly available LiDAR data encompassing the entire
Summary
Carolina Bays are enigmatic and widespread geomorphic features with an unknown origin They are round, shallow, and sandy-rimmed depressions with muddy interiors, with their major axes typically oriented northwest to southeast (see Figure 1). They span the Atlantic Coastal Plain (ACP) from southern New Jersey to southern Georgia. The Bays commonly house lakes, ponds, or ephemeral wetlands, while a significant number of Bays have been naturally or artificially drained, the latter mainly for agricultural use. Their origin is highly debated, with many unconfirmed hypotheses. Many [1,2,3,4,5,6] have argued, without substantial evidence, that they are meteoritic, comet, or glacial ice impact craters, while others [7,8,9,10,11,12,13,14,15,16,17] have tried to explain their origin using slower geological processes
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