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Geology topics

Kevin G. McKeehan

Publications and source records attributed to Kevin G. McKeehan.

6 recordsLinked to original sources
USGS2023

A guide to creating an effective big data management framework

Many agencies and organizations, such as the U.S. Geological Survey, handle massive geospatial datasets and their auxiliary data and are thus faced with challenges in storing data and ingesting it, transferring it between internal programs, and egressing it to external entities. As a result, these agencies and organizations may inadvertently devote unnecessary time and money to convey data without existing or outdated standards. This research aims to evaluate the components of data conveyance systems, such as transfer methods, tracking, and automation, to guide their improved performance. Specifically, organizations face the challenges of slow dispatch time and manual intervention when conveying data into, within, and from their systems. Conveyance often requires skilled workers when the system depends on physical media such as hard drives, particularly when terabyte transfers are required. In addition, incomplete or inconsistent metadata may necessitate manual intervention, process changes, or both. A proposed solution is organization-wide guidance for efficient data conveyance. That guidance involves systems analysis to outline a data management framework, which may include understanding the minimum requirements of data manifests, specification of transport mechanisms, and improving automation capabilities.

Journal of Big Data↗
USGS2023

The geography and progression of blowouts in the coastal dunes along the eastern shore of Lake Michigan since 1938

Coastal dunes along Lake Michigan's eastern shoreline are a unique system comprising perhaps the largest complex of freshwater coastal dunes in the world. Here, we examine the blowouts in this region and determine how they have evolved since the 1930s. We conducted a spatiotemporal analysis of 435 blowouts by comparing repeat aerial images of the coast beginning in 1938. Using an unsupervised machine learning classification known as iso-clustering, we mapped blowout morphologies at three timestamps: 1938, 1986–1988, and 2018. We then compared the blowout geographies through a technique known as a spatial-temporal analysis of moving polygons (STAMP) model, which allowed us to analyze how each blowout changed in time and space. Results show blowouts have contracted ~37% in size since 1938, mostly at the expense of vegetation, with many fragmenting. These findings comport with other regional and global studies detailing a trend in coastal dune stabilization from vegetation and suggest that an increase in precipitation or other environment drivers could be responsible. Moreover, we detected no new blowouts since 1938 along the ~500 km shoreline or on any of the Lake Michigan islands. This suggests blowouts here are artifacts of premodern conditions, perhaps the result of prior stormier or drier eras.

Indiana, Michigan↗
USGS2023

A 5500-year record of coastal dune evolution along the shores of Lake Michigan in the North American Great Lakes: The relationship of lake-level fluctuations and climate

Dune fields line much of the ∼1750-km long shore of Lake Michigan that extends from northwestern Indiana, north along the west coast of Lower Michigan, and west across southern Upper Michigan. Numerous geomorphic studies at specific sites or defined coastal reaches have demonstrated that dune formation began during the Nipissing maximum (∼5500–4500 calendar year Before Present (cal yr BP) and continued episodically to the present. Dune formation in these geographically confined areas is linked mostly to high lake stages and periods of increased storminess. Some recent research has also invoked drought as a potential causal mechanism on a site-specific basis. Although more than 250 collective absolute carbon-14 ( 14 C) and optically stimulated luminescence (OSL) ages have been acquired in these many studies, a comprehensive statistical assessment of this robust dataset has yet to be conducted to reconstruct a full chronological reconstruction of dune evolution. This study statistically analyzes the dataset of all known OSL (118) and 14 C ages (175) acquired directly from sand dunes. Application of a kernel density estimation (KDE) algorithm function to both datasets revealed the temporal patterns in the ages. Analysis of OSL ages indicates three major periods of dune growth, beginning between ∼5.0 and 3.8 ka during the Nipissing high stand and subsequent regression. The second phase occurred between ∼3.0 and 2.0 ka during the Algoma high stand and regression. Finally, the most recent interval of dune growth transpired between ∼1.3 and 0.8 ka in the aftermath of a high lake stage at ∼1.7 ka and another, shorter peak in lake level at ∼1.0 ka. These findings indicate that major pulses of dune building mostly occur during lake regressions. They also correspond to the relatively warmer and drier conditions of the Holocene Climate Optimum, Roman Climate Optimum, and Medieval Warm Period, respectively, as well as periods of relative drought in the region. These associations indicate that drier conditions may have contributed to dune activation. Plotting of 14 C data reveals no single variable, such as stability or burial, explains the timing of the four peaks in the kernel distribution of ages derived from organic remains. Intervals of higher 14 C age frequencies occurred between ∼4500 and 3800 cal yr BP, ∼3500 and 2600 cal yr BP, ∼2300 and 1600 cal yr BP, and ∼700 and 200 cal yr BP. The peak at ∼4200 cal yr BP corresponds with a similar OSL pattern, indicating that 14 C ages derived from weakly developed Entisols during this interval reflects their burial by eolian sand. Peaks in 14 C densities within the latter intervals at ∼3200, ∼1800, and ∼400 cal Yr BP align with distinct drops in the OSL distribution. This pattern indicates that 14 C ages from these intervals reflect episodes of stability and soil formation subsequent to episodes of dune building. Although this study demonstrates the general patterns of dune growth and stability, local variations in behavior likely occurred.

Indiana, Michigan↗
USGS2023

GeoAI for spatial image processing

The development of digital image processing, as a subset of digital signal processing, depended upon the maturity of photography and image science, introduction of computers, discovery and advancement of digital recording devices, and the capture of digital images. In addition, government and industry applications in the Earth and medical sciences were paramount to the growth of the technology. From the early days when photography was first introduced to science to today, artificial intelligence and deep learning technologies have been intensively used to analyze imagery. Spatial image processing has experienced breakthroughs and evolutions. This chapter presents an overview of the history of image processing, GeoAI-based image processing applications, and the role of GeoAI in advancing image processing methods and research. We also discussed the remaining challenges to using GeoAI for image processing regarding training data annotation, the issues of scale, resolution, and change in space over time.

Book chapter↗
USGS2023

Geomorphometric analysis of the Summit and Ridge classes of the Geographic Names Information System

This research aims to conduct a geosemantic comparison of landforms classified in the Summit and Ridge feature classes in the Geographic Names Information System (GNIS). The comparison is based on a 2D shape analysis of manually delineated polygons produced by USGS staff to correspond to 33,304 Summit and 8,006 Ridge features. Five shape measures were chosen for this specific geomorphometry-based analysis. Univariate and bivariate statistics are first calculated to compare the two feature classes. This is followed by unsupervised learning with k-means cluster analysis to identify two major geomorphometric clusters corresponding to Summit and Ridge features. Although this supports sufficient internal homogeneity to have stable Summit and Ridge feature classes, more than 7,500 (18%) special features were also identified, which were assigned by k-means to the cluster not corresponding to their given GNIS class. These features remain to be analyzed further to decide if GNIS features should be reclassified based on geomorphometric analysis of available polygonal representations.

Conference Paper↗