Remote sensing and three-dimensional photogrammetric analysis of glaciofluvial sand and gravel deposits for aggregate resource assessment in McHenry County, Illinois, USA

Authors

Xiaodong Miao, Linyi UniversityFollow
Christopher J. Stohr, University of Illinois at Urbana-Champaign
Paul R. Hanson, University of Nebraska-LincolnFollow
Qiansuo Wang, Luoyang Normal University

Date of this Version

6-5-2020

Citation

Published in Engineering Geology 274 (2020) 105695

doi:10.1016/j.enggeo.2020.105695

Comments

Copyright © 2020 Elsevier B.V. Used by permission.

Abstract

Sand and gravel deposits, one of the most common natural resources, are used as aggregates mostly by the construction industry, and their extraction contributes significantly to a region's economy. Thus, it is critical to locate sand and gravel deposits, and evaluate their quantity and quality safely and quickly. However, information on aggregate resources is generally only available from conventional two-dimensional (2-D) geologic maps, and direct field measurements for quality analysis at outcrops are time consuming and are often not possible due to safety concerns, or simply because exposures are too difficult to access. In this study, we presented a methodology to locate and evaluate aggregate resources, including the traditional methods of field surveying and borehole investigation for the entire McHenry County, Illinois, USA and new three-dimensional (3-D) photogrammetric models and remote sensing technologies at an active gravel pit. Thus acquired data sets allowed us to obtain key information for successful aggregate resource management: spatial occurrence, thickness, texture, paleocurrents, lithology and land use compatibility. In addition, remote sensing and photogrammetric techniques allowed for very quick and safe assessment of fundamental properties like particle size, paleocurrent direction and sorting, especially in inaccessible and/or unsafe outcrops. In summary, this paper demonstrated how remote sensing and photogrammetric technology can improve the efficiency and safety in resource assessment strategies, and the methodology used in our study can be applied to the development of autonomous mining and resource asset management elsewhere.