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Chapter 14. Geographic Information Systems and Glacial Environments CHAPTER GEOGRAPHIC INFORMATION SYSTEMS AND GLACIAL ENVIRONMENTS 14 K. Wagner Minnesota Geological Survey, Saint Paul, MN, United States 14.1 INTRODUCTION As the study of glacigenic sediments, landforms, and landsystems has advanced, new methods of data acquisition, storage, manipulation, analysis, and visualization have also developed rapidly. Modern approaches to the study of past glacial environments facilitate increasingly objective and numerically based modes of investigation, perhaps most importantly with regard to styles of mapping. Given that many research questions within palaeoglaciology are inherently spatially organized, mapping and sur- veying techniques have long been essential components of the glacial geologist’s toolkit, though recently, driven by the expansion of geospatial information technologies (GIT), these techniques have undergone expeditious change. In particular, geographic information systems (GIS) have become one of the most common frameworks for interpreting past glacial environments. GIS are combined systems of hardware and software that facilitate the storage, analysis, and display of spatial data. GIS, and the associated fields of geographic information science (GIScience) and remote sensing (RS), share many ideas and concepts with glacial geology and geomorphology. These include such foundational constructs as scale variation, time and space representation, feature identifi- cation and interrelation, data management, and geovisualization (Napieralski et al., 2007a). The ability to visualize the spatial and temporal distribution of a phenomenon (e.g., glacial landforms, clast dis- persal patterns, sediment textural or geochemical signatures, etc.) often reveals clues to its underlying process. In this respect, GIS have improved our understanding of glacial processes and landscape evo- lution, as they perform well when integrating information across varied spatial and temporal domains. For instance, continental-scale ice sheet reconstructions are increasingly being built from interpreta- tions of spatially referenced geological datasets, which are ingested into, managed, and processed within GIS (e.g., Boulton and Clark, 1990; Kleman et al., 1997, 2002, 2006; Clark et al., 2000, 2012; Boulton et al., 2001; Jansson et al., 2002; Jansson and Glasser, 2005; Greenwood and Clark, 2009a,b; Evans et al., 2009; Livingstone et al., 2010, 2012; Finlayson et al., 2014; Hughes et al., 2014). GIS have also been used recently to extract new patterns and relationships from geomorphological datasets (e.g., Clark, 1993; Clark and Meehan, 2001; Dunlop and Clark, 2006; Greenwood and Kleman, 2010), and have been instrumental in advancing glacial landsystem concepts (Evans, 2003). Traditionally, data gathering in glacial geomorphology relied extensively on field surveys, ste- reographic analysis of aerial photos, and the manual construction of contour maps (e.g., Rose and Past Glacial Environments. DOI: http://dx.doi.org/10.1016/B978-0-08-100524-8.00015-4 © 2018 Elsevier Ltd. All rights reserved. 503 504 CHAPTER 14 GEOGRAPHIC INFORMATION SYSTEMS Letzer, 1975), and though these methods remain central to site-specific studies with large-scale cartographic outputs, their high cost and time-intensiveness make most field-mapping techniques unsuitable for regional or broader-scale investigations (Smith et al., 2000). Alternatively, individual RS scenes, which are frequently managed with GIS, offer large areal coverage (commonly several tens to hundreds of km2 per tile or scene), and can thus be compiled and utilized by single operators within GIS environments to map glacial features rapidly at synoptic scales. 14.2 GIS DATA STRUCTURES AND REMOTELY SENSED DATA PRODUCTS Real-world spatial entities are stored and displayed in GIS using one of two primary data structures (a.k.a. data models): raster or vector (Fig. 14.1). Raster data represent space as consisting of a mesh or grid of square cells, each having a location defined by an individual row and column position which references a common origin. Raster cells store a value (z) for a particular attribute or vari- able of interest, although no implicit topological relationships between these values are recorded. In the vector data model, absolute spatial locations of points or nodes are defined on the basis of explicit coordinate pairs (x, y), which are pegged to a particular coordinate system. Individual points can be linked to form arcs or lines, or further, closed to form area-encompassing polygons. Thus, the derivation and extraction of important shape descriptors is made trivial within the vector data model. Numerical and nonnumerical attributes of vector features are stored within a separate FIGURE 14.1 Conceptual illustration of object representations in vector and raster data structures. The vector data structure stores and displays objects as points, lines, and polygons; the raster data structure represents objects as cells in a grid. Data conversions from raster to vector are easily handled in GIS environments and allow raster cell values to be extracted as vector feature attributes. 14.2 GIS DATA STRUCTURES AND REMOTELY SENSED DATA PRODUCTS 505 database management system (DBMS), and retrieved using a unique feature ID that is common to both data storage and display environments. Raster-to-vector conversions, and vice versa, are com- mon when dealing with most forms of spatial data (Fig. 14.1). Both raster and vector data structures play important roles in the mapping, modelling, and anal- ysis of glaciated terrains. In particular, the vector data model is well-suited for representing discrete features (i.e., glacial landforms, glacial striae observations, ice marginal positions, and/or sample locations collected in the field). Conversely, the raster data model is most useful for the representa- tion of continuous variables—those which might be measured or interpolated over an entire survey- able land surface (e.g., geochemistry ratios in surface sediments, isostatic rebound rates, etc.). Perhaps most important and commonly utilized are digital elevation models (DEMs), which com- prise gridded representations of ground elevation or topography. DEMs are often used in concert with other raster RS products, including satellite imagery and digital aerial photos. Most projects employ both raster and vector data structures, which highlights one of the unique advantages of GIS, involving the ability to partition datasets into separate layers that can be queried, integrated, or removed on-the-fly, lending to a seamless analysis and visualization workflow. In a glacial geo- logic context, this allows individual landform classes to be grouped by specific landsystem themes (Fig. 14.2). GIS layers may also consist of digitized legacy data that house important observations from the predigital literature (e.g., Clark et al., 2004; Smith et al., 2015). FIGURE 14.2 Schematic diagram illustrating the ability of GIS workflows to group landforms into separate data layers by class, and partition them into landsystem components. Layers can be variously ‘stripped’ away, or combined, to facilitate spatial and temporal analysis of landscape development. Modified from Napieralski, J., Harbor, J., Li, Y., 2007a. Glacial geomorphology and geographic information systems. Earth-Sci. Rev. 85, 1À22. 506 CHAPTER 14 GEOGRAPHIC INFORMATION SYSTEMS 14.2.1 SATELLITE IMAGERY Satellite imagery depicts the Earth’s surface at various spectral, temporal, radiometric, and increas- ingly detailed spatial resolutions, as is determined by each collection system’s sensing device, and the orbital path of its reconnaissance platform (Walsh et al., 1998). Spatial resolution refers to the smallest measurable area on the ground, the instantaneous field of view (IFOV), that can be sensed in a single pass by a given detector. The scale of a sensing target determines the adequacy of a sen- sor with regard to its spatial resolution. For instance, the detection of relatively minute features, like glacial flutes or annual push moraines, requires imagery with very high spatial resolution (VHR) (e.g., commercial sensors, like Worldview-III), whereas larger and/or more spatially homo- geneous targets, like proglacial outwash plains, can be easily mapped using coarser resolution data- sets (e.g., Landsat 8 OLI). Asensor’sspectral resolution defines the span of the range of wavelengths (λ1Àλ2)within the electromagnetic (EM) spectrum over which it is equipped to collect data (Fig. 14.3A). Spectral bandwidth (i.e., the width of a sensor’s band pass) is constrained by spectral resolution, and is equivalent to the wavelength (λ) width at 50% of a sensor’s maximum response level (Fig. 14.3B). Sensors that partition the EM spectrum (or a specific portion of it) into many dis- crete bands have high spectral resolution. Those which sample the spectrum near-continuously are referred to as hyperspectral sensors (or ultraspectral sensors for even higher resolutions) and frequently utilize over 200 spectral bands (Schaepman et al., 2009). Multi- and hyperspectral sensing is increasingly being used to detect anomalies in surface sediments based on the spectral response properties of indicator minerals and distinctive lithologies, which has assisted in deter- mination of glacial drift dispersal vectors for mineral exploration and prospecting (e.g., Kerr et al., 2002; Grunsky et al., 2009). Temporal resolution is controlled by a sensor’s swath width alongside the orbital parameters of its sensing platform; together these determine
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