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 identification 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 dispersal 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 evolution, 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, stereographic analysis of aerial photos, and the manual construction of contour maps (e.g., Rose and

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 variable 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 common 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 analysis 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 representation 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 comprise 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 geologic 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 increasingly 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 sensor 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 datasets (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 discrete 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 the periodicity (return interval) at which a fixed area on the ground is successively imaged by a spaceborne sensor. RS systems that are placed within orbital paths more distant from the Earth’s surface will have finer temporal resolutions than those placed within nearer orbital paths. Temporal resolution is a primary consideration when selecting an RS product for researchers studying contemporary glaciers (e.g., for monitoring marginal positions, supraglacial lake drainage, feature tracking, or detecting large tidewater glacier calving events) though it is less critical in the palaeoglaciological community, where imaging targets (glacial landforms) are usually static. Sensors with higher temporal resolution or more extensive imaging archives are, however, most likely to afford cloud-free, and seasonally favourable images, making them attractive options for all investigators. Finally, radiometric resolution describes the total number of quantization levels used by a sensor (e.g., its bit-depth, or the range of digital numbers it is capable of natively displaying) (Gupta, 2003). High radiometric resolution enables robust information storage, but at the cost of larger data volumes. Captured data, bound by these four resolutions (spatial, spectral, temporal, radiometric) is manipulated to generate imagery of the Earth’s surface, which can then be exploited to map glacial sediments and landforms (Walsh et al., 1998). Although there are many currently operational Earth-observation sensors, only a relatively small fraction produce data that are suitable for glacial geomorphological mapping applications 14.2 GIS DATA STRUCTURES AND REMOTELY SENSED DATA PRODUCTS 507

FIGURE 14.3 (A) Wavelength spans of the electromagnetic spectrum. Note the small range within which visible light occurs. (B) Generalized plot of the spectral bandwidth (spectral resolution) of a detector. Spectral range is determined

from the difference of spectral boundaries (λSB) at 50% of the detector’s maximum response level. The spectral aperture, or slit width of the detector, is shown with respect to its response curve above, and is calculated as the difference of λSSW.

(Table 14.1). The majority of these sensors can be categorized into those which collect in the visible and near-infrared (VNIR) ranges of the EM spectrum, versus those which collect in the microwave (i.e., radar) portion. The use of radar imagery in palaeoglaciology has been limited, however (e.g., Ford, 1984; Graham and Grant, 1991; Clark et al., 2000; Clark and Stokes, 2001; Heiser and Roush, 2001), relative to VNIR datasets, owing to error and geometric distortion associated with the off-nadir (side-looking) orientation of synthetic aperture radar (SAR) sensors, as well as the 508 CHAPTER 14 GEOGRAPHIC INFORMATION SYSTEMS

Table 14.1 Select Earth-Observing Satellites With Data Sources Relevant to Glacial Geomorphological Mapping Sensora Platform Spectrum Spatial Resolution (m) Spectral Bands MSS Landsat 1À5 VNIR 80 (60) 4 TM Landsat 4À5 VNIR 30 6 Thermal 120 1 ETM1 Landsat 7 Panchromatic 15 1 VNIR 30 6 Thermal 60 (30) 1 OLI Landsat 8 Panchromatic 15 1 VNIR 30 6 SWIR 30 2 TIRS Thermal 100 2 Hyperion EO-1 Hyperspectral 30 220 ALI Panchromatic 10 1 VNIR 30 9 ASTER Terra VNIR 15 3 SWIR 20 6 Thermal 90 5 À RapidEye VNIR 5 5 À EROS-A Panchromatic 1.9 1 À EROS-B Panchromatic 0.7 1 À Pleiades´ 1A Panchromatic 0.7 (0.5) 1 VNIR 2 4 À Pleiades´ 1B Panchromatic 0.7 (0.5) 1 VNIR 2 4 HRV SPOT 1À3 Panchromatic 10 1 VNIR 20 3 HRVIR SPOT 4 Panchromatic 10 1 VNIR 20 3 SWIR 20 1 HRS SPOT 5 Panchromatic 10 1 HRG Panchromatic 5 (2.5) 2 VNIR 10 3 SWIR 20 1 NAOMI SPOT 6À7 Panchromatic 1.5 1 VNIR 6 4 AVNIR-2 ALOS Panchromatic 2.5 1 VNIR 10 4 MSI Sentinel-2a and b VNIR 10/20/60 10 SWIR 20/60 3 14.2 GIS DATA STRUCTURES AND REMOTELY SENSED DATA PRODUCTS 509

Table 14.1 Select Earth-Observing Satellites With Data Sources Relevant to Glacial Geomorphological Mapping Continued Sensora Platform Spectrum Spatial Resolution (m) Spectral Bands À IKONOS Panchromatic 1 1 VNIR 4 4 À Quickbird Panchromatic 0.6 1 VNIR 2.5 4 À Geoeye Panchromatic 0.4 1 VNIR 1.65 4 À Worldview-1 Panchromatic 0.5 1 À Worldview-2 Panchromatic 0.46 1 VNIR 1.85 8 À Worldview-3 Panchromatic 0.31 1 VNIR 1.24 8 SWIR 3.7 8 CAVIS 30 12 KH1ÀKH6 CORONA/ARGON/ Panchromatic ,1.8 1 LANYARD À Formosat-2 Panchromatic 2 1 VNIR 8 4 ASAR Envisat Microwave 30 2 SAR ERS-1 and 2 Microwave 30 1 SAR RADARSAT-1 Microwave 8/30/50/100 1 SAR RADARSAT-2 Microwave 2/5/16/30/50/100 1 SAR Sentinel-1 Microwave 5/20/40 1 À COSMO-Skymed Microwave 1/3/15/16/20 1 À TerraSAR-X Microwave 1/3/18 1

aMSS, multispectral scanner; TM, thematic mapper; ETM1, enhanced thematic mapper plus; OLI, operational land imager; TIRS, thermal infrared sensor; ALI, advanced land imager; ASTER, advanced spaceborne thermal emission and reflection radiometer; HRV, high resolution visible; HRVIR, high resolution visible and infrared; HRS, high resolution stereoscopic; HRG, high resolution geometric; NAOMI, new astrosat optical modular instrument; AVNIR-2, advanced visible near infrared radiometer-2; MSI, multispectral instrument; KH, key hole; ASAR, advanced synthetic aperture radar; SAR, synthetic aperture radar.

specialist knowledge required to properly process and interpret their data (Clark, 1997; Palmann et al., 2008). The unique geometry of SAR sensors is useful for imaging positive relief landforms, though it also implies that layover occurs in front of tall features, and shadow behind. Layover can be processed out of SAR imagery, whereas shadow strictly generates areas of no data. Landform mapping from VNIR imagery is more intuitive and adaptable. VNIR sensors isolate spectral data within various bands, which can be assigned to red, green, and blue (RGB) channels and compiled in false-colour composite (FCC) ternary images to discretize surface materials according to their spectral response properties (Fig. 14.4). 510 CHAPTER 14 GEOGRAPHIC INFORMATION SYSTEMS

FIGURE 14.4 Ribbed moraines (subglacial bedforms) depicted using various multispectral VNIR band combinations. (A) Landsat 7 ETM1 4,3,2 (red, green blue; RGB) false-colour composite (FCC) of visible and near-infrared bands. (B) Landsat 7 ETM1 5,3,2 (RGB) FCC of visible and mid-infrared bands. (C) Landsat 7 ETM1 7,4,2 (RGB) FCC of infrared, near-infrared, and visible bands. (D) SPOT 5 panchromatic image (10 m resolution).

14.2.2 DIGITAL ELEVATION MODELS Perhaps the most significant development in geomorphological data collection within the past several decades has been the introduction of widely available surface elevation datasets, derived using various techniques, including traditional photogrammetry, radar altimetry, aerial laser scanning (ALS), and terrestrial laser scanning (TLS) (both varieties of light detection and ranging (LiDAR)), and interferometric synthetic aperture radar (IfSAR, also InSAR), as well as multi- and single-beam sound navigation and ranging (SoNAR), swath bathymetry, and seismic sounding in marine environments (Table 14.2). Digital elevation data are commonly processed into a gridded, regularly sam- pled dataset, and made available to the end-user as a DEM. Though often (and erroneously) used interchangeably, the term DEM is a superset of the designations ‘digital terrain model’ (DTM) and 14.2 GIS DATA STRUCTURES AND REMOTELY SENSED DATA PRODUCTS 511

Table 14.2 Select Digital Topographic Data Sources With Data Sources Relevant to Glacial Geomorphological Mapping Nominal Spatial Source Resolution Accuracy Ground survey Variable, but usually ,5 m Very high vertical and horizontal GPS Variable, but usually ,5 m Moderate vertical and horizontal, very high vertical and horizontal (dGPS) Photogrammetry (commerical ,1 m Very high vertical and horizontal optical sensors) LiDAR ,1À3 m 0.15À0.11 m vertical, 1 m horizontal InSAR/IfSAR 2.5À5m 1À2 m vertical, 2.5À10 m horizontal SRTM, Band C 90 (30) m 16 m vertical, 20 m horizontal SRTM, Band X 30 m 10 m vertical, 6 m horizontal Terra ASTER (GDEM) 30 m 7À50 m vertical, 7À50 m horizontal SPOT 5 (Stereo-Pair Mode) 30 m 10 m vertical, 15 m horizontal TerraSAR-X DSM 10 m 5À10 m vertical, 5À10 m horizontal

‘digital surface model’ (DSM). The latter differ in that DSMs depict the tops of buildings, forest canopies, etc., whereas DTMs represent ‘bare-earth’ models of the ground. DTMs tend to be most suitable for the majority of geomorphological mapping applications, and are now widely regarded as essential tools within the discipline, though require additional processing in order to remove surface clutter and noise (El-Sheimy et al., 2005). Many derivatives can be computed and displayed using DEMs (e.g., slope, fractal, curvature, aspect), however hillshaded or shaded relief surfaces are most intuitive and commonly employed for cartographic and general mapping purposes. The ability of the observer to vary illumination azimuth and sun angle on hillshaded surfaces within GIS environments is a particularly powerful technique and has been demonstrated to improve the detectability of glacial features (Clark and Meehan, 2001; Jansson and Glasser, 2005; Smith and Clark, 2005; Greenwood and Clark, 2008). Photogrammetrically derived DEMs rely on the collection and processing of repeat-pass (steer- able sensor array), or single-pass (multiple fore/aft sensors) stereographic satellite imagery. Terra ASTER, SPOT 5, and a number of more recently launched commercial VHR sensing systems sup- port stereo-pair acquisition modes. Terra ASTER possesses a second, aft-looking sensor, and has been used to produce a near-global (83 degrees NorthÀ83 degrees South) photogrammetric DEM (GDEM and GDEM-2) from along-track, single-pass imagery at 1 arc-second posting interval (B30 m spatial resolution), though use of this product has been somewhat limited in palaeoglaciological applications (e.g., Lytwyn, 2010). Unlike passive RS systems (e.g., VNIR) that rely on energy emission from the Sun and mea- surement of target surface reflectance or thermal emission, radar is an active form of RS which emits its own EM energy, and utilizes sensors that collect both wave phase and amplitude from backscattered signals. Signal backscatter information can be combined from different vantage points to construct an interferogram, where phase offsets reflect proportional displacements in 512 CHAPTER 14 GEOGRAPHIC INFORMATION SYSTEMS

surface height (Burgmann et al., 2000; Farr, 2011), thus forming the basis of topographic mea- surement using InSAR. Interferograms can be acquired in repeat passes with a single antenna, or instantaneously using two antennae. NASA’s Shuttle Radar Topography Mission (SRTM) is a popular example of spaceborne, single-pass InSAR. Flown onboard the Space Shuttle Endeavour over 11 days in February, 2000, SRTM generated a continuous DEM between latitudes 60 degrees North and 56 degrees South at 3 arc-second posting interval (B90 m spatial resolution), and 1 arc-second post spacing (B30 m spatial resolution) for the United States and Australia. These data have been extensively accessed for glacial geomorphological mapping applications, despite their relatively coarse spatial resolution and lack of coverage at high latitudes (e.g., Blundon et al., 2009; Ross et al., 2009; Shaw et al., 2010; Evans et al., 2014). More recently, the commercial collection of airborne InSAR has procured seamless digital elevation products at very high resolution (,5 m) for large areas of the globe. In particular, Intermap’s NextMap series of products provides coverage across most of the United States and Western Europe, and has been used widely in palaeoglaciological mapping applications throughout those areas (e.g., Everest et al., 2005; Smith et al., 2006; Bradwell et al., 2007; Finlayson and Bradwell, 2008; Livingstone et al., 2008, 2010, 2012; Clark et al., 2009, 2012; Evans et al., 2009; Finlayson et al., 2010; Hughes et al., 2010, 2014; Knight, 2010; Phillips et al., 2010; Spagnolo et al., 2011; Margold and Jansson, 2012). With the ability to generate high-quality digital terrain representations at decimetre-scale spatial resolution, LiDAR has expanded rapidly in recent years and now arguably represents the fore- front of terrestrial elevation capture methods. Applications in glacial geomorphology and palaeoglaciology have been substantial and are already too numerous to list, as collection has become widespread, often funded by national or regional survey initiatives. Airborne LiDAR instrumentation, or ALS systems, utilize scanner mounts beneath an aircraft platform that transmit many thousands of light pulses per second. Return times and intensity (sometimes multiples for each pulse) are recorded by a sensor, and the delay between transmission and reception is used to determine elevation (Baltsavias, 1999; Lillesand et al., 2008). A 3D vector point cloud is then generated by integrating positional data from the scanner or platform mount using a differential global positioning system (dGPS), allowing for either direct manipulation within GIS, or subsequent interpolation, using one of several methods, and generation of a gridded DEM (Pfeifer and Mandlburger, 2009). All but final returns within the point cloud can be processed out in order to generate ‘bare-earth’ DTMs with high precision and vertical accuracy, even in heavily forested regions (e.g., Haugerud et al., 2003). The unprecedented detail of LiDAR data comes with the caveat of requiring computer hardware and software capable of effectively handling such large datasets. In certain instances, LiDAR derivatives are resampled to coarser resolutions to reduce computational storage and processing requirements. Paralleling developments in the terrestrial domain, multibeam echo-sounding has revolution- ized palaeoglaciology by providing bathymetric data of the geomorphic imprint produced by the expansive lacustrine and marine-based sectors of former glaciers and ice sheets. With optimal processing, current SoNAR technologies permit cm-scale resolution in shallow waters, and ,25 m resolution at depths ,1000 m. Integration of marine and terrestrial remotely sensed datasets has the potential to produce more holistic understandings of glacier and ice sheet systems, although synergistic uses have been limited (e.g., Stoker et al., 2009; Freire et al., 2015; Greenwood et al., 2015). 14.2 GIS DATA STRUCTURES AND REMOTELY SENSED DATA PRODUCTS 513

14.2.3 DATA FUSION It is often the case that a particular mapping target is best resolved using more than one visualization technique, or by combining the display of multiple attributes or parameters simultaneously. Data fusion is an approach to optimally characterizing these features, which involves the integration of distinct datasets with varying thematic representations (vector) or resolutions (raster). Pansharpening, or the integration of (lower spatial resolution) multispectral with (higher spatial resolution) panchromatic satellite RS data is a standard example of a data fusion technique. Similarly, digital topographic maps, thematic data, and imagery are frequently fused with a DEM to provide three-dimensional representations of the land surface (Fig. 14.5). Many modern geovisualization techniques rely on data fusion to a considerable extent (cf. Mitasova et al., 2012). In certain applications, data fusion exposes new relationships between datasets. For instance, Clark et al. (2009) employed a drift-thickness raster in their mapping of glacial lineaments in northeast Scotland, which was interpolated from borehole records and visually fused with a DSM. A subsequent masking procedure using minimum-thickness boundaries enabled the authors to discriminate drumlins comprised of drift from crag-and-tails developed in the lee of bedrock outcrops and localized areas of thin glacial sediments (Fig. 14.6).

FIGURE 14.5 Examples of a common data fusion technique. (AÀD) Very-high-resolution (VHR) natural-colour composite multispectral satellite imagery visually fused with a 10 m DEM and derived contours, highlighting ‘cirque-like features’ of the Zardkuh Mountain, Bakhtiari Province, Iran using multiple oblique perspectives. After Seif, A., Ebrahimi, B., 2014. Combined use of GIS and experimental functions for the morphometric study of glacial cirques, Zardkuh Mountain, Iran. Quat. Intl. 353 (5), 236À249 (Seif and Ebrahimi, 2014). 514 CHAPTER 14 GEOGRAPHIC INFORMATION SYSTEMS

FIGURE 14.6 An illustration of data fusion techniques. (A) Shaded-relief image (NW illumination, 43 vertical exaggeration) of a NEXTmap 5 m digital surface model (DSM) from NE Scotland, revealing an assortment of streamlined landforms. (B) Advanced Superficial Thickness Model (ASTM) (in metres) overlain on DSM. (C) Visual fusing of DSM with ASTM data and discrete mapping of classical drumlins and crag-and-tails. After Clark, C.D., Hughes, A.L.C., Greenwood, S.L., Ng, F.S.L., 2009. Size and shape characteristics of drumlins derived from a large sample, associated scaling laws. Quat. Sci. Rev. 28, 677À692. 14.3 GLACIAL LANDFORM MAPPING 515

14.3 GLACIAL LANDFORM MAPPING Unlike traditional aerial photograph interpretation techniques, which involve mark-up of physical prints using transparencies and a stereoscope, glacial landform mapping from digital remotely sensed datasets is carried out by heads-up, or on-screen, digitization, either using specialized image analysis and manipulation software, or via direct input into a GIS environment. Note, however, that digital stereoplotters and soft-copy airphoto analysis now also enable 3D on-screen display of digital aerial photos. In GIS-based mapping, glacial features are stored as points, lines, or polygons, and are depicted according to their crest-line, mean centre, or break-of-slope, depending on the intended goal of feature representation, and desired cartographic scale of output (Fig. 14.1). Mapping is regularly conducted over repeat passes while varying scale, data source, and/or specific data parameters (e.g., illumination azimuth) in order to avoid any biases in detection (Smith and Wise, 2007). Prior to the proliferation of remote mapping techniques, glacial landforms were principally described in a qualitative fashion (e.g., Davis, 1884), with concern to establishing a consistent basis for taxonomic classification. Later studies developed quantitative methods, but were limited only to specific field regions by the necessity of collecting ground-based measurements from physically accessible areas (Chorley, 1959; Reed et al., 1962; Heidenreich, 1964; Vernon, 1966; Smalley and Unwin, 1968; Trenhaile, 1971). Subsequently, a shift toward digital GIS-based mapping has procured large inventories of glacial landforms, which deliver information concerning size, shape, and spatial distribution across broad and varied environments. This has fostered the marriage of both quantitative glacial geomorphology and landform ontology to geomorphometry (Smith and Mark, 2003), and has enabled researchers to develop sophisticated metrics which better describe landforms and relate their properties across physical settings.

14.3.1 GLACIAL GEOMORPHOMETRY Geomorphometry is the practice of quantitative digital topographic surface analysis (Pike, 1995; Pike et al., 2009). Classification and extraction of landforms from digital terrain surfaces is referred to as specific geomorphometry, and differs from general geomorphometry, which has interest in higher- level terrain classification (Evans, 1972; Goudie, 1990). Specific geomorphometry is a major activity in quantitative land surface analysis and now represents an important division of palaeoglaciological research, in particular as this relates to the study of subglacial bedforms, where geomorphometric techniques are increasingly used to test competing hypotheses of bed formation, and to provide much needed numerical constraints on morphological parameters for computational models of landform genesis (Fowler, 2000, 2009, 2010; Dunlop et al., 2008; Chapwanya et al., 2011; Fowler et al., 2013). At the most basic level, simple metrics of bedform size and shape, including length (L), width (W), and height/relief (H), are rapidly acquired using digital mapping techniques, which has prompted the development of large, unprecedented datasets for bedform geometry. These metrics are often considered in terms of their covariance, or utilized as shape-factor inputs in both graphical and numerical index-based approaches which seek to expose underlying properties in bedform populations that can be linked to specific ice-dynamical behaviours (Clark et al., 2009; Hillier et al., 2013; Dowling et al., 2015). In general, robust bedform geometry datasets serve three fundamental purposes by: (1) Enabling objective comparison of discrete landform assemblages, (2) assisting in the development of more precise definitions for glacial bedforms, and (3) providing numerical constraints for computational models which seek to characterize processÀform relationships. 516 CHAPTER 14 GEOGRAPHIC INFORMATION SYSTEMS

14.3.2 TECHNICAL CONSIDERATIONS OF MANUAL GLACIAL LANDFORM MAPPING FROM REMOTELY SENSED DATASETS Problems of bias (Box 14.1) when mapping landforms from remotely sensed datasets using human-cognitive approaches are well-characterized and have been comprehensively reviewed (Clark, 1997; Smith and Wise, 2007). These include issues stemming both from: (1) landform

BOX 14.1 BIASES AFFECTING MANUAL, SINGLE-OPERATOR GIS BASED GEOMORPHOLOGICAL MAPPING FROM REMOTELY SENSED DATASETS. Manual, single-operator GIS-based geomorphological mapping using remotely sensed datasets is subject to a number of limitations. Without proper acknowledgement, these can have adverse effects on mapping outcomes: Scale bias—The relative size of a target feature compared to both sensor spatial resolution and mapping scale. In general, small mapping scales and coarse sensor spatial resolutions inhibit the detection of comparatively small targets, whereas large mapping scales permit detailed representation of small features, but can prevent recognition of larger features. Azimuth bias—The relative angle of a linear target feature to the orientation of an (artificial or natural) illumination source. Orthogonal illumination sources generally permit the best detection of sublinear targets, but multiple orthogonal illumination azimuths should be consulted where possible to ensure that all targets are equally detected. Detection bias—The visual separability of a target relative to surrounding features/surfaces. Targets with high spectral, textural, and/or tonal contrast with their surroundings are typically the most detectable. Illumination declination can affect these properties (Fig. 14.7). Low angles (i.e., winter image acquisitions) generate shadow in the lee of positive relief targets, highlighting the target, but obscuring lee slopes. Alternatively, high illumination angles dampen textural contrast of the target object but do not mask lee side surfaces. Operator bias—Expectations shaped by the individual background and experience of an operator. Operator biases are generally difficult to quantify but can be minimized through proper training and education.

FIGURE 14.7 The effect of solar biasing on glacial landform identification. (A) Landsat 7 ETM1 panchromatic band 8 subscene acquired from southern Nunavut, Canada, during the summer season at high solar declination. (B) Landsat 7 ETM1 panchromatic band 8 subscene from the same WRS-2 path/row as (A), but acquired during the winter season under light snow cover and low solar declination. Palaeo-ice flow is from top right to bottom left. Note the increased detail (shadow, texture, contrast) available in winter imagery over summer imagery, providing optimal conditions for the detection of glacial landforms. 14.3 GLACIAL LANDFORM MAPPING 517

detectability and (2) observer ability. Whereas the latter can be difficult to quantify and varies with individual background and levels of experience, issues of landform detectability are more easily handled using careful preprocessing and data visualization/enhancement techniques (Clark, 1997). Nevertheless, even for the most experienced observer, reliance on any single methodology can still impart bias to mapping, and hence incorporating multiple data sources and processing/visualization techniques will always produce the best results. Optimal methods include the use of a DEM and combine slope-curvature mapping with multiple orthogonal illumination sources in order to reduce azimuth biasing (Smith and Clark, 2005; Smith and Wise, 2007). Landform detectability is closely associated with the spatial resolution and vertical accuracy of a digital representation, so choosing an appropriate data source is important. Data sources of insufficient quality can introduce a variety of errors, which then become subject to propagation down the workflow. Remote mapping from DEMs with #5mspatialresolutionand #1 m vertical accuracy (e.g., LiDAR, InSAR) tends to yield results comparable to field mapping for 1:10,000 scales or smaller (Smith et al., 2006), however, fused multispectral VNIR imagery and hillshaded DEMs have been shown to provide optimal glacial landform detection using moderate-resolution datasets (Jansson and Glasser, 2005).

14.3.3 AUTOMATION IN PALAEOGLACIOLOGY Traditional, single-operator manual techniques remain the most widely employed for mapping glacial features using remotely sensed datasets, yet they are also time-consuming and can be subjective. More recently, manual geomorphological mapping techniques have been shown to suffer from problems of underdetection (Hillier et al., 2014; Yu et al., 2015). These limitations have encouraged new research into automated and semiautomated GIS-based mapping routines across many subdisciplines of quantitative geomorphology (Bishop et al., 2012). In general, these can be divided into two categories: (1) Pixel-based classification schemes (e.g., MacMillan et al., 2003; Reuter et al., 2006; Iwahashi and Pike, 2007; Tagil and Jennes, 2008), and (2) object-based image analysis (OBIA) or geographic object-based image analysis (GEOBIA) (e.g., Dragu¸˘ tand Blaschke, 2007; van Asselen and Seijmonsbergen, 2006; Schneevoigt et al., 2008; Anders et al., 2011; Saha et al., 2011). Pixel-based techniques necessitate that individual pixels are either treated as spatial entities themselves, often on the basis of spectral properties alone (Mather, 2004), or else that they are assessed with respect to neighbouring pixels using statistical operations and moving window calcu- lations in order to generate arrays of land surface parameters (LSPs) (Hengl and Reuter, 2009). LSPs are usually derived from multivariate analyses which incorporate spectral properties and derivatives, slope, aspect, plan, and profile curvatures, relative height, and/or mean height, among other metrics, and are defined according to user-specified threshold values informed by expert knowledge. LSPs have been used with much success to discretize landform elements, or the separable constituents of landforms (e.g., pits, ridges, peaks, etc.) (Reuter et al., 2006; MacMillan and Shary, 2009), though integrating landform elements to characterize individual landforms has proven more problematic and requires subjective operator decision inputs (e.g., Wieczorek and Migon,´ 2014). Pixel-oriented approaches can also misrepresent Earth surface features, both when pixels are too large (spawning mixed pixels at target boundaries), or too small (leading to within-feature variation) relative to classification targets (Aplin et al., 1999; Aplin and Smith, 2011)(Fig. 14.8). For instance, Sulebak et al. (1997) used pixel-based isodata clustering and maximum likelihood 518 CHAPTER 14 GEOGRAPHIC INFORMATION SYSTEMS

FIGURE 14.8 Relationship between target objects and spatial resolution. (A) Coarse (low) resolution; targets are small relative to cell-size. Subpixel techniques are required. (B) Moderate resolution; targets are of the same order as cell-size. Pixel-based classification may be appropriate. (C) Fine (high) resolution; targets are large relative to cell-size. Regionalization is required. Classification of targets as image objects may be appropriate. classification of moderate-resolution DEM derivatives and were unable to constrain eskers and other large landforms to a single class. Even with enhanced treatments, like generalization or non- binary/fuzzy logic applications (e.g., MacMillan et al., 2000; Wang et al., 2010), pixel-based approaches remain limited in that they are incapable of accounting for the geometries and inherent topological relationships of and among classification targets (Blaschke and Strobl, 2001; Burnett and Blaschke, 2003; Blaschke et al., 2004). More recently, object-oriented approaches for automated mapping have been favoured in the geomorphological community by reason that they more closely resemble human-cognitive styles of interpretation through the accounting of spatial (e.g., distance, topology, neighbourhood, density) and geometric relationships, while shifting the scale of analysis to the real-world desired object (e.g., a particular glacial landform), rather than its constituent pixels (Hay et al., 2003; Blaschke et al., 2004; Gamanya et al., 2009). OBIA has benefited also from the recent wealth of accessible VHR remotely sensed data, as the technique can efficiently handle oversegmentation through regionalization procedures, whereas per-pixel analyses of VHR grids tend to generate noise, and resampling can contribute to loss of information (Blaschke, 2010). OBIA generally proceeds in two major steps beginning with pixel grouping/data segmentation (Dey et al., 2010) followed by analysis and classification of resulting objects using either a hierarchical decision-tree-based system or machine learning algorithm (Blaschke et al., 2008)(Fig. 14.9). In the context of glacial geomorphology, automated and semiautomated mapping techniques remain in their infancy, though they have recently yielded some promising results. A variety of semiautomated break-of-slope selection techniques, which involve contouring of a DEM and automated selection of closed loops, have been used to isolate glacial lineaments (Napieralski and Nalepa, 2010; Maclachlan and Eyles, 2013; Jorge and Brennand, 2014) and eskers (Broscoe et al., 2011) with reasonable success, though they are limited by the need for subsequent observer input to eliminate spurious objects, and the underrepresentation of features located on slopes that are not distinguished using contour data alone. Curvature has also been used as a metric to establish break-of-slope for automated drumlin delimitation, though it performs poorly when separating bedforms from surrounding terrain elements with similar gradients 14.3 GLACIAL LANDFORM MAPPING 519

FIGURE 14.9 Object relationships in object-based image analysis (OBIA), using a surficial geological example. Objects (units) exhibit both topological (neighbourhood) and hierarchical relationships. Object hierarchy can be exploited in image segment clustering, or segments can be classified to objects using machine learning algorithms.

(e.g., river banks) (Yu et al., 2015). Moreover, break-of-slope selection techniques tend to be sensitive to DEM resolution, where sub-10 m cells achieve results comparable to those produced from a 1 m DEM, though employing coarser resolution elevation data can contribute to landform positional drift and shape-related errors (Fig. 14.10). More recent applications incorporate terrain masking and contour tracking, for instance, around points of elevation minima in order to extract glacial overdeepenings and nested depressions from subglacial topography datasets (Patton et al., 2015). Experimentation with automated glacial feature classification using OBIA in select study areas has had variable results, and has yet to expand beyond experimental comparison of outputs with man- ually digitized reference datasets. Eskers (Parkinson et al., 2011) and drumlins (Saha, 2009; Saha et al., 2011) have been successfully demarcated using OBIA classification, though these workflows 520 CHAPTER 14 GEOGRAPHIC INFORMATION SYSTEMS

FIGURE 14.10 Variability in the size and shape of drumlins mapped using automated procedures, due to differences in the spatial resolution of the source DEM. Only subtle differences are observed between 1 and 20 m grid cell sizes, whereas coarser resolutions contribute to shape-related errors and positional drift. After Napieralski, J., Nalepa, N., 2010. The application of control charts to determine the effect of grid cell size on landform morphometry. Comput. Geosci. 36, 222À230. also tend to misclassify other linear structures (e.g., bedrock ridges, steep valley slopes), and underre- present targets with gentle slope gradients, lending to both substantial errors of commission and omis- sion. Furthermore, significant discrepancies have been observed between the morphometric statistics of targets delineated using OBIA versus those identified by manual procedures (Saha et al., 2011). The most promising classifications have been generated when applying OBIA to VHR LiDAR- derived DEMs. For instance, OBIA of a 2 m LiDAR-derived DEM using a multiple-step LSP-generation, image segmentation and decision tree hierarchy, with thresholds and fuzzy logic-based clustering, classified eskers, flutes, drumlins, and glacial outwash of the Breiðamerkurjo¨kull forefield, Iceland, to an overall accuracy of 77% (Robb et al., 2015). Refined target ontology (cf. Eisank et al., 2010) and incorporation of additional datasets (e.g., LiDAR intensity, multi-/hyperspectral imagery) could yield even higher rates in the future using a similar approach. 14.4 GLACIAL GEOLOGICAL INVERSION OF PALAEO-ICE SHEET BEDS 521

14.4 GLACIAL GEOLOGICAL INVERSION OF PALAEO-ICE SHEET BEDS AND GIS-BASED ICE SHEET RECONSTRUCTIONS The integration of remotely sensed imagery and elevation datasets with GIS-based mapping and analytical techniques has provided new bases for the reconstruction of palaeo-ice sheets. These are developed from spatiotemporal inversions of the glacial landform record and are closely linked to the current state of knowledge regarding generative mechanisms for subglacial bedforms. Ice sheet reconstructions built from inversion techniques are derived from the ‘bottom up’ using empirical datasets within GIS, and differ fundamentally from computational ‘top-down’ approaches, which reconstruct ice sheet extent and/or dynamics using numerical models. Empirically based ice sheet reconstructions have attracted considerable attention since the end of the 20th century, driven in large part by the expansion of GIT and a shift toward digital mapping practices. Continual refinement of standards and techniques has con- ditioned this approach into a quasisystematic and repeatable practice (Clark, 1993, 1994, 1997, 1999; Kleman and Borgstro¨m, 1996; Kleman et al., 2006; Greenwood and Clark, 2009a)(Box 14.2).

14.4.1 DATA REDUCTION AND FLOWSET ASSIGNMENT Flowsets (or fans) comprise the foundation of the inversion model, each representing a self- contained array of subglacial landforms (most critically, glacial lineations) that can be interpreted as the product of a single phase of ice flow. Flowsets are identified and differentiated on the com- parative basis of subglacial landform (bedform) morphology and spatial arrangement. This compart- mentalization of bedform patterns represents a distancing from the traditional practice of lumping and generalizing dissemblance in the ice flow record (e.g., Boulton et al., 1985). At the highest level, flowset designations are established using a minimum of three criteria that can be easily assessed within GIS environments (Clark, 1999): parallel concordance, close spatial proximity, and similar morphology. Parallel concordance implies that each bedform within a flowset exhibits like orientation to neighbouring bedforms of the same class (Fig. 14.12D and E). Since this check is commonly performed at large map scales, gradual deviations in bedform orientation along a flowline are to be expected. Close proximity requires that bedforms of a single flowset membership are tightly spaced, on the order of two to three times their average length or width (Clark, 1999; Clark et al., 2000)(Fig. 14.13). Lastly, bedforms are grouped into flowsets by similar morphology, which assumes that specific ice flow phases are traceable by a unique morphological signature or imprint (Fig. 14.13). Considerable work has been dedicated to developing conceptual templates that permit individual flowsets to be assigned formation scenarios within the context of specific glaciodynamic processes, based on the spatial and morphological properties of their constituent landforms (Kleman and Borgstro¨m, 1996; Clark, 1999; Clark et al., 2000; Kleman et al., 2006; Greenwood and Clark, 2009a). In this regard, flowsets are categorized as either isochronous or time-transgressive (Fig. 14.12A). Isochronous flowsets are envisioned as products of rapid generation; hence each bedform can be modelled as representing approximately the same age. These are typically generated some distance away from the ice margin and record interior-style flow patterns. In contrast, time-transgressive flowsets are created under gradual, back-stepping configurations of an ice sheet, under thin, lobate ice at a crenulate margin. Landforms belonging to this category may exhibit various ages and geometries, presumably younging in an ice-proximal direction (Fig. 14.12B and C). Whereas isochronous flowsets generally 522 CHAPTER 14 GEOGRAPHIC INFORMATION SYSTEMS

BOX 14.2 STEPS FOR GIS-BASED PALAEOGLACIAL RECONSTRUCTIONS USING A GEOLOGICAL INVERSION TECHNIQUE. A systematic approach to the inversion of palaeo-ice sheet beds within a GIS environment follows four steps: 1. Glacial geomorphological mapping from remotely sensed datasets (Fig. 14.11A and B). Digitization of legacy data. 2. Data reduction and generalization by amalgamation of individual landforms into cohesive flow patterns (Fig. 14.11C). 3. Deduction of flowsets from flow patterns and the construction of a relative chronology by flowset stack assembly (Fig. 14.11D). 4. Interpretation of glacial event sequences from the relative chronology of flowsets. Scenarios are assessed in terms of their glaciological plausibility, as well as their ability to fit available data and account for outlying and/or residual landforms/landform assemblages.

FIGURE 14.11 Illustration of the process of deriving flowlines and flowsets from the glacial landform record. Glacial lineations (A) and ribbed moraines (B) are mapped and their orientations used to develop separate flowlines, or flow patterns (C); black lines drawn parallel to lineation trend, grey lines drawn perpendicular to ribbed moraine crestlines. (D) A diachronous series of flowsets is developed by grouping landforms of similar spacing and morphometry along flowlines. Relative-age relationships (indicated on arrow tails; 1 5 oldest) are deduced from instances of cross-cutting and overprinting. Eskers (shown in red) and other meltwater features attest to the basal thermal regime of the ice sheet and the organization of its drainage during deglaciation. 14.4 GLACIAL GEOLOGICAL INVERSION OF PALAEO-ICE SHEET BEDS 523

FIGURE 14.12 Conceptual templates useful for interpreting isochronous and time-transgressive flowset scenarios. (A) A flowset

composed of glacial lineations interpreted as owing to isochronous (left, formed at time tn) or alternately, time- transgressive (right, formed from t1Àt3 or taÀtc) generation. (B) Flowlines and retreat isochrons that are implied by each interpretation. (C) Zone of bedform generation corresponding to scenario. Predicted patterns of lineation spacing, morphometry, and parallel conformity are shown arising from isochronous (D) and time-transgressive (E) flowset generation. Modified from Clark, C.D., 1999. Glaciodynamic context of subglacial bedform generation and preservation. Ann. Glaciol. 28, 23À32.

tend to reflect stable flow geometries, time-transgressive flowsets are built up incrementally and can record considerable volatility in ice margin configuration and flow direction through time. More sophisticated templates consider basal thermal regime transitions and the presence/absence of aligned meltwater features as metrics by which to assign flowsets to deglaciation (wet-based, cold-based), expansion, or contraction scenarios (Kleman and Borgstro¨m, 1996). Moreover, patterns of lineament spacing, elongation, and orientation are used to infer former ice streaming activity (Stokes and Clark, 1999), ice sheet thinning, and ice margin/divide migrations (Greenwood and Clark, 2009a). 524 CHAPTER 14 GEOGRAPHIC INFORMATION SYSTEMS

FIGURE 14.13 Using landform proximity and morphometry to distinguish between diachronous ice flow phases. (A) Hypothetical pattern of cross-cutting glacial lineations. (B) An interpretation of the lineation set shown in (A) that assumes all landforms were generated during the same flow event. (C) An alternative interpretation that assumes lineaments of different ages on the basis of cross-cutting flow patterns. (D) Spacing and morphometry (e.g., length) of landforms is used to differentiate between flowsets. Modified from Clark, C.D., 1997. Reconstructing the evolutionary dynamics of former ice sheets using multi-temporal evidence, remote sensing and GIS. Quat. Sci. Rev. 16, 1067À1092.

14.4.2 FLOWSET STACKING, AGE CONSTRAINT, AND PALAEOGLACIOLOGICAL RECONSTRUCTION Once a collection of flowsets has been established, the relative chronology of their formation can be determined on the basis of landform cross-cutting and overprinting relationships. This involves sorting flowsets into relative-age stacks according to the degree and location of superimposition and/or remoulding of their landforms. Flowset stacking is a meticulous, and time-intensive process, though the topological capabilities of modern GIS packages allow for systematic record-keeping of relative-age assignments within dedicated data layers. Stacks of sequentially aged flowsets convey both known and unresolvable age relationships. Field-collected data and legacy data (e.g., 14.5 FUTURE APPLICATIONS OF RS AND GIS IN PALAEOGLACIOLOGY 525

geochemical or lithological dispersal patterns, striae orientations, till fabrics) are often also incorpo- rated into the relative-age assignment procedure. Finalized stacks are deconvoluted into time-slice output sequences and arranged into broader ‘flow stages’ or phases (Boulton and Clark, 1990), and often attempts are then made to establish correlations with known stadials. Where available, pub- lished ages obtained by geochronological dating of glacial and encompassing nonglacial deposits are used to assign absolute ages to specific phases of ice flow. Residuals and conflicting lines of evidence must be handled with care; backtracking and iterative revision are almost always neces- sary in order to produce the best outputs.

14.5 FUTURE APPLICATIONS OF RS AND GIS IN PALAEOGLACIOLOGY 14.5.1 SPATIAL ANALYSIS GIS offer a platform conducive to the analysis of spatial trends within geological datasets, which can yield important insights into erosional and depositional histories in glacially modified terrains. The distribution of features and their attributes in geographic space are governed by three relationships: (1) distance, (2) connectivity, and (3) direction (Nystuen, 1963). The analytical procedures used to assess these relationships are well-supported in most GIS software and include spatial statistical and geostatistical analyses, exploratory spatial data analyses, and spatial modelling. Spatial statistical and geostatistical analyses are informed by a priori knowledge of a process which is then used to predict spatial patterns within a dataset, or to assess the likelihood that any observed patterns are resultant of a known process. With respect to distance, spatial statistical procedures commonly test the statistical significance of observed spatial distributions (i.e., uni- form, random, clustered, or dispersed) assuming a null hypothesis of complete spatial random- ness (i.e., the Poisson distribution). In contrast, exploratory spatial data analysis (ESDA) is performed without preexisting knowledge of patternÀprocess interactions, and is based largely on observer perception using increasingly dynamic and interactive univariate and multivariate visual and graphical methods (Anselin, 1999). ESDA is often employed as a first step to aid in hypothesis-forming (Unwin, 1996). Each of these former approaches differs from spatial modelling, which seeks to computationally replicate pattern-forming processes, and is deterministic in predictions of spatial configuration (see, e.g., the replication of glacial erosional patterns using GIS filtering in Jansson et al., 2011). Relative to other disciplines in the geosciences (e.g., hydrology, hydrogeology, meteorology), the spatial analysis capabilities of GIS have been underutilized in palaeoglaciology, despite some scattered recent applications (e.g., Dunlop and Clark, 2006; Maclachlan and Eyles, 2013; Stokes et al., 2013; Storrar et al., 2014a,b; Mink et al., 2014; Spagnolo et al., 2014; Cline et al., 2015; Ojala et al., 2015). Hence, there remains a great potential to expand the use of spatial analysis in palaeoglaciology in order to meet pressing research needs. Emerging GIS-based spatial analysis procedures in palaeoglaciology integrate empirical datasets (e.g., spatially referenced databases of glacial sediments, landforms, and absolute geochronol- ogies) with numerical ice sheet model outputs, either by overlay and visual comparison, or as boundary constraints on ice flow direction (glacial lineations), ice streaming (convergent 526 CHAPTER 14 GEOGRAPHIC INFORMATION SYSTEMS

megascale glacial lineations), subglacial drainage (eskers, tunnel channels), or glacial/ice sheet extent (end moraines, marginal meltwater channels, geochronologically dated materials) (Napieralski et al., 2006, 2007b; Napieralski, 2007; Li et al., 2007, 2008; Livingstone et al., 2015; Margold et al., 2015).

14.5.2 GROUND-BASED AND UNMANNED AERIAL VEHICLE REMOTE SENSING Even as the cost of aerial LiDAR continues to decrease, targeted site-specific acquisitions remain cost-prohibitive for many investigators. In response, localized elevation capture methods have seen increasing use. In particular, unmanned aerial vehicles (UAVs) demonstrate great potential as relatively low-cost tools for photogrammetric and LiDAR DEM generation. Persistent advances in image-matching technology, flight-planning software, and scanner instrumentation continue to enhance the usability and precision of UAVs such that they have been considered effective for glacial geomorphological mapping applications (e.g., Meyer, 2014; Hackney and Clayton, 2015; Chandler et al., 2015). TLS and ground-based photogrammetry have also seen increased use in glacial geomorphology, particularly in alpine settings (e.g., Heckmann et al., 2012; Carrivick et al., 2015), and novel ‘Virtual Outcrop’ techniques have been developed that combine imaging with close-range LiDAR, enabling 3D stratigraphic analysis (Bellian et al., 2005; Labourdette and Jones, 2007) and semiautomated classification of materials from surface exposures (Brodu and Lague, 2012)(Fig. 14.14).

14.5.3 WEB MAPPING AND WEB GIS Increasing emphasis is being placed on collaborative geoscience and data sharing in the 21st century, in particular through the dissemination of free and open-source software (FOSS), data, and web-based information technologies (Pope et al., 2014). Web GIS distribute the components of spatial information systems over the Internet, consisting at minimum of a (GIS) server and a web browser, mobile, or desktop client application. Open Geospatial Consortium (http://www. opengeospatial.org/) (OGC) standards facilitate common syntax for Web GIS development, enabling disjoint client softwares to request compiled map imagery, vector data, and geoproces- sing tasks from a server. The recent emergence of Web GIS has great potential for fostering integration of local data sources, and broadens user-base and access to traditionally proprietary, sophisticated and/or cost-prohibitive GIServices. Existing initiatives primarily emphasize basic Web mapping functionalities, for instance, by consolidating global geological map data (e.g.; OneGeology; www.onegeology.org), or offering interactive user education experiences (e.g., iGeology 3D, British Geological Survey; http://www.bgs.ac.uk/iGeology/3d.html). Services like Google Earth (www.google.com/earth) now also provide citizens and scientists alike with free access to commercial VHR satellite imagery of Earth (and beyond), and incorporate basic mapping utilities which interface with standalone GIS software. Moving forward, Web GIS could revolutionize how glacial geologists and geomorphologists collaborate and communicate, in both data collection and distribution domains. 14.5 FUTURE APPLICATIONS OF RS AND GIS IN PALAEOGLACIOLOGY 527

FIGURE 14.14 3D vector point cloud and classification of a ‘virtual outcrop’ obtained using terrestrial LiDAR. (A) Original scene (minimum point spacing 5 1 cm). (B) Classification according to an automated procedure (green 5 vegetation, grey 5 bedrock, red 5 gravel, blue 5 water). (C) Operator-improved classification. (D) Unclassified points (28.2% of total). After Brodu, N., Lague, D., 2012. 3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: applications in geomorphology. ISPRS J. Photogramm. Remote Sens., 68, 121À134.

14.5.4 PLANETARY GEOMORPHOLOGY Present-day ice sheets consisting of water- and carbon-dioxide-ice exist at both poles on Mars, and smaller glaciers occupy a number of craters at latitudes .70 degrees North and South. Rapidly accruing observational evidence suggests, however, that the Martian surface was more extensively glaciated across all latitudes in the past (Head et al., 2005, 2010; Garvin et al., 2006; Holt et al., 2008; Shean, 2010; Dickson et al., 2008, 2010; Baker et al., 2010; Fastook et al., 2011, 2014; Fastook and Head, 2014; Guidat et al., 2015; Scanlon et al., 2015a,b). Given the obvious inaccessi- bility of surfaces beyond Earth, present knowledge of extraterrestrial geology must be derived using remote data collection. Since the mid-1990s, a number of RS instruments have been launched into Mars’ orbit which provide VHR imagery and topographic data of its surface (Table 14.3). Digital landform mapping from these datasets is ongoing and often highlights both landform and 528 CHAPTER 14 GEOGRAPHIC INFORMATION SYSTEMS

Table 14.3 Recent Mars Missions and Associated Instruments and Data Sources Relevant to Martian Geomorphology Mission Name Date Instrumenta Type Spatial Resolution (m) Mars Global Surveyor 1996À2006 MOLA Laser Altimetry 0.375 (NASA) MOC Imaging 1.4 TES Spectrometry (IR) 8 Mars Odyssey (NASA) 2001À THEMIS Spectrometry (VIR) 18/100 Mars Express (ESA) 2003À HRSC Imaging 12 OMEGA Spectrometry (VIR) 100 Mars Reconnaissance 2005À HiRISE Imaging 0.3 Orbitor (NASA) CTX Imaging 6 CRISM Spectrometry (VIR) 20

aMOLA, Mars orbiter laser altimeter; MOC, Mars orbiter camera; TES, thermal emission spectrometer; THEMIS; thermal emission imaging system; HRSC, high resolution stereo camera; OMEGA, Observatoire pour la Mineralogie,´ l’Eau, les Glaces et l’Activite;´ HiRISE, high resolution imaging science experiment; CTX, context camera; CRISM, compact reconnaissance imaging spectrometer for Mars. landsystem-scale comparisons to terrestrial glacial analogues. Improved orbiter and rover technologies, increased integration with GIS infrastructures, and the potential for manned missions to Mars for ground-truth offer exciting prospects for the future of GIT-driven glacial research beyond Earth.

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