Terrestrial Laser Scanning

TLS offers the opportunity to collect high-precision and high-accuracy data over large spatial extents, at temporal frequencies commensurate with individual flood events.

From: Developments in Earth Surface Processes, 2011

Geomorphological Mapping

Takashi Oguchi, ... Thad Wasklewicz, in Developments in Earth Surface Processes, 2011

3.4.4 Terrestrial Laser Scanning

Terrestrial laser scanning (TLS), also referred to as terrestrial LiDAR (light detection and ranging) or topographic LiDAR, acquires XYZ coordinates of numerous points on land by emitting laser pulses toward these points and measuring the distance from the device to the target (Vosselman and Maas, 2010). The number of measurable points within a certain period is much larger than those of TS and LRF devices: a modern TLS device can measure 104–106 points per second with an accuracy of 10−1–100 cm. Bespoke software packages are generally required for managing and analysing the data because of the large amount of data stored in a TLS point cloud. A point cloud may be converted into a grid DEM to facilitate topographic mapping and spatial analyses.

TLS instruments are commonly broken into three categories based on the distance the laser light can travel to record a point in a field-of-view: short-, medium- and long-range scanners. TLS devices optimised for a long range (several hundreds of metres to kilometres) have been applied to measuring spatially larger areas (Hunter et al., 2003; Abellán et al., 2006), whereas shorter range scanners measure spatially smaller areas (up to several hundred metres) in greater detail and accuracy (Heritage and Large, 2009), reflecting a trade-off between the pulse rate and energy of laser light. For short-range scanners, the interval between adjacent measurement points can be up to 1 mm, although such densities are not practical for all but the smallest areas. A potential limitation to TLS approaches in geomorphology is the weight of the instrument (>20 kg including the battery), but as with many technologies lighter devices are being developed.

TLS use in geomorphology has been driven by the need to produce rapid topographic data that are accurate and precise (Heritage and Large, 2009). The precision and accuracy of TLS techniques permit scientists to conduct repeat surveys that are vital to unravelling complex space–time variations in landforms and landscapes. This, in conjunction with data describing process-mechanics, provides strong linkages between processes and forms that are needed to detect environmental change. This has been employed in a number of scenarios. Research in hillslope–channel coupling has combined hydrological and topographical changes in alpine drainages to provide an unprecedented view of channel changes (McCoy et al., 2010). This work has built upon TLS techniques that capture digital micro-topographic data used to analyse channel response to debris flow events (Wasklewicz and Hattanji, 2009; Figure 7.6). Similar approaches have been applied to other geomorphic features including gravel-bed rivers (Hodge et al., 2009) and fault surfaces (Candela et al., 2009; Sagy et al., 2009). Compared to airborne laser scanning, described later, the application of TLS to geomorphology is a relatively recent advancement that has concentrated on smaller spatial extents of the landscape (Heritage and Hetherington, 2007; Schaefer and Inkpen, 2010).

Figure 7.6. A point-cloud image of a headwater channel prior to debris flow event in Ashio, Japan (Wasklewicz and Hattanji, 2009).

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Mechanisms underlying the relationship between biodiversity and ecosystem function

Claudia Guimarães-SteinickeAlexandra WeigeltAnne EbelingNico EisenhauerJoaquín Duque-LazoBjörn ReuChristiane RoscherJens SchumacherCameron WaggChristian Wirth, in Advances in Ecological Research, 2019

4.4 A new method for biodiversity-ecosystem functioning research in grasslands

Terrestrial laser scanning (TLS) allowed us to follow the development of swards throughout the growing season at a high temporal resolution. The 3D point cloud produced by the TLS represented the canopy surface with a spatial resolution below 2 mm and was well suited to describe subtle changes in the canopy during the growing season. Much of the theory we are invoking (e.g. resource partitioning) refers to biomass production. The mean canopy height derived from TLS measurements by pooling years together explained 54% of dry mowed community biomass (Fig. S1 in Supplementary Material). It is important to note that we could only calibrate against high values of biomass corresponding to peak biomass at the time of the mowing (two times in the year) and that the biomass samples cover a considerable lower area than those studied with the TLS. This narrowed the range of values towards high values only and thus restricted our calibration precision. Far higher values of R2 may have been obtained if we also had included biomass measurements during low biomass periods.

Terrestrial laser scanning (TLS) allowed us to follow the development of swards throughout the growing season at a high temporal resolution. The 3D point cloud produced by the TLS represented the canopy surface with a spatial resolution below 2 mm and was well suited to describe subtle changes in the canopy during the growing season. Much of the theory we are invoking (e.g. resource partitioning) refers to biomass production. The mean canopy height derived from TLS measurements by pooling years together explained 54% of dry mowed community biomass (Fig. S1 in Supplementary Material in the online version at https://doi.org/10.1016/bs.aecr.2019.06.003). It is important to note that we could only calibrate against high values of biomass corresponding to peak biomass at the time of the mowing (two times in the year) and that the biomass samples cover a considerable lower area than those studied with the TLS. This narrowed the range of values towards high values only and thus restricted our calibration precision. Far higher values of R2 may have been obtained if we also had included biomass measurements during low biomass periods.

A major shortcoming of the method is that the TLS is not able to differentiate between species. It is therefore not possible to quantify relative abundances based on TLS data (at least for grassland communities), which would be important for differentiating between the contributions of different species to overyielding in mixtures (Loreau and Hector, 2001). Also, a visual inspection of the RGB pictures taken alongside the scanning does not yield reliable species abundance data. We, therefore, base our analysis on presence-absence data, although the trait-based indices could, in principle, be abundance-weighted. Thus, we can only assess the functional potential of our community to express diversity or identity control. It should be noted that basing the calculation of FDis and functional identity on presence-absence is equivalent to assuming perfect evenness and will yield results that maximize the range of FDis and minimize it for functional identity. This implies that identity effects may occur despite an intrinsic bias against identity effects. For future applications, we thus recommend recording species cover values based on visual inspection alongside the TLS measurements. Temporal constraints during fieldwork and image processing prevented the scanning of individual plots from different perspectives. The one perspective scan of 92 plots every second week provided a good overview of sward development and a feasible data storage potential. However, we understand that other scan perspectives could have considerably increased point information for the same position and decreased occlusion.

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Geomorphological Mapping

Richard Williams, ... Mark Neal, in Developments in Earth Surface Processes, 2011

3.2.2 Bathymetric Mapping

TLS surveys were undertaken at low flows. To derive channel-bed levels for the remaining areas, an empirical–optical mapping technique was used. This required aerial photographs of wetted channels and depth data, for model development and validation. Non-metric vertical aerial photographs were acquired at 1:5000 scale from a helicopter, using a Nikon D90 camera with fixed 28 mm lens and an automatic intervalometer set to acquire images every 5 s. Immediately following the acquisition of aerial photographs, depth data were obtained along two transects of the primary anabranches. These depth data were measured using the 1 MHz vertical acoustic beam of a Sontek S5 RiverSurveyor located using an integrated RTK GPS, mounted on a lightweight boat. This system enables precise geo-located depth soundings to be acquired at 10 Hz frequency as the boat was guided downstream on tethers by a single operator. Typical surveys of each anabranch took 30–40 min to acquire, resulting in 3000–4000 depth measurements along each anabranch at a point spacing of approximately 1 m.

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Applications for Societal Benefits

S..J. Walsh, ... C..F. Mena, in Comprehensive Remote Sensing, 2018

9.13.17 3-D Laser Scanner

Terrestrial laser scanning is a ground-based version of the airborne LIDAR frequently used for terrain and landscape mapping. Terrestrial laser scanners are a relatively recent development for high-resolution mapping. These scanners, originally developed for as-built modeling of architectural and engineering structures, can also be used for high-resolution mapping of terrain, vegetation, and other landscape features over limited distances in the range of 50–300 m. Like their airborne counterparts, these are active sensors that emit laser signals to calculate distances based on the time delay of the returned laser pulses. They are able to record a dense array of distance return values (e.g., in the order of several laser returns per square centimeter over ranges of 50 m), which are assembled into highly detailed, digital 3-D landscape models that can be transformed into GIS DEMs. Many of these terrestrial scanners also record digital photos of the same areas being laser-scanned, and can extract RGB color values from the photos and attach them to each laser distance return. This creates a highly photorealistic 3-D landscape model. For beach mapping, the resulting digital models are highly accurate renditions of in situ conditions at the time the scanning is done, and they can be used for both visualization purposes (as photorealistic models) as well as morphometric measurements (as DEMs). The spatial resolution of the resulting data is orders of magnitude higher than data derived from satellite- or airplane-based mapping and remote sensing techniques.

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Putting it all together: Geophysical data integration

Kenneth L. Kvamme, ... Jeremy G. Menzer, in Innovation in Near-Surface Geophysics, 2019

7.2.2 Methods

In January 2016, a terrestrial laser scanning system documented the accessible interior of Souterrain 1. The following summer, over 500 overlapping digital aerial photographs covering the entire property (about 1 ha) were acquired using a DJI Phantom 3 Advanced quadcopter. Additional photographs of areas under tree canopy were taken from the ground. Twelve ground control points were placed throughout the survey area with a RTK GNSS system. Agisoft PhotoScan Professional software was used to align 368 photographs and produce a point cloud of the ground surface and aboveground features, a bare-earth DEM, and a digital orthophoto.

GPR surveys conducted in 2015–16 employed a Geophysical Survey Systems, Inc. SIR4000 with 200 and 270 MHz antennae. Transects were spaced 1 m apart and covered 0.28 ha. GPR data processing included DC drift compensation, range gains, band-pass filters, and background removal. A ground velocity of 0.09 m ns 1 was estimated through hyperbola fitting, and the radargrams were terrain-corrected by the DEM. Fully processed data volumes of each GPR block were then exported as ASCII point clouds as were terrestrial laser scans acquired from inside the tunnel and the aboveground photogrammetric point cloud. In other words, all point cloud data were combined into a single 3D environment using CloudCompare (http://www.danielgm.net/cc/), a free and open-source software.

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Remote Sensing and GIScience in Geomorphology

T. Wasklewicz, ... T. Oguchi, in Treatise on Geomorphology, 2013

3.6.7.4 Temporal Data Acquisition

The advances in instruments such as TLS, ALS, and TPT have the potential to increase the temporal frequency of data acquisition and increase DTM inventories. As instrument weight and cost continue to decrease, it has become more feasible to mobilize equipment rapidly or set-up these instruments at various locations for extended periods of time. Instruments such as TLS are presently capable of being set-up and programmed at fixed locations to sample periodically throughout the day and night. This particular application has scarcely been applied in geomorphology, but is used in the mining industry to monitor rock fall and volumes of mined material. A similar approach should be used to increase our temporal understanding of landscape evolution and supplement existing information gathered at long-term sampling sites (e.g., stream gauges, weather stations, coastal-wave monitoring sites). Marzolff and Poesen (2009) demonstrated how using multiple DTMs can improve our space-time understanding of morpho-dynamics within gully systems. They examined changes to two different bank gully types using TPT. A major finding from their research was the large degree of storage of sediment within the gully network after headward erosion of the bank gullies. This was not anticipated, as most hypotheses would suggest that the gullies erode material into other parts of the drainage network with little deposition taking place within the gully system. Furthermore, TPT has been used to quantify the rate of parabolic dune migration (e.g., Arteaga et al., 2008).

A decrease in instrument weight and cost may also make it feasible to mount instruments on UAVs. This would greatly enhance the geomorphologists' ability to capture more data and provide greater flexibility in planning field-sampling campaigns. Missions (ALS) commonly take a great deal of time to mobilize because equipment is not located near relevant field sites. A UAV is much easier to mobilize and would permit measurement of time-sensitive data within a matter of hours. The ability to respond rapidly would permit the user to obtain digital elevation data immediately before and after an event. This flexibility greatly facilitates fieldwork and the assessment of geomorphological hazards. For example, multitemporal DTMs can be used to calibrate debris-flow sediment yield and prediction models after wildfires. This information is important for hazard mitigation schemes (Gartner et al., 2009; Cannon et al., 2010a,b). Furthermore, the ability to rapidly respond to events or forcing factors is critical, and DTMs can be combined with rainfall intensity data to study the timing, location, and run-out distances of debris flow-prone areas (Cannon et al., 2010a,b).

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Fluvial Geomorphology

T. Oguchi, ... Y.S. Hayakawa, in Treatise on Geomorphology, 2013

9.35.1 Introduction

Remote data such as aerial photographs, satellite remote sensing images, and the results of airborne/terrestrial laser scanning have been frequently used in fluvial geomorphology for a number of reasons. First, investigation of a broad area is commonly required. Remote data, particularly airborne and spaceborne data, are useful to observe extensive areas with a constant spatial resolution, although field surveys may be needed to obtain detailed information for some localized representative areas. Each fluvial landform needs to be understood within a framework of fluvial systems, including erosional and depositional components (Schumm, 1977). Therefore, even if the main target of research is a particular fluvial landform in a small area, observations of broader surrounding areas are generally required. In addition, one component of fluvial landforms can be fairly extensive. For example, megaalluvial fans such as those of the Kosi River in India (Wells and Dorr, 1987) and the Taquari River in Brazil (Assine, 2005) have an area on the order of 104 km2. Even a single channel can be quite wide in the case of large rivers such as the Amazon. Remote data are indispensable to observe such large landform components.

Second, even if research for a particular small area is sufficient, it may be impossible to visit a part or the whole of the area for field investigation. For example, remote areas such as the middle of a desert or a rugged mountain may be inaccessible. A more apparent case is research in planetary geomorphology. Fluvial landforms on Mars have attracted significant scientific attention (Baker, 1982, 2001), and studies so far are based on remote data including images of the orbiters of Viking and Mars Global Surveyors, except a few studies based on data collected by landers and rovers such as the Mars Pathfinder.

Third, observations from the air or space provide less distorted views of Earth surfaces than those from a ground location, allowing us to better understand the spatial distribution of landforms. This is particularly important for areas where large-scale maps or high-resolution digital spatial data are unavailable.

Fourth, not only aerial photographs and satellite images but also spatial data derived from these images are used for geomorphological studies. Digitizing objects on georeferenced or orthorectified photographs and images provide vector line/polygon data showing the spatial distribution of fluvial landforms such as channels, natural levees, river terraces, and alluvial fans. The data can be used for both efficient geomorphological mapping and quantitative analysis of landform distribution. Application of photogrammetry to stereo photographs and images also provide digital elevation models (DEMs).

Fifth, very detailed topographic data can be obtained directly if terrestrial and airborne LiDAR are employed, permitting efficient production of high-resolution DEMs. Radar remote sensing is another powerful tool to create medium-resolution DEMs for a broad area (e.g., SRTM from Space Shuttle). These data are useful for mapping and morphometric analyses of fluvial landforms, and detecting topographic changes due to fluvial erosion and deposition.

This chapter reviews remote data used in fluvial geomorphology. Attention is directed to (1) types of remote data and their historical development, (2) application examples in fluvial geomorphology, and (3) current problems and future perspectives. Classic remote data for geomorphological studies may include sketches and ground photographs of landforms drawn or taken in the field. Geomorphological articles and monographs published in the early twentieth century or earlier typically included such sketches and photographs. More recently, videos can also be used to collect information in the field. We do not deal with these data here, because their collection is generally not systematic and they are not used as major data for geomorphological studies anymore. However, ground photographs used for photogrammetry will be briefly introduced. Ground surveying is also related to remote data. In a sense, classic surveying using a plane table, a level, and transit collect a type of remote data because measurements for distant points are included. This chapter focuses on some modern methods of ground surveying such as terrestrial laser scanning, because the use of the classic methods has been decreasing rapidly. DEMs and similar topographic data derived from raw remote data such as aerial photographs will be introduced as remote data, because they have played a significant role in recent fluvial geomorphology. However, methods to derive subsurface data such as electrical resistivity measurements and ground penetration radar are not introduced here because they are essentially sedimentological and lithological rather than geomorphological, although they are sometimes called remote sensing. See Chapter 3.2 by Shroder in volume 3 for related material on ground, aerial, and satellite photography and geomorphic change.

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Methods in Geomorphology

L. Schrott, ... M. Geilhausen, in Treatise on Geomorphology, 2013

14.2.3.2.3 LiDAR – principles of data acquisition and processing

As ALS data acquisition is generally done by private companies or public authorities, this section – dealing with practical issues regarding data acquisition and processing – focuses on TLS systems. TLS surveys commonly comprise several scan positions. Their locations have to be chosen carefully and (if possible) prior to laser scanning measurements. The locations should ensure that shadowing effects are limited to a minimum, that angles of impact are as large as possible, and that the whole area of interest is covered homogeneously. This decision demands some experience, but GIS tools, as, for example, viewshed analyses (ArcGIS), might help in this regard. At each scan position, the area to be scanned and the desired scanning resolution have to be defined. Scanning designs using reflectors (in many cases fixed in the field with known GPS coordinates) commonly demand separate fine scans of them with the highest possible resolution. Reflectors are used for many purposes. Besides an accurate registration process, fixed reflectors in the field can simplify the comparison of repeated scan series (e.g., cut and fill analyses) and – if installed on ‘moving objects’ – the assessment of geomorphologic processes (e.g., rates of permafrost creep, solifluction, and landslides). Some systems also capture overlapping digital photographs covering the area of interest using a calibrated camera. These photographs allow for colorizing point clouds, texturing of meshes, or for creating orthorectified photographs of the study site.

After data acquisition, each scan position's point cloud has to be merged with each other (often called the registration process). This implies a rotation and translation of the point clouds to be registered as a whole, but one which would not change the relative positions of points within them. The registration of point clouds can be done in different ways, as for example with (a minimum of three) reflectors visible in two overlapping scenes. Additional tools, depending on the software package used, enable further (stepwise) registration processes without using reflectors, as for example the coarse registration by the manual setting of identical points or by using north angles (back sighting orientation) and the subsequent semiautomatic matching of polygon normals. The latter is probably the most accurate way and also recommended for point clouds already registered by means of reflectors. In contrast to the different locations relative to each scan position, all points are referred to one common project coordinate system after the registration process. Incorporating GPS data of reflectors or scan positions finally allows for data conversion in a global coordinate system of choice (e.g., UTM). To answer numerous geomorphological research questions these ‘raw’ LiDAR data have to be further processed in order to gain regular gridded elevation data with a homogeneous resolution or TINs, which is usually done using different filtering and thinning options (e.g., 2.5-D filter, octree approaches). If required, removing vegetation is another important issue to be managed using specific filters.

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Remote Sensing of Geomorphology

Anette Eltner, Giulia Sofia, in Developments in Earth Surface Processes, 2020

2.2 Accuracy considerations in geomorphological applications

Due to the many parameters that influence the accuracy of the final 3D model derived from the SfM-photogrammetry approach, error reliability is not as high as, for instance, point clouds derived from terrestrial laser scanning (TLS). Therefore, the need for robust error modeling is important when using SfM photogrammetry, especially considering the variety of applications in geomorphology at varying spatiotemporal scales. When performing error modeling, distinctions should be made between error reproducibility, i.e., error behavior under different conditions, and error repeatability, i.e., error behavior under the same conditions (Goetz et al., 2018). Furthermore, it is important to distinguish between constraining 3D accuracies due to internal and external causes (James et al., 2017b). Internal precision is influenced by image network geometry and tie-point measurements, whereas external precision relies on actual geo-referencing. Recent studies have focused on modeling of error behavior of SfM-photogrammetry data to improve data quality in geomorphic studies (James et al., 2017a,b; Wasklewicz et al., 2017).

SfM photogrammetry is not as rigorous in regards to the precision weights when compared to traditional photogrammetry, and therefore improvements to the accuracy of the final SfM-DEM (digital elevation model) are still possible if photogrammetric principles beneath SfM photogrammetry are considered (James et al., 2017a). James et al. (2017a) provide a workflow to consider and minimize errors when using SfM photogrammetry, which they illustrate with significantly improved error to distance ratios for two case studies. Thereby, the weight consideration of reference accuracy of GCPs in object space, as well as image measurement accuracy of tie points and GCPs in image space are important to avoid falsely fitting during bundle adjustment (James et al., 2017a).

Errors are spatially highly correlated when using SfM photogrammetry (James et al., 2017b), which is in contrast to other high-resolution topography methods such as TLS, where spatially independent, error behavior is assumed (e.g., Abellán et al., 2009; Kromer et al., 2015). Thus, rather than using one level of detection (LoD) applied to the entire DEM of difference for multitemporal change detection, consideration of spatial correlation is important (James et al., 2017b). James et al. (2017b) use Monte Carlo simulation to calculate precision maps, which they combined with an adopted M3C2 algorithm, which already considers a variable LoD depending on the complexity of the terrain (Lague et al., 2013) to estimate a spatially correlated error of the SfM-photogrammetry point cloud. However, it should be noted that precision maps are not able to detect systematic errors such as domes (e.g., Eltner and Schneider, 2015) and thus independent reference data, e.g., CPs, are needed for a robust accuracy estimation (James et al., 2017b).

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Geographic Information Systems and Glacial Environments

K. Wagner, in Past Glacial Environments (Second Edition), 2018

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 sampled 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 ‘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).

Table 14.2. Select Digital Topographic Data Sources With Data Sources Relevant to Glacial Geomorphological Mapping

SourceNominal Spatial ResolutionAccuracy
Ground surveyVariable, but usually <5 mVery high vertical and horizontal
GPSVariable, but usually <5 mModerate vertical and horizontal, very high vertical and horizontal (dGPS)
Photogrammetry (commerical optical sensors)<1 mVery high vertical and horizontal
LiDAR<1–3 m0.15–0.11 m vertical, 1 m horizontal
InSAR/IfSAR2.5–5 m1–2 m vertical, 2.5–10 m horizontal
SRTM, Band C90 (30) m16 m vertical, 20 m horizontal
SRTM, Band X30 m10 m vertical, 6 m horizontal
Terra ASTER (GDEM)30 m7–50 m vertical, 7–50 m horizontal
SPOT 5 (Stereo-Pair Mode)30 m10 m vertical, 15 m horizontal
TerraSAR-X DSM10 m5–10 m vertical, 5–10 m horizontal

Photogrammetrically derived DEMs rely on the collection and processing of repeat-pass (steerable 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 support stereo-pair acquisition modes. Terra ASTER possesses a second, aft-looking sensor, and has been used to produce a near-global (83 degrees North–83 degrees South) photogrammetric DEM (GDEM and GDEM-2) from along-track, single-pass imagery at 1 arc-second posting interval (~30 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 measurement 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 surface height (Burgmann et al., 2000; Farr, 2011), thus forming the basis of topographic measurement 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 (~90 m spatial resolution), and 1 arc-second post spacing (~30 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 forefront 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 revolutionized 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).

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