Using airborne LiDAR and USGS DEM data for assessing rock glaciers and glaciers

Geomorphology 195 (2013) 118–130

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Using airborne LiDAR and USGS DEM data for assessing rock glaciers and glaciers Jason R. Janke ⁎ Metropolitan State University of Denver, Department of Earth and Atmospheric Sciences, CB 22, Denver, CO 80217, United States

a r t i c l e

i n f o

Article history: Received 8 February 2013 Received in revised form 29 April 2013 Accepted 30 April 2013 Available online 9 May 2013 Keywords: Airborne LiDAR DEMs Rock glaciers Glaciers Rocky Mountains Accuracy assessment

a b s t r a c t Varying topographic and geologic conditions affect the location of rock glaciers. Despite being found worldwide, rock glaciers are often confused with glacier counterparts or other periglacial landforms. Light detection and ranging (LiDAR) data, because of its accuracy and resolution, may help the assessment of topographic variables needed to form rock glaciers or help reveal unique characteristics to enhance regional, automatic mapping. The objectives of this paper are to compare the elevation, slope, aspect, hillshade, and curvature for 1 m LiDAR and 10 m US Geological Survey (USGS) Digital Elevation Models (DEMs) from the Andrews and Taylor Glaciers with the Taylor Rock Glacier in Colorado. The utility of these data sources will be assessed for landform discrimination and to evaluate the uncertainty between the DEMs. According to the LiDAR data, the Taylor Rock Glacier exists at a lower elevation and has a gentler slope compared to the glaciers. Each landform has steep areas from which snow and debris are delivered. The Andrews Glacier has the most northern aspect, which helps maintain it through snow accumulation and reduced insolation. Glaciers exhibit a concave mean curvature, whereas the Taylor Rock Glacier has a convex mean curvature. The fine resolution of the LiDAR data clearly identifies some distinct characteristics. On the Taylor Rock Glacier, ridges, furrows, and a pronounced front slope were easily identifiable on the LiDAR DEM, whereas crevasses, the boundary between snow and debris covered surfaces, and a lateral moraine were detectable near the Andrews Glacier. The accuracy assessment revealed that at a common 10 m resolution, the USGS DEM estimated a maximum elevation about 150 m greater compared to the LiDAR data in areas of rugged topography surrounding the landforms. A comparison of root mean squared errors (RMSE) between the LiDAR and USGS DEMs showed that the Taylor Rock Glacier has the lowest RMSE for the elevation and the curvature variables. As a result, readily available USGS DEMs may better for analysis to characterize the topographic setting of landforms at the regional scale. At the fine scale, however, the micro-topography of rock glaciers is illuminated much more clearly on the LiDAR data, making it an ideal, yet costly source, for feature extraction. © 2013 Elsevier B.V. All rights reserved.

1. Introduction 1.1. Rock glaciers and glaciers Rock glaciers, an important component of high mountain systems, are found worldwide, but are often confused with glacier counterparts, debris covered glaciers, or other periglacial landforms such as blockfields, blockslopes, or talus slopes (Haeberli, 2000). Rock glaciers occur in areas with high production of talus and are different from glaciers in that they have a matrix of debris on the surface, display ridges and furrows produced from flow, possess an internal ice structure, and produce a steep front slope at the toe of the rock glacier (Janke et al., 2013). Glaciers lack visible surface material other than medial moraines running parallel to the direction of flow or end moraines that cover ice at the toe of the glacier. Rock glaciers and glaciers can often co-exist in the same valley. In other cases, rock glaciers may form in one valley, whereas the adjacent valley is occupied only by ⁎ Tel.: +1 303 556 3072. E-mail address: [email protected]. 0169-555X/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.geomorph.2013.04.036

glaciers. It is important to study the climates induced by topography that can possibly explain the occurrence of two different landforms that are located in close proximity (Morris, 1981; Humlum, 1998; Janke, 2007). Most rock glaciers currently contain some form of massive or interstitial ice, which in a future warmer climate could melt, providing an additional late-summer water source. However, in some mountain regions, the hydrologic importance of rock glaciers is not recognized because inventories are incomplete; knowledge of topographic characteristics that are common to rock glaciers impedes identification and mapping. For instance, the amount of water in rock glaciers in sections of the Andes has been estimated to be an order of magnitude higher than in rock glaciers in the Alps because of their size and abundance; however, areas where rock glaciers are known to exist are missing from national and international inventories (Brenning, 2008; Azocar and Brenning, 2010). It is important to determine distinguishing characteristics of rock glaciers that will enhance identification or mapping through automated extraction techniques. Fine resolution (1 m) IKONOS satellite imagery and filtering techniques have shown promise to categorize texture related to the spacing

J.R. Janke / Geomorphology 195 (2013) 118–130

between ridges and furrows on the surface of rock glaciers for image extraction (Brenning et al., 2012). If rock glaciers can be identified with greater certainty using other unique topographic characteristics, inventories can be completed more quickly with greater certainty to identify and account for an often-unnoticed water resource contained within rock glaciers. 1.2. DEMs and geomorphology In alpine areas, DEMs are the basis for modeling mass movements such as snow avalanches, debris flows, or rockfalls as well as estimating hydrologic parameters such as flow paths, flood zones, or soil moisture (Kenward et al., 2000; Christen et al., 2010). Using DEMs, landslide activity was determined to be influenced by slope, aspect, curvature, a wetness index, and a stream power variable (Oh and Lee, 2011). Mean slope, curvature, and distance to the ridge are important variables that influence the frequency of avalanche release (Maggioni and Gruber, 2003). Digital elevation models (DEMs) provide a reliable data source to enhance the understanding of landform distribution. In Glacier National Park, upslope topography in the snow receiving area affects glacier morphology and compactness (Allen, 1998). Glaciers are heavily reliant on topoclimates, or temperature differences as the result of elevation, slope, and aspect, which provide snow through avalanching and reduce solar radiation (Coleman et al., 2009). The location of rock glaciers and glaciers appear to be controlled by local topoclimatic differences rather than regional climates despite being separated by short distances (Humlum, 1998; Janke, 2007). Rock glacier area is controlled by the size of the contributing area as well as a variety of talus production factors such as the availability of erodible material and the number, intensity, and duration of freeze–thaw events (Janke and Frauenfeldeler, 2007). 1.3. Mountains and LiDAR applications Accurate, fine spatial resolution data are needed to represent the heterogeneity and complexity of mountainous terrain. With highresolution light detection and ranging (LiDAR) data, several geomorphic processes and landforms can be examined at unprecedented scales. LiDAR data have been used to assess hazards, model surface processes, and detect subtle changes over time (Prokop and Panholzer, 2009; Jaboyedoff et al., 2012). High-resolution DEM data, obtained from terrestrial laser scans, are ideal for mapping micro-topography to assess subtle inundation and erosion risk (Coveney and Fotheringham, 2010). In the Bhutan Himalaya, terrestrial laser scanning has been used to uncover the structure of landslides (Dunning et al., 2009). Detecting small-scale, temporal surface change with terrestrial or airborne LiDAR shows much promise, but minimal research has been conducted to assess how LiDAR data can be used to enhance the identification and understanding of periglacial landforms (Abermann et al., 2010). 1.4. Glaciers and LiDAR applications Multi-temporal LiDAR surveys have been used to measure shortterm change in glacial geometry as well as mass balance in many European alpine regions (Favey et al., 1999; Geist et al., 2003, 2005; Abermann et al., 2010; Knoll and Kerschner, 2010). LiDAR data are able to obtain data in areas that are covered by shadows, which has hindered traditional glacial mass balance calculations using stereopairs of aerial photographs. As a result, LiDAR data are often preferred because of their horizontal resolution, vertical accuracy, and ability to discern bare-Earth surfaces (Sanders, 2007). LiDAR data are ideal for mapping small glaciers (b 0.5 km 2) and boundaries that are often difficult to detect (transitional debris-covered surfaces)

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on space-borne multispectral data or high-resolution (0.5 m) orthophotos (Abermann et al., 2010). 1.5. Rock glacier LiDAR applications Airborne LiDAR as well as terrestrial laser scanning is a relatively new technique that is just beginning to be applied to periglacial environments (Bauer et al., 2003; Abellan et al., 2006; Avian et al., 2008). Terrestrial laser scans have been conducted on the Hinteres Langtalkar Rock Glacier in Austria since 2000, which has a maximum displacement that ranges from 1.75 to 3.60 m/yr (Avian et al., 2009). Comparison of temporal DEMs revealed positive vertical displacements over most of the rock glacier surface (Avian et al., 2009). In the Southern French Alps, repeat terrestrial LiDAR scans were useful for investigating flow processes on the Laurichard Rock Glacier (Bodin et al., 2008). A comparison of scans and previously generated DEMs revealed that the toe of the rock glacier is experiencing extensional flow to the northeast (Bodin et al., 2008). These studies provided detailed measurements of flow processes using terrestrial LiDAR data, but it is also important to use high-resolution DEM data to understand the resulting form of rock glaciers (Vitek, 2012). 1.6. Impacts of resolution and accuracy As sources of new digital elevation data, such as LiDAR data, become more readily available, it becomes important to assess their utility and functionality. For instance, hydrologic indices are often lost with coarser resolution DEMs. A relatively small difference occurs between LiDAR derived DEMs and field surveyed DEMs; however, fine resolution LiDAR derived DEMs provide a good representation of the surface compared to contour derived DEMs (Vaze et al., 2010). DEM resolution affects flow paths and runout distance when modeling gravity driven processes, such as debris flows or pyroclastic density currents or avalanche parameters such as flow paths, run-out distances, deposition, and associated hazardous areas (Bühler et al., 2011; Capra et al., 2011). A 25 m DEM is sufficient, but must be examined for consistency and artifacts because US Geological Survey DEMs can have large local, spatially correlated errors despite having a small global (average) error (Holmes et al., 2000; Bühler et al., 2011). Mass balance, glacial extent, and landform characterization measurements can also be improved using LiDAR data. In Switzerland, helicopter-based LiDAR data were compared with DEMs created through digital photogrammetric techniques (Bühler et al., 2012). LiDAR data are ideal for small areas or steep terrain, whereas digital imagery is more suitable for large areas with slopes less than 30°. Above 50°, the root mean square error between elevational data grows to 2 m. Using multi-temporal airborne laser data, glacier thickness change was 35% greater than previously reported (Joerg et al., 2012). In another study, the boundaries of glaciers were shown to be within 4 m for 80% of the ground truth measurements, illustrating the capability of LiDAR data for improving glacial measurements (Abermann et al., 2010). Not only is it important to assess the utility of high-resolution DEMs, but it is equally important to weigh the monetary costs and benefits of LiDAR with other less costly data sources, such as USGS DEMs. 1.7. Objectives The first objective of this paper is to characterize rock glacier and glacier topography using Geographic Information System (GIS) calculations of elevation, slope, aspect, curvature, and hillshade according to 10 m US Geological Survey (USGS) and 1 m aerial LiDAR data. This will provide a better understanding of the topographic settings that create two distinct landforms in adjacent valleys. The second objective of this paper is to evaluate the utility of the two DEM sources and associated topographic variables for landform identification,

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which could be applied to other mountain systems for regional identification and mapping. The final objective of this paper is to assess the uncertainty between the two DEMs. The aforementioned topographic variables will be evaluated using the root mean squared error (RMSE) between DEMs at different scales and locations.

through late 1970s, and again began shrinking during the 1990s (Hoffman et al., 2007). Radiocarbon-dated bison remains, exposed at a glacier, suggest that recession may be greater than any point in the last 2000 years BP (Lee et al., 2006; Hoffman et al., 2007). 3. Methods

2. Study area Rocky Mountain National Park (RMNP) contains at least 30 glaciers and 127 rock glaciers. Glaciers range in size from 0.006 to 0.127 km 2 with an average size of 0.034 km 2; rock glaciers range in size from 0.005 to 0.747 km 2 with an average size of 0.087 km 2 (Hoffman et al., 2007; Janke, 2007). Most glaciers occupy north to east facing cirques along the east side of the Continental Divide (Madole, 1976). Rock glacier locations are more variable, but those that are flowing are more likely to be found below glaciers in cirques (Janke, 2005a, b, 2007). In RMNP, Taylor Rock Glacier and Andrews Glacier are separated by only 1.75 km in the Loch Vale Watershed; nevertheless, topoclimates create two distinct landforms (Fig. 1). Andrews Glacier and Taylor Rock Glacier are representative of glaciers and rock glaciers that typically occur in Colorado. Taylor Rock Glacier has an average flow rate of about 6–7 cm/yr (Janke, 2005c). Displacements are generally greater toward the toe of the rock glacier as well as toward the interior (Janke, 2005c). Andrews Glacier exists in a unique topographic setting and receives roughly 8 times the regional snow accumulation. In addition, summer ablation is slowed by topographic shading (Outcalt and MacPhail, 1965) (Fig. 1). Glaciers in the Front Range are relatively insensitive to changes in winter accumulation; changes in glacier area are more affected by summer temperature and spring snowfall (Outcalt and MacPhail, 1965; Hoffman et al., 2007). Regionally, the glaciers shrank in the early part of the 20th century, grew during the 1950s

Aerometric, Inc. acquired the airborne LiDAR imagery using an Optech Scanner in August 2010 as part of a USGS data acquisition project. A nominal pulse spacing of 0.7 m was obtained at about 1500 m above the terrain. Vertical accuracy was 0.12 m for the bare earth readings. The points were interpolated to a 1 m DEM and downloaded via the USGS Seamless Data Warehouse. National Elevation Dataset (NED) 1/3 arc-second 10 m DEMs, the primary elevation dataset of the USGS, were obtained from the USGS National Map and were compiled based on aerial photographs from 1999. Rock glacier and glacier polygons were digitized on orthophotos of the study area. Two catchments in each of the cirques were chosen for analysis at the watershed scale. Contributing areas, regions that accumulate either snow or are the source of talus for rock glaciers, were digitized above the rock glacier and glaciers. Taylor Glacier occurs above Taylor Rock Glacier and can contribute talus to the rock glacier by having rock slide across the surface of the glacier. As a result, two contributing areas needed to be defined around Taylor Rock Glacier. The sole contributing area occurs only in the talus and rock walls surrounding the rock glacier, whereas the entire contributing area includes the Taylor Glacier. Using ArcGIS 10© surface raster functions, contours were interpolated, and elevation, slope, aspect, curvature, and hillshade were calculated for USGS and LiDAR DEMs. Zonal statistics were employed to overlay the aforementioned polygons onto the raster surfaces and calculate minimum, maximum, range, mean, and standard deviation

Fig. 1. Location of Taylor Rock Glacier and Andrews Glacier in Rocky Mountain National Park, Colorado, USA.

J.R. Janke / Geomorphology 195 (2013) 118–130

for each polygon. The circular aspect data required a special calculation in vector space because the mean of 1° and 359° cannot be
 180° (Davis, 1986). The vector mean direction θ was calculated using GIS map algebra with the following formula: S ¼ ∑ sin θ; C ¼ ∑ cos θ

θ ¼ arctan

S C

ð1Þ

where S = the sum of sine calculations of aspect (θ), and C = the sum of cosine calculations of aspect. The strength of the resultant vector (mean resultant length), which varies between 0 and 1 and can be thought of as the vector equivalent of variance, was calculated using the following formula: R¼

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi S2 þ C2 : n

ð2Þ

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Rock Glacier (3473 m). The contributing area for the rock glacier and glaciers is about 60% greater on average compared to the size of the associated landform. The approximate 2:1 ratio of contributing area size compared to rock glacier size is common for rock glaciers located in the Front Range (Janke and Frauenfeldeler, 2007). Interpolated 25 m contours from USGS and LiDAR DEMs can vary by as much as 50 m horizontal distance (Fig. 2). Given the slow vertical and horizontal movement (6–7 cm/yr) of Taylor Rock Glacier, it is more likely that differences are because of uncertainty rather change between acquisition dates (LiDAR: 2010; USGS DEM: 1999). Differences on Andrews Glacier may be the result of glacial thinning; however, comparable uncertainty exists on stable areas not on the surface of the glacier. The complexity of the terrain appears to be better represented by the fine resolution LiDAR DEM. Longitudinal and transverse profiles of each landform reveal some unique features that are not observable on the 10 m USGS DEM

4. Results and discussion 4.1. Area and elevation The Sky Pond Catchment, which contains the Taylor Rock Glacier, has a greater area, range of elevation, and a greater mean elevation than the Andrews Tarn Catchment (Table 1). Andrews Glacier is about 36,650 m 2 larger than Taylor Glacier. Taylor Rock Glacier is the largest landform in each of the watersheds, approximately double the size of Andrews Glacier. Taylor Glacier has the highest mean elevation (3625 m), followed by Andrews Glacier (3576 m) and Taylor

Table 1 Elevation comparisons for catchments, glaciers, rock glaciers, and contributing areas. Name

LiDAR 1 m DEM Sky Pond Catchment Andrews Tarn Catchment Andrews Glacier Taylor Glacier Taylor Rock Glacier Andrews Glacier contributing area Taylor Glacier contributing area Taylor Rock Glacier sole contributing area Taylor Rock Glacier entire contributing area USGS 10 m DEM Sky Pond Catchment Andrews Tarn Catchment Andrews Glacier Taylor Glacier Taylor Rock Glacier Taylor Glacier contributing area Andrews Glacier contributing area Taylor Rock Glacier sole contributing area Taylor Rock Glacier entire contributing area Difference Sky Pond Catchment Andrews Tarn Catchment Andrews Glacier Taylor Glacier Taylor Rock Glacier Andrews Glacier contributing area Taylor Glacier contributing area Taylor Rock Glacier sole contributing area Taylor Rock Glacier entire contributing area

Area (m2)

Range (m)

Mean (m)

Standard deviation (m)

1,980,880 341,138 61,773 25,124 133,869 153,603 98,285 194,393

698 338 179 243 202 273 344 507

3622 3606 3576 3625 3473 3631 3788 3708

167 77 41 51 43 64 71 130

317,802

507

3726

120

1,981,000 340,800 61,600 24,800 134,100 98,500 153,000 194,200

686 327 177 235 196 324 270 475

3618 3610 3580 3621 3475 3779 3632 3699

162 79 42 48 44 72 65 123

317,500

47

3718

115

−120 338 173 324 −231 603 −215 193

12 11 2 8 7 3 19 33

4 4 −4 4 −2 −1 8 9

5 −2 −2 3 −1 −1 −1 7

302

33

8

5

Fig. 2. Contours interpolated at a 25 m interval for Andrews Glacier (A) and Taylor Rock Glacier (B). A hillshade is provided as a base map for reference.

Fig. 3. Longitudinal and transverse profiles of Taylor Rock Glacier and Andrews Glacier. Descriptions and notable features are provided in A–D.

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(Fig. 3). The longitudinal profile of Taylor Rock Glacier across the LiDAR 1 m DEM clearly show ridges and furrows that indicate flow and a pronounced front slope near the toe of the rock glacier. Morphologically, these are quite different from the smooth surface of Andrews Glacier (Fig. 3). Without the detailed microtopography, the two features could easily be misclassified as both being glaciers using only the USGS 10 m DEM. The LiDAR transverse profile of Taylor Rock Glacier clearly shows the edges of the rock glacier, whereas the transverse profile of Andrews Glacier illustrates lateral moraines found beyond the sides of the glacier (Fig. 3). According to the LiDAR data, the range of elevation was greater for catchments, glaciers, rock glaciers, and contributing areas in comparison to the USGS DEM (Table 1). LiDAR data showed a lower minimum elevation (8 m maximum difference) and a greater maximum elevation (25 m maximum difference). The minimum elevation was consistently lower on the LiDAR DEM for all features. As a result, the range of elevations was greater on the LiDAR data. The mean difference in elevation ranged from 1 to 9 m (Table 1). Certainly, this difference can affect the results of landform characterization studies. 4.2. Slope According to the LiDAR data, the Sky Pond Catchment was slightly steeper (40°) with greater variation (±17°) than the Andrews Tarn Catchment (38° ± 15°). Andrews Glacier was flatter on average (24°) than Taylor Glacier (34°) (Table 2). The greater slope of Taylor Glacier is responsible for delivering weathered rock to the rock glacier below through sliding. The lower slope of Andrews Glacier makes it difficult to deliver material to the toe of the glacier, hence the lack of rock glacier presence in this valley. Taylor Rock Glacier had the gentlest slope (22°). On average, contributing areas were steeper than other categories, ranging from 40° to 48°. Steeper contributing areas are necessary to deliver snow or debris to the landforms that exist beneath them (Table 2).

Table 2 Slope comparisons for catchments, glaciers, rock glaciers, and contributing areas. Name

LiDAR 1 m DEM Sky Pond Catchment Andrews Tarn Catchment Andrews Glacier Taylor Glacier Taylor Rock Glacier Andrews Glacier contributing area Taylor Glacier contributing area Taylor Rock Glacier sole contributing area Taylor Rock Glacier entire contributing area USGS 10 m DEM Sky Pond Catchment Andrews Tarn Catchment Andrews Glacier Taylor Glacier Taylor Rock Glacier Andrews Glacier contributing area Taylor Glacier contributing area Taylor Rock Glacier sole contributing area Taylor Rock Glacier entire contributing area Difference Sky Pond Catchment Andrews Tarn Catchment Andrews Glacier Taylor Glacier Taylor Rock Glacier Andrews Glacier contributing area Taylor Glacier contributing area Taylor Rock Glacier sole contributing area Taylor Rock Glacier entire contributing area

Range (°)

Mean (°)

Standard deviation (°)

89 87 74 61 76 85 84 88 88

40 38 24 34 22 40 48 48 47

17 15 7 5 11 13 12 18 16

78 71 43 47 36 65 70 69 76

39 36 23 36 16 37 47 48 47

16 14 8 9 8 12 11 15 14

11 15 31 14 40 20 14 19 12

1 2 1 −1 5 3 1 0 0

2 1 −1 −5 4 1 2 3 2

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It has been suggested that the standard deviation of slope provides a good measure of topographic “roughness” (Grohmann et al., 2010). Interestingly, the LiDAR data revealed that Taylor Glacier (5°) and Andrews Glacier (7°) had the lowest standard deviation because of smooth surfaces compared to Taylor Rock Glacier (11°). This relationship was not apparent on the USGS DEM where Taylor Rock Glacier had the lowest standard deviation (Table 2). This “roughness” variable calculated with the LiDAR data provides a reliable method of detection that will help automatic extraction of rock glaciers and glaciers. LiDAR and USGS DEM calculations of slope for Andrews Glacier and Taylor Rock Glacier are provided in Fig. 4. The USGS DEM slope calculations show some general flow lobes in the center of Taylor Rock Glacier; however, detail is greatly enhanced on the LiDAR slope calculation. A longitudinal furrow, transverse flow lobes and ridges, a distinct front slope, and even a large rock (≈ 110 m 2) can be seen on the surface of the rock glacier. Similar observations are apparent when comparing the LiDAR and USGS slope calculations of Andrews Glacier (Fig. 4). The LiDAR slope calculation shows crevassing on the surface of the glacier and a clear textural difference between the debris covered and snow covered sections of the glacier, which are not apparent on the USGS DEM. In general, the LiDAR data indicate lower minimum slopes and greater maximum slopes. The LiDAR data indicate a 40° greater range of slope exists on Taylor Rock Glacier compared to the USGS slope calculations (Table 2). The LiDAR data have a greater mean slope for every feature except Taylor Glacier. According to the LiDAR data, a 5° greater slope occurs on Taylor Rock Glacier, the result of the steepness of the front slope being better represented at a 1 m resolution. The steep front slope is a distinguishing characteristic of flowing rock glaciers that is not as apparent on the USGS DEM slope calculation (Table 2; Fig. 4). Profiles of slope along and across the rock glacier and glacier are illustrated in Fig. 5. The longitudinal and transverse profiles of Taylor Rock Glacier reveal high variation in local slope on the LiDAR data because of the complex surface consisting of ridges and furrows. The variation is not visible on the slope calculation from the USGS DEM. On Andrews Glacier, the LiDAR slope surface calculation has low variation compared to surrounding terrain. The texture created on the LiDAR data could be used to improve rock glacier mapping and detection by using filters to isolate regions with about a 20° slope with a variance of about 10° (Table 2). 4.3. Aspect and hillshade According to the LiDAR data, Sky Pond Catchment has a mean aspect of 51°, whereas Andrews Tarn has a more northerly mean aspect of 44°. The USGS DEM suggests the opposite: Sky Pond Catchment has a more northerly aspect (46°) compared to Andrews Tarn (49°). Andrews Glacier exists at a northerly aspect (1°) compared to Taylor Glacier which is oriented more toward the east (73°) (Table 3). In comparison, Taylor Rock Glacier has an intermediate northeastern aspect (51°). The northern aspect of Andrews Glacier may help preserve, through shading and reduced insolation, the glacier more so compared to the east facing aspect of Taylor Glacier. Aspects varied considerably on the USGS DEM, but still showed a similar north to northeast trend (Andrews Glacier = 11°; Taylor Glacier = 32°; and Taylor Rock Glacier = 48°). Contributing areas are northeasterly (range from 40° to 56°). Mean resultant lengths indicate that a dominant direction does not exist on most features, likely the product of the fine resolution LiDAR data that detects micro-topography. The standard deviation of hillshade values is lower for glacier surfaces, compared to rock glaciers (Table 3). For example, Andrews Glacier had a standard deviation of 38°, whereas Taylor Rock Glacier had a standard deviation of 50°. The USGS DEM hillshade standard deviation measurements for glaciers and rock glaciers varied from 33 to

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Fig. 4. Slope comparisons for LiDAR and USGS DEM sources for Taylor Rock Glacier and Andrews Glacier.

34°. Again, the texture or “roughness” of the surface (snow and ice versus a mixture of fine to coarse debris) is clearly distinguishable on the LiDAR data, a characteristic that may help improved automatic detection using this data source. The generalization of the USGS DEM is clearly demonstrated on the aspect grids (Fig. 6). Micro-topography, such the orientation of flow ridges, is easily discernible on the LiDAR data, but not on the USGS DEM. The front slope of the rock glacier is shown to transition from east to northeast to north, but this is not visible on the USGS data. On Andrews glacier, a crevasse and a lateral moraine have distinct orientations on the LiDAR data. A comparison of mean aspect measurements from USGS DEM and LiDAR data revealed that large (41°) differences exist for Taylor Rock Glacier (Table 3). This is likely because the feature is small and located in rugged terrain that is generalized on the USGS data. Contributing areas also exhibit large differences in mean aspect because of pronounced peaks and narrow ravines that are generalized on the USGS DEM calculation. The largest difference in mean hillshade occurs on Taylor Rock Glacier because the surface texture is smoothed on the USGS DEM data. 4.4. Curvature The LiDAR data have a greater range of curvature values because it captures micro-topography (Table 4). According to the LiDAR data, Taylor Rock Glacier has a greater range of curvature (2341°) compared to Taylor Glacier (1193°) and Andrews Glacier (1548°); this relationship is not apparent with the USGS DEM. The mean curvature of glaciers is concave, whereas Taylor Rock Glacier has the greatest

convex curvature (Table 4). The mean curvature of the contributing area of Taylor Rock Glacier is concave, but the contributing area of Andrews Glacier is slightly convex and almost flat, providing an ideal surface for snow to blow across and accumulate on the glacier. The concave slope of the contributing area of Taylor Rock Glacier helps capture and deliver rock to Taylor Rock Glacier. These relationships are not detectable with the USGS DEM, which show concave mean curvatures for all features: glaciers, rock glaciers, and contributing areas (Table 4). On the LiDAR data, mean plan curvature (perpendicular to the maximum slope) is more concave for the glaciers compared to Taylor Rock Glacier, which is flat (0). Profile curvature (the direction of the main slope) is concave for glaciers and convex for Taylor Rock Glacier (Table 4). The plan and profile curvature of glaciers creates a shape that can enhance shading and snow trapping ability, preserving the existence. As far as contributing areas, the mean profile curvature of Andrews Glacier is convex, whereas Taylor Rock Glacier has a mean profile curvature that is concave. A comparison of the LiDAR and USGS DEMs reveals that the linear, concave edges of the landforms are more clearly depicted on the USGS DEMS because of the coarse resolution (Fig. 7). This could be useful for automatic feature extraction algorithms designed for outlining the perimeter of rock glaciers and glaciers. On the LiDAR data, steep cliffs and narrow ravines are clearly depicted. The convex apex of a lateral moraine next to Andrews Glacier is also visible (Fig. 7). A comparison of the two data sets reveals a more concave plan curvature for Andrews Glacier according to the LiDAR data, but the remaining features all have more convex plan curvatures. Taylor

Fig. 5. Longitudinal and transverse slope profiles of Taylor Rock Glacier and Andrews Glacier. Descriptions and notable features are provided in A–D.

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Table 3 Aspect and hillshade comparisons for catchments, glaciers, rock glaciers, and contributing areas. Name

Mean aspect (°)

LiDAR 1 m DEM Sky Pond Catchment Andrews Tarn Catchment Andrews Glacier Taylor Glacier Taylor Rock Glacier Andrews Glacier contributing area Taylor Glacier contributing area Taylor Rock Glacier sole contributing area Taylor Rock Glacier entire contributing area USGS 10 m DEM Sky Pond Catchment Andrews Tarn Catchment Andrews Glacier Taylor Glacier Taylor Rock Glacier Andrews Glacier contributing area Taylor Glacier contributing area Taylor Rock Glacier sole contributing area Taylor Rock Glacier entire contributing area Difference Sky Pond Catchment Andrews Tarn Catchment Andrews Glacier Taylor Glacier Taylor Rock Glacier Andrews Glacier contributing area Taylor Glacier contributing area Taylor Rock Glacier sole contributing area Taylor Rock Glacier entire contributing area

Mean resultant length (0–1)

Mean hillshade (0–255)

Standard deviation hillshade (0–255)

51 44 1 73 51 40 56 48 56

0.004 0.005 0.006 0.007 0.006 0.005 0.004 0.004 0.038

96 145 125 61 138 135 41 94 75

70 85 38 21 50 91 43 76 69

46 49 11 32 48 18 51 40 33

0.008 0.002 0.029 0.001 0.044 0.015 0.014 0.018 0.000

100 147 130 66 143 135 31 103 78

70 87 34 34 33 92 33 81 74

5 −5 −10 41 2 22 6 8 24

−0.003 0.003 −0.023 0.006 −0.038 −0.010 −0.010 −0.014 0.038

−4 −2 −5 −4 −5 0 10 −9 −3

0 −2 3 −13 17 −1 10 −5 −6

Fig. 6. Aspect comparisons for LiDAR and USGS DEM sources for Taylor Rock Glacier and Andrews Glacier.

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Table 4 Curvature comparisons for catchments, glaciers, rock glaciers, and contributing areas. Name

LiDAR 1 m DEM Sky Pond Catchment Andrews Tarn Catchment Andrews Glacier Taylor Glacier Taylor Rock Glacier Andrews Glacier contributing area Taylor Glacier contributing area Taylor Rock Glacier sole contributing area Taylor Rock Glacier entire contributing area USGS 10 m DEM Sky Pond Catchment Andrews Tarn Catchment Andrews Glacier Taylor Glacier Taylor Rock Glacier Andrews Glacier contributing area Taylor Glacier contributing area Taylor Rock Glacier sole contributing area Taylor Rock Glacier entire contributing area Difference Sky Pond Catchment Andrews Tarn Catchment Andrews Glacier Taylor Glacier Taylor Rock Glacier Andrews Glacier contributing area Taylor Glacier contributing area Taylor Rock Glacier sole contributing area Taylor Rock Glacier entire contributing area

Range curvature (°)

Mean curvature (°)

Standard deviation curvature (°)

Range plan curvature (°)

Mean plan curvature (°)

Standard deviation plan curvature (°)

Range profile curvature (°)

Mean profile curvature (°)

Standard Deviation profile curvature (°)

43,533 5744 1548 1193 2341 5744 7669 19,560

0.0 −0.1 −0.8 −0.9 0.1 0.1 −1.0 −0.2

197 103 25 21 33 103 139 258

23,295 3567 705 489 1162 3294 4606 9617

0.2 0.2 −0.4 −0.6 0.0 0.0 −0.3 0.5

92 49 123 12 18 50 60 115

22,722 3631 979 875 1425 3084 5463 12,947

0.2 0.2 0.4 0.3 −0.1 0.0 0.8 0.7

132 69 15 12 20 66 102 184

19,560

−0.5

216

9617

0.2

96

12,947

0.7

154

167 104 19 28 17 42 95 147

−0.1 −0.1 −0.5 −1.6 0.0 −0.1 −0.6 −0.3

7 6 2 3 2 4 8 12

79 60 9 19 8 25 35 65

0.0 0.0 −0.3 −0.8 0.0 −0.2 −0.3 0.0

4 3 1 2 1 3 3 6

112 57 13 23 12 28 72 95

0.1 0.1 0.2 0.7 0.0 0.0 0.3 0.4

5 3 1 2 1 3 6 8

147

−0.5

10

65

−0.1

5

95

0.4

7

43,366 5640 1529 1165 2324 5703 7574 19,413

0.1 0.0 −0.4 0.6 0.1 0.2 −0.4 0.1

190 98 23 18 31 98 131 247

23,216 3507 696 470 1154 3269 4570 9552

0.2 0.2 −0.1 0.3 0.0 0.3 0.0 0.4

88 46 12 11 17 47 57 109

22,611 3574 966 853 1414 3056 5391 12,852

0.1 0.2 0.2 −0.4 −0.1 0.0 0.5 0.3

128 65 14 10 19 64 96 176

19,413

0.0

206

9552

0.3

91

12,852

0.3

147

Glacier and Rock Glacier profile curvatures are more convex on the USGS DEM (Table 4). The subtle changes in curvature are better represented by the LiDAR data.

4.5. LiDAR and USGS accuracy assessment Calculations of elevation, slope, aspect, hillshade, and curvature from the LiDAR DEM were resampled to 10 m using a cubic convolution technique to match the resolution of the USGS DEM. Calculations based on the USGS DEM were then subtracted from the calculations based on the 10 m LiDAR DEM to calculate a RMSE to compare residuals from the USGS and LiDAR DEMs at different scales as well as locations (Table 5). In general, the larger the area covered, the lower the RMSE; the Sky Pond and Andrews Tarn catchments had better agreement compared to other smaller areas (Table 5). Contributing areas tended to have the greater RMSE for all topographic categories (Table 5). Within the contributing areas, elevation and curvature had the greatest RMSE because these areas have narrow peaks, ravines, and rugged alpine topography that are often generalized on USGS DEMs. In addition, these areas often contain abundant shadows that make it difficult to produce accurate DEMs using aerial photographs. Taylor Rock Glacier had the lowest RMSE and therefore best overall agreement for the elevation and the curvature categories. As a result, readily available USGS DEMs may be better for landform analysis at the regional scale when using an existing inventory to characterize topographic settings. However, the fine scale, micro-topography of rock glaciers is illuminated much more clearly on the LiDAR data, making it ideal for feature extraction to establish an initial inventory.

Taylor Glacier had the greatest RMSE for slope, aspect, and hillshade compared to the other categories. This is because of the small size of the glacier, which is obstructed by shadowing and rugged topography on aerial photographs used to create DEMs. Andrews Glacier showed intermediate RMSE values for the majority of topographic categories compared to the other features, suggesting that the coarser resolution DEM would be suitable for regional landform characterization. A visual representation of differences in elevation is provided in Fig. 8. To the northwest of Taylor ROCK glacier, the USGS DEM estimated a maximum elevation that was about 150 m greater compared to the LiDAR data (Fig. 8). To the south of the rock glacier, the LiDAR data were a maximum of 143 m greater. This represents a significant uncertainty between the data sources. The areas surrounding the landforms consist of rugged terrain – pinnacles, peaks, and horns – that is likely generalized on the USGS DEM causing a high relative difference. On the surface of each feature, the USGS DEM indicated slightly greater elevations. The 1999 USGS DEM was higher on the north side of Andrews Glaciers, which may indicate some thinning of the glacier surface over time. According to the zonal statistics for Andrews Glacier, differences ranged between 7 and − 25 m with a mean difference of − 7 m. On Taylor Rock Glacier, differences ranged from 9 to − 14 m with a mean difference of − 3 m. 5. Conclusions The high resolution and precision of LiDAR data are well suited for change detection or measurement of small-scale geomorphic processes. The data can also be used to measure topographic characteristics of

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Fig. 7. Curvature comparisons for LiDAR and USGS DEM sources for Taylor Rock Glacier and Andrews Glacier.

alpine landforms. Despite being separated by only 1.75 km in adjacent valleys, Andrews Glacier and Taylor Rock Glacier exhibit unique topographic characteristics which explains their formation and current existence. Andrews Glacier is located at about 100 m higher elevation compared to Taylor Rock Glacier. Taylor Rock Glacier has a gentler slope, but is convex in comparison to the concave shape of Andrews Glacier, which helps trap snow. Taylor Rock glAcier has a northeastern orientation, whereas Andrews Glacier faces more northerly, which helps maintain snowpack. The contributing area of Taylor Rock Glacier is almost double the size of the contributing area of Andrews Glacier. The contributing area of Taylor Rock Glacier is concave, has a steeper slope, and is easterly in comparison to the convex, less steep, and slightly more northeasterly orientation of Andrews Glacier, which helps

deliver rock to Taylor Rock Glacier. These topographic characteristics help maintain the respective rock glacier and glacier forms. Landform characteristics that are unique to rock glaciers are needed to complete regional inventories. The LiDAR data provide detailed micro-topography that differentiates the two landforms. Terrain complexity appears to be better represented by the fine resolution of the LiDAR DEM. A longitudinal furrow, transverse flow lobes and ridges, and a distinct front slope are visible on Taylor Rock Glacier, whereas crevassing, an ice to debris transition, and a lateral moraine are identifiable on the LiDAR data of Andrews Glacier and vicinity. Because these features can be detected, the LiDAR data, enhanced by image extraction filters, are ideal for conducting an initial rock glacier inventory. Topographic “roughness” criteria can help discern rock glacier

Table 5 Root mean squared error (RMSE) of topographic variables for catchments, glaciers, rock glaciers, and contributing areas. Name

Elevation

Slope

Aspect

Hillshade

Curvature

Plan curvature

Profile curvature

Sky Pond Catchment Andrews Tarn Catchment Andrews Glacier Taylor Glacier Taylor Rock Glacier Andrews Glacier contributing area Taylor Glacier contributing area Taylor Rock Glacier sole contributing area Taylor Rock Glacier entire contributing area

0.14 0.25 0.40 0.42 0.13 0.30 0.60 0.48 0.35

0.09 0.23 0.31 0.51 0.33 0.33 0.44 0.37 0.26

0.009 0.022 0.051 0.083 0.035 0.032 0.041 0.029 0.023

1.07 2.62 2.38 7.16 2.28 4.24 5.35 3.43 2.73

0.97 1.33 0.88 1.17 0.66 1.94 3.95 4.90 3.22

0.39 0.58 0.25 0.89 0.39 0.87 1.44 1.95 1.27

0.70 0.91 0.36 0.53 0.40 1.26 2.19 3.13 2.02

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Fig. 8. Elevation difference between a USGS 10 m DEM and a resampled 10 m LiDAR DEM for Taylor Rock Glacier (A) and Andrews Glacier (B).

locations: slopes near 20° with a 10° standard deviation and a mean hillshade of about140° with a standard deviation of 50° are ideal. With LiDAR data, these could be used as an initial filter to narrow results that would later be verified on aerial or satellite imagery. However, the high cost of acquiring aerial LiDAR data because of logistics, planning, and support must be considered as well. When comparing the USGS and LiDAR DEMs, Taylor Rock Glacier had the lowest RMSE for the elevation and the curvature classes. As a result, readily available USGS DEMs may be better for landform analysis at the regional scale when using an existing inventory to characterize topographic settings such as those analyzed in this study. Acknowledgments Jack Vitek, Rick Giardino, Anders Schomacker, and an anonymous reviewer greatly improved this manuscript by providing their thoughtful suggestions and edits. I would like to offer a special thanks to Stephanie Kampf, Blaine Hastings, Katie Williams, and Jill Baron at Colorado State University for providing access to the LiDAR imagery. Stacey Curry at the USGS processed the LiDAR imagery and created the 1 m DEM. Appendix A. Supplementary data Supplementary data associated with this article can be found in the online version, at http://dx.doi.org/10.1016/j.geomorph.2013.04. 036. These data include Google maps of the most important areas described in this article.

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