UNIVERSITY OF GOTHENBURG Department of Economy and Society, Human Geography & Department of Earth Sciences Geovetarcentrum/Earth Science Centre

Detecting small-scale glacial

in LiDAR derived

digital elevation models

A case study of roches moutonnées on the Swedish west coast

Gunnar Palm

ISSN 1400-3821 B882 Bachelor of Science thesis Göteborg 2015

Mailing address Address Telephone Telefax Geovetarcentrum Geovetarcentrum Geovetarcentrum 031-786 19 56 031-786 19 86 Göteborg University S 405 30 Göteborg Guldhedsgatan 5A S-405 30 Göteborg SWEDEN Abstract The purpose of this study was to develop a method to detect and map what Sugden and John (1976) calls, “the hallmark of glacial ”, namely the characteristically asymmetrical rock mounds known as roches moutonnées. The roches moutonnées was to be detected in the new national elevation model provided by the Swedish mapping authority. The national elevation model was produced using an active remote sensing technique called light detection and ranging (LiDAR). LiDAR uses laser light to measure elevation and can produce very accurate digital elevation models (DEM). During the study data from the national elevation model was processed and a high resolution raster DEM was generated, processed and analyzed using geographical information systems (GIS) including ArcGIS and FME Desktop. The location of a number of roches moutonnées were marked using a GPS receiver during a field campaign. The coordinates from the GPS receiver were imported into a GIS environment in order to study the values in and around the perimeters of the surveyed roches moutonnées. The aim of this was to identify the distinctive properties of the , which in turn would permit a search for these properties, and thus the landform, in the entire study area. To put the performance of the national elevation model in perspective, the same method was applied to a local elevation model with higher resolution provided by the City of Gothenburg. The GIS method detected 996 roches moutonnées in the national elevation model within an area of two square kilometers on the west coast of Sweden. A comparison of the results from the local elevation model provided by the City of Gothenburg revealed that the method detected the same roches moutonnées in the two different elevation models. The method detected 31% more roches moutonnées in the local elevation model than in the national elevation model. A validation of a sample of the predicted roches moutonnées verified that the method in that case was 97.6% accurate, and that a majority all the roches moutonnées that the method detected in the national elevation model most likely corresponded to actual roches moutonnées found in the study area.

Key words Roches Moutonnées Geographical Information Systems Digital Elevation Models

Table of Contents 1. Introduction ...... 1

2. Background ...... 3

2.1 LiDAR - a brief overview ...... 3

2.2 The National Elevation Model ...... 4

2.2.1 The Local Elevation Model ...... 5

2.3 Roches Moutonnées ...... 5

3. Study Area ...... 7

4. Materials and Methods ...... 9

4.1 Data ...... 9

4.1.1 LASer (LAS) file format ...... 9

4.1.2 Glacial striations ...... 9

4.2 Data processing ...... 10

4.2.1 Preprocessing of LAS files ...... 10

4.2.2 Generating a Raster Digital Elevation Model ...... 12

4.3 Survey of Roches Moutonnées ...... 13

4.4 Calculation and Analysis of DEM Derivatives ...... 14

4.4.1 Slope ...... 15

4.4.2 Curvature ...... 16

4.4.3 Aspect ...... 17

4.4.4 Contour lines ...... 18

4.5 Classification Predicted Roches Moutonnées ...... 19

4.5.1 Stoss and Lee Sides ...... 19

4.5.2 Delimitation of Predicted Roches Moutonnées ...... 21

4.6 Validation of Predicted Roches Moutonnées ...... 21

5. Results ...... 23

5.1 Results of the GIS Analysis ...... 23

5.1.1 Results from the National Elevation Model ...... 23

5.1.2 Results from the Local Elevation Model ...... 24

5.1.3 Comparison of the Results ...... 26

5.2 Validation of Predicted Roches Moutonnées in the Field ...... 28

6. Discussion ...... 29

6.1 Results Discussion ...... 29

6.1.1 Predicted Roches Moutonnées ...... 29

6.1.2 Validation ...... 30

6.2 Method Discussion ...... 30

6.2.1 Input Data ...... 30

6.2.2 Classification criteria ...... 30

6.3 Further studies ...... 31

7. Conclusion ...... 32

Acknowledgements ...... 32

References ...... 33

1. Introduction In 2009 the Swedish mapping, cadastral and land registration authority (hereafter referred to as the Swedish mapping authority) began work on a new LiDAR derived high resolution digital elevation model (DEM) with national coverage (Lantmäteriet, 2014). A DEM is a digital representation of the land surface, stripped from vegetation and anthropogenic structures. Although the data set was originally intended for landslide and flood management, several other applications have been identified in different research sectors since its arrival (Dowling, Alexanderson, & Möller, 2013). The national elevation model has for example been used to map the distribution and structure of urban vegetation (Lindberg, Johansson & Thorsson, 2013). This project will examine the potential application of the new high resolution DEM for glacial geomorphology, specifically for the detection of small-scale glacial landforms. A number of studies concerning the segmentation and classification of land cover or fluvial geomorphological features using similar LiDAR derived DEMs have been published internationally (Brennan & Webster, 2006; Tarolli, 2014). The Geological Survey of Sweden has used the new DEM to map large scale glacial landforms, including and (Peterson, 2014); still the prospect of mapping smaller scale glacial landforms using the national DEM remains largely unexplored. The definition and delimitation of landforms is one of the central aspects of geomorphology (Eisank, Smith & Hillier, 2014). In order to measure the geomorphological characteristics of a landform, it must to be separated from its surroundings by a closed border marking its limits (Evans, 2012). Traditionally this process has included rigorous fieldwork and visual interpretation of printed topographic maps and aerial photographs (Roering et al., 2013). The coarse resolution of the earlier DEMs made the detection and delimitation of small and medium glacial landforms impossible (Dowling et al., 2013), giving geomorphologist no choice but to study them in the field. The new high resolution DEM enables the detection of previously unnoticed landforms (Roering et al., 2013), pointing geomorphologists towards new areas of interest and thus constitutes a great new opportunity to study and learn more about of the Quaternary history of Sweden (Dowling et al., 2013). This study focused on roches moutonnées, a glacial landform which distinctive shape and relative abundance on the Swedish west coast makes it an ideal target for detection.

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This project aims to develop a method to detect and delimit roches moutonnées in a reproducible way, using a combination of several DEM derivatives to optimally isolate individual roches moutonnées (see figure 1). Apart from the national elevation model, a vector dataset provided by the Geological Survey of Sweden containing the location and orientation of glacial striations, will be used to inform the classification of the roches moutonnées. The national DEM will be processed and analyzed using geographical information systems (GIS) with the intention of developing a method more robust than visual analysis of topographic maps, that has the potential to replace or at least supplement manual landform delimitation in the future (Eisank et al, 2014). For comparison, the method will also be applied to a local DEM with a higher resolution provided by the City of Gothenburg.

Figure 1. Flowchart depicting the study process.

Problem statements:

 Is it possible to detect and delimit roches moutonnées in the LiDAR derived digital elevation model provided by the Swedish mapping authority?  What is the difference if any between the results from the national and the local elevation model?

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2. Background

2.1 LiDAR - a brief overview Light detection and ranging (LiDAR) is an active remote sensing system, that utilizes a pulse of laser light as the sensing carrier to measure the distance between the sensor and an object of interest (e.g. the Earth surface) (Wehr & Lohr, 1999). When installed on an airplane or a helicopter, a LiDAR is often referred to as an airborne laser scanner or ALS (Liu, 2008).

Like all active remote sensors the LiDAR system has two key components, a transmitting and a receiving apparatus. In the case of the LiDAR, a pulse of laser light is transmitted, and the reflection or backscatter of the laser light is received. Laser light has a number of properties that makes it highly advantageous as a sensing carrier, and soon after its advent it was employed as an instrument for ranging (Wehr & Lohr, 1999). The LiDAR system measures the distance between the sensor and an object of interest by multiplying the travel time of the laser light pulse (i.e. the time between the initial emission of the laser light pulse and the receipt of the reflection or backscatter) by the speed of the laser light pulse (i.e. the speed of light) (Liu, 2008). Lastly the total travel time is divided by two, since the laser light pulse traveled from the sensor to the object of interest, and back to the sensor again (Wehr & Lohr, 1999). The range combined with information on the angle at which the laser light pulse was emitted, gives the exact position of the object of interest in relation to the LiDAR.

By the end of the last century, numerous advances in LiDAR technology, including a considerable increase in pulse repetition rate, made ALS a favorable method for elevation data acquisition (Liu, 2008). In particular, the development of differential global navigation satellite systems (DGNSS) permitted very accurate position and orientation systems (POS), the third vital component of an ALS (Wehr & Lohr, 1999).

As mentioned above, the position of the object of interest is only known in relation to the LiDAR. The purpose of the POS is to track the position and orientation of the LiDAR with respect to a coordinate system, thus permitting the object of interest to be located in the relevant coordinate system (Wehr & Lohr, 1999). The POS consists of a DGNSS receiver and an inertial measurement unit (IMU) that tracks the attitude (roll, pitch and yaw) of the airplane (Wehr & Lohr, 1999).

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A LiDAR can emit thousands of laser light pulses per second, up to 250 kHz (Liu, 2008), and generates a so-called point cloud, consisting of millions of data points with x, y and z coordinates (longitude, latitude and ellipsoidal height) as output (Dowling et al., 2013). The accuracy of this points cloud is directly related to the precision of the POS (Liu, 2008).

2.2 The National Elevation Model In 2007 the final report from the Swedish commission on climate and vulnerability, SOU 2007:60, was published. The report included a number of recommendations aimed at reducing Sweden’s vulnerability to climate change, among them a proposal to give the Swedish authority additional funding in order to create a new full coverage national elevation model. In order to produce accurate predictions of natural disasters related to climate change (e.g. landslides and floods) the commission proposed that the new elevation model would have a higher resolution and be more accurate than the elevation model available at that time. Moreover the elevation model would be available in digital format, free of charge for Swedish municipalities and government agencies, tasked with analyzing, planning and adapting to climate change (SOU 2007:60).

The Swedish mapping authority had already conducted tests with LiDAR derived DEMs in 2004 (Klang & Burman, 2006), and work on the new elevation model began in 2009, under the name nationell höjdmodell or NH.

Completion of the entire elevation model is planned for 2016, however, the Swedish mapping authority is continually releasing new data from areas where the survey is completed (Lantmäteriet, 2014). Data from the NH is delivered in two formats, either as a processed point cloud in a LAS file format (see section 4.1.1); or as a 2 meter resolution raster DEM in a ASCII grid format (Lantmäteriet, 2014). Compared with the foregoing 50 meter resolution raster DEM, based on photogrammetric measurements, the new 2 meter resolution raster DEM represents a substantial increase in detail.

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2.2.1 The Local Elevation Model In 2010 the City of Gothenburg commissioned the production of a LiDAR derived digital elevation model similar to the NH. Compared to the NH, this elevation model has a smaller geographical coverage, it only includes Gothenburg municipality, but a considerably higher spatial resolution (see 1), permitting very accurate hydrological modeling and landslide monitoring (Fredén, 2015). Similar to the NH, data from this local elevation model is delivered in several formats, including LAS file format.

Table 1. Metadata on the two elevation models utilized in the study . The point density is for ground classed points only. (Lantmäteriet, 2014; Göteborgs Stad, 2013)

Data provider City of Gothenburg Swedish mapping authority Coordinate system SWEREF 99 1200 SWEREF 99 TM Flight level 550 meters a.s.l. 1700 – 2300 meters a.s.l. Point density (resolution) 8 points/square meter 0.5 points/square meter Laser footprint diameter 0.13 meters 0.5 meters Geographical coverage Gothenburg municipality Sweden Classes* 1, 2, 3, 4, 5, 6 1, 2 *1 Unclassified, 2 Ground, 3 Non vegetation, 4 Medium vegetation, 5 High vegetation, 6 Building

2.3 Roches Moutonnées The objective of this study is to detect and map glacial landforms. One of the most common medium-scale landforms associated with glacial erosion is the characteristically asymmetrical rock mounds called roches moutonnées. Roches moutonnées are formed by erosion and their asymmetrical form is the result of on one side and on the other side (De Blij, Muller, Burt & Mason, 2013). The shape and size of the roches moutonnées is related to the distribution of joints and fractures as well as the lithological composition of the underlying bedrock, and thus they exist in a great variety of sizes (Olvmo & Johansson, 2001).

Apart from the influence of the local lithology, the orientation of the roches moutonnées is also strongly related to the direction of movement (Sugden & John, 1976). When the advancing glacier is forced over a rock outcrop, pressure increases, the basal ice is partially melted and the underlying rock is abraded (i.e. scraped) by rock fragments carried in the basal ice. This results in a smooth convex surface on the so called stoss side, the upstream side of the rock with respect to 5 the basal flow of the glacier. On the opposite side of the rock, pressure drops and the liquid water re-freezes, fractures the rock and carries away rock fragments. This process results in a sharp steepened edge on the so called lee side, the side of the rock facing away from the flow direction of the glacier (see figure 2). Because of this relationship, information on the orientation and distribution of roches moutonnées can be used to reconstruct the path of and increase our understanding of the subglacial conditions during the most recent ice ages (De Blij et al, 2013).

Figure 2. A typical roche moutonnée located in the study area. The smooth convex stoss side (to the left) and the steepened lee side (to the right) are clearly visible. The arrow indicates the direction of the ice movement (northeast to southwest). Rosenberg, R (Photographer).

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3. Study Area The study area is situated on the island of Hisingen in the northwestern part of Gothenburg municipality (see figure 3). The extent of the study area is 1 x 2 kilometers. Small-scale glacial landforms, mainly roches moutonnées, are clearly visible in the study area, both in the field and in aerial photographs, and there is a minimum of disturbances in the form of high vegetation or buildings. Most importantly, having the objective of this study in mind, the area is represented both in the national elevation model provided by Swedish mapping authority and the local elevation model provided by the City of Gothenburg.

Norway Sweden

Study Area Hisingen

Denmark

Gothenburg

Figure 3. The location and extent of the study area in relation to northern Europe.

The local bedrock was formed 1.5 – 1.6 billion years ago and consists of metagreywacke, quartzite, paragneiss and metabasalt (SGU, 2015). During the latest glaciation the inland advanced across the area from the northeast, as evident from the northeast southwest orientation of the glacial striations in the local bedrock. The land cover of the study area is mostly exposed bedrock (see figure 5), in areas where there is a soil cover, it consists of postglacial sand and fine glacial clay (SGU, 2015). Most of the roches moutonnées in the study area are found in series 7 along rock ridges or in large complexes (see figure 4 and 5), but there are also cases of isolated roches moutonnées in the sediment filled valleys between the ridges. Most of the roches moutonnées observed in the study area were roughly five to ten meters in diameter. The stoss sides of the roches moutonnées were in general oriented towards the northeast.

Figure 4. A complex of roches moutonnées located in the study area. Rosenberg, R (Photographer).

Figure 5. Aerial photo of a subsection of the study area. The position and orientation of rock ridges (not to be confused with glacial striations) along which the roches moutonnées were found are highlighted with red lines.

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4. Materials and Methods The LiDAR derived elevation model was processed and a high resolution raster DEM was generated, processed and analyzed (see figure 7) using GIS-software including ArcMap and FME Desktop. The location of a number of roches moutonnées were marked using a GPS receiver during a field campaign. The coordinates from the GPS receiver were imported into a GIS environment in order to study the values in and around the perimeters of the surveyed roches moutonnées. The aim of this is to identify the distinctive properties of the landform, which in turn would permit a search for these properties, and thus the landform, in the entire study area.

4.1 Data

4.1.1 LASer (LAS) file format Both the national elevation model from the Swedish mapping authority and the local elevation model from the City of Gothenburg were provided in a LAS file format. The LAS format is a public file format created and maintained by the American Society for Photogrammetry and Remote Sensing that has been specifically developed to make the interchange of LiDAR derived point cloud data less time consuming and more convenient (ASPRS, 2015). A LAS file contains binary data consisting of a public header block with generic information such as the number of point and the bounds of the point cloud; variable length records containing various metadata; and a Point Data Record containing information on each individual point such as intensity, return number and classification (ASPRS, 2013).

4.1.2 Glacial striations A vector dataset from the Geological Survey of Sweden containing the location and orientation of glacial striations (see figure 6) was used to identify which aspect the stoss sides and the lee sides should have respectively. Glacial striations are scratch marks left in the bedrock by advancing glaciers. The mapping of glacial striations was made by the Geological Survey in connection with the soil type survey for the production of the Quaternary map of Sweden. The glacial striations reveal that the ice movement across the study area was from the northeast; consequently the stoss sides (see figure 2) of the roches moutonnées should have a northeasterly aspect, and the lee sides of the roches moutonnées should have a southwesterly aspect (Adrielsson & Fredén, 1987).

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Figure 6. The direction of glacial striations on western Hisingen. The location of the study area is marked with a square. Data set provided by the Geological Survey of Sweden.

4.2 Data processing

4.2.1 Preprocessing of LAS files The first step of the preprocessing was to combine the LAS point cloud tiles that collectively covered the study area, into one single point cloud, the FME Desktop transformer PointCloudCombiner was used for this purpose. To produce an accurate DEM, the bare-Earth points that represent the ground needed to be extracted from the combined point cloud. This was accomplished by filtering the point cloud based on the point classification made by the data providers (see table 1). The FME Desktop transformer PointCloudFilter was utilized for this purpose. The output of the filter was a point cloud that only contained points classified as ground by the data providers.

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Figure 7. Flowchart depicting the GIS analysis. The inputs to the analysis are the LAS elevation data provided by the Swedish mapping authority and the vector dataset from the Geological Survey of Sweden containing the location and orientation of glacial striations. The outputs of the analysis are the predicted roches moutonnées (PRMs). Rectangles denote processes and parallelograms denote data.

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4.2.2 Generating a Raster Digital Elevation Model The next step of the preprocessing was to generate a raster DEM (see figure 8) based on the LAS point cloud. The FME Desktop transformer RasterDEMGenerator was utilized for this purpose. This transformer constructs a Delaunay triangulation based on the input point cloud. A Delaunay triangulation is an interpolation method, that is, a method of filling the gaps between points with elevation values, based on the elevation values of the surrounding points. This operation produces a triangulated irregular network (TIN) that is uniformly sampled to produce a raster DEM. A raster DEM is a digital representation of the land surface in the form of a rectangular grid of cells organized in rows and columns. Each cell in the raster DEM is assigned an elevation value based on the elevation values of the sampled TIN.

Two raster DEMs were generated, one derived from the national elevation model provided by the Swedish mapping authority and one derived from the local elevation model provided by the City of Gothenburg. Both raster DEMs covered the same two square kilometers and both had a cell size of 1 x 1 meter.

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Figure 8. A color coded raster digital elevation model overplayed with a shaded relief model illuminated from the northeast. The model depicts a section of the study area. Both rasters are generated using the national elevation data provided by the Swedish mapping authority.

4.3 Survey of Roches Moutonnées During a field campaign on April 16, a number of representative roches moutonnées were located in the study area (see figure 9). The coordinate location of 28 roches moutonnées were collected in the field using a GPS receiver. The coordinate locations were collected as close to the peak or center of each landform as possible. These coordinate locations will hereafter be referred to as the surveyed roches moutonnées or SRMs. However, since the GPS receiver only had an accuracy of ±3 meters, the SRMs should not be considered the exact, but rather the approximate location of the roches moutonnées.

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Figure 9. A photograph showing some of the roches moutonnées located in the study area. The photograph is taken towards the southwest highlighting the smooth convex stoss sides of the roches moutonnées (marked with red arrows). Rosenberg, R (Photographer).

4.4 Calculation and Analysis of DEM Derivatives In order to identify the properties of the roches moutonnées, five different raster images were derived from the raster DEM. These images visualize five different surface properties: slope, curvature, aspect and contour lines. The surveyed locations of the roches moutonnées were inspected in the DEM derived images to identify the distinctive properties of landform. The results of this visual analysis guided the identification and classification of stoss and lee side surfaces and are therefore presented here and not result section.

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4.4.1 Slope To distinguish the steep lee sides of the roches moutonnées, a slope raster was calculated from the raster DEM (see figure 10). The ArcMap tool Slope was utilized for this purpose. The Slope tool assigns each DEM cell a value based on the maximum change in elevation from that cell to its neighbor cells. Low slope values indicate surfaces; high slope values indicate steep surfaces.

Figure 10. Five surveyed roches moutonnées (SRM), displayed on top of a slope raster, calculated using the national elevation data provided by the Swedish mapping authority. The arrows mark some of the cell clusters with high slope, possibly the lee sides of roches moutonnées, which can be seen to the southwest of the surveyed locations.

Elongated cell clusters with high degrees of slope were observed southwest of most surveyed locations. These clusters were often oriented along a northwest southeast axis, possibly corresponding to the steep lee sides of the roches moutonnées (see figure 10).

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4.4.2 Curvature To distinguish the smooth convex stoss sides of the roches moutonnées, a curvature raster was calculated from the raster DEM (see figure 11). The ArcMap tool Curvature was utilized for this purpose. The Curvature tool assigns each DEM cell a value based on relation between that cells elevation and the elevation of its neighboring cells. Negative curvature values indicate concave surfaces; positive curvature values indicate convex surfaces and curvature values of zero indicate flat surfaces.

Figure11. Five surveyed roches moutonnées (SRM), displayed on top of a curvature raster, calculated using the national elevation data provided by the Swed ish mapping authority. Some of the cell clusters with positive curvature, possibly roches moutonnées, are encircled.

Cell clusters with positive curvature were observed in close proximity to most surveyed locations. These clusters possibly correspond to the smooth convex stoss sides of the roches moutonnées. Elongated cell clusters with negative curvature were observed southwest of most surveyed locations. These clusters possibly correspond to the steepened lee sides of the roches moutonnées (see figure 11).

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4.4.3 Aspect To distinguish the stoss sides from other convex surfaces, and the lee sides from other steep slopes, an aspect raster was calculated from the raster DEM (see figure 12). The ArcMap tool Aspect was utilized for this purpose. The Aspect tool assigns each cell a value based on the compass direction that the surface is facing at that location.

Figure 12. Five surveyed roches moutonnées (SRM), displayed on top of an aspect raster, calculated using the national elevation data provided by the Swedish mapping authority. Some of the aspect patterns suggestive of mounds, possibly roches moutonnées, are encircled.

Aspect patterns suggestive of small mounds were observed in close proximity to most surveyed locations. The surveyed locations were for the most part closest the northeastern face of these mounds (see figure 12), which possibly correspond to the stoss sides of the roches moutonnées.

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4.4.4 Contour lines To define the boundary of individual roches moutonnées, contour lines with an interval of 0.25 meter were calculated from the raster DEM (see figure 13). The ArcMap tool Contour was utilized for this purpose. The Contour tool generates isolines connecting points with equal elevation. Closed contour lines denote either peaks or depressions. The presence of roches moutonnées, which are small mounds, should result in small closed contour lines.

Figure 13. Five surveyed roches moutonnées (SRM), displayed together with contour lines with an interval of 0.25 meters, calculated using the natio nal elevation data provided by the Swedish mapping authority. The arrows mark some of the steep slopes, possibly the lee sides of roches moutonnées, which can be seen to the southwest of the surveyed locations.

Small closed contour lines were observed in close proximity to most surveyed locations. The average length of the closed contour lines was around 50 meters. The distance between the contour lines was in some cases narrow, a pattern suggestive of steep slopes, on the southwestward facing side of these mounds (see figure 13). These steeper slopes possibly correspond to the steepened lee sides of the roches moutonnées.

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4.5 Classification Predicted Roches Moutonnées

4.5.1 Stoss and Lee Sides The visual analysis of the DEM derivatives revealed the distinctive properties of stoss and lee side surfaces (see table 2). The next step in the process was to delimit surfaces with these properties within the study area.

Table 2. Classification scheme of stoss and lee side surfaces.

Classification Aspect Slope Curvature Stoss side 0° – 90° (North to East) - Positive (> 0) Lee side 180° – 270° (South to West) > 25° -

All raster DEM derivatives were reclassified using the ArcMap tool Reclassify (see table 3).

Table 3. Reclassification scheme of (A) the slope raster, (B) the curvature raster, and (C) the aspect raster. The process was done with the ArcMap tool Reclassify.

A. Reclassification of the slope raster Old values New values 0° – 25° No Data 25° – 84.5° 1 B. Reclassification of the curvature raster Old values New values - 4.15 – 0 No Data 0 – 4.21 1 C. Reclassification of the aspect raster Old values New values 0° – 90° 1 90° – 180° No Data 180° – 270° 2 270° – 360° No Data

All raster DEM derivatives were converted from raster to polygon features using the ArcMap tool Raster to Polygon. The results of this conversion will hereafter be referred to as the slope polygon features, the curvature polygon features and the aspect polygon features respectively.

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In order to delimit the stoss sides of the roches moutonnées, surfaces with a northeasterly aspect and a positive curvature were traced using the ArcMap tool Intersect. The Intersect tool computes the geometric intersection of two input features, in this case the curvature polygon features and the aspect polygon features (0° – 90°). Where the two input features overlap a new polygon feature is generated. This new polygon features represents surfaces with a northeasterly aspect and a positive curvature and should thus signify the stoss sides of roches moutonnées. These polygon features will hereafter be referred to as the stoss side polygon features (see figure 14).

In order to delimit the lee sides of the roches moutonnées, surfaces with a southwesterly aspect and a slope over 25° were traced, again using the Intersect tool. For this operation the input features were the slope polygon features and the aspect polygon features (180° – 270°). The resulting polygon features represents surfaces with a southwesterly aspect and a slope over 25° and should thus signify the lee sides of roches moutonnées. These polygon features will hereafter be referred to as the lee side polygon features.

Figure 14. Five surveyed roches moutonnées (SRM) together with surfaces classified as stoss sides and lee sides. Some of the possible roches moutonnées are encircled.

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The lee side polygon features and the stoss side polygon features were both observed in close proximity to most surveyed locations. The spatial relation between stoss and lee side pairs was also consistent with the field observations, with lee sides located southwest of stoss sides.

4.5.2 Delimitation of Predicted Roches Moutonnées The boundary of individual roches moutonnées were defined as closed contour lines with a length equal or less than 50 meters. These closed curvature lines were converted to polygon features using the ArcMap tool Line to Polygon. These polygon features will hereafter be referred to as the boundary polygon features.

To distinguish the roches moutonnées from other mounds, the ArcMap tool Select Layer by Location was utilized in order to select boundary polygon features that contained stoss side polygon features and lee side polygon features. These selected polygon features represents small mounds containing surfaces with a northeasterly aspect and a positive curvature and surfaces with a southwesterly aspect and a slope over 25° and should thus denote roches moutonnées. These polygon features will hereafter be referred to as the predicted roches moutonnées or PRMs. In order to enable import to a GPS receiver, the PRMs were converted to point features using the ArcMap tool Feature to Point.

4.6 Validation of Predicted Roches Moutonnées During a field campaign on April 23 a test validation was conducted. The coordinate locations of a selected sample of 20 PRMs were imported to a GPS receiver and visited in the field.

To ensure the objectivity of the validation the coordinate locations of a random sample of 25 PRMs were imported to a GPS receiver and visited in the field during two subsequent field campaigns on May 4 and 6. The PRMs were noted as correct or incorrect depending on if an actual roche moutonnée was found at their location or not.

An analysis of the incorrect PRMs revealed that most of the errors were caused by boundary polygon features that represented depressions and not peaks. To correct these errors the ArcMap tool Zonal Statistics as Table was used to exclude the boundary polygon features that represented depressions. The Zonal Statistics as Table tool summarizes the values of a raster feature within the zones of another dataset. The boundary polygon features were input as the zonal features and the curvature raster was input as the value raster. The statistical operation calculated the mean 21 curvature value within each boundary polygon feature. Boundary polygon features that had a negative mean curvature were excluded from the analysis.

The final validation was conducted during a field campaign on May 14. The validation was performed within a 200 square meter subsection of the study area with a high density of PRMs. The coordinate locations of all PRMs within the subsection of the study area was imported to a GPS receiver and visited in the field. The PRMs were noted as correct or incorrect depending on if an actual roche moutonnée was found at their location or not (see figure 15).

Figure 15. The author standing on a correct Predicted Roche Moutonnée. Rosenberg, R (Photographer).

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5. Results In the first part of the result section the results from the GIS analysis of the two different elevation models are presented. In the second part of the result section the results of the validation process are presented.

5.1 Results of the GIS Analysis

5.1.1 Results from the National Elevation Model The GIS analysis (see figure 7) of the national elevation data provided by the Swedish mapping authority resulted in 996 PRMs (see figure 16) within the study area. PRMs are detected in the entire study area and most PRMs are distributed along rock ridges, consistent with the field observations of actual roches moutonnées. There is a high density of PRMs along the coast line (se figure 17) and a low density in the central part of the study area.

Figure 16. The 996 Predicted Roches Moutonnées (PRMs) resulting from the GIS analysis of the national elevation data provided by the Swedish mapping authority.

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Figure 17. Density of Predicted Roches Moutonnées (PRMs) resulting from the GIS analysis of the national elevation data provided by the Swedish mapping authority. The image was generated using the ArcMap tool Point Density.

5.1.2 Results from the Local Elevation Model The GIS analysis of the local elevation data provided by the City of Gothenburg resulted in 1312 PRMs (see figure 18) within the study area. PRMs are detected in the entire study area and their distribution along ridges is consistent with the field observations of actual roches moutonnées, the pattern is even stronger because of the additional PRMs. There is a high density of PRMs along the coast line (se figure 19) and a low density in the central part of the study area.

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Figure 18. The 1312 Predicted Roches Moutonnées (PRMs) resulting from the GIS analysis of the local elevation data provided by the City of Gothenburg.

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Figure 19. Density of Predicted Roches Moutonnées (PRMs) resulting from the GIS analysis of the local elevation data provided by the City of Gothenburg. The image was generated using the ArcMap tool Point Density.

5.1.3 Comparison of the Results There is a similarity in the spatial distribution of PRMs detected in the national and local elevation model respectively. However, the amount of PRMs detected in each elevation model differs considerably (see table 4). 1312 PRMs were detected in the local elevation model; this is 316 or 31% more PRMs than the 996 PRMs detected in the national elevation model. 74% of the PRMs detected in the national elevation model were also detected in the local elevation model (see figure 20), but only 56.5% of the PRMs detected in the local elevation model were also detected in the national elevation model. Some PRMs were only detected in one of the elevation models (see figure 20).

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Table 4. Statistics on the Predicted Roches Moutonnées resulting from the analysis of the national elevation data provided by the Swedish mapping authority and the local elevation data provided by the City of Gothenburg.

Number of PRMs detected in the national elevation model 996 Number of PRMs only detected in the national elevation model 258 Number of PRMs detected in the local elevation model 1312 Number of PRMs only detected in the local elevation model 574 Number of PRMs detected in the national and the local elevation model 738 Total number of PRMs detected in the national or the local elevation model 1570

Figure 20. All 1570 PRMs resulting from the GIS analysis of both elevation models. Yellow polygons denote PRMs only detected in the national elevation data provided by the Swedish mapping authority. Blue polygons denote PRMs only detected in local elevation data provided by the City of Gothenburg. Red polygons denote PRMs detected both in the national and the local elevation data.

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5.2 Validation of Predicted Roches Moutonnées in the Field 80 of the 82 PRMs validated in the subsection of the study area were correct (see figure 21). That is, the PRMs that were detected using method on the national elevation model corresponded to an actual roche moutonnée in the field. This means that the method detects roches moutonnées in the national elevation model with an accuracy of 97.6%. All of the correct PRMs in the subsection of the study area were also detected in the local elevation model together with an additional 48 PRMs which were not validated.

Figure 21. 82 Predicted Roches Moutonnées (PRM) resulting from the GIS analysis of the natio nal elevation data provided by the Swedish mapping authority, validated during a field campaign on May 14. Green crosshairs denote correct Predicted Roches Moutonnées, which were actual roches moutonnées. Red crosshairs denote incorrect Predicted Roches Moutonnées, which were not actual roches moutonnées.

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6. Discussion

6.1 Results Discussion

6.1.1 Predicted Roches Moutonnées The spatial distribution of PRMs detected in the national elevation model is similar to spatial distribution of PRMs detected in the local elevation model. The number of PRMs detected in each elevation model is however quite different. Moreover, there were PRMs detected in each elevation model that were not detected in the other elevation model. Both the elevation models represent the same land surface, so how can the results be so different? There are numerous explanations to why the two elevation models detect different PRMs and different numbers of PRMs. The two elevation models are both produced using LiDAR elevation data, but the LiDAR data used to construct the national elevation model was acquired at a much higher flight level, than the LiDAR data used to construct the local elevation model,1700 meters above sea level, compared to 500 meters above sea level. Furthermore, the LiDAR data used to construct the local elevation model was acquired during the summer and the LiDAR data used to construct the national elevation model was acquired during the autumn. Consequently there could be more noise in the form of vegetation in the local elevation model. There is also a great difference in the spatial resolution of the two elevation models, and the local elevation model with a point density of 8 points per square meter, is probably more accurate than the national elevation model with a point density of 0.5 point per square meter. It is therefore not surprising that more PRMs were detected in the local elevation model. That 50 % of the PRMs that were detected in the local elevation model also were detected in the national elevation model is however surprising, considering that the spatial resolution of the local elevation model is 16 times higher than the spatial resolution of the national elevation model.

Considering all this, what is the true number of roches moutonnées in the study area? There are two ways to look at the difference in the number of PRMs detected in each elevation model. The two elevation models could be supplementing each other, meaning that the true number of roches moutonnées in the study area is close to the total 1500 PRMs detected in the national or the local elevation model. Or the elevation models could confirm each other, meaning that the true number of roches moutonnées in the study area is close to the 700 PRMs detected

29 both in the national and in the local elevation model. To find out the true number of roches moutonnées in the study area a comprehensive field observation would have to be conducted.

6.1.2 Validation The objectivity of the validation process is questionable, bearing in mind the internal verity of the landform. To identify the exact morphology of a landform while in the field is exceedingly difficult, even for experienced geomorphologists. The process of validating PRMs is therefore prone to bias. Another issue with the validation process is the selection of the PRMs for the final validation. Due to time constraints, this validation was only done in a subsection of the study area and may not be statistically representative for the entire area. Because of this, the 97.6% accuracy of the method probably does not apply on the 996 PRMs detected in the entire study area; there are without a doubt more incorrect PRMs among them.

6.2 Method Discussion

6.2.1 Input Data The method depends on data from external sources; the accuracy of this data therefore controls the accuracy of the results. The LAS data provided by the Swedish mapping authority was originally intended for the production of a 2 meter resolution raster DEM (Lantmäteriet, 2014). In this study the same data was used to produce a 1 meter resolution raster DEM. The concern here is the low point density. This may in some cases been counterbalanced where flight lines overlapped resulting in higher point density. Another concern is that the LAS data was interpolated in order to generate the raster DEM. This means that the elevation values of some cells in the raster DEM is not based on surveyed elevation values, but rather on interpolated elevation values which may be far from correct.

The point classification made by the data providers is not perfect and there could be artifacts in the data, such dense vegetation incorrectly classified as ground (Lantmäteriet, 2014). These artifacts could mistakenly be classified as roches moutonnées.

6.2.2 Classification criteria The classification of the PRMs was based on a few representative roches moutonnées, and probably does not encompass every single variant of the landform. There is a risk for errors of

30 omission since the method does not detect all roches moutonnées but only the ones that conform to the classification. Consequently there is most probably more roches moutonnées in the study area than the method predicted. Errors of commission are also a concern. In its current version the method does not take into account the geology of the surface. The classification of PRMs is strictly based on form and orientation and does not differentiate between soil or rock formations for example. Consequently earthen mounds or anthropogenic landforms resulting from earthwork connected to road or house construction are incorrectly classified as roches moutonnées. These commission errors could be avoided if a high resolution geologic map is incorporated in the method. Another solution to this problem could be to take advantage of the intensity value of the LiDAR return signals to distinguish hard rock surfaces from softer vegetation surfaces. The process of delimiting individual PRMs is also problematic. Roches moutonnées are not clearly-bounded landforms and there is often a question of where one begins and another one ends, especially in areas where they occur in large complexes.

The greatest source of potential errors in the study is the GPS receiver used to mark and find locations in the field. The accuracy of this GPS receiver was ±3 meters. This causes problems both concerning the visual analysis of the SRMs and the validation of the PRMs. This problem could have been avoided if a more accurate differential GPS receiver was used instead.

6.3 Further studies The model could be developed to extract more information from individual roches moutonnées than their location and area. The strike and dip of each landform could be automatically calculated and added to an attribute table. The vector dataset from the Geological Survey of Sweden containing the location and orientation of glacial striations could be further incorporated into the method. The vector data could be interpolated and converted to raster format, creating a kind of ‘glacial direction surface’. This raster could be used to automatically define which aspect the stoss side surfaces should have in a particular area. If it is further developed, the method could be used to map the frequency of roches moutonnées in all of Sweden. The search window of the method could also be modified to detect differences in the spatial orientation of the landforms, indicating regional differences in the direction and influence of the glacial ice flow.

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7. Conclusion It is possible, using the method developed during this study, to detect roches moutonnées in the national elevation model provided by the Swedish mapping authority. The method detected 996 roches moutonnées within the study area of two square kilometers. A validation of 82 predicted roches moutonnées confirmed that the method was had an accuracy of 97.6%. The roches moutonnées that the method detected in the national elevation model corresponded to actual roches moutonnées located in the study area. The comparison of the results from the local elevation model provided by the City of Gothenburg revealed that the method detected the same roches moutonnées in the two different elevation models. The method detected 31% more roches moutonnées in the local elevation model than in the national elevation model. This is to be expected considering the difference in resolution between the two elevation models, and 31% more is not enough to renounce further studies of roches moutonnées using the national elevation model, bearing in mind its geographical coverage. Nevertheless, the method needs to be developed further, the influence of the lithology and structure of the bedrock on the shape of the roches moutonnées needs to be considered. The strength of the method is its simplicity and that it does not require large amounts of data. If it is further developed, the method could be used to map the variations in the frequency of roches moutonnées on a local, regional and national scale, potentially reshaping our understanding of glacial dynamics during the last .

Acknowledgements I would like to express my great appreciation to Assoc. Prof. Olvmo and PhD. Lindberg, my project supervisors, for answering all my questions concerning geomorphology and geographical information systems, and for assisting me during the first field campaigns. I would also like to thank Assoc. Prof. Thorson for her patient reading and helpful feedback during the project seminars. My thanks are also extended to PhD Student Islam for his instructions on using the differential GPS, to Mr. Rosenberg for assisting me with the photo documentation and to Mr. Penttinen for his assistance during the final field campaign. Finally, I wish to express my deep gratitude to Ms. Rosenberg for assisting me during the last field campaigns and for putting up with all my anxiety during this project.

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