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A New Method for Deriving Glacier Centerlines Table 2 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | The Cryosphere Discuss., 7, 5189–5229, 2013 Open Access www.the-cryosphere-discuss.net/7/5189/2013/ The Cryosphere TCD doi:10.5194/tcd-7-5189-2013 Discussions © Author(s) 2013. CC Attribution 3.0 License. 7, 5189–5229, 2013 This discussion paper is/has been under review for the journal The Cryosphere (TC). A new method for Please refer to the corresponding final paper in TC if available. deriving glacier centerlines A new method for deriving glacier C. Kienholz et al. centerlines applied to glaciers in Alaska and northwest Canada Title Page Abstract Introduction C. Kienholz1, J. L. Rich1, A. A. Arendt1, and R. Hock1,2 Conclusions References 1Geophysical Institute, University of Alaska Fairbanks, USA 2Department of Earth Sciences, Uppsala University, Uppsala, Sweden Tables Figures Received: 6 October 2013 – Accepted: 16 October 2013 – Published: 25 October 2013 J I Correspondence to: C. Kienholz ([email protected]) Published by Copernicus Publications on behalf of the European Geosciences Union. J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion 5189 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Abstract TCD This study presents a new method to derive centerlines for the main branches and major tributaries of a set of glaciers, requiring glacier outlines and a digital elevation 7, 5189–5229, 2013 model (DEM) as input. The method relies on a “cost grid – least cost route approach” 5 that comprises three main steps. First, termini and heads are identified for every glacier. A new method for Second, centerlines are derived by calculating the least cost route on a previously es- deriving glacier tablished cost grid. Third, the centerlines are split into branches and a branch order centerlines is allocated. Application to 21 720 glaciers in Alaska and northwest Canada (Yukon, British Columbia) yields 41 860 centerlines. The algorithm performs robustly, requiring C. Kienholz et al. 10 no manual adjustments for 87.8 % of the glaciers. Manual adjustments are required pri- marily to correct the locations of glacier heads (5.5 % corrected) and termini (3.5 % cor- rected). With corrected heads and termini, only 1.4 % of the derived centerlines need Title Page edits. A comparison of the lengths from a hydrological approach to the lengths from Abstract Introduction our longest centerlines reveals considerable variation. Although the average length ra- 15 tio is close to unity, only ∼ 50 % of the 21 720 glaciers have the two lengths within Conclusions References 10 % of each other. A second comparison shows that our centerline lengths between Tables Figures lowest and highest glacier elevations compare well to our longest centerline lengths. For > 70 % of the 4350 glaciers with two or more branches, the two lengths are within 5 % of each other. Our final product can be used for calculating glacier length, conduct- J I 20 ing length change analyses, topological analyses, or flowline modeling. J I Back Close 1 Introduction Full Screen / Esc Glacier centerlines are a crucial input for many glaciological applications. For example, centerlines are important for determining glacier length changes over time (Leclercq Printer-friendly Version et al., 2012; Nuth et al., 2013), analyzing velocity fields (Heid and Kääb, 2012; Melko- Interactive Discussion 25 nian et al., 2013), estimating glacier volumes (Li et al., 2012; Linsbauer et al., 2012), and one-dimensional modeling of glaciers (Oerlemans, 1997a; Sugiyama et al., 2007). 5190 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Also, glacier length, derived from centerlines, is an important parameter for glacier in- ventories (Paul et al., 2009). TCD So far, most of the above applications have relied on labor-intensive manual digi- 7, 5189–5229, 2013 tization of centerlines. The few studies that derive centerlines fully automatically use 5 a hydrological approach and/or derive only one centerline per glacier (Schiefer et al., 2008; Le Bris and Paul, 2013). Here, we present a new algorithm that allows for deriving A new method for multiple centerlines per glacier based on a digital elevation model (DEM) and outlines deriving glacier of individual glaciers. Moreover, this algorithm splits the centerlines into branches and centerlines classifies them according to a geometry order. The approach is tested on all glaciers 2 C. Kienholz et al. 10 in Alaska and adjacent Canada with an area > 0.1 km , corresponding to 21 720 out of 26 950 glaciers. We carry out a quality analysis by visual inspection of the centerlines, and compare the derived lengths to the lengths obtained from alternative approaches. Title Page Abstract Introduction 2 Previous work Conclusions References The automatic derivation of glacier centerlines and thereof retrieved glacier length is 15 considered challenging (Paul et al., 2009; Le Bris and Paul, 2013). Consequently, only Tables Figures few automated approaches have been proposed so far (Schiefer et al., 2008; Le Bris and Paul, 2013) and often, centerlines have been digitized manually even for large- J I scale studies. In their large-scale study, Schiefer et al. (2008) applied an automated approach to J I 20 all British Columbia glaciers that is based on hydrology tools. For each glacier, this Back Close approach derives one line that represents the maximum flow path that water would take over the glacier surface. Schiefer et al. (2008) find that these lengths are 10–15 % Full Screen / Esc longer than distances measured along actual centerlines. Because lower glacier areas (∼ ablation areas) are typically convex in cross-section, their flow paths are deflected Printer-friendly Version 25 toward the glacier margins, which leads to length measurements that are systematically Interactive Discussion too long in these cases. While systematically biased lengths in a glacier inventory can be corrected for, the actual lines need major manual corrections before they can 5191 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | be used in glaciological applications such as flowline modeling. Therefore, Paul et al. (2009) suggest the use of the above automated algorithm in the concave (i.e., higher) TCD part of the glacier, combined with manual digitization in the convex (lower) part of the 7, 5189–5229, 2013 glacier. 5 Le Bris and Paul (2013) present an alternative method for calculating glacier cen- terlines based on a so-called “glacier axis” concept, which derives one centerline per A new method for glacier between the highest and the lowest glacier elevation. Le Bris and Paul (2013) deriving glacier first establish a line (the “axis”) between the highest and the lowest glacier point, which centerlines is then used to compute center points for the glacier branches. These center points are C. Kienholz et al. 10 connected, starting from the the highest glacier elevation and following certain rules (e.g., “always go downward”, “do not cross outlines”). Smoothing of the resulting curve leads to the final centerline. The algorithm is applicable to most glacier geometries, Title Page yielding results similar to manual approaches (Le Bris and Paul, 2013). A limitation of their approach is the fact that the derived centerline between the highest and lowest Abstract Introduction 15 glacier elevation does not necessarily represent the longest glacier centerline or the centerline of the main branch, either one of them often required in glaciological ap- Conclusions References plications. For example, for glacier inventories, it is recommended to measure glacier Tables Figures length along the longest centerline, or, alternatively, to average the lengths of all glacier branches (Paul et al., 2009). J I J I 20 3 Test site and data Back Close Our algorithm is tested on glaciers located in Alaska and adjacent Canada (Fig. 1a). For brevity, we hereafter refer to these glaciers as Alaska glaciers. From the complete Full Screen / Esc Alaska glacier inventory (Arendt et al., 2013), we extract all glaciers with a minimal area threshold of > 0.1 km2, thus eliminating small glacierets and possible perennial Printer-friendly Version 2 25 snowfields. This results in 21 720 glaciers with 86 400 km of ice total, which accounts Interactive Discussion for more than 99 % of the area of the complete Alaska inventory. 5192 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | The Alaska glacier inventory comprises glacier outlines derived from satellite im- agery taken between ∼ 2000 and 2012. The outlines used herein either stem from TCD manual digitization at the University of Alaska Fairbanks or from automated band ratio- 7, 5189–5229, 2013 ing followed by visual quality checks and manual corrections (Bolch et al., 2010; Le Bris 5 et al., 2011, Fig. 1a). Four DEM products are combined to create a continuous 60 m DEM consistent with A new method for the time-span covered by the glacier outlines (Fig. 1b). South of 60◦ N, we rely on the deriving glacier Shuttle Radar Topography Mission (SRTM) DEM (http://www2.jpl.nasa.gov/srtm, ac- centerlines cess: 25 July 2013), taken in February 2000 (Farr et al., 2007). Over Alaska, the SRTM C. Kienholz et al. 10 DEM has a native spatial resolution of 30 m, while the resolution is 90 m over Canada. North of 60◦ N, we use a high quality DEM derived from airborne Interferometric Syn- thetic Aperture Radar (IFSAR) data obtained in 2010 (Geographic Information Network Title Page of Alaska GINA, http://ifsar.gina.alaska.edu, access: 25 July 2013). For areas not cov- ered by the IFSAR DEM, we use DEMs derived from data from the High Resolution Abstract Introduction 15 Stereo (HRS) imaging instrument onboard the Système Pour l’Observation de la Terre (SPOT) satellite taken within the scope of the SPIRIT program (time span 2007–2008, Conclusions References Korona et al., 2009) and the Advanced Spaceborne Thermal Emission and Reflection Tables Figures Radiometer (ASTER) instrument onboard the Terra satellite (ASTER GDEM2, time span 2000–2011, Tachikawa et al., 2011, http://asterweb.jpl.nasa.gov/gdem, access: J I 20 25 July 2013).
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