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Colombia Venezuela Table 1. Optimum images of the glaciers of Venezuela and Colombia Glacier Series Date Sensor Type Path Row Ruiz-Tolima Massif Sierra Nevada de Santa Marta Sierra Nevada del Cocuy Pico Bonpland Massif (Siniguis) 01/01/1959 * Aerial — — 02/01/1976 * Landsat 1 9 57 10/24/1997 Landsat 5 9 57 01/28/2001 Landsat 7 9 57 01/01/1957 * Aerial — — 01/01/1973 * Landsat 1 8 53 12/16/1984 Landsat 5 8 53 01/18/1991 Landsat 4 8 53 12/20/2000 ASTER 8 53 01/14/2004 ASTER 8 53 01/01/1959 * Aerial — — 01/18/1973 * Landsat 1 7 56 01/13/1986 Landsat 5 7 56 12/23/1992 Landsat 4 7 56 05/25/1999 Landsat 5 7 56 01/14/2001 ASTER 7 56 01/30/2001 ASTER 7 56 03/06/2002 ASTER 7 56 03/02/2003 ASTER 7 56 01/01/1952 * Aerial — — 03/24/1985 Landsat 5 6 54 01/20/1988 Landsat 4 6 54 01/29/2000 Landsat 5 6 54 02/11/2002 ASTER 6 54 03/02/2003 ASTER 6 54 02/01/2004 ASTER 6 54 * Measurements taken from Hoyos-Patiño (1998) and Schubert (1998)I Image Preprocessing For ASTER scenes, the six SWIR bands were spatially oversampled to 15 meter resolution to match the VNIR bands using nearest neighbor resampling. For the Landsat images, all bands were oversampled from 28.5 meter to 15 meter resolution. Resizing all images to 15 meters allowed for better comparisons of snow and ice classification between the ASTER and Landsat platforms and facilitated visual interpretation of glacier area and comparisons of the classified glacier extent between platforms. The images for each glacier region were coregistered using ground control points (GCPs) spaced throughout each scene. A minimum of 9 GCPs were used with all images. Images for each glacier area were coregistered to a master image (Table 2) selected for each glacier series. All images were projected into Universal Transverse Mercator, Zone 19N with a WGS-84 datum. Once the images in a glacial series were coregistered, they were overlaid to visualize glacial retreat over time for each area. 184
Colombia Venezuela Glacier Series Ruiz-Tolima Massif Sierra Nevada de Santa Marta Sierra Nevada del Cocuy Pico Bonpland Massif (Sinigüis) Table 2. Master Images for Image Coregistration Date 185 Sensor Type Landsat WRS Path Landsat WRS Row 01/28/2001 Landsat 7 9 57 01/14/2004 ASTER 8 53 03/06/2002 ASTER 7 56 02/11/2002 ASTER 6 54 Snow and Ice Classification Many image processing techniques could have been used in this study including manual digitization methods, band ratios and normalized difference ratios. Because this study focuses on digital area assessments, manual techniques were not considered. Further analysis of the band ratio method and normalized difference method were completed before selecting the best technique to use for all satellite images. The Normalized Difference Snow Index (NDSI) and Band Math Ratio were both considered and computed for all ASTER images for comparison. The NDSI (equation 1) is a commonly employed snow detection method that helps to differentiate between cloud cover and snow/ice (Hall et al. 1995). The Band Math Ratio (equation 2) is a simple ratio between two bands, a and b ( NIR − Red) ( NIR + Red) NDSI = (1) Band a Band Math Ratio = (2) Band b Both methods were analyzed for accuracy using several ASTER band combinations: one and five, two and five, and three and five. After visually assessing both methods and all band combinations, the NDSI method using ASTER band two (0.63–0.69 µm) and five (2.145–2.185 µm) was selected as best for determining glacial area for these study areas. The NDSI method was then applied to the Landsat scenes using the Landsat band combination, three (0.63–0.69 µm) and five (1.55–1.75 µm), that best approximated the ASTER bands. To determine the glacial area in each image using the NDSI ratio, a density slice was performed to classify pixels containing snow and ice. For both ASTER and Landsat, pixels with NDSI values ranging between 0.4 and 1.0 were classified as snow and ice. To compute the total glaciated area for each area, a region of interest (ROI) was drawn around glaciated area to determine the number of pixels with NDSI values in the selected range. This ROI approach helped eliminate clouds and other features misclassified as snow/ice. In some instances, transient snow cover still may be misinterpreted as glacier.
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Proceedings of the 63rd ANNUAL EAST
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FOREWORD T his proceedings volume c
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CONTENTS Foreword..................
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STATEMENT OF PURPOSE The Eastern Sn
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EXECUTIVES FOR THE 63rd EASTERN SNO
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THE PRESIDENT’S PAGE The 63rd ann
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Weisnet Medal for Best Student Pape
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3 63 rd EASTERN SNOW CONFERENCE New
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Figure 1. Layer 1 represents the so
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Figure 4. The general layout of the
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lue color represented no convection
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Total Density of Water Profiles The
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Figure 11(a). Simulated snow grain
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Jordan R, 1991. A one-dimensional t
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Campbell Scientific Award for Best
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19 63 rd EASTERN SNOW CONFERENCE Ne
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R ∑i ∑ n 2 = 1 NS = − n i = 1
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Snow and Climate 37
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Spatial distributions of changes in
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Snow and Climate Posters 43
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Figure 2 presents correlation maps
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CONCLUSIONS Three regional-continen
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Discharge (mm) 20 18 16 14 12 10 8
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ABSTRACT 55 63 rd EASTERN SNOW CONF
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This paper analyzes the synoptic pa
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Cyclones that track west of the App
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The synoptic-scale atmospheric circ
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CONCLUSIONS The record snowfall in
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correlations between April-May snow
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Figure 3. Linear correlations betwe
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winter/early spring could contribut
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Snow Remote Sensing 73
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height averaged 11.4 m with an aver
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strings were buried 20 - 30 cm belo
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Although the λE/Rn fraction was on
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estimated snow depth based on the 2
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important to note that the average
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Schmidt, R. A., C. A. Troendle, and
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METHODS The IMS Product The IMS was
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Figure 1. Microwave spectral charac
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snow depth and SWE on a weekly basi
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Figure 4. Example of blended SWE pr
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Evaluation of the global distributi
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Figure 9. Inter-comparison plots of
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Helfrich, S.R., D. McNamara, B.H.Ra
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105 63 rd EASTERN SNOW CONFERENCE N
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and validated regionally so they ca
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109 Test Site Figure 2. Variation o
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Correlation Coe. Correlation Coe. C
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Figure 8. SSM/I scattering signatur
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Considering the facts mentioned abo
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Besides the temporal validation, th
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RMSE Non linear Azar Chang Goodison
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63 nd EASTERN SNOW CONFERENCE Newar
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http://www.ccin.ca); from simulatio
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over the period 1988-2000. With the
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Figure 3: Explained variance (R 2 )
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correspondence between simulated an
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values. Continued development of ne
Page 145 and 146: National Climatic Data Center (2005
Page 147 and 148: Snow Remote Sensing Posters 135
Page 149 and 150: ABSTRACT 63 rd EASTERN SNOW CONFERE
Page 151 and 152: day and has a mean pixel resolution
Page 153 and 154: Table 1. Wheaton River basin EASE-G
Page 155 and 156: The SSM/I snowmelt onset algorithm
Page 157 and 158: coarse-resolution pixel with the hi
Page 159 and 160: Figure 5. (a) Scatterplot of snowpa
Page 161 and 162: For both 2004 and 2005, the AMSR-E
Page 163 and 164: threshold of ±18 K that reflects t
Page 165 and 166: ABSTRACT 153 63 rd EASTERN SNOW CON
Page 167 and 168: DATA USED SSM/I & QuikSCAT Coverage
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Page 179 and 180: independent of regions. This greatl
Page 181 and 182: slightly vary the IMS with this pro
Page 183 and 184: imagery, the higher latitudes rely
Page 185 and 186: MODIS Land Science Team, NASA Godda
Page 187 and 188: FUTURE OF IMS Enhancements to the I
Page 189 and 190: information never present before on
Page 191 and 192: Brubaker, K.L., R. Pinker and E. De
Page 195: The Sierra Nevada del Cocuy region
Page 199 and 200: Table 4. Glacier Areas of the Ruiz-
Page 201 and 202: Pico Bonpland Massif-Sinigüis Glac
Page 203 and 204: Linder W, Jordan E, 1991. Ice-mass
Page 205 and 206: Snowpack Processes 193
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Page 209 and 210: In the following paragraph the main
Page 211 and 212: Figure 1: Variation of the season a
Page 213 and 214: Figure 3: 8-day composite maps (24
Page 215 and 216: The third pair (Figure 5) represent
Page 217 and 218: Figure 9: Time evolution of SWE ave
Page 219 and 220: melting occurs, whereas at the high
Page 221 and 222: Colbeck SC, Anderson EA. 1982. The
Page 225 and 226: A B Figure 1. Low-magnification sec
Page 227 and 228: GB ridge GB groove Figure 3. Higher
Page 229 and 230: CONCLUSION Figure 5. Indexed electr
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Page 233 and 234: Meteorological data for the winter
Page 235 and 236: monthly probability adjusted data 4
Page 237 and 238: 225 Pullman WA Rawlins WY Leadville
Page 239 and 240: The daily wind speed can exceed 6.5
Page 241 and 242: NCDC. 2006. National Climate Data C
Page 245 and 246: and mass balances for 540 environme
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Temperature, precipitation, windspe
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the left in response to snow compac
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Pinched-cone dimensions for each sl
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Terrain slope (degrees) Terrain slo
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ACKNOWLEDGEMENTS This work was fund
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Glacial and Periglacial Processes 2
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From linear regression the constant
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DATA COLLECTION Figure 1. Location
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Table 1. Comparison of GPS measured
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Transverse Velocity Profiles Surfac
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is shown in Figure 4. This variatio
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Table 4. The calculated volume flux
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Figure 1. Cordillera Blanca regiona
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MATERIALS AND METHODS Field Measure
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Data analysis techniques We synthes
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season, but unacceptable for the dr
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significant, although more informat
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(a) Liquid Water (mm) Dry Period: M
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Future Work We are installing addit
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Shuttleworth, W.J., and J.S. Wallac
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Snow and Periglacial Processes Post
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No one to date has shown why snowfl
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APPENDIX A Northern Hemisphere Year
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62nd ESC Papers 291
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62 nd EASTERN SNOW CONFERENCE Water
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each other; and, they both decrease
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297 62 nd EASTERN SNOW CONFERENCE W
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comes in the form of precipitation.
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averaged to reduce bias in this var
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RESULTS Figure 2. Flow chart of reg
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Table 6: Actual-Conditions SWE, dep
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307 Table 9: Regression results bet
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Goita, K., A. Walker, B. Goodison,
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TO ERR IS HUMAN, TO FORGET IT WOULD
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