Assessing 50 years of tropical Peruvian glacier volume change from multitemporal digital elevation models
Kyung In Huh1,3, Bryan G. Mark1, Chris Hopkinson2, Yushin Ahn3
1Department of Geography, The Ohio State University, 1036 Derby Hall, 154 North Oval Mall, Columbus, Ohio, 43210, U.S.A. (firstname.lastname@example.org, email@example.com)
2Department of Geography, University of Lethbridge, 4401 University Drive West, Alberta T1K 3M4, Canada (firstname.lastname@example.org)
3School of Technology, Michigan Technological University, 1400 Townsend Drive, Houghton, Michigan, 49931, U.S.A. (email@example.com)
Assessing 50 years of tropical Peruvian glacier volume change
from multitemporal digital elevation models (DEMs)
Figure 1. The Coverage of 1962 Aerial photos and 2008 LiDAR flights over Cordillera Blanca, Peru.
Background is ASTER mosaic image. Yellow boxes show the LiDAR data coverage and Red boxes show 4
glaciers. 1962 aerial photo coverage is also shown as Black boxes. Green boxes show GPS base stations
for the LiDAR flight in 2008.
Figure 2. The selection of Non-glacierized terrain (within overlap of LiDAR DEM and aerial photo DEM) for
the comparison of DEMs in Site 2 (near Uruashraju).
Although far smaller than large polar ice caps, mountain glaciers are significant contributors
to sea level rise and tropical glaciers in particular are sources of critical water resources to
regional societies. Monitoring tropical glaciers using remotely sensed data with time-lapse
analysis has provided important variability information of tropical glacier recession. The
motivation of this study is to quantify glacier volume change on four target glaciers in the
Cordillera Blanca, Peruvian Andes, for almost 50 years by intercomparison of the surface
elevation DEMs based on aerial photo remotely sensed image data: stereo-paired aerial
photo photography and airborne LiDAR. We characterize the limitations inherent in
processing historic aerial photo photography with different viewing geometries over highly
rugged terrain relief and uncertainties in the processing stage as well as DEM comparison
by analyzing DEMs over non-glacierized terrain.
Results and Discussion
LiDAR Strip by Strip Comparison
To evaluate the LiDAR DEM data quality, we checked vertical discrepancies between
overlapping strips of flight line elevations, all flight lines were converted to a triangulated
surface, and then overlapping flight line data common to both flight lines were extracted as
points. These points were compared to the surface of the overlapping adjacent flight line using
a residual analysis (Table 1).
Data and Method
High resolution airborne LiDAR (Light Detection and Range) data acquired during July 2-
16, 2008 (Red boxes in Figure 1) and DEMs (Digital Elevation Models) from stereo-paired
aerial photographs on a scale of 1:60,000 taken on June 17,1962 (Black boxes in Figure 1)
can reveal both current glacial surface topography and glacial profiles spanning almost 50
years, enabling calculation of the surface area changes and the total volume loss.
However, the accuracies of data from LiDAR, aerial photo, and ASTER can vary at different
sites. To calculate a reasonable accuracy level for the measured glacier volume changes
based on different DEMs from various different aerial sensors, we first compared the
surface elevations in non-glacierized regions of the airborne LiDAR DEM and the aerial
photo derived DEM.
Delineation of non-glacierized terrain
To assess the consistency of the three different DEMs (LiDAR, aerial photo and ASTER),
the surface elevation profiles of non-glacier terrains in the study area were compared. The
non-glacierized terrains were defined as areas without continuous glacier ice and fresh
snow that were included within the spatial extent of all three DEMs for each study area.
This method for the definition of non-glacierized terrains was adapted and formulated from
the Global Land Ice Measurements from Space (GLIMS) application (Racoviteanu and
others, 2009). The non-glacierized terrains for the comparison of all three DEMs were
delineated by manually digitizing using ASTER false-color imagery of 2007- 2008 taken
during the dry season (April to October) with ArcGIS software (Figure 2).
Aerial photo DEM vs. LiDAR DEM over non-glacierized terrain
(June 27, 2004)
Ames, A., G. Muñoz, J. Verástegui, R. Vigil, M. Zamora and M. Zapata, 1989. Glacier Inventory of Peru. Part I.
Hidrandina S.A. Unit of Glaciology and Hydrology. Huaraz-Peru
Hastenrath, S. and A. Ames, 1995a. Recession of Yanamarey Glacier in Cordillera Blanca, Peru, during the
20th century. Journal of Glaciology, 41(137), 191-196.
Kaser, G., A. Ames, and M. Zamora, 1990. Glacier fluctuations and climate in the Cordillera Blanca, Peru,
Annals of Glaciology, 14, 136-140.
Mark, B.G., Seltzer, G.O. 2005. Evaluation of recent glacier recession in the Cordillera Blanca, Peru (AD 1962
-1999): spatial distribution of mass loss and climatic forcing. Quaternary Science Reviews 24, p2265-2280.
Racoviteanu, A.E., P. Frank, B. Raup, S. J. S. Khalsa, R. Armstrong, 2009. Challenges and recommendations
in mapping of glacier parameters from space: results of the 2008 Global Land Ice Measurements from Space
(GLIMS) workshop, Boulder, Colorado, USA. Annals of Glaciology 50(53), pp. 53 – 69.
Vullie, M., B. Francou, P. Wagnon, I. Juen, G. Kaser, B.G. Mark, R.S. Bradley, 2008. Climate change and
tropical Andean glaciers: Past, present and future. Earth-Science Reviews 89,79-96..
31. 2% Loss 38.9% Loss 85.7% Loss 56.3% Loss
Volume change of all glaciers (1962-2008)
Figure 3. Plots of Surface Area change of glaciers between 1962 and 2008 in the four glaciers (red boxes, Figure 1).
This project was funded from NASA New Investigator Program (NASA Grant #NNX06AF11G), National Geographic and The
Climate, Water and Carbon (CWC) Program from The Ohio State University.
Table 2. Statistics of Aerial photo DEM minus LiDAR DEM over non-glacierized terrains
(a) Site 1 (near Glacier #1 at Laguna Cuchillacocha) (b) Site 2 (near Glacier #2 at Uruashraju)
(c) Site 3 (near Glacier #3 at Yanamarey) (d) Site 4 (near Glacier #4 Queshque and Mururaju)
(a) Site 3
(b) Site 4
Table 1. Comparison of LiDAR flight lines within Site 3
(a) and Site 4 (b)
Surface Area Change of All glaciers
Figure 1 Figure 2
Each mean values from the comparison (Aerial photo DEM  minus LiDAR DEM ) were
applied for the adjustment to the calculation of volume change (Table 2, Red underlined).
Glacier1 Glacier2 Glacier3 Glacier4 (QueMain) Glacier4(QueEast) Glacier4(Muru)
Min ‐29.47 ‐25.12 ‐29.47 ‐19.53 ‐28.95 ‐28.95 ‐29.09 ‐23.95 ‐14.26 ‐19.90 ‐29.88 ‐24.74
Max 79.44 83.79 121.77 129.48 151.70 157.56 170.10 175.14 135.76 140.90 89.91 95.05
Mean 8.83 9.50 31.61 39.58 58.31 64.06 58.44 63.01 44.50 47.69 23.66 28.62
[km3] Glacier1 Glacier2 Glacier3 Glacier4 (QueMain)
Before 0.013 0.103 0.054 0.137 0.021 0.028
Adjustment 0.019 0.128 0.060 0.150 0.026 0.034
Surface Lowering (1962-2008)
Figure 4. Plots of Volume change of glaciers between 1962 and 2008.
Glacier #1 Glacier #2 Glacier #3
Glacier #4 (Main & East) Glacier #4 (Mururaju)
[km2] Glacier1 Glacier2 Glacier3
1962 1.38 3.52 1.16 3.53
2008 0.95 2.15 0.165 1.54
Glacier #1 Glacier #2 Glacier #3 Glacier #4
Site Lat Lon DGPS LiDAR
1 -9.415 -77.354 4682.40 4682.46 -0.06
3-(1) -9.660 -77.275 4704.60 4696.64 7.96
3-(2) -9.661 -77.274 4702.15 4702.08 0.07
3-(3) -9.674 -77.289 4350.30 4350.91 -0.61
4-(1) -9.798 -77.252 4774.69 4771.73 2.96
4-(2) -9.796 -77.289 4767.99 4766.95 1.10
Table 3. Statistics of DGPS points minus LiDAR DEM over
non-glacierized terrains (Left)
Table 4. Surface Area Change of All glaciers (Below)
Table 5. Statistics of Surface Lowering of all glaciers by comparing two DEMs (1962-2008)
Table 5. Volume change of all
glaciers with before and after
adjustment (adjustment values
from Table 2)