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High-resolution monitoring of Himalayan glacier dynamics using unmanned aerial vehicles

Steven M. De Jong, Walter Immerzeel, Philip Kraaijenbrink, Marc Bierkens Geosciences Utrecht University NL

F. Pellicciotti (ETH-Zurich), Joe Shea (ICIMOD)

ITC, 6 March 2014

Himalayan Mountains: water towers of Asia • Rivers fed from Tibetan plateau & adjacent mountain ranges

• 1.6 billion people depend on the water resources of the rivers:

Ganges, Yangtze, Brahmaputra, Indus, etc

• Snow & glacial melt are important, so what does climate change? Normalized Melt Index:

Importance of glacial/snow melt runoff generated in the upstream part of the basin divided by runoff generated downstream. Indus: 60% melt runoff Ganges: 9% melt runoff

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Overall project objectives: • Understanding distribution of P in mountain areas • Understanding response of glaciers and snow to climate change • Quantifying the contribution of melt water to stream flow in

time and space Catchment scale field observations (Langtang & Lirung): • Advanced precipitation measurements (high altitude (4500m+),

snow & rain) • Discharge measurements • Glacier dynamics (pristine & debris covered glaciers) using

UAVs Ice mass balances and runoff modelling

Precipitation regimes

Source: Bookhagen and Burbank, JGR, 2010

• Monsoon driven • Very large spatial and temporal variability

Cherrapunji, Megalaya hills: ~ 12 m annual precipitation

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Recent glacier changes

Source: Gardelle et al, 2013, TC

Recent glacier changes

Source: Gardelle et al, 2013, TC

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Objective of this sub-project: Monitoring a debris covered glacier toe (Lirung) using an Unmanned Aerial Survey System (UASS)

Lirung toe Debris Covered & terminal lake

Why work with UAVs? • Provides very (ultra) high resolution imagery, pixels in centimetres • Provides very precise XYZ-data allowing quantitative mapping • Various payloads: VI, CIR, Thermal, LIDAR, Hyperspectral • Flexible, cost effective, self-controlled, fit in a regular car • Own priority settings compared to space/airborne missions • Bridge the gap between terrestrial - space - airborne data collection

Use auto-pilot or contract a skilled pilot:

Henk Markies UU Darren Turner UTAS

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UAV platforms: Copters, helicopters, wings, planes

Aims of sub-project • Prove the potential of UAVs in glaciology

• Understand the dynamics of debris-covered Himalayan glaciers:

– Mass balance / downwasting of glaciers

– Flow velocity of glaciers

– To map ice cliffs, supra-glacial lakes and englacial conduits

• Improve current and future hydrological modelling efforts by integrating the mass balance results

<- englacial conduits

^Debris cover glacier

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Common glacier monitoring methods

Direct (on-site):

• Glaciological: stakes, snow pillows

• Hydrological: precipitation (rain, snow), glaicier discharge

Indirect (remote sensing):

• Anaysis of satellite-derived elevation models (altimetry, photogrammetry)

• Feature tracking on satellite imagery (using optical or SAR imagery)

Common glacier monitoring methods

Direct (on-site): • Often time consuming and expensive • Small spatial scale & tiny spatial support • High detail Indirect (remote sensing): • Relatively easy and affordable • Large spatial scale • Lower detail So, we study what we can do better using UAV imagery. High resolution, flexible, timeseries

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Flow velocity estimations by automated feature tracking on ERS/Envisat SAR imagery (~30 m/pixel)

Quincey et al., JoG, 2009

Langtang National Park

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Yala Plateau 4950m

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Methodology

• Pre and post monsoon surveys: May & Oct 2013

• Generation of high resolution DEMs and Ortho-mosaics using Structure from Motion algorithm

• Determination of down-wasting by DEM comparison

• Determination of surface deformation using manual feature tracking

The UASS (SenseFly Swinglet CAM)

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Acquisition of DGPS reference points

Defining the autopilot flight plan on-site

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Typical processing line for regular RGB or CIR photos …… Photogrammetric Structure from Motion (SfM) image processing line 1) Set of UAV collected RGB, CIR, TIR photos: sufficient overlap, quality, viewing angles 2) SIFT: scale invariant feature transform image objects, auto detection, matched between photos 3) Bundle Block adjustment camera positions with respect to matched features XYZ position of each feature -> Sparse 3D point cloud 4) Multi-view stereopsis densification of the point cloud into a 3D model 5) Ground Control Points or UAV camera GPS locations used for georeferencing to cartographic coordinate system

Photogrammetric SfM image processing line in pictures

MVS

Full size image stack Resized image stack Extract SIFT features

Bundle adjustment

derives camera

parameters

Remove radial distortion

from full size images

Densify point cloud via MVS

Match

SIFT

features

Bundler

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Processing the acquired images

Lateral moraine

Ice cliff & lake

Glacier terminus lake

Results: uncertainties

1. dGPS geodetic accuracy: ~20 cm in x, y and z

2. SfM processing technique

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Results: Mass loss and surface deformation First detailed reconstruction of a debris-covered glacier

Results: Ice cliffs and supra-glacial ponds

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Results: Ice cliffs and supra-glacial ponds

Conclusions • UAVs have high potential and may revolutionize

classical methods of glacier monitoring

• Average mass loss on the Lirung glacier is limited, but spatial variation is high

• High variability and mass loss near ice cliffs and supra-glacial ponds

• Englacial conduits may play large role in facilitating the mass loss of debris-covered glaciers

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Thanks for your attention