Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view groundbased pictures and snow drills (East Loven glacier, Spitsbergen, Svalbard) Dominique LAFFLY1, Eric BERNARD2, Jean-Michel FRIEDT3 Gilles MARTIN3, Christelle MARLIN4, Madeleine GRISELIN2 1
Université de Toulouse, GEODE UMR 5602 CNRS, Toulouse, France Besançon, France 3 Université de Franche-Comté, FEMTO-ST UMR 6174 CNRS, Besançon, France 4 Université de Paris-Sud 11, IDES UMR 8148 CNRS , Orsay, France 2 Université de Franche-Comté, ThéMA UMR 6049 CNRS,
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Summary Introduction 1. Field localization 2. Flood event of september 2008 3. In situ image data collection 4. In situ image geometric correction 5. Snow and ice melt quantification Conclusion
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
In the frame of the Hydro-Sensor-FlOWS program funded by the French National Research Agency and the French Polar Institute (IPY 16), the East Loven glacier, located in Spitsbergen (78°N, 12°E, Norway) has been closely monitored during the last 4 years (2007 to 2010) in order to analyze at the basin scale (10 km2) and at various time scales (hour, day, month, year) the ice and snow mass-balance and their direct and indirect hydrological consequences. For obvious cost reasons as well as due to poor weather/cloudy conditions, daily satellite imaging coverage is not always accessible: eleven images are acquired each year but only two monthly FORMOSAT data sets have been selected as representative of general snow cover. Nevertheless, fast events appear as significant in the ice and snow budget while being ignored by satellite based studies since the slower sampling rate is unable to observe such fast events. In this project, satellite imagery is complemented with a series of ground based autonomous automated high resolution digital cameras. An example of the complementarity of database was presented about a flood period in september 2007.
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Field localization
La GOULE Corbel station
East Loven
Calcareous ridge
Spitsberg 10°
15°
80°
Limit of the LIA moraine
Brogger 78°
Le DIABLE
76°
74°
East Loven glacier
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Flood period at the end of 2008 Summer T°C in Ny Alesund
hydrological summer 2008
rain snow
P mm in Ny Alesund
129 mm
Total precipitation 129 mm Runoff in w.eq Goule
Runoff in w.eq / day
680 mm
Diable 174 mm
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Flood period at the end of 2008 Summer 1st week of September: cold, no water in rivers it seems that it is the beginning of winter
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Flood period at the end of 2008 Summer 2d week, sudden increase of T and 10% of the yearly precipitation
Due to the rain, snow and ice melt on the glacier (flood water in the Goule river) while the moraine absorbs the rain like a sponge (Diable river stays constant)
Goule Diable
The rain stops one day: immediate response of the Goule river. Then a new rain event gives again flood water in the Goule. The moraine is than saturated and the Diable begins to grow up.
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Flood period at the end of 2008 Summer The last week, T decreases and the rain stops. It snows. The fall in level is slow, sustained by the subglacial runoff.
BUDGET
76% of summer precipitation (100 mm) 40 % of the summer runoff (260 mm) Goule Diable
160 mm of melting of snow and ice
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Ablation vs Accumulation
ELA 09/05/08 250 m
ELA 09/25/08 350 m
Snow high and quality field measures
During that flood period, the equilibrium line (ELA) of the glacier growed up from 250m to 350m. D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
15th of august
30th of september
FORMOSAT specifications Products
B&W : 2 m Color: 2 m (pansharpened) MS (R, V, B, PIR) : 8 m
Spectral bands
P : 0,45 – 0,90 µm B1 : 0,45 – 0,52 µm (blue) B2 : 0,52 – 0,60 µm (green) B3 : 0,63 – 0,69 µm (red) B4 : 0,76 – 0,90 µm (NIR)
Coverage Repetiivity Angle
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
daily lateral &front-back: +/- 45°
Programmation
Yes
Image dynamics
8 bits/pixel
Image size (1A level)
Only two Formosat images are available around this flood event (August 15th and September 30th) ... showing the glacier totally cover of snow
24 km x 24 km
MS : 35 MB Pan : 137 MB
Six digital cameras are positioned around the glacier basin, providing complete glacier coverage D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
in situ acquisition – 3 images per day…
… weather conditions + electronics: only a fraction of the available data is usable ! D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
08/ 15/ 2008
09/ 03/ 2008
09 / 08/ 2008
09 / 10/ 2008
09 / 15/ 2008
09/20/ 2008
09/ 17/ 2008
09/30/ 2008
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
09/ 04/ 2008
09 / 13/ 2008
09/ 19/ 2008
10/03/ 2008
Oblique views provide a qualitative information on daily glacier evolution.
Geometrical orthorectification models are not appropriate due to the tangential views, especially since several images taken from different cameras must be combined
In order to be used on a map, these images must be projected. Classical calibration models fail due to several constraints. The Delaunay triangulation (rubber sheeting) model is adapted if enough reference points are available for a dense initial triangulation.
Hinkler, J. , Pedersen, S. B. , Rasch, M. and Hansen, B. U.(2002) 'Automatic snow cover monitoring at high temporal and spatial resolution, using images taken by a standard digital camera', International Journal of Remote Sensing, 23: 21, 4669 — 4682
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Delaunay Triangular Irregular Networks (TIN), network of contiguous triangles defined so that no vertex lies within the interior of any of the circumcircles of the triangles in the network
X2,Y2
X1,Y1
X3,Y3
X2,Y2
X1,Y1 X3,Y3
Xest=a1X +b1Y+ε1 Yest=a2X +b2Y+ε2
Latitudes and longitudes are estimated from regression plane equations or a spline surface.
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
In situ 22 july 2009
Formosat 22 july 2009
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Reference points are defined on the glacier using flags
Detail
No reference control point on the glacier Localisation GPS des points D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
GCP #274
GCP #264 GCP #266 GCP #265
GCP #216
GCP #218 GCP #215 GCP #231 GCP #237 GCP #233 GCP #234 GCP #214 GCP #250GCP #251 GCP #257 GCP #272 GCP #238 GCP #255 GCP #271 GCP #127 GCP #249 GCP #51 GCP #273 GCP #212 GCP #256 GCP #267 GCP #91 GCP #228GCP #232 GCP #99 GCP #172 GCP #202 GCP #253 GCP #236 GCP #270 GCP #223 GCP #40 GCP #38 GCP #200 GCP #258 GCP #33 GCP #97GCP #52 GCP #49 GCP #263 GCP #32 GCP #48 GCP #259GCP #268 GCP #94 GCP #224 GCP #199 GCP #129 GCP #95GCP #98 GCP #35 GCP #269 GCP #27 GCP #196 GCP #46 GCP #50 GCP #93 GCP #280 GCP #222GCP #36 GCP #247 GCP #220 GCP #1 GCP #41 GCP #67 GCP #193 GCP #211GCP #34 GCP #31 GCP #207GCP #44 GCP #66 GCP #15 GCP #39 GCP #42 GCP #221 GCP #2 GCP #203 GCP #68GCP #69 GCP #210 GCP #43 GCP #209 GCP #279 GCP #260 GCP #3 GCP #74 GCP #281 GCP #70 GCP #14 GCP #262 GCP #4 GCP #245 GCP #170GCP #76 GCP #261 GCP #13 GCP #181 GCP #164 GCP #45 GCP #5 GCP #283 GCP #9 GCP #77 GCP #28 GCP #81 GCP #284 GCP #208 GCP #12 GCP #188 GCP #185 GCP #26 GCP #87 GCP #6 GCP #29 GCP #82 GCP #86 GCP #184 GCP #11 GCP #183 GCP #25 GCP #30 GCP #177GCP #178GCP #85 GCP #7 GCP #84 GCP #175 GCP #10 GCP #187 GCP #174GCP #90 GCP #24 GCP #176 GCP #180 GCP #8 GCP #173 GCP #16 GCP #276
GCP #213
GCP #19 GCP #18
GCP #17
GCP #21
GCP #286 GCP #22
GCP #23 GCP #20
Ground Control Point
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Delaunay interpolation triangles (TIN)
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Latitude and longitude simulation
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Actual limit of the glacier
Not visible area
Final rectification D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Combination of images provided by different cameras (over 98 % of the glacier surface is mapped)
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Geometrical models for each camera are only valid as long as images are acquired in the exact same conditions. Technical constraints associated with the instruments and harsh environmental conditions require replacing the camera several times.
Camera replacement necessarily induces some frame change/motion.
Images for which the geometrical model was not defined must be corrected through a double geometrical transformation to be consistent with the mapping projection. D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Source image to be modified
Modified image using a second order polynom, consisitent with the geometry of data acquired in 2009 used as reference for the `` rubber sheeting ’’ method
2009 2008
Images are corrected to match even though the orientation of the camera was changed: a first geometrical transform allows fitting with the parameters of the projection model.
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
2009 2008
Generalization to all the in situ images (about 1000/camera/year, or a total of 26 000 images since 2007) In situ images
UTM 33 Database of In situ data
Referencial geometry
t1 -> t2
t2 -> t3
Rubber sheeting model Delaunay triangulation
t3 -> t4
t4 -> t5
Very high density of ground control points (more than 200 for each in situ image)
t5 -> t6
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
S N O W / I C E C O V E R A G E
Snow / Ice coverage and quality estimation Hinkler, J. , Pedersen, S. B. , Rasch, M. and Hansen, B. U.(2002) ‘Automatic snow cove r monitoring at high temporal and spatial resolution, using images taken by a standard digital camera', International Journal of Remote Sensing, 23: 21, 4669 — 4682
Binary snow and ice visual interpretation…
Normalized Difference Snow Index - NDSI
ICE With digital camera approximation – NDSIRGB
Where :
a and b empirical parameters depending of camera
E M P I R I C A L
N O T E F F I C I E N T
SNOW
W I T H S E V E R A L C A M E R A S
… completed by regular snow drills field measurments (high and snow density) Snow high/density database
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
september13th
ELA
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
september15th
ELA
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
September 20th
ELA
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Estimating the fraction of glacier melt in the hydrologic budget … march – april – may – june – july – august – september – october – november – december – january – february – march – april – may – june – july – august - september …
UTM 33 Database of In situ data
FORMOSAT regular data acquisition NDSIRGB index/ELA map
33 temperature data logger (each hour)
Daily ELA multitemporal evolution
2 1
Positive mean temperature of the glacier
0
Snow melt modelling
-1 -2 -3 -4
Negative mean temperature of the glacier
-5 -6
Water snow melt estimation Daily map temperature of the glacier
Daily snow high map (interpolate) Daily map precipitation D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Simple model of snow melt – day degree fusion model Drills field measures since 2008
Day-degree fusion model: Can be simplified as:
k : coefficent defining the influence of natural and climatic conditions of the basin on melting, 5 mm/degC for snow and 7 mm/degC for ice. Ti: mean air temperature or maximum daily temperature. T0: threshold temperature above which snow melts.
Considering precicipitations, one obtains: α: fitted parameter, usually 0.0035 mm-1. Pi : total daily precipitations
Network of Data loggers since 2008
One should additionnaly consider the influence of progressive stock reduction and the accumulation of rain water in snow. Both satellite images and ground pictures give a binary information concerning the presence of snow or presence of ice. This differenciation is very important to determine, for each point on the glacier surface, the melting coefficient k of the moment which determines the amount of water coming from the melting of snow and ice.
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Simple model of snow melt – day degree fusion model September 15th 2008 Mean daily air temperature IDW interpolate map
Water height equivalent of snow-ice melt using model map
Snow/ice coverage from mosaïc of projected in situ images
Ice ELA
Snow
INPUT
INPUT
OUTPUT
7.1 °C
49 mm
3.9 °C
19 mm
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Simple model of snow melt – day degree fusion model September 15th 2008 nb_pix 3827 8152 17192 73070 9954 17897 81230 124249 62874 50187 86301 122140 10175 13808 12734 16221 17345 32994 29389 37509 28924 48314 39769 45200 34438 35221 18501 15516 19447 6727 7363 1126668
volume area (m²) (m3) 15308 290.852 32608 652.16 68768 1444.128 292280 6430.16 39816 915.768 71588 1718.112 324920 8123 496996 12921.896 251496 6790.392 200748 5620.944 345204 10010.916 488560 14656.8 40700 1261.7 55232 1767.424 50936 1680.888 64884 2206.056 69380 2428.3 131976 4751.136 117556 4349.572 150036 5701.368 115696 4512.144 193256 7730.24 159076 6522.116 180800 7593.6 137752 5923.336 140884 6198.896 74004 3330.18 62064 2854.944 77788 3656.036 26908 1291.584 29452 1443.148 4506672 144777.796
Water height equivalent of snow-ice melt using model _ Statistic - Diagram
volume (m3) 10000
Budget 13.8 mm mean of water equivalent snow melt on the drainage basin.
1000
144.7 103 m3 with 56% from snow (81 103 m3 vs 63.7 103 m3 from ice)
100
10
1
19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Total
melt (mm) 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Total
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Simple model of snow melt – day degree fusion model Each day during the flood period
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Conclusion
As a conclusion, we demonstrate in this presentation that the conversion of ground pictures into aerial recomposed images may be successfully made for an Arctic glacierized system, especially during summer period when the hydrological activity is the most intense. This original approach is very relevant for Arctic where the dynamics of processes is rarely observed and therefore is not easily quantified by classical methods.
We can now estimate snow melt, and hence the water equivalent thickness for each pixel, in order to define the fraction of ice and snow melt in hydrological budgets
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
Thank you
ISIS Incitation à l’utilisation Scientifique des Images Spot
D. Laffly et al., Snow cover monitoring using combined FORMOSAT satellite imaging, in situ sensing oblique view ground-based pictures and snow drills
