1. Five Years of Land Cover Reflectance in a Large
Scale Hydrological Manipulation in
an Arctic Tundra Landscape
1Santonu Goswami
2John A. Gamon
1Craig E. Tweedie
1Environmental Science and Engineering
University of Texas at El Paso
2Dept. of Earth and Atmospheric Sciences
University of Alberta
-
.

2. Importance of Surface Hydrology (Soil
Moisture) in ecosystem structure and function
• Many observed and modeled change responses of
arctic terrestrial ecosystems are related to surface
hydrology:
– Surface energy budgets (Chapin et al. 2005, Euskircken et
al. 2007)
– Land-atmosphere carbon exchange (Merbold et. al 2009,
Wolf et al. 2008)
– Geomorphic processes (Lawrence and Slater 2005,
McNamara and Kane 2009)
– Provision of ecosystem goods and services (Milennium
Ecosystem Assessment)
– Plant phenology and response to warming (Arft et al. 1999,
Walker et al. 2006)

3. Plants as an indicator of change
• Detecting biotic responses to a changing environment is
essential for understanding the consequences of global
change.
• Plants can work as effective indicators of
• changing conditions and, depending on the nature of the
change,
• respond by increasing or decreasing amounts of green-leaf
biomass, chlorophyll, and water content.
• important effects on ecosystem processes such as NPP and
nutrient cycling.
• Plant phenology is detectable using remote sensing
technologies and therefore scaleable over space and time
(e.g. NDVI).

4. Study Site - Biocomplexity Experiment,
Barrow, Alaska

5. Experimental Setup: Biocomplexity
Experiment
Tramlines (300m long)
• Large-scale Hydrological
Manipulation experiment
to study the effect of Controlled
varying Soil Moisture on
ecosystem Carbon Drained
Balance in the Arctic Flooded
Dikes
100km Barrow (71º19'N 156º37'W)
Atqasuk
Coastal Plain
Eddy covariance towers
Brooks Range

6. The Tram System

7. The Tram System

8. The Tram System

9. The Tram System
Near surface remote sensing technology
• Measurements are made at every meter along each of the
three 300 meter tramlines (precise and repeatable)
• Collects hyperspectral data in the vis-nir region and digital
images of vegetation
• More than 200,000 data files collected in 5 years
• Has the ability to carry various sensors packages
• Can be used to study change in phenology, sun angle
effect on vegetation etc
• Standardized sampling design is part of a larger effort
(SpecNet) to compare ecosystem responses using a
combination of spectral reflectance and flux
measurements
Tramline system in the biocomplexity experiment is the
largest ground based spectral data monitoring system
that we are aware of internationally

10. Measurement of Reflectance
• Reflectance
= ITarget / IDownwelling
= Radiance/Irradiance
• Reflectance correction
= (RTarget / RDownwelling) X (RDownwelling/ RPanel)
• NDVI (Normalized Difference
Vegetation Index) calculation
= (R800-R680)/ (R800+R680)
• Also measures water table depth, thaw
depth.

11. Questions being asked in this study
• How does land-surface phenology (i.e. surface
reflectance properties) change inter-annually?
• Is there any detectable effect of experimental
treatment (flooding and draining) on surface
reflectance?

12. Transition Throughout the Season
avg corrected reflectance south 070613
1
H2O Band
reflectance
0.5
1st Week of June
0
350 450 550 650 750 850 950 1050
wavelength
avg corrected reflectance south 070618
0.2
Chl Feature
reflectance
0.1
3rd Week of June
0
350 550 750 950
wavelength
0.3 Corrected reflected south 080806
Chl Feature
Reflectance
0.2
0.1 1st Week of August
0
350 550 750 950
Wavelength

13. Inter-annual variability of NDVI in the treatment areas for
pre-treatment years (2005, 2006, 2007) and treatment years
(2008 and 2009)
Peak growing time
Peak growing time
0.70
0.70
0.60 0.60
0.50 0.50
0.40
NDVI
0.40
NDVI
0.30 Flooding 0.30
treatment effect Snow fall
0.20 0.20
0.10 Heavy flooding event 0.10
0.00 0.00
Snow-melt Snow-melt
-0.10 -0.10
-0.20 -0.20
150 170 190 210 230 250 150 170 190 210 230 250
Day of Year
Day of Year
NDVI North 2005 NDVI North 2006 NDVI North 2007 NDVI Central 2005 NDVI Central 2006 NDVI Central 2007
NDVI North 2008 NDVI North 2009 NDVI Central 2008 NDVI Central 2009
FLOODED DRAINED

14. 25
20
Water Table Depth (cm)
15
10
5
Flooding
treatment Flooded
0
-5 2009
-10 2008
-15
-20
2007
160 170 180 190 200 210 220 230
0 Day of Year
Comparison of inter-
Thaw Depth (cm)
Increased thaw in 2009
-15
annual variability of
-30
NDVI to water table
-45
160 170 180 190 200 210 220 230
depth and thaw depth
0.70
Day of Year in the flooded section
0.60 Heavy flooding
0.50 event for 2007 (pre-
0.40
0.30
0.20
treatment) and 2008
NDVI
0.10 Flooding treatment in 2008
0.00
Snow-melt
and 2009 (treatment
-0.10
-0.20
160 170 180 190 200 210 220 230
years)
Day of Year

15. 30
Water Table Depth (cm)
20
10 Drained
0
2009
-10
2008
-20
2007
160 170 180 190 200 210 220 230
0
-15
Comparison of inter-
Thaw Depth (cm)
-30
annual variability of
-45
160 170 180 190 200 210 220 230
NDVI to water table
depth and thaw depth
0.70
0.60 in the drained section
0.50
0.40
0.30
for 2007 (pre-
0.20
treatment) and 2008
NDVI
0.10 Snowfall
0.00
-0.10 Snow-melt and 2009 (treatment
-0.20
160 170 180 190 200 210 220 230 years)
Day of Year

16. 30
Water Table Depth (cm)
20
10
Control
0 2009
-10 2008
-20 2007
160 170 180 190 200 210 220 230
0 Dry year 2007
Thaw Depth (cm)
-15
Comparison of inter-
-30
annual variability of
-45
160 170 180 190 200 210 220 230
NDVI to water table
0.70
depth and thaw depth
0.60
0.50 in the control section for
0.40
0.30 2007 (pre-treatment)
NDVI
0.20
0.10
0.00
Snowfall and 2008 and 2009
-0.10 Snow-melt
-0.20 (treatment years)
160 170 180 190 200 210 220 230
Day of Year

17. 0.50
0.43
0.40
0.40
0.33
0.30
NDVI
0.20
0.10
0.00
11% surface 28% surface 83% surface
water water water
Standing surface water
has a significant effect on
reflectance spectra.
This has profound effect
on the NDVI values.

18. The spectral range (400-1000nm) offers the ability to detect
several things, including water. Using a spectral index (NDSWI)
capable of capturing multi-dimensional surface hydrological
dynamics can improve the sampling capabilities of this system
NDSWI (Normalized
Difference Surface
Low Water Index) =
(R490-R1000)
(R490+R1000)
High
Quickbird 2002 Quickbird 2008 Goswami et. al – submitted, JGR
Biogeosciences

19. Summary and Conclusion
• Changes in vegetation cover during the growing season was
clearly detectable using surface reflectance.
• Seasonal patterns of the NDVI values showed some
differences with flooding. The flooded section showed bigger
differences than the drained section.
• The seasonal patterns in NDVI was least different for the
control section among the years.
• NDVI values were compromised by varying water cover
(among other things), so simultaneously tracking NDVI and
NDSWI might be a better approach in this landscape.
• Alternatively, other methods like Spectral Mixture Analysis
can probably help here.

20. Acknowledgements
• National Science Foundation (ASSP-0421588).
• Barrow Arctic Science Consortium.
• CH2M Hill Polar Field Services
• Ukpeagvik Inupiat Corporation – landholder
• Spectral Network.
• UTEP Cybershare Center of Excellence.
• Sergio Vargas, Perry Houser, Christian Andresen, Adrian
Aguirre, Sandra Villarreal, Ryan Cody, David Lin.
• Systems Ecology Laboratory, University of Texas at El Paso.