Five Years of Land Cover Reflectance in a Large

Scale Hydrological Manipulation in

an Arctic Tundra Landscape

1Santonu Goswami2John A. Gamon

1Craig E. Tweedie

1Environmental Science and Engineering

University of Texas at El Paso2Dept. of Earth and Atmospheric Sciences

University of Alberta

-

.

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)

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, 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).

Study Site - Biocomplexity Experiment,

Barrow, Alaska

Experimental Setup: Biocomplexity

Experiment

• Large-scale Hydrological Manipulation experiment to study the effect of varying Soil Moisture on ecosystem Carbon Balance in the Arctic Flooded

Drained

Controlled

Tramlines (300m long)

Barrow (71º19'N 156º37'W)

Atqasuk

100km

Brooks Range

Coastal Plain

Dikes

Eddy covariance towers

The Tram System

The Tram System

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

The Tram System

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

Measurement of Reflectance

• Reflectance

= ITarget / IDownwelling

= Radiance/Irradiance

• Reflectance correction

= (RTarget / RDownwelling) X (RDownwelling/ RPanel)= (RTarget / RDownwelling) X (RDownwelling/ RPanel)

• NDVI (Normalized Difference

Vegetation Index) calculation

= (R800-R680)/ (R800+R680)

• Also measures water table depth, thaw

depth.

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 • Is there any detectable effect of experimental

treatment (flooding and draining) on surface

reflectance?

Transition Throughout the Seasonavg corrected reflectance south 070613

0

0.5

1

350 450 550 650 750 850 950 1050

wavelength

refl

ec

tan

ce

avg corrected reflectance south 070618

0.2

1st Week of June H2O Band

0

0.1

350 550 750 950

wavelength

refl

ec

tan

ce

0

0.1

0.2

0.3

350 550 750 950

Re

fle

cta

nce

Wavelength

Corrected reflected south 080806

3rd Week of June

1st Week of August

Chl Feature

Chl Feature

Inter-annual variability of NDVI in the treatment areas for

pre-treatment years (2005, 2006, 2007) and treatment years

(2008 and 2009)

0.50

0.60

0.70Peak growing time

0.50

0.60

0.70

Peak growing time

-0.20

-0.10

0.00

0.10

0.20

0.30

0.40

0.50

150 170 190 210 230 250

ND

VI

Day of Year

NDVI North 2005 NDVI North 2006 NDVI North 2007

NDVI North 2008 NDVI North 2009

Snow-melt

Heavy flooding event

-0.20

-0.10

0.00

0.10

0.20

0.30

0.40

0.50

150 170 190 210 230 250

ND

VI

Day of Year

NDVI Central 2005 NDVI Central 2006 NDVI Central 2007

NDVI Central 2008 NDVI Central 2009

Snow-melt

Snow fall

FLOODED DRAINED

Flooding treatment effect

Comparison of inter-

annual variability of

NDVI to water table

-20

-15

-10

-5

0

5

10

15

20

25

160 170 180 190 200 210 220 230

Wa

ter

Tab

le D

ep

th (

cm)

Day of Year

-30

-15

0

Th

aw

De

pth

(cm

)

Flooding treatment

Increased thaw in 2009

2009

20082007

Flooded

NDVI to water table

depth and thaw depth

in the flooded section

for 2007 (pre-

treatment) and 2008

and 2009 (treatment

years)

-45

-30

160 170 180 190 200 210 220 230

Th

aw

De

pth

(cm

)

Day of Year

-0.20

-0.10

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

160 170 180 190 200 210 220 230

ND

VI

Day of Year

Flooding treatment in 2008

Snow-melt

Heavy flooding event

-20

-10

0

10

20

30

160 170 180 190 200 210 220 230

Wa

ter

Tab

le D

ep

th (

cm)

-30

-15

0

Th

aw

De

pth

(cm

)

Comparison of inter-

annual variability of

2009

20082007

Drained

-45

160 170 180 190 200 210 220 230

Th

aw

De

pth

(cm

)

-0.20

-0.10

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

160 170 180 190 200 210 220 230

ND

VI

Day of Year

annual variability of

NDVI to water table

depth and thaw depth

in the drained section

for 2007 (pre-

treatment) and 2008

and 2009 (treatment

years)

Snow-melt

Snowfall

Comparison of inter-

annual variability of-30

-15

0

Th

aw

De

pth

(cm

)

-20

-10

0

10

20

30

160 170 180 190 200 210 220 230

Wa

ter

Tab

le D

ep

th (

cm)

Dry year 2007

2009

20082007

Control

annual variability of

NDVI to water table

depth and thaw depth

in the control section for

2007 (pre-treatment)

and 2008 and 2009

(treatment years)

-45

-30

160 170 180 190 200 210 220 230

Th

aw

De

pth

(cm

)

-0.20

-0.10

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

160 170 180 190 200 210 220 230

ND

VI

Day of Year

Snowfall

Snow-melt

0.430.40

0.33

0.00

0.10

0.20

0.30

0.40

0.50

11% surface

water

28% surface

water

83% surface

water

ND

VI

Standing surface water

has a significant effect on

reflectance spectra.

This has profound effect

on the NDVI values.

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

Water Index) =Low

Quickbird 2002 Quickbird 2008 Goswami et. al – submitted, JGR

Biogeosciences

Water Index) =

(R490-R1000)

(R490+R1000)

High

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 • 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.

Acknowledgements

• National Science Foundation (ASSP-0421588).

• Barrow Arctic Science Consortium.

• CH2M Hill Polar Field Services

• Ukpeagvik Inupiat Corporation – landholder

• Spectral Network. • 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.