int. j. remote sensing, 1999 , vol. 20 , no. 4, 683± 698
Estimating CO 2 exchange at two sites in Arctic tundra ecosystems
during the growing season using a spectral vegetation index
C. E. McMICHAEL*, A. S. HOPE, D. A. STOW and J. B. FLEMING
Department of Geography, San Diego State University, San Diego, CA 92182,
USA
G. VOURLITIS and W. OECHEL
Global Change Research Group, Department of Biology, San Diego State
University, San Diego, CA 92182, USA
(Received 29 January 1997; in ® nal form 23 March 1998 )
Abstract.
Measurements of carbon ¯ uxes in Arctic tundra landscapes are generally obtained through intensive ® eld work and involve the use of chamber and/ or
micrometeorological tower techniques. However, ® ndings in a variety of nonArctic ecosystems have demonstrated the potential of remote sensing-based techniques (particularly spectral vegetation indices) to provide estimates of CO2
exchange in a more timely and e cient manner. As the ® rst step towards modelling
Arctic regional and circumpolar ¯ uxes of CO2 using remotely sensed data, we
investigated the relationships between plot-level ¯ uxes of CO2 and a vegetation
spectral re¯ ectance index derived from hand-held radiometric data at two sites.
These relationships were evaluated for variations in vegetation cover type and
environmental factors using data collected during the short Arctic growing season.
Overall, this study demonstrated a relationship between the Normalized
Di€ erence Vegetation Index (NDVI) and measurements of mean site gross photosynthesis and ecosystem respiration at two sites in Arctic tundra ecosystems on
the North Slope of Alaska.
1.
Introduction
Approximately 14% of stored terrestrial carbon is found in Arctic tundra ecosystems (Oberbauer et al. 1991). During the recent and historic geologic past the
Arctic region is assumed to have been a sink for carbon, but research ® ndings over
the past few years suggest that tundra ecosystems on the North Slope of Alaska may
have become a source of carbon to the atmosphere (Oechel and Billings 1992, Oechel
et al. 1993 ). Because of the presumed sensitivity of tundra ecosystems to climate
change (Miller et al. 1983, Shaver et al. 1992), these results support the hypothesis
that Arctic ecosystems may become a signi® cant positive feedback to potential global
warming when ecosystem respiration exceeds gross photosynthesis (Oechel and
Vourlitis 1994).
The Land± Atmosphere± Ice Interaction (LAII) Trace Gas Flux study (TGFS)
*e-mail: [email protected]
International Journal of Remote Sensing
ISSN 0143-1161 print/ ISSN 1366-5901 online Ñ 1999 Taylor & Francis Ltd
http:// www.tandf.co.uk/ JNLS/ res.htm
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684
C. E. McMichael et al.
was initiated in 1992 by the US National Science Foundation to model Arctic carbon
source/ sink strengths on the North Slope of Alaska using meteorological and landscape variables measured at a variety of scales. In order to document, understand
and predict the e€ ects of surface warming on Arctic ecosystem processes, investigators
in this study established three primary objectives: (1) to measure trace gas ¯ uxes of
Arctic ecosystems; (2) to determine the primary driving variables controlling these
¯ uxes; and (3) to scale ¯ ux measurements and model predictions from the local to
the regional and ultimately to the circumpolar scale.
Flux chamber and micrometeorological tower measurements are necessary for
characterizing CO2 ¯ uxes at local scales. These data are representative of relatively
small, discrete patches in space. Regional-scale estimates may be made by extrapolation of these `point’ data over time and space, but these extrapolations generally
require some form of statistical or mechanistic modelling procedures. However,
regional-scale estimates of carbon exchange will require spatially explicit input data
for these models and observations from satellites or aircraft may be the only viable
means for obtaining these data in a timely and cost-e€ ective manner.
Many studies have successfully related vegetation spectral re¯ ectance in the red
and near-infrared wavelengths to vegetation photosynthetic ¯ uxes at a variety of
2
spatial scales, ranging from less than 1 m to large regions. These studies have been
conducted in a variety of environments using hand-held, aircraft and satellite-based
remote sensing techniques (e.g. Tucker and Sellers 1986, Fung et al. 1987, Whiting
et al. 1992, Potter et al. 1993). For example, satellite-derived values of the Normalized
Di€ erence Vegetation Index, NDVI ((near-infraredÕ red)/ (near-infrared+ red)), have
been shown to correspond to global-scale variations in the photosynthetic drawdown of CO2 from the atmosphere and to above-ground primary production (Tucker
et al. 1986, Tucker and Sellers 1986, Fung et al. 1987 ). The NDVI values derived
from hand-held radiometry have been related to plot-level net carbon dioxide
exchange in a mid-latitude grass canopy (Bartlett et al. 1990) and in sub-Arctic
tundra (Whiting et al. 1992).
A goal of the LAII-TGFS is to develop models to estimate regional carbon
balance of Arctic tundra on the North Slope of Alaska using aircraft and satellite
remote sensing data. Investigating relationships between plot-level ¯ uxes of CO2 and
a vegetation spectral re¯ ectance index derived from hand-held radiometric data
represents the ® rst step towards this goal. Observations made using hand-held
radiometric data provide the necessary `control’ for developing relationships between
biophysical quantities and spectral re¯ ectance data at larger scales. The area contributing to the measured CO2 ¯ ux can be identi® ed precisely at this small scale and
corresponding radiometric measurements can be made over this area. Relationships
based on such plot-level measurements can then be used to guide the development
of larger, regional-scale models that would utilize satellite or aircraft data.
Few authors have applied remote sensing techniques to the problem of estimating
CO2 ¯ uxes in tundra landscapes. A closed gas-exchange system (chamber) was used
by Whiting et al. (1992) to measure plot-level CO2 ¯ uxes at two sites in sub-Arctic
tundra over a one-month period. They recorded the multi-spectral re¯ ectance characteristics of the vegetation using a portable, hand-held radiometer while making
concurrent measurements of CO2 ¯ ux. Whiting et al. (1992) demonstrated that net
ecosystem exchange (NEE) of CO2 at the two sites could be predicted successfully
using a spectral vegetation index (i.e. NDVI). These authors concluded that gross
primary photosynthesis (GPP) was the dominant carbon exchange mechanism
Estimating CO 2 exchange at two sites in Arctic tundra ecosystems
685
a€ ecting NEE in this landscape and that ecosystem respiration (ER) could be treated
as a constant value (Sellers 1985). Thus, the NDVI± NEE relationships reported by
Whiting et al. (1992) largely re¯ ect the relationship between the NDVI and GPP.
In contrast to these ® ndings for sub-Arctic sites, research results presented by
Grulke et al. (1990) and Oechel et al. (1993) for Arctic tundra ecosystems have
shown ecosystem respiration to be variable and often the dominant component of
daily NEE. Thus, the use of spectral vegetation indices (SVIs) to predict NEE in
these locations probably necessitates separate models for GPP and ER.
2.
Research objectives
Studies have typically shown GPP to exhibit a non-linear dependence on photosynthetically active radiation (PAR) (Farquhar et al. 1980), although some authors
have reported only slight non-linearities in the relationship (Tucker and Sellers 1986,
Semikhatova et al. 1992, Whiting et al. 1992). A linear association between the
NDVI and the incident photosynthetically active radiation intercepted by the vegetation (IPAR) has been theorized by Asrar et al. (1984 ) and Tucker and Sellers (1986),
and empirically demonstrated in studies such as those reported by Bartlett et al.
(1990) and Gamon et al. (1995 ). Together, these ® ndings suggest the existence of a
strong, near-linear relationship between the NDVI and the draw-down of atmospheric CO2 . This relationship has been demonstrated at various scales in modelling
studies (e.g. Potter et al. 1993) and empirical research (Tucker et al. 1986, Fung et al.
1987, Whiting et al. 1992, Verma et al. 1993).
Previous research has pointed to the existence of a strong, near-linear relationship
between the NDVI and the uptake of atmospheric CO2 by vegetation (i.e. GPP) for
many non-Arctic environments. We hypothesized that a similar relationship would
be found in Arctic tundra ecosystems. The general goal of this research was to
investigate relationships between plot-level ¯ uxes of CO2 (GPP and ER) and a
spectral vegetation index (NDVI) derived from hand-held radiometric data in selected
Arctic tundra ecosystems on the North Slope of Alaska. These ecosystems were
selected to be representative of the LAII-TGFS study area on the North Slope
of Alaska.
An additional hypothesis was that the NDVI would be related to the magnitude
of ER in Arctic tundra ecosystems. Many authors have noted the signi® cant in¯ uences
of soil moisture, depth of thaw and soil temperature on the magnitude of soil
respiration in a variety of environments (e.g. Oberbauer et al. 1991, Potter et al.
1993, Oechel et al. 1995), while air temperature, growth rate and, to a lesser extent,
soil heat loss have been suggested as factors in¯ uencing respiration in Arctic tundra
plants (Tieszen 1973, Semikhatova et al. 1992). Unfortunately, accurate and reliable
remote sensing methods for estimating these environmental factors are not well
developed, implying that estimating ER via remote sensing techniques (e.g. the
NDVI) may be di cult. However, Hope et al. (1993) and Stow et al. (1993) demonstrated that variations in the NDVI in Arctic tundra ecosystems followed patterns
of vegetation community composition and state (e.g. green biomass). These variables
are largely controlled by di€ erences in soil type and in temperature and moisture
regimes on the North Slope (Ostendorf 1993 ), so we expected the NDVI to be an
integrative measure of those variables in¯ uencing rates of ecosystem respiration.
Finally, previous research has demonstrated that Arctic tundra carbon exchange
rates are also in¯ uenced by environmental conditions such as air temperature, soil
temperature and incident photosynthetically active radiation (PAR) (e.g. Tieszen
686
C. E. McMichael et al.
1973, Tieszen and Dietling 1980, Oberbauer et al. 1991, Nadelho€ er et al. 1992 ).
Thus, in examining the GPP± NDVI and ER± NDVI relationships, we also investigated how these relationships were a€ ected by transient environmental conditions.
3.
Study sites
The North Slope of Alaska can be divided into two distinctive physiographic
regions, the Arctic Coastal Plain and the Foothills Region of the Brooks Mountain
Range. Soils along a latitudinal gradient extending from Prudhoe Bay at the coast
to Happy Valley in the foothills region are primarily Histic Perigelic Cryaquepts
(Rieger et al. 1979) in which a 15± 30 cm organic layer overlays the mineral soil
(Marion and Oechel 1993). Mean monthly temperatures range between Õ 12.8 and
10.3ß C at the coast and from Õ 11.1 to 6.7ß C in the foothills (Oechel 1989). Mean
annual precipitation is between 180 and 210 mm at the coast and between 225 and
325 mm in the foothills. Most of the annual precipitation on the North Slope falls
as snow during the winter and spring months (Kane et al. 1992).
The Arctic Coastal Plain is a wet sedge tundra ecosystem dominated by Carex
aquatilis and Eriophorum angustifolium . It is characterized by low local relief (< 2 m)
consisting of shallow thaw lakes nested within larger drained lake basins, highand low-centred polygons, and ice wedges. Poor subsurface drainage results in
shallow active layer depths (< 1 m); standing water in many of the thaw lakes and ice
wedges persists over much of the summer. The Foothills region comprises moist
tussock/ dwarf shrub tundra dominated by Eriophorum vaginatum and Salix pulchra ,
and Betula nana within the water tracks. This area is characterized by low rolling
hills (< 30 m relief ), water track drainages at the base of hillslopes and shallow active
layer depths (< 1 m).
These di€ erences in topography, climate and surface and sub-surface hydrology
result in variations in vascular and non-vascular plant species composition between
physiographic regions. The resulting regional di€ erences in plant community structure, function and composition could a€ ect both the observed carbon balance and
the spectral re¯ ectance of the vegetation. Hence, one study site was located within
each physiographic region. While not encompassing the entire range of land surface
cover types in these areas (e.g. open water, peat hummocks, dry ridge-tops), these
sites were representative of the two dominant land surface cover types found on the
North Slope (i.e. wet sedge tundra and moist tussock/ dwarf shrub tundra). The ® rst
site was situated just inland from the Arctic Ocean at Prudhoe Bay (70ß 21¾ N,
148ß 58¾ W) (® gure 1). The second site was located in the foothills of the Brooks
Mountain Range, approximately 193 km inland (south) at Happy Valley (69ß 09¾ N,
2
148ß 50¾ W) (® gure 1). Twelve study plots (approximately 0.5 m ) were located at
each research site along micro-topographical gradients encompassing the range of
vegetation types present at each site.
4.
M ethodology
4.1. CO2 ¯ ux measurements
Data for this study were collected at the Prudhoe Bay and Happy Valley sites
from mid-June through late-July, 1994. A closed gas-exchange system was used to
measure CO2 ¯ uxes at each plot for both study sites ( Vourlitis et al. 1993 ). After
sealing the chamber onto the permanently installed polycarbonate base, the system
was allowed to come to equilibrium (approximately 30 s) and the change in CO2
concentration over three consecutive 10 s intervals was measured using a Li-Cor
Estimating CO 2 exchange at two sites in Arctic tundra ecosystems
687
Figure 1. Location of the research sites on the North Slope of Alaska.
6250 Infrared Gas Analyzer; NEE was calculated as the average value of these three
observations. After removing the chamber for approximately 30 s to allow ambient
conditions to re-establish, dark ecosystem respiration was measured in a similar
manner, except that a cloth blanket was placed over the chamber to keep out light.
GPP was calculated as the di€ erence between NEE and ER. (See Vourlitis et al.
(1993) for a more complete description of the closed chamber system.)
Measurement of NEE and ER (and calculation of GPP) was conducted over
a 24-h period approximately once a week at each site. During each diurnal
688
C. E. McMichael et al.
measurement period, individual plots were sampled approximately every 1.5± 2.0 h
resulting in 12± 16 observations for each plot. These measurements were then integrated over the 24-h observation period using trapezoidal integration in order to
obtain daily values for each variable. Concurrent with NEE observations, 12± 16
measurements of air temperature were made inside the closed chamber, and 12± 16
measurements of soil temperature were made next to each plot at 0, 5 and 10 cm
depth; a quantum sensor a xed to the chamber top measured incident PAR.
4.2. Radiometric measurements
An Exotech 100 hand-held radiometer was used to measure spectral radiance
over all plots at each study site (with chamber removed) from early-June through
July 1994. The spectral responses of the four-channel radiometer corresponded to
Landsat Thematic Mapper (TM) Band 1 (blue, 0.45± 0.52 mm) and to Systems
Probatoire d’Observation de la Terre (SPOT) high resolution visible (HRV) bands
1 (green, 0.50± 0.59 mm), 2 (red, 0.61± 0.68 mm) and 3 (near-infrared, 0.79± 0.89 mm).
The radiometer was equipped with a 15ß ® eld of view and, when held at nadir
approximately 1 m over the vegetated surface, sampled spectral radiant exitance from
a circle approximately 0.4 m in diameter. Five radiometric observations were made
above each plot on each sampling date and then arithmetically averaged to obtain
a mean value for each target. Spectral radiance was subsequently converted to
spectral re¯ ectance by standardizing mean plot radiance by reference panel ( barium
sulphate, BaSO4 ) radiance measurements made both before and after each target
observation. The NDVI was calculated for each plot from the red and near-infrared
spectral re¯ ectances.
The North Slope of Alaska is characterized by frequent and patchy cloud cover
during the short growing season from June to August (Maxwell 1992). In June 1994
stratus (e.g. nimbostratus) and/ or cirrus cloud types were common, while July was
typically dominated by stratocumulus, altocumulus, cirrus and cumulonimbus
(table 1). Findings reported by Pinter et al. (1987) demonstrate the usefulness of
band ratio spectral vegetation indices (e.g. the NDVI) for estimating biomass under
a variety of sky cover conditions. These authors point out that such indices have
the particular bene® t of retaining most of their information content under varying
topographical and illumination conditions. That is, the NDVI should not be noticeably sensitive to changes in illumination intensities due to clouds passing near to
and/ or in front of the sun because both the red and near-infrared bands react
similarly to reductions in solar irradiance (Pinter et al. 1987).
Previous hand-held radiometric work in this environment focused on obtaining
spectral measurements on clear or virtually clear days (e.g. Hope et al. 1993, Stow
et al. 1993 ). However, as these authors point out, this practice may preclude analyses
of certain portions of the growing season and/ or severely limit sample sizes. Therefore,
both Hope et al. (1993) and Stow et al. (1993) recommend undertaking experiments
which investigate the possibility of using data collected under a variety of illumination
conditions.
A limited number of experiments were conducted during the 1994 ® eld season in
order to evaluate the sensitivity of hand-held radiometric measurements to varying
illumination conditions in this Arctic environment. On two occasions at Happy
Valley (July 11 and 14) and once at Prudhoe Bay (July 9) measurements of spectral
radiant exitance were made over a 24-h period by attaching the Exotech 100 radiometer to a support structure and suspending it approximately 0.75 m above a
689
Estimating CO 2 exchange at two sites in Arctic tundra ecosystems
Table 1. Dates and locations of radiometric data collection and concurrent weather
observations made on the North Slope of Alaska during the 1994 growing season.
Date
Weather
6 June
11 June
12 June
13 June
17 June
19 June
21 June
22 June
26 June
28 June
29 June
2 July
5 July
7 July
10 July
14 July
16 July
18 July
19 July
20 July
24 July
25 July
Clear
Clear
Clear
Clear
Cloudy
Cloudy
Cloudy
Cloudy
Cloudy
Sun with clouds
Sun with clouds
Sun with cirrus
Cm.cngts./ no direct sun
Clear
100% stratus
100% stratus
Ast. and cirrus with direct sun
100% stratus
Broken stratus
100% cb. and nb.
100% stratus
Sun with cst. and ccum.
Happy Valley
X
X
X
X
X
X
X
X
Prudhoe Bay
X
X
X
X
X
X
X
X
X
Abbreviations: cm.cngts. = cumulus congestus ; ast. = altostratus ;
nb. = nimbostratus ; cst. = cirrostratus; ccum = cirrocumulus .
X
cb. = cumulonimbus ;
homogeneous vegetated surface. The NDVI was calculated from the red and
near-infrared spectral radiance. Plots of the radiometric values versus time of day
(® gure 2) show that the individual red and near-infrared bands are a€ ected by the
presence of clouds, while the NDVI is insensitive to changes in cloud cover. Thus,
radiometric data for the present study were obtained under all weather conditions
except rain.
4.3. Data analysis
We used paired radiometric and chamber data (table 2) to investigate the aforementioned research hypotheses. Although every e€ ort was made to collect these
datasets as simultaneously as possible, the sampling dates did not usually coincide
over the study period. Barring extreme changes in precipitation and/ or radiation
regimes it was assumed that the NDVI was relatively stable over a period of 2± 3
days at a given site. Mean daily values for each variable (GPP, PAR, ER and NDVI)
for each site were computed by pooling all plots at a given location for each date
(table 2). Simple linear regression analysis was used to evaluate the relationships
between daily GPP (normalized for variations in PAR) and ER and the NDVI. Daily
GPP was normalized by daily PAR after Bartlett et al. (1990) and Whiting et al.
(1992) in order to compare the CO2 uptake of the wet sedge tundra and moist
tussock/ dwarf shrub tundra under similar sunlight conditions. Stepwise linear regression analysis was used to evaluate the in¯ uence of transient environmental conditions
on the relationships between daily GPP and ER and the NDVI.
690
C. E. McMichael et al.
Figure 2. Diurnal trends for the red and near-infrared bands and the NDVI.
Table 2. Pairing of radiometric and chamber data for the 1994 growing season.
PB NDVI
PB chamber
HV NDVI
HV chamber
22 June
28 June
2 July
14 July
19 July
25 July
21 June
27 June
4 July
11 July
18 July
26 July
17
26
29
10
16
24
18 June
24 June
1 July
8 July
15 July
22 July
June
June
June
July
July
July
PB= Prudhoe Bay; HV= Happy Valley.
5.
Results and discussion
The arithmetic mean and the coe cient of variation (CV) for the NDVI, GPP
and ER for the data collected on each date at each site are listed in table 3. Mean
NDVI increased over the growing season at both Happy Valley (HV) and Prudhoe
Bay (PB), a trend which corresponds to the growth of tundra vegetation during the
short Arctic summer. Mean GPP and mean ER also tended to increase over the
season at both sites. Mean values of GPP, NDVI and ER at Happy Valley were
greater than at Prudhoe Bay. Gross primary production of Arctic tundra ecosystems
generally increases with decreasing latitude as temperatures rise and moisture availability increases. Consequently, the NDVI could be expected to follow a similar
latitudinal trend. The CO2 eƒ ux of wet sedge tundra at Prudhoe Bay was less than
the eƒ ux at the drier inland site at Happy Valley because rates of organic decomposition at the coast are slower due to poorly aerated, waterlogged soils (Oechel et al.
1993 ).
An examination of table 3 reveals that the CV values for each variable were less
than 37%, indicating a high degree of similarity among the 12 plots at each site on
a given date. The CVs for the NDVI, GPP and ER were larger for the observations
Estimating CO 2 exchange at two sites in Arctic tundra ecosystems
691
Table 3. Mean and coe cient of variation (CV) of NDVI, GPP and ER by site and date.
Site
Date
Mean
NDVI
CV
NDVI
Mean
GPP1
CV
GPP
Mean
ER1
CV
ER
HV
HV
HV
HV
HV
HV
17 June
26 June
29 June
10 July
16 July
24 July
0.5634
0.5810
0.6060
0.6648
0.6924
0.6936
0.144
0.137
0.102
0.107
0.123
0.077
0.5549
1.1104
1.9619
2.3228
3.1280
2.6145
0.150
0.161
0.185
0.141
0.290
0.184
0.9475
1.2395
2.3224
2.8646
3.1293
2.8121
0.139
0.183
0.156
0.259
0.297
0.176
PB
PB
PB
PB
PB
PB
22
28
2
14
19
25
0.3244
0.3397
0.3516
0.4612
0.4462
0.5077
0.203
0.235
0.158
0.112
0.126
0.105
0.2050
0.3585
0.8727
1.5092
1.3283
2.0156
0.838
0.330
0.249
0.206
0.205
0.197
0.3586
0.6569
0.8684
1.4024
0.7664
1.5912
0.367
0.363
0.302
0.351
0.311
0.198
1
June
June
July
July
July
July
gC mÕ
2
dayÕ
1
.
made at Prudhoe Bay than for those at Happy Valley. The relatively low CV values
for a given site could be expected as a result of the close spacing (usually < 1 m) of
the ¯ ux chambers at each site. Indeed, previous work in this environment has
demonstrated that plots spaced less than 100 m apart tend to be spatially autocorrelated (Hope et al. 1995 ). Further, the variation in micro-topography over short
distances at Prudhoe Bay is more pronounced than at Happy Valley, perhaps
accounting for the relative di€ erence in CV values for the NDVI, GPP and ER
between sites. The larger early-season coe cients of variation for the NDVI and
GPP at both sites probably resulted from the initial di€ erential growth rates of the
various species found at each site. In general, the values tended to decrease as the
season progressed as vegetation biomass and CO2 exchange processes at a site
approached their peak values and were likely to be in¯ uenced by more regional
constraints such as precipitation, temperature and solar radiation. Values of the CV
for ER were larger at Prudhoe Bay than at Happy Valley over the growing season,
presumably representing the contrast in organic decomposition rates between the
drier polygonal areas and the waterlogged lake basin areas.
5.1. NDV I± GPP/ PAR relationship
A scatterplot showing the relationship between daily GPP/ PAR and the NDVI
for both study sites is given in ® gure 3. A linear relationship between GPP/ PAR and
the NDVI is depicted for each site. GPP/ PAR was regressed on the NDVI for each
site separately and the regression models were evaluated using a t-test to determine
whether the regression slope was signi® cantly di€ erent from zero. An F -test described
by Pindyck and Rubinfeld (1981) was used to determine whether the regression
slopes for the two sites were signi® cantly di€ erent from one another. For each study
site, the regression slope coe cient was signi® cantly di€ erent from zero at the 0.02
con® dence level. The NDVI accounted for almost 68% of the variation in GPP/ PAR
at Happy Valley (equation (1)) and approximately 74% for Prudhoe Bay (equation
(2)). Additionally, the slope coe cients were signi® cantly di€ erent from each other
as the calculated value of F (11.39) exceeded the critical value (10.04) at the 0.01
692
C. E. McMichael et al.
Figure 3. Scatterplot of GPP/ PAR versus NDVI for Prudhoe Bay and Happy Valley study
sites.
con® dence level. These results should be viewed with caution given the small sample
size (n = 6).
GPP/ PAR= 0.477NDVIÕ 0.249
2
(adj. R = 0.675; SE = 0.018)
(1)
GPP/ PAR= 0.211NDVIÕ 0.061
2
(adj. R = 0.740; SE = 0.009)
(2)
These results are in agreement with similar hand-held remote sensing studies
conducted in other environments (e.g. wetland grass canopies, Bartlett et al. 1990;
sub-Arctic tundra, Whiting et al. 1992 ). Further, these results are analogous to the
® ndings of global-scale studies which have demonstrated that the carbon uptake of
whole biomes (i.e. all vegetation types pooled) is related to satellite-derived vegetation
spectral re¯ ectances (e.g. Tucker et al. 1986, Fung et al. 1987 ). The di€ erent relationships at the two sites may be explained by the di€ erences in climate, topography
and hydrology between ecosystems which, over time, have led to contrasting vegetation characteristics such as species composition, dominant species type, biomass and
leaf-area index. These variables and their control over rates of ecosystem photosynthesis and PAR absorption could result in di€ erent responses in GPP for a given
¯ ux of PAR for tussock (Happy Valley) and wet sedge (Prudhoe Bay) tundra
ecosystems. Moreover, variations in the magnitude of observed NDVI values between
ecosystems could be due to di€ erences in vegetation composition, function and
structure.
5.2. NDV I± ER relationship
The observed relationship between ER and the NDVI for both study sites is
given in ® gure 4. A linear relationship between ER and the NDVI was evident for
both sites and the regression of ER on the NDVI indicated that the NDVI accounted
for approximately 86% of the variation in ER at Happy Valley (equation (3)) and
for almost 70% at Prudhoe Bay (equation (4)). The regression slope coe cients for
Happy Valley and Prudhoe Bay were signi® cantly di€ erent from zero at the 0.01
693
± 2
± 1
ER (gC m d )
Estimating CO 2 exchange at two sites in Arctic tundra ecosystems
Figure 4. Scatterplot of ER versus NDVI for Prudhoe Bay and Happy Valley study sites.
and 0.02 levels, respectively. Further, these slope coe cients were signi® cantly di€ erent from each other with the calculated value of F (18.16) exceeding the critical
F -value (12.83) at the 0.005 con® dence level.
ER= 14.992NDVIÕ 7.278
2
(adj. R = 0.857; SE = 0.346)
(3)
ER= 5.338NDVIÕ 1.222
2
(adj. R = 0.697; SE = 0.257)
(4)
The results of the ER± NDVI analyses point to the NDVI as a potential estimator
of ecosystem respiration rates in moist tussock/ dwarf shrub and wet sedge tundra.
However, the relationship between the two variables is most likely indirect rather
than direct. ER is the combined respiration from plants, roots and soil microbes and
is in¯ uenced by soil organic matter, soil environmental conditions (e.g. moisture and
temperature), root mass and above-ground biomass. Above-ground biomass has
been shown to covary with the NDVI in Arctic tundra vegetation (Hope et al. 1993)
and the variations in root mass are likely to follow the long-term average variations
in above-ground biomass (although year-to-year relationships may not be constant).
Therefore, below-ground biomass could be indirectly related to the NDVI. Further,
the organic matter in the soil is a function of the integrated historical production of
vegetation and spatial variations in soil moisture characteristics are also likely to be
re¯ ected in vegetation patterns. The soil environment, in turn, is a function of the
interaction of climate, topography, geology, etc. over time. The `undisturbed’ nature
of the North Slope landscape has produced a relatively high correspondence between
observed vegetation and soil patterns. This implies that vegetation in this environment is a good indirect indicator of soil environmental conditions and, therefore, of
ecosystem respiration.
This premise seems to be supported by a scatterplot of GPP/ PAR versus ER for
both sites (not shown) which depicted a linear relationship between these variables
2
(R = 0.63, n = 12). This implies that, as the environment became more favorable for
vegetation growth over the growing season (e.g. increasing availability of nutrients
694
C. E. McMichael et al.
as the depth of the active layer increases), it also favored increased rates of ecosystem
respiration (e.g. increasing soil aeration with increasing active layer depth). Since the
NDVI has been widely used as an indicator of vegetation processes, and because it
is strongly related to mean site GPP/ PAR in this environment, it is reasonable to
expect that the NDVI may serve as an indirect indicator of the magnitude of ER in
Arctic tundra ecosystems. However, while our method produced statistically signi® cant relationships between ER and NDVI and, thus, supports our plot-scale
hypothesis, a more physically based approach may be required to account for
environmental factors (e.g. standing water, slope/ aspect) controlling variations in ER
at larger scales.
5.3. NDV I and ecosystem carbon ¯ uxes Ð the in¯ uence of environmental factors
As stated previously, Arctic tundra carbon ¯ uxes have been shown to be in¯ uenced by variations in transient environmental conditions (e.g. air/ soil temperature,
incident PAR). Since the relationships between vegetation spectral re¯ ectance and
ecosystem carbon ¯ uxes were distinct for Happy Valley and Prudhoe Bay, the
in¯ uence of environmental factors on these relationships was investigated separately
for each study site using stepwise linear regression.
5.3.1. GPP/ PAR versus NDV I
The results from the stepwise regression analysis for Happy Valley indicated that
a combination of the NDVI with the measured chamber and soil variables did not
explain any more of the variability in GPP/ PAR than did the NDVI alone. However,
the resulting regression model for Prudhoe Bay (equation (5)) suggested that a
combination of maximum soil surface temperature (TS0MAX) and the NDVI
improved the predictive power of the model, with 97% of the variation in GPP/ PAR
being associated with these variables (an increase of 23% over the NDVI alone,
equation (2)). Both regression slope coe cients were signi® cantly di€ erent from zero
at the 0.01 con® dence level.
GPP/ PAR= 0.335NDVIÕ 0.003TS0MAXÕ 0.076
2
(adj. R = 0.969; SE = 0.003)
(5)
We cannot, however, be certain that soil temperature was the variable directly
a€ ecting GPP/ PAR, since there is a complex interaction between soil temperature
and moisture and it can be di cult to separate out the e€ ects of each factor on
ecosystem carbon exchange (Nadelho€ er et al. 1992, Oechel and Vourlitis 1994).
Increases in soil surface temperatures may result from a reduction in soil moisture
and a declining water table, which would lead to increased rates of soil organic
matter decomposition and nutrient mineralization (e.g. Oberbauer et al. 1991).
Therefore, observed values of both CO2 uptake and eƒ ux may be expected to
increase under these drier conditions.
Because soil temperatures are logistically di cult to gather over large areas in
Arctic landscapes, a remote sensing-based model of carbon uptake which includes
this variable may be hard to implement operationally. Large-scale observations of
air temperature are much more readily available than soil temperatures in this
environment. As maximum soil surface temperature was highly correlated (> 95%)
with chamber air temperatures, GPP/ PAR was regressed on the NDVI and chamber
air temperature variables (i.e. maximum, minimum, mean) for Prudhoe Bay in order
to assess the utility of using air temperature (in addition to the NDVI) to estimate
Estimating CO 2 exchange at two sites in Arctic tundra ecosystems
695
GPP/ PAR at this site. Although a model including both the NDVI and mean
chamber air temperature (CTMEAN) (equation (6)) explained somewhat less variation in GPP/ PAR than the model based on maximum soil surface temperature, it
accounted for 17% more variation in carbon uptake at Prudhoe Bay than the model
which estimated GPP/ PAR from the NDVI alone (equation (2)).
GPP/ PAR= 0.0.308NDVIÕ 0.002CTMEANÕ 0.076
2
(adj. R = 0.906; SE = 0.006)
(6)
5.3.2. ER versus NDV I
Following the arguments presented in the preceding section, ER was regressed
on the NDVI, chamber and soil variables using stepwise linear regression. The results
of this analysis for Happy Valley indicated that the combination of maximum soil
surface temperature (TS0MAX) and the NDVI greatly improved the predictive
2
power of the regression model, with the adjusted R increasing from 0.857 (NDVI
alone, equation (3)) to 0.985 (NDVI + TS0MAX, equation (7)).
ER= 11.069NDVI+0.072TS0MAXÕ 5.851
2
(adj. R = 0.985; SE = 0.112)
(7)
However, due to the logistical di culties associated with making measurements of
soil temperatures over large areas on the North Slope, the combination of the NDVI
and chamber air temperature variables was also investigated. Although stepwise
regression results from this analysis (equation (8)) explained slightly less of the
variability in ER than the (NDVI + TS0MAX) model (equation (7)), they still
accounted for more of the variability in ER at Happy Valley (97%) than did the
NDVI-only model (86%) (equation (3)).
ER= 8.601NDVI+ 0.082CTMEANÕ 4.357
2
(adj. R = 0.970; SE = 0.158)
(8)
Together, the NDVI and CTMEAN explained approximately 94% of the variation in ER at Prudhoe Bay (equation (9)), compared with 74% using the NDVI as
the only independent variable (equation (4)).
ER= 2.389NDVI+ 0.066CTMEANÕ 0.749
2
(adj. R = 0.942; SE = 0.112)
(9)
It should be noted that this model is based on the same two independent variables
as the Happy Valley model to estimate ER, the NDVI and CTMEAN.
6.
Conclusions
The overall purpose of this research was to investigate the relationship between
the NDVI (derived from hand-held radiometric data) and chamber carbon exchange
(i.e. GPP and ER) in Arctic tundra ecosystems on the North Slope of AlaskaÐ the
® rst step in estimating the regional carbon balance of Arctic tundra on the North
Slope of Alaska using aircraft and/ or satellite remotely sensed data. This relationship
was evaluated for variations in vegetation cover type and environmental factors. All
analyses were based on data collected during a portion of the short Arctic growing
season (green-up through peak-greenness, early June± late July, 1994 ) under all
weather conditions except rain. The conclusions drawn from these analyses, therefore,
apply only to comparable surface, meteorological and environmental conditions.
Overall, this study demonstrated a relationship between a remotely sensed
696
C. E. McMichael et al.
spectral vegetation index (i.e. the NDVI) and measurements of mean site GPP/ PAR
and ER at two study sites in Arctic tundra ecosystems. Moreover, in most cases the
inclusion of chamber air and soil temperatures in the regression analyses improved
the modelled relationships. However, because the radiometric data used in this study
were collected under di€ erent cloud cover conditions, future research should examine
how much of the residual variance in these relationships may be associated with
non-uniform sky conditions. Further, it is suggested that the next step in this study
should focus on validating the models developed in this research with data collected
in subsequent summers at a variety of sites with diverse land cover characteristics
(e.g. peat hummocks and open water). Additionally, future work should focus on
developing techniques for `scaling-up’ these models for use at larger scales (i.e.
regional and circumpolar). The use of mean site values of the NDVI, GPP/ PAR and
ER in this study is consistent with this goal. As previously discussed, the mean site
values are the result of pooling all plots at a study site. The use of these pooled
values e€ ectively integrates the variability in these variables at each study site in a
manner analogous to the way in which aircraft / satellite sensors integrate sub-pixel
variability of surface variables to the pixel level.
While conventional chamber and micrometeorological techniques permit the gas
exchange processes of di€ erent vegetation types (ecosystems) to be measured at
selected locations, these analyses are limited in both space and time due to the
intensive nature of the ® eld work and/ or the costs associated with the instrumentation. However, remote sensing-based techniques have demonstrated the potential to
provide estimates of carbon ¯ uxes in a more timely and e cient manner. A variety
of authors have investigated both process-based and empirical remote sensing
approaches to estimate the net ecosystem exchange of carbon (NEE) at di€ erent
scales in many environments. However, to our knowledge no one has applied these
techniques to the problem of estimating CO2 ¯ uxes in Arctic tundra landscapes.
Acknowle dgm ents
The authors would like to thank Dr Arthur Getis, Department of Geography,
San Diego State University, for his time and expert statistical advice. The helpful
comments of the two anonymous reviewers are also appreciated.
The research presented in this article was funded in part by the National Science
Foundation, ARCSS-LAII Grants DPP 9216109 and OPP 9318527.
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