Predictions of the future evolution of the earth's atmospheric chemistry and its impact on global circulation patterns are based on Global Climate Models (GCMs) that integrate the complex interactions of the biosphere, atmosphere and the oceans. Most of the available records of climate and environment are short-term records (from decades to a few hundred years) with convolved information of real trends and short-term fluctuations. GCMs must be tested beyond the short-term record of climate and environment to insure that predictions are based on trends and therefore are appropriate to support long term policy making. Unfortunately different parts of the world, weather stations are scattered, records extend over a period of only few years, and there are no systematic climate records for large portions of the globe

D'Antoni, Hector L.

Schaebitz, Frank

English

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World Resource Review Vol. 7 No. 2
REMOTE SENSING AND HOLOCENE VEGETATION:
HISTORY OF GLOBAL CHANGE
Hector L. D'Antoni
NASA Ames Research Center MS 239-20
Moffett Field CA 94035-1000 USA
Frank Sch_bitz
Universit_it Bamberg, Phys Geographie, Am Kranen I, Postfach 1549
D-96045 Bamberg GERMANY
Key'words: pollen analysis, remote sensing, global change, NDVI, hindcasting, paleoenvironment
INTRODUCTION
Predictions of the future evolution of the earth's atmospheric chemistry
and its impact on global circulation patterns are based on global climate models
(GCMs) that integrate the complex interactions of the biosphere, atmosphere and
the oceans. Most of the available records of climate and environment are short-
term records (from decades to a few hundred years) with convolved information
of real trends and short-term fluctuations. GCMs must be tested beyond the
short-term record of climate and environment to insure that predictions are based
on trends and therefore are appropriate to support long term policy making. An
appropriate timeframe should extend over the Holocene period (the last 10,000
years) when most contemporary climate and environmental processes began.
Since its inception in 1916, pollen analysis has successfully reconstructed
the paleoecology of the last 10,000 years for many sites around the world, thus
providing a powerful time-link between short- and long-term processes in the
biosphere. However, pollen analytic results cannot be used in physiological
models driven by remotely sensed data. Further, modern ecology and climate
data are necessary to calibrate pollen analytical models. These are available for
extensive regions in the northern hemisphere, particularly for eastern United
States and Canada, and western Europe. In other parts of the world, weather
stations are scattered, records extend over a period of only few years, and there
are no systematic climate records for large portions of the globe. This is the case
of Patagonia in Argentina where a few weather stations are located close to the
Atlantic seaports, fewer stations are in towns located near the eastern Andean
foothill, and fewer still are scattered on the extensive Patagonian plateau.
This problem became evident after completion of the Argentine-German
Program of Palynology (PROPAL), a cooperative effort of National University of
Mar del Plata (Argentina) and University Bamberg (Germany) to produce a
modern pollen database for the Pampa and Patagonia regions. PROPAL pollen
data were used by undergraduate and graduate students for their theses and
© 1995 World Reso_arceReview. All rights rcserved. 282
World Resource Review Vol. 7 No. 2
dissertations. Five of them
analyzed Quaternary pollen
profiles but none was able to
perform calibration due to
the lack of modern climate
data.
To solve this
problem, a new approach to
calibration was attempted by
D'Antoni and Spanner
(1993). By calibrating
modern pollen data of
southern Patagonia (Figure
1) with satellite remote
sensing data they produced
predictive models of
reflectance in the red (RED)
and near infrared (NIR)
regions of the
electromagnetic spectrum.
As sensed by the
Advanced Very High
Resolution Radiometer
(AVHRR) mounted on the
NOAA-9 satellite, RED
(580-680 nm) includes the
wavelengths absorbed by
chlorophyll pigments
Figure 1 The study area - Transect CT) 1, sites 1A5-LI9; T 2,
sites C28-C1; T 3, sites SI-$29, T 4, sites 30-35; and, T 5, sites
T30-T0, Location of the study area en South America (box)
(Hoffer, 1978), while NIR (725-1100 nm) is characterized by scattering and
increased reflectance of infrared wavelengths by the cellular structure of leaves
(Gausman and Allen, 1973). Typically, dense vegetation absorbs more RED and
reflects more NIR. Deserts and other environments with highly reflective soil
surfaces also have high reflectance in the NIR band.
The RED and NIR bands have been used for calculating a simple ratio
index (SR=NIR/RED) and a normalized difference vegetation index
(NDVI=[NIR-RED]/[NIR+RED]). NDVI has been used for mapping vegetation.
Linking remote sensing data with ecosystem process models is more complex and
requires additional research work (Running, 1990). Sellers (1985, 1987) found
important relationships among leaf area index (LAI), absorbed photosynthetically
active radiation (APAR), and NDVI. Thus, under specified canopy properties,
the APAR is linearly related to NDVI and curvilinearly related to LAI. Further,
the net primary productivity NPP was shown to be a function of the APAR and
the energy-conversion efficiency (e), as NPP=f (Y. APAR) e, integrated over the
growing season (Running, 1990): It is not our intention to further discuss these
relationships but to initiate a change toward a substantially richer palynological
paleoecology than the one available at present. This work refines the method,
© 1995 World RcsourceReview. All rights reserved. 283
World Resource Review Vol. 7 No. 2
discusses results of a remote sensing "snapshot" study of vegetation of southern
Patagonia and Tierra del Fuego, and compares these results with predictions by
calibrated equations. These equations are then used to predict reflectances in the
RED and NIR in the past, which are then used to hindcast past vegetation
indices from a fossil pollen profile sampled from a sedge bog at Meseta Latorre,
(51°31'S, 72°03'W, 1,000 m above sea level) with a basal _4C date of 9,055±140
years B.P. (Schfibitz, 1991).
MATERIALS AND METHODS
Remote Sensing
Remote sensing data of vegetation in southern Patagonia and Tierra del
Fuego (latitude range = 45°-55 ° South), from the Andes to the Atlantic coast
were obtained for the RED and NIR channels of the AVHRR. These data were
acquired from the afternoon overpass on February 18, 1987. The study sites are
distributed no further away than 15 ° from the center of the scene, thus
minimizing illumination variations due to scan angle. The spatial resolution of
AVHRR data is 1 km. The mean of a 2x2 km area was calculated for each
sample and converted to albedo for the RED and NIR using the calibration
coefficients proposed by Price (1987). Albedo represents the spectral reflectance
that AVHRR would measure from a Lambertian surface, with the sun directly
overhead at the mean Earth-Sun distance, and with no intervening atmosphere
(NOAA, 1986). These constraints are not met but conversion to albedo provides
a basis for normalization and comparison to other data (Spanner, et al., 1990).
From the data converted into albedo values, NDVIs were calculated for each of
74 study sites.
Pollen Analysis
Surface soil samples were taken at the study sites in the summer of 1987
and early fall of 1989. Samples consisted of ca. 100 ml of soil collected with a
spatula sweeping the surface to a depth of ca. 1 cm. Samples were placed in
labelled plastic bags. Pollen extraction included subsampling, addition of foreign
markers to allow pollen concentration estimates and then: (1) a pre-treatment
aimed at neutralizing 'humic acids' in acid soils and calcium carbonate in arid
soils; (2) sieving through a 200 Izm mesh under a water jet; (3) floatation in a
water solution of zinc chloride calibrated at 2.1 g ml -_ specific gravity (separation
was enhanced by a 5 minutes ultrasonic treatment); (4) acetolysis treatment (up to
10 minutes) to break cellulose molecules into soluble sugar fractions then removed
by water rinses; and (5) embedding residues in glycerol. Slides were made and
analyzed under a light microscope. Modem pollen data were produced by L.
Burry (in prep.), D'Antoni & Spanner (1993), M. Lombardo (in prep.) and M.
Mancini (1989). Fossil samples were obtained by H. Stingl and K. Garleff with a
Dachnowski corer. Subsamples were taken at 100 mm intervals and then several
intermediate subsamples were taken at critical portions of the stratigraphy.
Pollen was extracted and analyzed by F. Sch_ibitz (1991). Pollen sums were
© 1995 World ResourceR¢view. All rights reserved, 284
World ResourceReview Vol. 7 No. 2
established by multinomial confidence intervals for 95% probability (Mosimann,
1965) using an unpublished program for personal computers. Pollen counts
ranged between 300 and 627 grains per sample.
Hindcasting
Predictive equations were calibrated for the RED and NIR with modem
pollen data, using the approach discussed by D'Antoni and Spanner (1993), and
further discussed by D'Antoni (1993). Thus, all the pollen sums were recalculated
based on 30 pollen types that satisfied the constraints established by Webb (1985).
The values of each pollen type were plotted against those of AVHRR's RED and
NIR reflectances to visualize their relationships. Pollen variables not linearly
related to RED or NIR were transformed (linearized) by rising their values to
either the .5 or the .25 power. These simple procedures allowed the use popular
multiple linear regression algorithms for the models.
RESULTS
This 'snapshot' study of vegetation is based on one AVHRR scene
acquired on February 18, 1987. Therefore, large differences in signature can be
expected when a more thorough study is performed. For this work, NDVIs were
calculated for all 74 calibration sites. In general terms, NDVIs for the
northwestern sector of Patagonia vary between .159 and .001. NDVIs of the arid
central sector (Patagonian plateau) vary around .050. Humid sectors with open
vegetation have NDVIs above .150 and forest locations have NDVIs between .250
and .320.
A linear regression model for all possible subsets (BMDP 9R) was used
to generate the models. Mallows' CP statistic was used to control bias. Square
multiple correlation adjusted to the number of predictors, and other statistics
were used to further control the models. Cross validation was performed as
described by D'Antoni (1993). In Figure 2, predicted NDVIs are plotted against
NDVIs calculated from AVHRR reflectances, thus NDVIs were calculated from
red and near infrared bands of the AVHRR sensor mounted on NOAA-9
satellite. Odd-numbered samples were used to calibrate the model and predict the
NDVIs of pair-numbered samples and vice versa.
These equations were used to generate the Pa]eo-NDVIs for the fossil
profile. The models used follow. The abbreviations are, CA: Caryophyllaceae;
CH: Cenopodiaceae; CO: Compositae Tubuliflorae; CR: Cruciferae; CY:
Cyperaceae; EP Ephedra; GR: Gramineae', GU Gunnera; NA: Nassauvia; PA:
Papilionaceae; PO: Polygala; and SO: Solanaceae.
RED = 6.7 + .17_/(CA) + .73(CH) 2' + .24_'(CO) + .174(CR) + .37,/(NA)
Statistics for the model: number of predictors (p): 5; Mallows' CP for
p=5, 3.02; adjusted squared multiple correlation, for p=5: .79. Standard error for
intercept =.60; for coefficients, CA=.09; CH=.15; CO=.07; CR=.07; NA =.05.
Standard error of estimations =.73. RED values range [12.13;5.94].
© 1995WorldResourceReview.Allrightsreserved. 285
World Resource Review Vol. 7 No. 2
4'NIR = 3.86 -.02_/(CO)-.009(CY)-.005(EP)-.04,/(GR)-.04(GU)-.08,/(MI)-.003(NA) -
.016(PA) -.037(PO) -.083(SO)
Statistics for the model: number of predictors (p): 10; Mallows' CP for
p=10: 8.9; Adjusted squared multiple correlation, for p=10: .52. Standard error
for intercept = .099; for coefficients, CO=.01; CY=.005; EP=.003; GR=.01;
0d -
Figure 2 Model crossvalidation
GU=.017; MI=.029; NA=.002; PA=.009; PO--.011; SO=.038. Standard error of
estimations =.12; ,/(NIR) values range [3.85;2.40].
VEGETATION HISTORY
Profile Meseta Latorre I (51°31'S, 72°03'W) was sampled in a sedge bog
at 1,000 meter elevation, near the upper timberline of a forest of Nothofagus spp.
Classification of samples by stratigraphically constrained cluster analysis
(CONISS) produced a finer zonation than that proposed by Sch_bitz (1991) but
fully compatible with it (Figure 3).
Zone 1 (Z1) reflects an early postglacial environment, with dominance of bog
elements, low proportions of grasses and bushes, and a still distant forest. The
site was then located considerably above the upper timberline. It includes
Sch_bitz's zones MLI, ML2, and ML3.
Subzone 1 (Sl) Paleo-NDVIs vary between .098 and .178. This range is
similar to those found in modem Andean locations and this zone developed
between years 9,000 and 8,000 B.P. (Sch/ibitz's ML1).
Subzone 2 (S2)Paleo-NDVIs vary between .191 and .218 but includes
sample 27 with Paleo NDVI =. 154. Sch/ibitz (1991) placed this sample in zone
ML3. It is a rather peculiar sample. Higher soil coverage, lower proportion of
sedge, higher proportion of grasses. The upper timberline of Nothofagus spp.
forest is near by. Tundra conditions are still prevalent. It can be placed between
8,000 and 6,800 years B.P.
© 1995WorldResoureeReview.Allrightsreserved. 286
World Resource Review Vol. 7 No. 2
Subzone 3
($3) Paleo-
NDVIs vao
between
•192 and
.214.
Tundra
conditions
are still
prevalent.
A richer
flora is
developing
and lower
proportions
of sedge are
observed.
This zone
developed
4
between Figure 3 Pollen diagram of Profile Meseta Latorre I
6,800 and
5,000 years
B.P., and is approximately equivalent to Sch_ibitz zone ML3.
Zone 2 (Z2) is characterized by the processes leading to today's vegetation.
Subzone 1 (Sl) Paleo-NDVIs vary between .201 and .249. This is a
transition period with reduction of sedges, advance of grasses and the arrival of
the Nothofagus spp. forest. It has vegetation similar to that of the upper
timberline. This subzone developed between 5,000 and 3,720 ± 60 years B.P. and
is similar to Sch_ibitz's ML 4a.
Subzone 2 (S2) Paleo-NDVIs vary between .247 and .281. Proportions are
minimum for sedge, medium for grasses, and high for forest. In the upper
timberline environment, Nothofagus spp. dominate the pollen spectra. This
subzone developed between 3,720 and 2,500 years B.P., and corresponds to the
lower third of Sch_ibitz's ML 4b.
Subzone 3 (S3) Paleo-NDVIs vary between .228 and .252. It includes an
advance of sedges, stable values of grasses and some reduction of forest elements.
This subzone developed between 2,500 and 2,000 years B.P. It corresponds to the
central section of Sch_ibitz's ML 4b.
Subzone 4 (S4) Paleo-NDVIs vary between .252 and .285 (profile's highest).
Subzone developed between 2,000 and 1,000 years B.P. including a _4C of 1,315 ±
65 years B.P. The highest values of the Nothofagus spp. forest, and lower values
for grasses and sedges. Conditions prevalent today below the upper timberline.
It corresponds to the upper section of Sch_ibitz's subzone ML 4b.
© 1995 World ResourceRcview. All rights reserved. 287
World Resource Review Vol. 7 No. 2
Subzone 5 ($5) Paleo-NDVI vary between .245 and .274. Modern
conditions (the last 1,000 years): an open forest not far from the upper timberline,
high proportion of grasses and a reduced sedge bog. It corresponds to ML 4c.
CONCLUSIONS
Remote sensing data are useful in palynologic predictive models of
paleoclimate and paleoecology taking advantage of world-wide coverage and
appropriate spatial scale. Remote sensing data are available for any point on the
earth's surface, and repeated observations permit time-series analysis.
AVHRR data of vegetation match field observations. NDVI and 'Paleo-
NDVI' values can be used to estimate canopy-level photosynthesis and
transpiration as well as other functional parameters required by predictive
ecophysiological models of global change. NDVI is related to LAI, APAR and
NPP. At this point, one can foresee that calibration of pollen data in terms of
remote sensing data will lead to paleoecological reconstructions of photosynthetic
capacity, leaf area, absorbed radiation and net primary productivity. Such
reconstructions will in turn be used to drive ecosystem process models
contributing a much larger time window than those in use today. As a
consequence, the GCMs will produce more accurate predictions.
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Remote Sensing and halocene Vegetation: History of Global Change

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