Natural Variability of the Land Surface - Phenology

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Natural Variability of the Land Surface -
Phenology

• Lecture 12
• CLIM 714
• Paul Dirmeyer

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Main Climate Drivers for Natural Land Cover

• Climatic Constraints
• Temperature
• annual and seasonal means, extremes, timing of
  first frost, ice free days, growing degree days
• Moisture
• annual and seasonal means, extreme events (floods
  and droughts), precipitation, actual and
  potential evapotranspiration

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Regional Patterns

• Climate Zones
• Köppen (1928) - map coauthored by student Geiger
  (P T)
• Holdridges Life Zones (P T)
• USDA Hardiness Zones (minimum T)
• Potential Natural Vegetation
• climax vegetation
• A.W. Kuchler (1964)
• EPA and other groups have updated

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Köppen-Geiger Climate Classification System
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Köppen Classification Scheme
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Holdridges Life Zones
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USDA Plant Hardiness
Gardeners in the U.S. are familiar with the USDA
classification scheme, which is based solely on
minimum temperature extremes (assuming
precipitation is irrelevant because gardeners can
water their plants during dry weather).
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Geographic Controls on Land Cover

• Geomorphology
• Landforms are a product of the interaction of
  geology and climate
• Topography can modify climate
• Soils develop as a result of weathering of
  geologic substrates

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Geologic map modified from King and Biekman (1974)
http//tapestry.usgs.gov/
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Terrain of the Conterminous United States shaded
relief map
Dry adiabatic lapse rate Ga g / cp 9.7 K /
1000 m Observed lapse rate G 6.5 K / 1000
m This correction can be used to estimate
local temperatures near observation stations that
are at a different altitude.
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A Statistical Solution A Neural Data-Driven
Assessment of Global Vegetation Classes
Global distribution of feature types obtained
after simulation with a SOM (Self-Organizing Map)
and the topological arrangement of the categories
on the network. As training data the monthly
means of temperature, precipitation, insolation
and the water storage capacity of soils are used.
(Kropp 1999)
Neural nets are black boxes. They dont tell
why or how.
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Based on observed 1971-2000 climatology, along
with soils and other information (e.g. native
species habitats).
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Predicting current vegetation
Henderson-Sellers (1990) used a GCM and
Holdridges Life Zones to estimate the
distribution of vegetation that would be
consistent with GCM climate.
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Todays models

• 2001 - a great deal of variation in coupled
  GCM-DVM ability to simulate current vegetation,
  as driven by climate.

Cramer, W., A. Bondeau, F.I. Woodward, I.C.
Prentice, R.A. Betts, V. Brovkin, P.M. Cox, V.
Fisher, J.A. Foley, A.D. Friend, C. Kucharik,
M.R. Lomas, N. Ramankutty, S. Sitch, B. Smith, A.
White, and C. Young-Molling, 2001 Global
response of terrestrial ecosystem structure and
function to CO2 and climate change results from
six dynamic global vegetation models. Global
Change Biology, 7, 357-373.
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Monitoring Vegetation Variations

• Satellite are appropriate to a range of dynamic
  monitoring tasks
• monitoring vegetation dynamics over course of a
  year
• link to (crop) growth models to provide yield
  estimates
• distinguish cover types (classification)

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Issues

• temporal sampling
• reconcile requirements of monitoring task with
  sensor characteristics and external influences
• repeat cycle of sensor
• spatial resolution of sensor
• lifespan of mission / historical data
• cloud cover aerosol effects on optical /
  thermal data

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Issues

• discriminating surface changes from external
  influences on remote sensing data
• Viewing and illumination conditions can change
  over time
• Viewing
• wide field of view sensors
• pointable sensors
• Illumination
• variations in Sun position (visible and nearIR
  channels)
• variations in atmospheric conditions

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Issues

• cloud cover

Composite image from U. Wisconsin SSEC
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Issues

• Sensor calibration
• degradation over time
• variations between instruments
• Co-registration of data
• effects of mis-registration (practical)
• Quantity of data
• volume of data can be very large
• preprocessing requirements can be very large
• move towards formation of databases of
  remote-sensing derived 'products' (e.g., EOS
  Earth Observing System)

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Mean Annual Cycles - Snow
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Mean Annual Cycles Soil Wetness
Magnitude of annual cycle (top) and standard
deviation of April soil wetness (bottom).
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Albedo movie
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PAR movie
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LAI movie
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Green-up and climate
Is the green-up causing a cool spell, or is this
an artifact of weather variability?
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Dates of start and length of growing season
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Climate regimes reflected in growing seasons
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First green-up
DMA delayed moving average Detects the time of
the first increase in NDVI from base
level. Year-to-year variations are caused by
climate variations (mainly temperature anomalies,
but many factors play a role, including
precipitation/soil moisture, and solar radiation).
1995
1996
Schwartz et al. (2002 Int. J. Climatol.)
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Peak greening
SMN Seasonal Midpoint NDVI Detects the time of
the crossing of NDVI over the (maxmin)/2 value.
1995
Satellite NDVI measurements
Remove cloud-contaminated points
Fit a spline curve
Determine midpoint
1996
DMA looks for this first positive trend.
Schwartz et al. (2002 Int. J. Climatol.)
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Hysteresis means you cant just map weather to
vegetation physical and biological processes
must be modeled
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Soil moisture and diurnal cycle
Where extremes control vegetation (e.g. freezes),
this can be a factor.
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Dealing with issues

• Vegetation Indices (VIs)
• measured reflectance / radiance sensitive to
  variations in vegetation amount
• BUT also sensitive to external factors
• want contiguous data (clouds)
• Typically take VI compositing approach
• Assume highest measured VI is actual VI.
• Interpolate across missing data.

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Use of VIs

• no one ideal VI - NDVI used historically
• empirical relationships will vary spatially and
  temporally
• direct
• attempt to find (empirical) relationship to
  biophysical parameter (e.g. LAI)
• indirect
• look at timing of vegetation events (phenology)
• VI can still be sensitive to external factors
  (Especially bi-directional reflectance
  distribution function (BRDF) effects)

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BRDF explained
BRDF gives the reflectance of a target as a
function of illumination geometry and viewing
geometry. The BRDF depends on wavelength and is
determined by the structural and optical
properties of the surface, such as
shadow-casting, multiple scattering, mutual
shadowing, transmission, reflection, absorption
and emission by surface elements, facet
orientation distribution and facet
density. However, it should not be overlooked
that the BRDF simply describes what we all
observe every day that objects look differently
when viewed from different angles, and when
illuminated from different directions.
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Real-world examples of BRDF variations
This is a black spruce forest in the BOREAS
experimental region in Canada. Left
backscattering (sun behind observer), note the
bright region (hotspot) where all shadows are
hidden. Right forward-scattering (sun opposite
observer), note the shadowed centers of trees and
transmission of light through the edges of the
canopies.
A barren field with rough surface Left
backscattering (sun behind observer), note the
bright region (hotspot) where all shadows are
hidden. Right forward-scattering (sun opposite
observer), note the specular reflection.
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VI Issues

• IDEAL
• Attempt to make VI sensitive to vegetation amount
  but not to external factors
• atmospheric variations
• topographic effects
• BRDF effects (view and illumination)
• soil background effects
• SAVI, ARVI etc.
• PRACTICE
• VIs maintain some sensitivity to external factors
  
• Be wary of variations in satellite calibration
  etc. for time series

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VI Issues
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VI Issues
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Examples/Techniques

• land cover change detection
• Vegetation Indices eg
• change in VI - infer change in vegetation state
• NDVI variation in Mozambique (UN World Food
  Programme)

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Variance of Boreal Summer LAI over 8 years
(1987-1994)
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Mean annual NPP (1981-2000) estimated with a DVM
at 8km resolution
NPP (gC m-2 yr-1)
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Inter-annual variation in global total NPP(Gt C/
yr)
Year
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Interannual trends in mean land NDVI and in total
land NPP
Annual growth rate K, significance 95, 99.
1991-92 omitted because of the Mount Pinatubo
eruption.
Trends in mean land NDVI
Trends in total land NPP
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Trends in length of growing season

• Evidence exists both for both an earlier spring
  green-up and later autumn senescence over the
  later half of the 20th Century.

Sparks, T. H., and A. Menzel, 2002 Observed
changes in seasons An overview. Int. J.
Climatol., 22, 1715-1725.
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GLO-PEM estimate of changes in annual NPP
1982-2000
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Temporal changes in NPP for the globe and each
hemisphere 1981-2000.
Global
MEI is an indicator of the intensity of El Niño
(ve) and La Niña (-ve). The 10-day anomaly is
the deseasonalized change in NPP. NPP anomalies
in g C m-2 per 10 days.
Interannual variability and trend in global
terrestrial net primaryproductivity satellite
analysis 1980-2000 M. Cao, S.D. Prince, J. Small,
S.J. Goetz Department of Geography, University of
Maryland
Northern hemisphere
Southern hemisphere
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The responses of NPP to ENSO
Difference between NPP for the stated year and
mean value for whole period 1981-2000.
Anomalies. Yr. x-(mean of 1981-2000) gCm-2 per
10 d
La Niña - 1998-99
El Niño - 1986-87
Normal - 1995-96
El Niño 1993-94
El Niño - 1982-83
El Niño 1997-98
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AFRICAWhole continent
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NPP vs ENSO in Africa
Interannual Variation in NPP and ENSO Cycles for
the Whole African Continent
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El Niño
1997
1987
1983
Normal
Negative anomaly
1996
1990
No change
La Niña
Positive anomaly
1989
1999
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Coefficient of Variation and Mean Annual NPP 1982
- 1999
Low High
Low High
Coefficient of variation
Mean Annual NPP
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Areas exhibiting model sensitivity to
phenologyNumber of months per yearLAI-Phen
statistically different from LAI-Mean
HadAM3MOSES2
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Simulating disturbances
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Fire in Boreal Forests

• Many ecosystems have fire as a natural element
• One of less obvious is the boreal forests, where
  despite typically high soil moisture, most areas
  outside Northeast Asia have a 50-200y recurrence
  of fire.
• Outside the growing season, the forest crown and
  undergrowth can be very dry fueling fires
  started by lightning.

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BOREAS Southern Study Area

• BOREAS was a forerunner to the GEWEX CSEs
• Recent burns are red in classification map
  Young, medium and old regenerating forests are
  shaded as olive, tan and brown respectively.
• TMI satellite image (lower right)

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Key to Global Change Forecasts

• One must be able to simulate observed (historical
  and present) variations in vegetation phenology
  before one can believe true predictions.
• The same is true for modeling vegetation
  distribution, as we will see next week.