Transcript Satellite phenology

NATIONAL PHENOLOGY
NETWORK (NPN)
Challenges of Building
a Phenological Research Infrastructure
in the USA
UW-Milwaukee Geography
Research Contributions
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Research Collaborators: R. Ahas, A. Aasa, X. Chen,
B. Reed, M. White, and T. Zhao
Phenology data from J. Caprio, DWD, and A. Menzel
Climate data from Chinese Meteorological
Administration, German Weather Service (DWD),
Instytut Meteorologii i Gospodarki Wodnej
(Poland), and USA National Climatic Data Center
NSF Grants ATM-9510342, 9809460, and 0085224
Base maps from ESRI data
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Definition of Phenology
 Phenology which is derived from the Greek word
phaino meaning to show or to appear, is the study of
plant and animal life cycle events, which are
triggered by environmental changes, especially
temperature. Thus, timings of phenological events
are ideal indicators of global change impacts.
 Seasonality is a related term, referring to similar
non-biological events, such as timing of the fall
formation and spring break-up of ice on fresh water
lakes.
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Phenological Research
 Traditional
approach: agriculturecentered, and local-scale events
 Recent approach: Earth systems
interactions, and global-scale events
 Question: What roles for phenology in
current and future agricultural
research?
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Decadal Averaged Cherry Bloom in Kyoto, Japan
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Data Source: web file (no longer available)
Mean onset of spring phenophases in the
International Phenological Gardens (Europe)
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Source: Menzel et al. 2001, Global Change Biology, Figure 1
Cloned lilac first leaf and first bloom dates
at a single station in Vermont
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Simulated phenology developed from lilac and
honeysuckle data combined with climate data
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Source: Schwartz and Reiter 2000, Plate 4 (updated)
Critical Research Areas
Atmosphere-Biosphere
Interactions
Long-term Organism
response to Climate Change
Global Phenology Databases
for monitoring and
management
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Integrated Approach
Satellite
Observations
(AVHRR-NDVI)
Indicator Species
Phenology
Native Species Phenology
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Lilac First Leaf
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Lilac First Bloom
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DMA NDVI Start of Season 1995
(Schwartz et al. 2002, mean day = 74, March 15th)
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Critical Research Areas
Atmosphere-Biosphere
Interactions
Long-term Organism
response to Climate Change
Global Phenology Databases
for monitoring and
management
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Diurnal Range Change with Lilac First Leaf
15.5
14.5
Snow Date
Mean = -27.9
s.e. = 1.6
+
Diurnal Range (°C)
13.5
12.5
11.5
10.5
Freeze Date
Mean = +12.5
s.e. = 0.9
+
9.5
8.5
7.5
-56
-42
-28
-14
0
14
Days After First Leaf Date
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Source: Schwartz 1996, Figure 3
28
42
56
Comparative Net Ecosystem Exchange
6
4
Mean Daily NEE (umol/m2/s)
2
0
-2
-4
-6
-8
-10
-12
Park Falls, WI
-14
M-Monroe, IN
H. Forest, MA
Oak Ridge, TN
-16
-18
-70
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-56
-42 -28 -14
0
14
28
42
Days after Spring Index First Bloom
56
70
Comparative Net Ecosystem Exchange
Annual “Downturn” Rates
Days after SI First Bloom that NEE = 0
40
20
0
-20
Park Falls, WI
M-Monroe, IN
H. Forest, MA
Oak Ridge, TN
-40
-10
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0
10
20
30
Days after SI First Bloom that NEE = -5
40
Critical Research Areas
Atmosphere-Biosphere
Interactions
Long-term Organism
response to Climate Change
Global Phenology Databases
for monitoring and
management
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Terrestrial Biosphere
Dynamic Change Detection
Satellite
Phenology
Simulated Phenology (Models)
Cloned Species Phenology
Native Species Phenology
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Satellite Phenology
Advantages:
1) Global coverage;
2) Integrated signal
Limitations:
1) Short period-of-record;
2) Cloud cover interference;
3) Interpretation issues;
4) Small set of measures
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SMN NDVI Start of Season 1995
(Schwartz et al. 2002, mean day = 124, May 4th)
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Simulated Phenology
Advantages:
1) Broad coverage if using simple input;
2) Standardized response
Limitations:
1) Model inadequacies;
2) Small set of events and plants
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Spring Indices
Suite of Measures
-2.2oC freeze date in Autumn
 Composite chill date (SI models)
 First leaf date (SI models)
 First bloom date (SI models)
 Last -2.2oC freeze date in Spring
 -2.2oC Freeze period
 Damage index value (first leaf – last frost)
 Average annual, average seasonal, and twelve
average monthly temperatures
 First
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SI First Leaf Date 1961-2000 Slope
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North. Hem. SI First Leaf Date Departures
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North. Hem. Last –2.2oC Freeze Date Departures
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SI Damage Index Value 1961-2000 Slope
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Cloned Species Phenology
Advantages:
1) Ideal for model development;
2) Standardized response to environment;
3) Broad range
Limitations:
1) Lack of network geographical coverage;
2) Not adapted to local environment
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Lilac First Leaf 1961-2000 Slope
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Lilac First Bloom 1961-2000 Slope
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Native Species Phenology
Advantages:
1) Adapted to the local environment;
2) Precise signal
Limitations:
1) Lack of network geographical coverage;
2) Limited range;
3) Geographical variations in response
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Integrated Approach
Example: Wisconsin
Zhao and Schwartz (2003)
Satellite
phenology (DMA SOS)
Simulated phenology
(SI first bloom dates)
“Native” species phenology
(WPS records of first bloom date
for 21 introduced and 32 native
species)
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Integrated Species Indices (ISI)
southwestern Wisconsin
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Critical Research Areas
Atmosphere-Biosphere
Interactions
Long-term Organism
response to Climate Change
Global Phenology Databases
for monitoring and
management
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Critical Data/Analysis Needs
Interpretation/Comparison
of
satellite phenology with “spatial”
surface data
Interpretation of “ripple effects” in
biomes and managed systems
National, continental, and global
scale phenology networks
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USA National Phenology
Network (NPN)
a
continental-scale network observing
regionally appropriate native plant species,
cloned indicator plants (lilac), (and
selected agricultural crops?)
 designed to complement remote sensing
observations
 data collected will be freely available to the
research community and general public
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Prototype for web-based NPN
http://www.npn.uwm.edu
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Select appropriate native species
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Submit data over the Internet
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What might be possible with 20 years
(or less) of phenological data?
Facilitate understanding of plant phenological
cycles and their relationship to climate
 Comprehensive evaluation of satellite-derived
measurements
 Detection of long-term phenological trends in
response to climate variability/global warming
 Evaluate impacts of longer growing seasons on
pollinators, cattle, crop and forest pests,
wildfires, carbon storage, and water use
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Issues for NPN Implementation
Workshop (Aug. 23-25, 2005 in Tucson, AZ)
Native species selection for regions
 Expansion of indicator plants to entire country
 Web-based reporting and feedback system
 Network infrastructure design and function
 Collaborative and cooperative agreements
 Deployment and development strategies
 Public engagement and awareness
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