Download Modeling of Ecological Processes

Modeling of Ecological Processes:
Applications in Arizona and Chile
Wim van Leeuwen
w/ contributions from ARSC
School of Natural Resources and the Environment
School
Sc
oo o
of Geography
Geog ap y and
a d Development
e e op e t
Arizona Remote Sensing Center
University of Arizona, Tucson, USA
Overview
• Phenology?
gy
• Remote Sensing?
• Is
I land
l d surface
f
phenology
h
l
a proxy
for climate?
• Sky Island Research
• Chile
• The Future
What is phenology?
Phenology is a proxy for climate
(IPCC, 2007)
 “Phenology
gy – the timing
g of seasonal activities of
animals and plants – is perhaps the simplest
process in which to track changes in the ecology
off species in response to climate change.”
 “Observed
Observed phenological events include leaf
unfolding, flowering, fruit ripening, leaf colouring,
leaf fall of plants, bird migration, chorusing of
amphibians, and appearance/emergence of
butterflies.”
Phenology?
• Biological
g
life cycle
y
* Abiotic * Biotic
• Flower phenology
• Butterfly phenology
• Green-up phenology
Flower phenology - Cherry blossom
Washington DC has traditionally had a
Cherry Blossom Festival during the first
t
two
weeks
k in
i A
April,
il culminating
l i ti iin mid
id
April with a parade. Over the past few
decades, the cherryy trees have been
blooming earlier and earlier, so that
peak bloom is now at the beginning,
rather than during the celebration at the
end of the festival……
http://www.windows.ucar.edu/citizen_science/bud
burst/phenology_important.php
Phenology - Monarch butterflies face plight
of flight in hot Texas (Washington Post Oct 7,7 2011)
http://www.learner.or
g/jnorth/tm/monarch/
MigrationPathwayM
exicoCalvert.html
NDVI green-up Phenology
NDVI green-up Phenology
Edwards Plateau
6500
Mean NDVI
6000
NDVI * 1
10000
5500
5000
4500
4000
3500
3000
Date
MODIS NDVI 16-day Composite Image
Refle
ectance
1
BGR NIR
0.8
SWIR
Portion of Electromagnetic Spectrum
0.6
Normalized Difference
Vegetation Index
0
0.4
0.2
0
300
900
1500
2100
Wavelength (nm)
2700
Green Vegetation
Dry Grass
Soil
NDVI =
(NIR - RED)
(NIR + RED
NDVI
MODIS - TERRA
Jan, 2004
MODIS NDVI 16-day Composite Image
Normalized Difference
Vegetation Index
NDVI = (NIR - RED)
(NIR + RED
MODIS – TERRA/AQUA NDVI
How does phenology impact
the world?
Phenology is an essential component of the biosphere
Adapted from Bonan (2002)
Ecol. Climatology
Climate….
Cli
t
and
phenology…
Fig. 1. (A) Geographic distribution of potential climatic constraint to plant growth derived from longterm climate statistics
Temperature
Vapor Pressure Deficit
Solar Radiation
R. R. Nemani et al., Science 300, 1560 -1563 (2003)
Published by AAAS
Phenology is a proxy for climate
(IPCC, 2007)
 “Phenology
gy – the timing
g of seasonal activities of
animals and plants – is perhaps the simplest
process in which to track changes in the ecology
off species in response to climate change.”
 “Observed
Observed phenological events include leaf
unfolding, flowering, fruit ripening, leaf colouring,
leaf fall of plants, bird migration, chorusing of
amphibians, and appearance/emergence of
butterflies.”
Vegetation Trajectories
© Kamel Didan,VIP lab
Colorado Rocky Mountains
Sierra Mariquita
Sierra Elenita
MODIS NDVI
MODIS
NDVI
May 25, 2010
Sierra Madres
Sonoran Desert
Ponderosa Pine
Shrubland
Cottonwood,Pinyon, Juniper, Oak
ATMOSPHERIC
COMPOSITION
ANTHROPOGENIC
ACTIVITES
CLIMATE
VARIABILITY
& CHANGE
Key to:
BIOSPHERE MODELS
MANAGEMENT
TOURISM
HEALTH
LAND-USE/LANDLANDUSE/LANDCOVER CHANGE
and DISTURBANCE
© K. Didan,VIP-lab
WATER and
CARBON
CYCLE
Background
ac g ou d o
of tthe
e Area
ea
Biogeography (Lomolino et al., 2010)
Goals and Objectives
Develop an assessment of changes in
landscape scale phenology
vegetation along elevation gradients
mountain sky islands in the drylands of the
Southwest US and Northern Mexico.
Examine the variability in climate and
vegetation
egetation green
green-up
p relationships
seasonally and interannually,
along the elevation and latitudinal gradients.
Precipitation (PRISM)
Elevation
NDVI
Average summer
Precipitation 2007
Pinaleño
Catalina
Rincon
Picacho
Sierra El Pinito
mm (x100)
Average winter
A
i t
Precipitation 2007
mm (x100)
Sierra San Louis
NDVI
NDVI phenology
7000
Fire 1 2
6000
NDVI (x10000)
5000
4000
3000
2000
1000
0
Drought
Penaleno
S t C t li Mountains
Santa Catalina M
t i
Sierra El Pinito
El Picacho
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
23 Observation/Year
Photo credits: Dave Bertelsen
5500
5000
4500
4000
3500
3000
2500
2000
1500
1000
500
NDVI
Elevation ((m))
'2005-15
Aspect changes...
NDV
a
a119
a18
a17
b16
c6
b115
d15
c4
d114
e15
d13
f3
e112
f14
g13
h12
0
h1i9
h11
i12
0
j9
k
i19
1
l9
k1l8
l10
m0
8
n
m7
o79
n
o68
n
o55
p5
o4
p4
o1
p1
o
o23
NDVI and E
Elevati
MODIS NDVI and Elevation for pixels
along Finger Rock trail
pixels
8000
Elevation 3200m
2100m - Forest Woodland
Snow‐dips
1100m - Desert
Drough
7000
Phenological asynchronies (start, peak and end of growing season)
t
700m
NDVI (x10000)
6000
5000
4000
3000
2000
Bimodal
Unimodal
1000
2000 - 1
2001 - 1
2002 - 1
2003 - 1
2004 - 1
2005 - 1
Year - period
8000
NDVI (x10000)
7000
y = 2.7359x ‐ 180.2
R2 = 0.8524
Pine‐Oak Forest & Woodland
6000
5000
4000
3000
2000
Desert Desert
1000 Cacti & Scrub
800 1000 1200 1400 1600 1800 2000 2200 2400
Sierra Mariquita
Sierra Elenita
MODIS NDVI
May 25, 2010
Elevation (m) Methods and Data Analysis:
•
NDVI : 2/26/2002- 03/11/2002
R
Remotely
t l sensed
d data
d t
 (MODIS) NDVI time series 2000 -2010
 250 m spatial resolution
 23
23-period-per-year
period per year (16-day
(16 day
composite)
NDVI : 6/26/2007- 07/11/2007
•
Climate data
p
((PRISM)) at 4 km
 Precipitation
 Average summer precipitation (JJSO)
 Average winter precipitation (DJFM)
 Temperature (min and max)
 Average
A
summer ttemperature
t
(JJSO)
 Average winter temperature (DJFM)
•
Topographical data
 Elevation
 Aspect
 Slope
Elevation in meter
Methods and Data Analysis
1
1.
Pheno-metrics
Pheno
metrics from NDVI time
time-series
series
from 2000 - 2010
 TIMESAT analysis software
–
–
–
2.
Start of growing season (SOS) from 2000 to 2010
Length of season (LS)
Small integral (SI)
Statistical Analysis

Spatio-temporal trend analysis of phenological
parameters

Coefficient of variation of phenological parameters

Multiple Linear Regression
•
Phenological response varibles: SOS, SI, LS
•
Cli t d
Climate
data:
t Precip,
P i Temp
T
•
Topographical data : Elevation, Slope, and Aspect
Evaluate interannual trends
and spatial variation in
vegetation dynamics
across sky islands
Evaluate seasonal and
interannaul varibility
across sky islands
Evaluate temporal variation
in vegetation dynamic with
the relation to climate and
topographical data
TIMESAT: Phenological metrics (t, NDVI)
l. deriv
r. deriv
Peak (t)
Amplit.
Sm Integr
Length
End (t)
Lg Int.
Start (base,t)
2000
2001
2002
2003
2004
2005
TIMESAT: pheno-metrics
Large Int. NDVI (Productivity)
Small Int. NDVI
S
Senescence
INPUT:
stack of
time-series
time
series
images
Greenup
Amplitude
Peak
Base
Length
End
Mid.
Start
OUTPUT:
11 “layers” of
pheno metrics
pheno-metrics
Start of growing season 2007
Small integral 2007
Phenological metrics
The Pinaleño Mountains
southeastern Arizona
260
240
220
Linear ((SOS2000))
Linear (SOS2004)
Linear (SOS2008)
Linear ((SOS2001))
Linear (SOS2005)
Linear (SOS2009)
Linear ((SOS2002))
Linear (SOS2006)
Linear (SOS2010)
Linear ((SOS2003))
Linear (SOS2007)
200
Start of season (DO
OY)
180
160
140
120
100
80
60
40
20
0
1500
1700
1900
2100
2300
2500
2700
Elevation (m)
2900
3100
3300
3500
Elevation
Startt of season (DOY)
The Sierra El Pinito is a large
Sky Island mountain range
located in southeast of
Nogales, Sonora
260
Linear (SOS2000)
240
Linear (SOS2004)
(
)
220
Linear (SOS2008)
200
180
160
140
120
100
80
60
40
20
0
1400
1500
1600
Linear (SOS2001)
Linear ((SOS2005))
Linear (SOS2009)
Linear (SOS2002)
Linear ((SOS2006))
Linear (SOS2010)
Linear (SOS2003)
Linear ((SOS2007))
Elevation
1700
1800
1900
El ti (m)
Elevation
( )
2000
2100
2200
2300
260
240
220
Linear (SOS2000)
Linear (SOS2004)
Linear (SOS2008)
Linear (SOS2001)
Linear (SOS2005)
Linear (SOS2009)
Linear (SOS2002)
Linear (SOS2006)
Linear (SOS2010)
200
The Pinaleño Mountains
180
Start of se
eason (DOY)
Linear (SOS2003)
Linear (SOS2007)
160
140
120
100
80
60
40
20
Star
rt of season (DOY)
0
1500
1700
260 Linear (SOS2000)
240 Linear (SOS2004)
220 Linear (SOS2008)
200
180
160
140
120
100
80
60
40
20
0
1400 1500 1600
1900
2100
Linear (SOS2001)
Linear (SOS2005)
Linear (SOS2009)
2300
Linear (SOS2002)
Linear (SOS2006)
Linear (SOS2010)
2500
2700
Elevation (m)
Linear (SOS2003)
Linear (SOS2007)
Sierra El Pinito.
1700
1800 1900
Elevation (m)
2000
2100
2200
2300
2900
3100
3300
3500
Annual
SOS
trends
80000
70000
60000
Linear (SI2000)
Linear (SI2001)
Linear (SI2002)
Linear (SI2003)
Linear (SI2004)
Linear (SI2005)
Linear (SI2006)
Linear (SI2007)
Linear (SI2008)
Linear (SI2009)
Linear (SI2010)
Sm
mall Integral
Pi l ñ Mountains
Pinaleño
M
t i
50000
40000
30000
20000
10000
0
1500
60000
Small IIntegral
50000
2000
Linear (SI2000)
Linear (SI2003)
Linear (SI2006)
Linear (SI2009)
Linear (SI2001)
Linear (SI2004)
Linear (SI2007)
Linear (SI2010)
2500
Elevation( m)
Linear (SI2002)
Linear (SI2005)
Linear (SI2008)
40000
30000
20000
10000
0
1400
Sierra El Pinito
1600
1800
2000
Elevation (m)
2200
3000
3500
Inter-annual variability
of productivity changes
as a function of
elevation
Coefficient of variation (COV)
values for start of season
(2000-2010)
Elevation
Coefficient of variation for the start
of growing season was variable
among the sky islands, highest for
the northern sky islands vs southern
This suggested that the landscape
of the northern sky islands is
responding strongly to interannual
changes in climate and/or
disturbance
The observed high COV values for all
of the sky Islands are located in
Higher elevation
The desert areas shows low and
high variability in the season start
dates likely related to distribution of
winter/spring/summer precipitation
COV of SOS
Coefficient of
variation of Small
I t
Integral
l
Elevation
Distribution of coefficient of
variation
i ti
(COV) values
l
for
f small
ll
integral
• The COV of vegetation
g
p
production in
the northern sky islands are higher
comparing to these in the south.
• This suggested that the landscape of
the northern sky islands is responding
strongly to interannual changes in
climate and/or disturbance
COV of Small Integral
Elevation
COV of length of season
Coefficient of variation
(COV) for length of
growing season
Intercept
SOS Spatio-temporal
trends.
1. P value shows significance
and spatial
p
variability
y for all
sky islands
Slope
P Value
Intercept
Spatio-temporal
trends for Small
integral
Slope
P Value
Intercept
Spatio-temporal
Spatio
temporal
regression for
l
length
th off
growing season
Slope
P Value
Pinaleno Mountains
MLR:
SOS response to
Climate and
Topographical Data
Madrean Pinyon-Juniper
Woodland communities
in the Pinaleno
mountains (2003)
DEM
PinyonJuniper
Woodland
A
Aspect
t
Sl
Slope
P
Precw
x
x
x
P
Precs
T
Tmaxw
T
Tmaxs
x
T i
Tmins
T i
Tminw
T i
Tminw
R2
x
x
0.428
Pinaleno Mountains MLR:
Small Integral in response to
Climate and Topographical Data
Precw= Winter Precipitation
Precs= Summer Precipitation
Tmaxs= Max Summer Temperature
Tmaxw= Max Winter Temperature
Tmins= Min Summer Temperature
Tminw= Min Winter Temperature
Elevation
Aspect
SI2000
x
x
SI2001
x
x
SI2002
x
SI2003
x
SI2004
SI2005
Slope
x
Precs
Precw
Tmaxs
Tmaxw
Tmins
Tminw
R2
x
x
x
x
x
0.245
x
x
x
x
x
x
0.287
x
x
x
x
x
x
0.263
x
x
x
x
x
x
x
0.478
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
SI2006
x
SI2007
x
x
SI2008
x
x
x
x
x
x
x
SI2009
x
x
x
x
x
x
x
SI2010
x
x
x
x
x
x
0 552
0.552
x
0.400
x
0.051
x
x
x
0.364
x
0.186
x
0.533
0.505
Findings to date
• Satellite time series data can be used to derive sky
island vegetation phenological information to
monitor and assess vegetation response to climate
variability
i bilit and
d change
h
and
d disturbance
di t b
events
t such
h
as wildfires
• The spatio-temporal phenological characterization
utilizing MODIS time series data show distinctive
vegetation response patterns and trajectories
• Phenology of sky islands and elevation clines
respond to topography and interannual variability in
precipitation
i it ti and
d temperature
t
t
van Leeuwen et al., 2010. Phenological Characterization of Desert Sky Island Vegetation
Communities with Remotely Sensed and Climate Time Series Data. Remote Sens., 2, 388-415.
Davison et al., 2011. Remotely sensed vegetation phenology and productivity along a climatic
gradient: on the value of incorporating the dimension of woody plant cover. Global Ecology and
Biogeography.; Online July 10, 2010. http://dx.doi.org/10.1111/j.1466-8238.2010.00571.x
MODIS NDVI Transect (North South)
DOY 241
DOY 33
Winter DOY 241
Summer DOY 33
MODIS NDVI TRANSECTS
(WEST EAST) Summer DOY 33
Winter DOY 241 (WEST-EAST)
croplands
http://daac.ornl.gov/glb_viz_2/16Oct2011_18:33:47
_840455417L-37.448497L71.841869S9L9_MOD13Q1/index.html
Oak Ridge National Laboratory
Distributed Active Archive Center
(ORNL DAAC). 2010. MODIS
subsetted land products,
Collection 5. Available on-line [
http://daac ornl gov/MODIS/modi
http://daac.ornl.gov/MODIS/modi
s.html] from ORNL DAAC, Oak
Ridge, Tennessee, U.S.A.
Accessed 10-16-2011.
MODIS NDVI
interannual time
series - Shrubland
http://daac.ornl.gov/glb_viz_2/06Oct2011_
http://daac
ornl gov/glb viz 2/06Oct2011
20:31:49_841728164L31.353636941500987L71.015625S25L25_MOD13Q1/index.html
NDVI interannual
ti
time
series
i Forest
http://daac.ornl.gov/glb_viz_2
/16Oct2011_18:51:41_35704
1069L-37.448497L71.521944S9L9 MOD13Q1/i
71.521944S9L9_MOD13Q1/i
ndex.html
Oak Ridge National Laboratory
Distributed Active Archive Center
(ORNL DAAC). 2010. MODIS
subsetted land products, Collection 5.
Available on-line [
http://daac.ornl.gov/MODIS/modis.htm
l] from ORNL DAAC, Oak Ridge,
Tennessee, U.S.A. Accessed 10-162011.
NDVI
interannual time
series -Forest
Oak Ridge National Laboratory
Distributed Active Archive Center
(ORNL DAAC). 2010. MODIS
subsetted land products, Collection 5.
Available on
on-line
line [
http://daac.ornl.gov/MODIS/modis.htm
l] from ORNL DAAC, Oak Ridge,
Tennessee, U.S.A. Accessed 10-162011
http://daac.ornl.go
v/glb_viz_2/16Oct
2011 18:51:41 35
2011_18:51:41_35
7041069L37 448497L37.448497L
71.521944S9L9_
MOD13Q1/index.h
tml
MODIS NDVI
interannual time
series - AG
http://daac.ornl.gov/glb_viz_2/06Oct2011_2
04
0:45:55_913454647L-38.343697L9134 464 L 38 34369 L
72.382719S25L25_MOD13Q1/index.html
The Future
• F
Further
h characterize
h
i pheno-metrics
h
i for
f different
diff
sky islands and associated vegetation types
• Examine impact of disturbance and climate
change and variability
• Sky island RS phenology model development
• Are Sky
y Islands g
good barometers for climate
change?
• IPCC - Land surface phenology: a proxy for
climate change and variability?
• The phenology of Chile?
• Longer time series
Muchas Gracias!
[email protected]