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Spatial variation in autumn leaf color
Matt Hinckley
EDTEP 586
Autumn 2003
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Introduction Background Initial model
Methods Results
Data, maps, graph Discussion
Evidence for claim Revision of model
Introduction: background
Leaves change color in the fall when they lose their chlorophyll
Altitudinal succession mirrors latitudinal succession Does this principle hold true in this case?
Trees “know” when it’s fall
Introduction: background
Leaves change color in the fall when they lose their chlorophyll
Altitudinal succession mirrors latitudinal succession Does this principle hold true in this case?
Trees “know” when it’s fall
Introduction: background
Leaves change color in the fall when they stop making chlorophyll
Altitudinal succession mirrors latitudinal succession Does this principle hold true in this case?
Trees “know” when it’s fall
Introduction: background
Leaves change color in the fall when they stop making chlorophyll
Altitudinal succession mirrors latitudinal succession Does this principle hold true in this case?
Trees “know” when it’s fall Factors:
Light, temperature, precipitation
Introduction: background
Leaves change color in the fall when they stop making chlorophyll
Altitudinal succession mirrors latitudinal succession Does this principle hold true in this case?
Trees “know” when it’s fall Factors:
Light, temperature, precipitation
?
Introduction: background
Leaves change color in the fall when they stop making chlorophyll
Altitudinal succession mirrors latitudinal succession Does this principle hold true in this case?
Trees “know” when it’s fall Factors:
Light, temperaturetemperature, precipitation
?
Introduction: background
Leaves change color in the fall when they stop making chlorophyll
Altitudinal succession mirrors latitudinal succession Does this principle hold true in this case?
Trees “know” when it’s fall Factors:
Light, temperaturetemperature, precipitationDefinitely changes by altitude in the Cascades
?
Introduction: initial model
Leaf color
When leaves fall off
Spatialvariability
Introduction: initial model
Leaf color
When leaves fall off
Spatialvariability
Temp.
Precip.
Correlation
Causal
Introduction: initial model
Leaf color
When leaves fall off
Spatialvariability
?Temp.
Precip.
Light
Correlation
Causal
Adiabatic cooling
Adiabatic cooling
Introduction: initial model
Leaf color
When leaves fall off
Spatialvariability
?Temp.
Precip.
Light
Correlation
Causal
Elevation Adiabatic cooling
Adiabatic cooling
Introduction: assumptions
Trees across the sample area will have leaves that can be observed on them Most problematic assumption: high elevation deciduous
trees had lost all leaves Conducting observations ≥ 1 week apart would be
OK It was not – leaves change fast, so only one observation
was conducted I would be able to control for tree species
Methods
Driving the Puget Sound area Digital photography Image analysis
Quantification of color GIS analysis of quantitative data
Mapping Spatial interpolation
Methods
Driving the Puget Sound area Digital photography Image analysis
Quantification of color GIS analysis of quantitative data
Mapping Spatial interpolation
Study area – drivingStudy area – driving
Digital photos
Methods
Driving the Puget Sound area Digital photography
Digital photos
Methods
Driving the Puget Sound area Digital photography
Digital photos
Methods
Driving the Puget Sound area Digital photography
Methods
Hue
Driving the Puget Sound area Digital photography Image analysis
Quantification of color GIS analysis of quantitative data
Mapping Spatial interpolation
Methods
Driving the Puget Sound area Digital photography Image analysis
Quantification of color GIS analysis of quantitative data
Mapping Spatial interpolation
Results
The data
Sample Number Color Elevation Location
2 103 303 66 30
4 70 70 SR 18 interchange5 47 70 SR 18 interchange
6 67 507 48 50
8 68 509 71 80
10 41 100 SR 167 Puyallup curve11 76 100
12 46 3013 55 30 Puyallup River
14 69 100 South Hill17 75 500 S.I.R.
18 77 500 S.I.R.19 69 600 NW Trek
20 50 70021 31 800
22 69 125023 70 1200 Almost Elbe
24 53 1200 Almost Elbe25 37 1250
26 39 125027 45 1300
28 53 150029 69 1700
30 69 1800 Past Ashford31 44 1850
32 37 190033 70 2100 In MRNP
34 19 3200 Cougar Rock35 10 3400
36 19 345037 19 3500 Christine Falls
38 12 3900 Past Nisqually bridge39 17 4200 Snow zone
40 14 420044 0 6000
Results
The data How to interpret it?
Sample Number Color Elevation Location
2 103 303 66 30
4 70 70 SR 18 interchange5 47 70 SR 18 interchange
6 67 507 48 50
8 68 509 71 80
10 41 100 SR 167 Puyallup curve11 76 100
12 46 3013 55 30 Puyallup River
14 69 100 South Hill17 75 500 S.I.R.
18 77 500 S.I.R.19 69 600 NW Trek
20 50 70021 31 800
22 69 125023 70 1200 Almost Elbe
24 53 1200 Almost Elbe25 37 1250
26 39 125027 45 1300
28 53 150029 69 1700
30 69 1800 Past Ashford31 44 1850
32 37 190033 70 2100 In MRNP
34 19 3200 Cougar Rock35 10 3400
36 19 345037 19 3500 Christine Falls
38 12 3900 Past Nisqually bridge39 17 4200 Snow zone
40 14 420044 0 6000
Results: mappingResults: mapping
Results: mappingResults: mapping
Results: mappingResults: mapping
Results: mappingResults: mapping
0
20
40
60
80
100
120
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
Sample Number
Hue
0
1000
2000
3000
4000
5000
6000
7000
Feet
Color Elevation
Leaf color and elevation
0
20
40
60
80
100
120
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
Sample Number
Hue
0
1000
2000
3000
4000
5000
6000
7000
Feet
Color Elevation
Leaf color and elevation
Freezing level ?
Spatial interpolationSpatial interpolation
Spatial interpolationSpatial interpolationSpatial interpolationSpatial interpolation
Data limitations
Image analysis problems Differences in lighting Selecting a tree to sample in each picture
Tree species loosely controlled Limited sample size Snapshot in time and on Earth
Therefore, claims may not be widely applicable
Final Claim
Generally, leaf color hue decreases along the visible spectrum as elevation increases Shown by data
Temperature drops as altitude increases Known principle, observable in Cascades
Therefore, lower temperature = more intense leaf color
Initial revised model
Leaf color
When leaves fall off
Spatialvariability
?Temp.
Precip.
Light
Correlation
Causal
Elevation Adiabatic cooling
Adiabatic cooling
Final model
Leaf color
When leaves fall off
Temp.
Precip.
Light
Correlation
Causal
Elevation Adiabatic cooling
Latitude
Otherfactors
Hard to test locally
More easily tested
?
Conclusions Data shows:
Lower temperature = more intense leaf color We know that:
Altitudinal succession = latitudinal succession
Remains unclear whether these two principles can be applied together on a larger scale Regional/local limitation Further research: road trip to Alaska
Control for tree species!
