mike madritch - appalachian state university phil townsend –university of wisconsin
DESCRIPTION
Remote Sensing of Forest Genetic Diversity and Assessment of Below Ground Microbial Communities in Populus tremuloides Forests . Mike Madritch - Appalachian State University Phil Townsend –University of Wisconsin Karen Mock – Utah State University Rick Lindroth – University of Wisconsin. - PowerPoint PPT PresentationTRANSCRIPT
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Remote Sensing of Forest Genetic Diversity and Assessment of Below Ground Microbial Communities in Populus tremuloides
Forests
Mike Madritch - Appalachian State University Phil Townsend –University of WisconsinKaren Mock – Utah State UniversityRick Lindroth – University of Wisconsin
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Genotype
Phenotype
Nutrient Cycles
Litter Chemistry
Environment
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Hyperspectral data
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Objectives
1. Estimate the genetic diversity of aspen stands across multiple ecoregions using remotely sensed data.
2. Build predictive models of genetically-mediated leaf chemistry using remotely sensed hyperspectral data.
3. Measure belowground microbial biodiversity and functional diversity that results from genetically determined variation in plant chemistry.
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1. Genetic2. Nutrient/microbial3. Remotely-sensed
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1. Genetic – aspen phylogeography
• Hundreds of genotypes with multiple ramets– Midwest tend to be
small – West tend to be large– Polyploidy issues– Progress
• 2009 complete ~8 microsatellites
• 2010 nearing completion
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Leaf• Carbon, nitrogen• Condensed tannins, lignin
• Soil• Nutrient: C, N, NH4
+, NO3-
• Microbial: extracellular enzymes, • t-RFLP
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2. Leaf and Soil analyses
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Hyperspectral data
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3. Remote sensing
• LANDSAT time series– Use fall phenology to identify aspen clones– Build time series databases normalized to end of
season dates• Mid-summer AVIRIS imagery
– Spectral variation to estimate clonal differences– Estimate canopy chemistry
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LANDSAT – end of season
USGS remote sensing phenology
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AVIRIS Pre-processing: Steps
Uncorrected image 1) Cloud, shadow, water mask
2) Cross-track correction
3) Remove redundant bands
4) Atmospheric correction 5) Terrain normalization
Aditya Singh
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AVIRIS spectral analysis
400 900 1400 1900 24000
1000
2000
3000
4000
5000
6000
f090713t01p00r11rdn
T151T176T76T26T1
wavelength (nm)% r
eflec
tanc
e x
10,0
00
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400 900 1400 1900 24000
1000
2000
3000
4000
5000
6000
AVIRIS 7-13-2009
T151T176T76T26
wavelength (nm)% r
eflec
tanc
e x
10,0
00 2.37%2.31%2.20%2.32%2.11%
Nitrogen Concentration
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400 900 1400 1900 24000
1000
2000
3000
4000
5000
6000
f090713t01p00r11rdn
T151T176T76T26T1
wavelength (nm)
% r
eflec
tanc
e x
10,0
00
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Hyperspectral data
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Remote sensing summary
• LANDSAT – Promising, long data series needed, may work
better on larger clones• AVIRIS
– Expected relationships between canopy % N and reflectance persists within species
– MORE PROMISE than with LANDSAT• Visable spectra show no difference, spectra associated
with canopy chemistry shows differences among genotypes
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Acknowledgements
• NASA• Clayton Kingdon• Peter Wolter• Timothy Whitby• Aditya Singh• Jacqui Bryant
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Preliminary analysis shows that bands known to correlate with N agree with canopy nitrogen measurements. Too few corrected AVIRIS images to present correlation.
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0
0.2
0.4
0.6
0.8
1
Soil
N (%
)02468
1012141618
Soil
C (%
)
05
101520253035
Aspen genotype
NH4
-N u
g/ g
soil
• Aspen genotype influences– Belowground N– Belowground C– Belowground NH4
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0.5
1
1.5
2
2.5
3
3.5
Aspen genotype
Leaf
N (%
)
0
5
10
15
20
25
30
Tann
in (%
)• Aspen genotype influences– Canopy tannin– Canopy N
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0
10
20
30
40
50
BG m
Mol
/hr
/gso
il05
1015202530354045
CB m
Mol
/hr
/gso
il
050
100150200250300350400
Aspen genotype
LA m
Mol
/hr
/gso
il
• Aspen genotype influences– Belowground
microbial community
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