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Gradients or hierarchies? Which assumptions make a better map?. Emilie B. Grossmann Janet L. Ohmann Matthew J. Gregory Heather K. May. How does the world work?. The World is a Gradient Curtis 1957 The Vegetation of Wisconsin The World is a Hierarchy Delcourt et al. 1983 - PowerPoint PPT Presentation
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Gradients or hierarchies? Which assumptions make a better map?
Emilie B. GrossmannJanet L. Ohmann
Matthew J. GregoryHeather K. May
How does the world work?
• The World is a Gradient– Curtis 1957
• The Vegetation of Wisconsin
• The World is a Hierarchy– Delcourt et al. 1983
• The World is Shaped by Many Different Things– Wimberly and Spies 2001 Influences of environment and
disturbance on forest patterns in coastal Oregon watersheds
– “No single theoretical framework was sufficient to explain the vegetation patterns observed in these forested watersheds.”
Regional-Scale Vegetation in Western Oregon:a (very) simple conceptual model.
Tree Species Distributions
Rainfall-Temperature GradientCool/Wet Hot/Dry
Loca
l Sca
le
Reg
iona
l Sca
le
Short-term Long-term
Forest Structure
Can
opy
Clo
sure
Time Since Disturbance
Spatial Data Covering Regional Scales in Western Oregon
Tree Species Distributions
Rainfall-Temperature GradientCool/Wet Hot/Dry
Loca
l Sca
le
Reg
iona
l Sca
le
Short-term Long-term
Forest Structure
Can
opy
Clo
sure
Time Since Disturbance
ElevationClimate (PRISM)Soil Parent Material
Local TopographyLANDSAT (bands and transformations)
Our Quest
• Make a highly accurate regional-scale vegetation map, that simultaneously represents detailed forest composition and structure.
• Peril #1:– The world is a complex place.
• Solution #1:– Use statistical models to sort out the complexity, and make a
prediction.
• Peril #2:– Statistical models often come with ASSUMPTIONS that cause
problems when violated.
• Solution #2:– Try to find a model with reasonable assumptions.– See whether it works any better than other methods.
Perils
Methods– Maps built from:
– 1677 plots (FIA annual plots)
– 19 possible mapped explanatory variables.
Landsat Bands 3,4,5, Tassled Cap
Climate PRISM: Means, seasonal variability
Topography Elevation, slope, aspect, solar
Location X, Y
studyarea
(2) Place new pixel
withinfeature space
(3) find nearest-neighbor plot within feature
space
(4) impute nearest
neighbor’s value to
pixel
Methods: k-NN
feature space geographic space
Elevation
Rainfall
(1)Place plots
within feature space
(2) calculate
axis scores of pixel from
mapped data layersstudyarea
(3) find nearest-
neighbor plot in
gradient space
(4) impute nearest
neighbor’s value to
pixel
Methods: GNNgradient space geographic space
CCAAxis 2
(e.g., Temperature, Elevation)
CCAAxis 1
(e.g., Rainfall, local
topography)
(1)conductgradient
analysis ofplot data
ASSUMPTION: Species exhibit unimodal responses to environmental variables.
Methods: Classification Tree
|Elevation < 1244
August Maximum < 23.24 Temp
August Maximum < 25.60 Temp
Summer Mean < 12.79 Temp
Aug. to Dec. Temperature < 12.79 Differential
Elevation < 1625LANDSAT Band 5 < 24
PSME TSHEPSME THPL
ABAM TSMEPSME PIPO
High Elevation ( > 1244)High August Temp (> 23.24°C)High reflectance in Band 5 (> 24)
Methods: Random Forest
• A “Forest” of classification trees.
• Each tree is built from a random subset of plots and variables.
|ANNHDD < 4271.43
SMRPRE < 5535.09
X < 8808.88ANNHDD < 3950.45
SMRPRE < 5576.65
SMRTP < 2088.19
MR4300 < 166.968
ANNHDD < 4779.98
4215 4222 4224 4224
4228
4267 42154272 4228
|ANNTMP < 665.874
ANNVP < 591.82
ANNHDD < 4710.98X < 7248.68
STRATUS < 3.7435
X < 7762.43 X < 6340.86
ANNHDD < 3901.34215 42284215 4272
4215 4205
4224
4226 4224
|ANNGDD < 2578.11
ANNVP < 591.82
ANNGDD < 2190.48
ANNPRE < 740.947
STRATUS < 40.8768
R5400 < 117.208
ANNGDD < 3028.96
4228 4215
4272
4215 42154224
4224 4224
|ANNFROST < 1693.8
ANNFROST < 1271.82
CONTPRE < 788.967IDSURVEY < 456
ANNFROST < 2051.42
IDSURVEY < 423ADR5700 < 70.8343
4224 4224 4224 4224
4215 4272 4267 4228
|SMRTMP < 1206.3
ANNVP < 608.87
R5400 < 158.673
SMRTMP < 1105.53
ANNVP < 660.51
ANNVP < 610.822
TC200 < 134.347
SMRTMP < 1444.82
CONTPRE < 785.7484228 42154267
4272
4267 42154215
4224
4214 4224
|ANNHDD < 4204.74
DIFTMP < 2847.06
ANNHDD < 3669.42
CVPRE < 8079.84
DIFTMP < 3022.3
DIFTMP < 2854.2
SMRTMP < 1123.01SMRTMP < 1184.12
4226 42144224 4224 4215
4228 4272 4228 4215
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k.NNGNNRFNN
Ka
pp
a
0.0
0.2
0.4
0.6
0.8
1.0
Species Presence-Absence(Kappa statistics)
Forest Structure
Ba
sal A
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- L
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Ca
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- A
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k-NNGNNRFNN
Forest Structure: Basal Areak-NN GNN RFNN
PERIL!
COMPUTING TIME! Random forest took over a week to run.
Just finished last Friday morning.
If you are in a rush to prepare for a
conference, don’t take this route!!!
Summary
• Species Kappas– Each model had strengths and weaknesses.– All did well with the dominants.
• Structure– RFNN consistently just a little bit better.
• Maps– Broad-scale: Indistinguishable– Local-scale: GNN noisiest
• Overall Community Structure– RFNN best.
Conclusion
• Random forest did the best all around. broad-scale (species composition)
AND
local-scale (structure)
But, there’s still room for improvement.