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The West Cascades
Park City
The West Cascades
• NaNationwide FForest IImputation SStudy
Gradients in Plant Community Ecology
• Plant species exhibit distributional patterns that are a reflection of changing environmental conditions.
500 1000 1500 2000
01
02
03
0
Douglas-fir
Elevation
Ba
sal A
rea
Hierarchies from landscape ecology
• “… a system of interconnections wherein the higher levels constrain the lower levels to various degrees...” (Turner et al. 2001)
• Broad-scale, factors (e.g., climate) constrain local species pools.• Local topography, disturbance, succession and competition
determine which species from that pool occupy a given site.
Time ------------------------------------->
Sp
atia
l E
xten
t --
----
-- > CLIMATE
Disturbance
Local Topography
Objective
• Explore vegetation-environment relations in the context of imputation mapping.
• Different modeling techniques make different assumptions about the world.
– Euclidean Nearest Neighbor. – Gradient Nearest Neighbor.– Random Forest.
Methods
– Maps built from:
– 784 records from our plot database (FIA annual plots)– and 16 mapped explanatory variables.
Landsat Bands 3,4,5
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: Euclidean Nearest Neighbor Imputation
feature space geographic space
Elevation
Rainfall
(1)Place plots
within feature space
• Advantages – Simplicity.– Quick to run.– Makes no assumptions about how vegetation
relates to the environment.
• Disadvantages– May not represent species-environment
relations well.
Pros and Cons: Euclidean Imputation
(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: Gradient Nearest Neighbor Imputationgradient space geographic space
CCAAxis 2
(e.g., Temperature, Elevation)
CCAAxis 1
(e.g., Rainfall, local
topography)
(1)conductgradient
analysis ofplot data
• Advantages to GNN– Shapes environmental space as it relates to forest
composition. – Model structure is straightforward, reasonably
intuitive.
• Disadvantage to GNN– Assumes that species show a unimodal response to
environmental gradients (Gauch, 1982; ter Braak and Prentice, 1988).
Pros and Cons: Gradient Nearest Neighbor Imputation
500 1000 1500 2000
01
02
03
0
Douglas-fir
Elevation
Ba
sal A
rea
studyarea
Methods: Random Forest Nearest Neighbor Imputation
Random Forest space geographic space
Methods: Random Forest• One 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
|
Methods: Random Forest Imputation
|
157915
23610
81413
11181925
242317
1620
302726
2829
26162028
• Advantages– Models vegetation-environment relations – Free from distributional assumptions– High accuracy
• Disadvantages– Computing time– Interpretation is difficult
Pros and Cons: Random Forest Imputation
Comparisons
• Root Mean Square Difference (RMSD) for species basal area.
• Mapped distribution (presence/absence)– Douglas-fir (Pseudotsuga menziezii)– Sugar Pine (Pinus lambertiana)
Results
Accuracy
(scaled RMSD)
GNN Advantage
Random Forest Advantage
Methods Equally Good
Pacific Dogwood
Sugar Pine
Red Alder
Bigleaf Maple
Englemann Spruce
Grand Fir
Incense Cedar
Ponderosa Pine
California Black Oak
Giant Chinkapin
Shasta Red Fir
Western Red Cedar
Pacific Madrone
Western Hemlock
Lodgepole Pine
Western White Pine
Noble Fir
Mountain Hemlock
Pacific Silver Fir
Oregon White Oak
Douglas-Fir
White Fir
Subalpine Fir
Pacific Yew
0.0
0.5
1.0
1.5
RFGNNEuclidean*
Euclidean model not shown. Results were comparable, but never best.
Douglas-fir
• Often dominant.
• Widespread, early colonizer, long-lived.
• Only disappears at v. high elevations.
Douglas-fir Range
Euclidean78.0%
GNN59.2%
RandomForest74.3%
Estimated Actual Area79.77%
PresentAbsent
Douglas-fir Range
RMSD
0.0
0.2
0.4
0.6
0.8
1.0
Euclidean GNN Random Forest
(scaled)
Sugar Pine
• Spotty distribution, wide elevation range, mostly in the South.
Sugar Pine Range
Euclidean5.4%
GNN3.5%
RandomForest4.3%
Estimated Actual Area4.6%
PresentAbsent
Sugar Pine RangeEuclidean GNN Random Forest
RMSD
0.0
0.2
0.4
0.6
0.8
1.0
1.2
(scaled)
Conclusions
• The answer is...
YES!!
– The world can be seen as a gradient.
– But in some cases, the world is better described by a hierarchy.
Conclusions: Which model?• Broad-scale patterns are consistently predicted
by all 3 model types.• GNN works well most of the time.• If rare, or quirky species are our focus, however,
Random Forest may provide a very useful alternative.
• Both Random Forest and GNN are an improvement over simple euclidean imputation in terms of RMSD, but euclidean was often less biased in the range-maps.
The End.
• Questions?