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Random Forests and Nearest Neighbors: Methods for mapping the West Cascades of Oregon. Emilie Grossmann, Oregon State University Janet Ohmann, U. S. Forest Service James Kagan, Oregon State University Kenneth Pierce, U.S. Forest Service Heather May, Oregon State University - PowerPoint PPT Presentation
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Random Forests and Nearest Neighbors: Methods for mapping the West Cascades of Oregon
Emilie Grossmann, Oregon State University Janet Ohmann, U. S. Forest Service
James Kagan, Oregon State University Kenneth Pierce, U.S. Forest Service
Heather May, Oregon State University Matthew Gregory, Oregon State University
The West Cascades
Madison
The West Cascades
USGS Pacific Northwest ReGAP
• GAP project needs broad-scale, but also detailed vegetation base-maps.
• Consistent classification system:
NatureServe’s Ecological Systems
North Pacific Mesic-Wet Douglas-fir Western Hemlock Forest
• This ecological system is a significant component of the lowland and low montane forests of western Washington, northwestern Oregon, and southwestern British Columbia.
• ... In Oregon, it occurs on the western slopes of the Cascades, around the margins of the Willamette Valley, and on the west side of the Coast Ranges, and is reduced to locally small patches in southwestern Oregon.
... continued
North Pacific Mesic-Wet Douglas-fir Western Hemlock Forest
...• They differ from North Pacific Maritime Dry-Mesic
Douglas-fir-Western Hemlock Forest primarily in having more hydrophilic undergrowth species ...
• In many rather drier climatic areas, it occurs as small to large patches within a matrix of North Pacific Maritime Dry-Mesic Douglas-fir-Western Hemlock Forest; in dry areas, it can occur adjacent to or in a mosaic with North Pacific Dry Douglas-fir Forest and Woodland, and at higher elevations it intermingles with either North Pacific Dry-Mesic Silver Fir-Western Hemlock-Douglas-fir Forest or North Pacific Mesic Western Hemlock-Silver Fir Forest.
Can you see the problem?
• We need more information than LANDSAT
• This is why we need statistics for building the GAP maps.
What type of model to use?
Objective
• Compare Random Forest (RF) and Gradient Nearest Neighbor (GNN) modeling techniques with respect to:
1) classification accuracy
2) class area representation
3) spatial patterns
4) explanatory variables used
Methods
– GNN and RF models built from – 4222 records from our plot database– and mapped explanatory variables, selected from 115
possible layers
Landsat Bands, transformations, texture
Climate Means, seasonal variability
Topography Elevation, slope, aspect, solar
Disturbance Past fires, harvest, insects and disease
Location X, Y
Soil Parent Material e.g., Ultramafic rocks, sandstone, basalt, etc.
|
Methods: Random Forest• One Classification Tree.
Elevation < 1244
August Maximum < 2324 Temp
August Maximum < 2560 Temp
Summer Mean < 1279 Temp
Aug. to Dec. Temperature < 1279 Differential
Elevation < 1625LANDSAT Band 7 < 24
4224 42244224 4215
4272 42284215 4267
North Pacific Dry-Mesic Silver Fir-Western Hemlock-Douglas-fir Forest
Methods: Random Forest
• A “Forest” of classification trees.
• Each tree is built from a random subset of plots and variables.
• When the model is applied to a pixel, each tree ‘votes’ for an Ecological System.
|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: Adjusting The Random Forest Map
• The Random Forest model tends to over-map some systems, and under-map others.
• We can map the votes for the under-mapped systems, creating single-system maps.
• ...which can be used to expand their area in the final map.
Methods: Adjusting The Random Forest Map
Single System Map of: North Pacific Mesic
Western Hemlock-Silver Fir Forest
(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 Imputation
gradient space geographic spaceCCA
Axis 2(e.g., Climate)
CCAAxis 1
(e.g., elevation, Y)
(1)conductgradient
analysis ofplot data
The Maps
Without Landsat TMRF
RF_ADJ
GNN
With Landsat TMRF_TM
RF_ADJ_TM
GNN_TM
Results
RF:
0.340.68
RF_TM:
0.380.73
RF_ADJ:
0.340.70
RF_ADJ_TM:
0.380.70
GNN:
0.300.63
GNN_TM:
0.290.60
Top #: Kappa, Bottom #: Fuzzy Kappa
Best Maps: Class By Class (assigned by Kappa)RF RF_Adj RF_TM RF_Adj_TM GNN GNN_TM
Mediterranean California Mixed Evergreen Forest
Mediterranean California Mesic Mixed Conifer Forest and Woodland
Northern Rocky Mountain Western Larch Savanna
North Pacific Maritime Mesic Subalpine Parkland
Mediterranean California Lower Montane Black Oak-Conifer Forest and Woodland
Northern Rocky Mountain Subalpine Woodland and Parkland
North Pacific Maritime Dry-Mesic Douglas-fir-Western Hemlock Forest
East Cascades Mesic Montane Mixed-Conifer Forest and Woodland
Sierra Nevada Subalpine Lodgepole Pine Forest and Woodland
North Pacific Mesic Western Hemlock-Silver Fir Forest
Mediterranean California Dry-Mesic Mixed Conifer Forest and Woodland
California Montane Woodland and Chaparral
Rocky Mountain Lodgepole Pine Forest
Mediterranean California Red Fir Forest
Rocky Mountain Poor-Site Lodgepole Pine Forest
North Pacific Dry Douglas-fir Forest and Woodland
North Pacific Dry-Mesic Silver Fir-Western Hemlock-Douglas-fir Forest
North Pacific Maritime Mesic-Wet Douglas-fir-Western Hemlock Forest
North Pacific Broadleaf Landslide Forest and Shrubland
North Pacific Mountain Hemlock Forest
North Pacific Wooded Volcanic Flowage
Northern California Mesic Subalpine Woodland
North Pacific Lowland Mixed Hardwood Conifer Forest and Woodland
Northern Rocky Mountain Ponderosa Pine Woodland and Savanna
Rocky Mountain Subalpine Dry-Mesic Spruce-Fir Forest and Woodland
0 2 9 10 1 3
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RF
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_AD
J
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_AD
J_T
M
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N_T
M
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_AD
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Shannon Weaver Diversity
Top 5 VariablesRF RF _ TM GNN GNN_TM
Y Y Mean annual temperature
Mean annual temperature
Annual
short-wave radiation
Annual
short wave radiation
December
minimum temperature
December
minimum temperature
Summer
moisture stress
X Elevation Elevation
Elevation Summer
moisture stress
Annual vapor pressure Annual vapor pressure
Summer temperature Summer temperature Summer
moisture stress
Summer
moisture stress
First TM Variables: Median Filtered Tasseled Cap axis 2
(26 out of 36)
Median Filtered LANDSAT band 3
(13 out of 23)
RF_ADJAccuracy OK
Area OK
RFAccuracy OK
Area lousy
Coarse-grained
RF_TMBest Accuracy
Area lousy
RF_ADJ_TMAccuracy Good
Area OK
Incorporates Imagery
GNNAccuracy OK
Area Good
No Imagery
GNN_TMLeast accurate
Area good
Fine-grained
XX XX X
Conclusions
• Buyer Beware.– The patterns in a map are at least partly a function of model
choice.
• The most appropriate map depends upon intended application.– Importance of area estimations vs. incorporation of imagery – For some applications, the GNN base-map may be better.
• We chose RF_Adj_TM, because it balanced a variety of concerns well.
Acknowledgements:
• USGS GAP analysis program
• LEMMA research group at Oregon State University
• Jimmy Kagan – reality-check and systems identification
• Brendan Ward – programming help
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