Why Worry?
• Predictive models of vegetation-environment relationships are an important first step in mapping vegetation classes at regional scales.
• There are many modeling techniques available for building maps.
• Because different models may produce different maps, attention to model-choice is important.
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
The West Cascades
Asheville
The West Cascades
Mapping Methods
– We map NatureServe's Ecological Systems – Using GNN and RF models built from
– 8,109 records from our plot database– and mapped explanatory variables, selected from 115
possible layers
– At a 30m grain
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:
|MTM100 < 21.1095
DEM < 679.825
MTM300 < 13.874
SLPPCT < 29.3858
YFIRE < 3.15519
CANOPY < 42.085930725365 3415
4323
8793 4847
5767
Methods: Random Forest
• A whole forest of classification trees!
• Each tree model is built from a random subset of explanatory variables and input data.
• When the model is applied to mapped data, each tree ‘votes’ on which Ecological System a pixel should be.
|TM100 < 22.9069
TM100 < 19.0223 FOG < 0.5
TM100 < 33.82934108 5120
5977 86398622
|ANNHDD < 2766.21
SLPPCT < 10.3216 STDTM100 < 16.1739
ANNHDD < 3469.43STDTM100 < 46.7235
9148 5675 3517 4192 4607 5832
|TM200 < 22.9549
STRATUS < 201.108
TM200 < 35.1356
4156
5559 8269
8694
|MTM300 < 28.9494
MTM300 < 13.8683 IDSURVEY < 0.5
MTM300 < 43.8013
3922 4672
6770 8947
5136
|DECMINT < 48.1696
MTM700 < 27.8564
DECMINT < -214.708DECMINT < -276.567
R5700 < 145.622
R5700 < 185.313
4280 36687896 4737
6104
8506 5086
|TM100 < 22.9069
TM100 < 19.0223 DISTNF:b
4108 5120
5833 8480
Methods: Adjusting The Random Forest Map
• The RF model may favor some classes to maximize overall accuracy. – Over-mapping some systems– And under-mapping others
• We can map the votes for the under-mapped systems, creating single-system probability maps.
• ...which can be used to expand their area in the final map.
Methods: Adjusting The Random Forest Map
Single System Map of: Mediterranean California Dry-Mesic
Mixed Conifer 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: GNN
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:
83.8%91.8%
RF_TM:
82.5%91.0%
RF_ADJ:
82.9%91.0%
RF_ADJ_TM:
82.5%90.4%
GNN:
82.5%89.7%
GNN_TM:
78.6%87.5%
Top #: Accuracy, Bottom #: Fuzzy Accuracy
MC
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PlotRF
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PlotRF
RF_ADJRF_TM
RF_ADJ_TM
Class Area Representation
RF
RF
_AD
J
RF
_TM
RF
_AD
J_T
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GN
N
GN
N_T
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20
40
60
80
% o
f la
ndsc
ape
Largest Patch Indexp < 0.001
RF
RF
_AD
J
RF
_TM
RF
_AD
J_T
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GN
N
GN
N_T
M
0.0
0.2
0.4
0.6
0.8
edge
s /
cells
Edge Densityp < 0.001
RF_ADJ:Accuracy OK
Area good
RF:Most Accurate
Area lousy
Coarse-grained
RF_TM:Accuracy OK
Area lousy
RF_ADJ_TM:Accuracy OK
Area good
GNN:Accuracy OK
Area good
GNN_TM:Least accurate
Area good
Fine-grained
? ? ? XX X
Conclusions
• No single map is perfect.
• Each has its strengths.
• ...and weaknesses.
• The maps vary most with respect to class areas, and pattern.
• Unfortunately, we lack reference data for pattern.
• And yet, we still need to choose ‘the best’ technique for the GAP vegetation maps.
Discussion
• If you were choosing which methods to use to build a GAP map, which one seems best to you?
Why?• Acknowledgements:
– USGS GAP analysis program– LEMMA research group at Oregon State
University
Landscape Ecology Modeling Mapping & Analysis
