1. 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.

2. 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

3. The West Cascades Asheville The West Cascades

4. 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 LandsatBands, transformations, texture ClimateMeans, seasonal variability TopographyElevation, slope, aspect, solar DisturbancePast fires, harvest, insects and disease LocationX, Y Soil Parent Materiale.g., Ultramafic rocks, sandstone, basalt, etc.

5. Methods: Random Forest One Classification Tree:

6. 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.

7. 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.

8. Methods: Adjusting The Random Forest Map Single System Map of: Mediterranean California Dry-Mesic Mixed Conifer Forest

9. (2) calculate axis scores of pixel from mapped data layers study area (3) find nearest- neighbor plot in gradient space (4) impute nearest neighbor’s value to pixel Methods: GNN gradient spacegeographic space CCA Axis 2 (e.g., Climate) CCA Axis 1 (e.g., elevation, Y) (1) conduct gradient analysis of plot data

10. The Maps Without Landsat TM RF RF_ADJ GNN With Landsat TM RF_TM RF_ADJ_TM GNN_TM

11. Results

12. 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

13. Class Area Representation

15. 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 ? ? ? X X X

16. 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.

17. 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