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Gradients or hierarchies? Which assumptions make a better map?

Explore how assumptions impact vegetation mapping using gradients or hierarchies and the methods to overcome computational perils like complexity and model assumptions. Discover the strengths and challenges of different models such as k-NN, GNN, CCA, and Random Forest for accurate regional-scale vegetation mapping. Understand the importance of statistical models and their assumptions in creating precise vegetation maps.

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Gradients or hierarchies? Which assumptions make a better map?

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  1. Gradients or hierarchies? Which assumptions make a better map? Emilie B. Grossmann Janet L. Ohmann Matthew J. Gregory Heather K. May

  2. 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.”

  3. Tree Species Distributions Rainfall-Temperature Gradient Cool/Wet Hot/Dry Local Scale Regional Scale Forest Structure Canopy Closure Time Since Disturbance Short-term Long-term Regional-Scale Vegetation in Western Oregon:a (very) simple conceptual model.

  4. Tree Species Distributions Rainfall-Temperature Gradient Cool/Wet Hot/Dry Local Scale Regional Scale Forest Structure Canopy Closure Time Since Disturbance Short-term Long-term Spatial Data Covering Regional Scales in Western Oregon Elevation Climate (PRISM) Soil Parent Material Local Topography LANDSAT (bands and transformations)

  5. Our Quest • Make a highly accurate regional-scale vegetation map, that simultaneously represents detailed forest composition and structure.

  6. Perils • 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.

  7. You Are Here

  8. Methods Maps built from: 1677 plots (FIA annual plots) 19 possible mapped explanatory variables.

  9. Methods: k-NN (2) Place new pixel within feature space study area (4) impute nearest neighbor’s value to pixel (3) find nearest-neighbor plot within feature space feature space geographic space Elevation (1) Place plots within feature space Rainfall

  10. Methods: GNN (2) calculate axis scores of pixel from mapped data layers study area (4) impute nearest neighbor’s value to pixel (3) find nearest-neighbor plot in gradient space ASSUMPTION: Species exhibit unimodal responses to environmental variables. gradient space geographic space CCA Axis 2 (e.g., Temperature, Elevation) (1) conduct gradient analysis of plot data CCA Axis 1 (e.g., Rainfall, local topography)

  11. Methods: Random Forest Nearest Neighbor Imputation study area Random Forest space geographic space ?

  12. Methods: Classification Tree Elevation < 1244 August Maximum < 25.60 Temp August Maximum < 23.24 Temp Summer Mean < 12.79 Temp Aug. to Dec. Temperature < 12.79 Differential LANDSAT Band 5 < 24 Elevation < 1625 PSME PIPO TSHE PSME ABAM TSME PSME THPL High Elevation ( > 1244) High August Temp (> 23.24°C) High reflectance in Band 5 (> 24)

  13. Methods: Random Forest A “Forest” of classification trees. Each tree is built from a random subset of plots and variables.

  14. Methods: Random Forest Imputation 28 29 26 16 20 28 30 27 26 2 3 6 10 1 5 7 9 15 8 14 13 11 18 19 25 24 23 17 16 20

  15. Accuracy Assessment • Species Kappa • RMSD • Bray-Curtis Distance

  16. Results

  17. Species Presence-Absence(Kappa statistics)

  18. Forest Structure

  19. Forest Structure: Basal Area k-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!!!

  20. Crater Lake Closeup

  21. Forest Structure: Basal Areak-NN

  22. Forest Structure: Basal AreaGNN

  23. Forest Structure: Basal AreaRFNN

  24. Community Structure

  25. 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.

  26. Conclusion • Random forest did the best all around. broad-scale (species composition) AND local-scale (structure) But, there’s still room for improvement.

  27. Acknowledgements

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