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

How does the world work? The World is a Gradient –Curtis 1957 The Vegetation of Wisconsin The World is a Hierarchy –Delcourt et al 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.”

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

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

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

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. Perils

You Are Here

Methods –Maps built from: –1677 plots (FIA annual plots) –19 possible mapped explanatory variables. LandsatBands 3,4,5, Tassled Cap ClimatePRISM: Means, seasonal variability TopographyElevation, slope, aspect, solar LocationX, Y

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

(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., Temperature, Elevation) CCA Axis 1 (e.g., Rainfall, local topography) (1) conduct gradient analysis of plot data ASSUMPTION: Species exhibit unimodal responses to environmental variables.

study area Methods: Random Forest Nearest Neighbor Imputation Random Forest spacegeographic space

Methods: Classification Tree Elevation < 1244 August Maximum < Temp August Maximum < Temp Summer Mean < Temp Aug. to Dec. Temperature < Differential Elevation < 1625 LANDSAT Band 5 < 24 PSME TSHE PSMETHPL ABAM TSME PSME 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.

Methods: Random Forest Imputation

Accuracy Assessment Species Kappa RMSD Bray-Curtis Distance

Results

Species Presence-Absence (Kappa statistics)

Forest Structure

Forest Structure: Basal Area k-NN GNNRFNN 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!!!

Crater Lake Closeup

Forest Structure: Basal Area k-NN

Forest Structure: Basal Area GNN

Forest Structure: Basal Area RFNN

Community Structure

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.

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

Acknowledgements