TerrAdapt:Cascadia Documentation
TerrAdapt:Cascadia
  • QUICK START GUIDE
  • HOW TO USE TERRADAPT:CASCADIA
    • Public License and Citation
    • Appropriate Uses and Key Limitations
    • TerrAdapt:Cascadia Map Portal
    • TerrAdapt:Cascadia Dashboard
  • DATA INPUTS
    • Data Inputs Overview
    • Remote Sensing Data
    • Climate
    • Energy and Transportation Infrastructure
    • Topography, Hydrology, & Soils
  • METHODS AND VALIDATION
    • Methods Overview
    • Landcover
      • Taxonomy
      • Training Data
      • Covariates
      • Model Development
      • Model Validation
    • Forest Structure
      • Training Data
      • Covariates
      • Model Development
      • Model Validation
    • Rangeland Fractional Cover
      • Training Data
      • Covariates
      • Model Development
      • Model Validation
    • Change Detection and Ecological Disturbance Modeling
      • Taxonomy
      • Covariates
      • Training Data
      • Model Development
    • Human Footprint
    • Habitat
      • Species Distribution Modeling
      • Ecosystem-based Models
      • Core Habitat
      • Habitat Centrality
    • Connectivity
      • Mapping Connectivity Networks
      • Corridors
      • Corridor Centrality
      • Mapping Barriers
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  1. METHODS AND VALIDATION
  2. Forest Structure

Model Development

PreviousCovariatesNextModel Validation

Last updated 9 months ago

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We used the random forest classifier () implemented in Google Earth Engine (GEE) to predict tree height (m), tree diameter (cm), and canopy cover (%) from the at all locations. We trained 10 replicate models in regression mode per variable in a k-fold approach () with a unique 'fold' of 1/10th of the data withheld for . The final model was calculated as the average of the 10 replicates.

Breiman 2001
covariates
training data
Fushiki 2011
validation