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. Rangeland Fractional Cover

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 fractional cover of sagebrush, shrubs, perennial grasses, and annual grasses 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