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Root Turnover Analysis Data

Source: R/data.R
TurnoverDPC_data.Rd

Root turnover analysis from a sedge fen in northern Finland, comparing root presence between two time points (June 2023 and October 2023) using RootDetector root tracking feature.

Usage

data(TurnoverDPC_data)

Format

A RasterBrick object with dimensions:

  • 2550 columns (width)

  • 2273 rows (height)

  • 3 layers (decay, growth, persistent)

Source

Images by J.Cunow

Details

Multi-layer representation of root dynamics where:

  • Layer 1: Root decay (value = 1)

    • Roots that disappeared between time points

    • Foreign objects (e.g., tape)

    • Persistent roots

  • Layer 2: Root growth (value = 1)

    • New roots that appeared

    • Persistent roots

  • Layer 3: Persistent roots (value = 1)

    • Roots present in both time points

Background is represented as 0 in all layers.

Author

Johannes Cunow johannes.cunow@gmail.com

Examples

if (FALSE) { # \dontrun{
  data(TurnoverDPC_data)
  img = terra::rast(TurnoverDPC_data)
  # Plot individual layers
  terra::plot(img)
  # Calculate turnover statistics
  decay <- sum(terra::values(img[[1]]) == 1)
  growth <- sum(terra::values(img[[2]]) == 1)
  persistent <- sum(terra::values(img[[3]]) == 1)
} # }