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RecoverMetricsR: Post-Disturbance Vegetation Recovery Analysis

Hammadi Achour, Amal Daboussi, Wahbi Jaouadi

2026-10-03

Overview

RecoverMetricsR is an R package designed to analyze and quantify vegetation recovery following disturbance events (particularly forest fire) using remote sensing data. It provides tools to compute recovery metrics, model recovery trajectories, classify recovery typologies, and visualize uncertainty. The package is especially suited for ecosystem monitoring in Mediterranean landscapes.

Key Features

1. Recovery Metrics

Computes pixel-wise spectral recovery metrics:
- DM: Disturbance Magnitude
- Slope: Trend of post-disturbance recovery
- YrYr_early / YrYr_late: Early vs. late post-disturbance annual recovery
- Recovery_Rate_Change: Difference between early and late recovery rates
- RRI: Relative Recovery Index
- R80P: Recovery to 80% of pre-disturbance state
- YrYr: Annual recovery rate
Metrics are computed over user-defined pre- and post-disturbance windows.
Optional masking of areas outside the study region.
Values can be clamped to theoretical ranges for robustness.

2. Recovery Time Computation

Calculate Time-to-Recovery (TTR) for different thresholds.
Account for persistence (consecutive years above threshold) and baseline pre-disturbance conditions.
Compute uncertainty bands for TTR across multiple thresholds.

3. Parametric Recovery Modeling

Fit exponential, logistic, or Gompertz models to mean post-disturbance spectral trajectories.
Extract model coefficients, standard errors, and goodness-of-fit metrics (ME, RMSE, R², AIC, BIC).
Overlay fitted curves and equations on plots for visual interpretation.

4. Recovery Typology Classification

Classify post-disturbance recovery into four categories based on TTR and uncertainty:
1. Fast & stable recovery
2. Fast but threshold-sensitive recovery
3. Slow & stable recovery
4. Slow & threshold-sensitive recovery
Generate spatial maps of recovery typologies with customizable color schemes.

5. Visualization

Plot spectral recovery trajectories with confidence bands.
Annotate fitted model equations on recovery plots.
Map spatial patterns of TTR, uncertainty, and recovery type.

6. Summary Statistics

Compute per-class statistics including count, percentage, mean TTR, and uncertainty metrics.
Add descriptive labels to recovery classes for clear interpretation.

Intended Users

Ecologists, remote sensing specialists, and environmental scientists seeking reproducible, quantitative analyses of vegetation recovery from spatial data (e.g., satellite or drone imagery).

Dependencies

terra for raster and spatial data operations
sf (optional) for vector spatial data handling
ggplot2 for plotting
dplyr and rlang for data manipulation

Example Workflow

The following example illustrates a typical workflow on a small synthetic dataset. In practice, r_stack_processed would be a multi-layer terra::SpatRaster of yearly spectral-index values, and Fire_scars a vector polygon of the fire perimeter.

library(RecoverMetricsR)
library(terra)
#> terra 1.8.42

# --- Small synthetic dataset -------------------------------------
set.seed(42)

r_stack_processed <- rast(nrows = 20, ncols = 20, nlyrs = 8)
names(r_stack_processed) <- paste0("NBR_", 1998:2005)
years <- 1998:2005

# Simulate a burn in 2000
vals <- matrix(
  runif(20 * 20 * 8, 0.4, 0.7),
  ncol = 20 * 20
)
vals[, ] <- vals
for (i in 1:8) {
  values(r_stack_processed[[i]]) <- vals[i, ]
}
values(r_stack_processed[["NBR_2000"]]) <- values(r_stack_processed[["NBR_2000"]]) * 0.3

Fire_scars <- vect(
  "POLYGON ((2 2, 18 2, 18 18, 2 18, 2 2))",
  crs = crs(r_stack_processed)
)

# --- 1. Compute pixel-wise recovery metrics ----------------------
metrics_stack <- compute_recovery_metrics(
  raster_stack = r_stack_processed,
  years        = years,
  event_year   = 2000,
  pre_window   = 2,
  post_window  = 4,
  clamp        = TRUE,
  metrics      = c("DM", "Slope", "YrYr_early",
                   "YrYr_late", "Recovery_Rate_Change",
                   "RRI", "R80P", "YrYr"),
  mask_layer   = Fire_scars
)

print(metrics_stack)
#> class       : SpatRaster 
#> dimensions  : 20, 20, 8  (nrow, ncol, nlyr)
#> resolution  : 18, 9  (x, y)
#> extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (CRS84) (OGC:CRS84) 
#> source(s)   : memory
#> names       :        DM,       Slope, YrYr_early, YrYr_late, Recov~hange,      RRI, ... 
#> min values  : 0.3547119, -0.03079411,  0.2711621, 0.1034210,   0.1677410, 1.078587, ... 
#> max values  : 0.3995375, -0.01651226,  0.3162783, 0.1081637,   0.2081146, 1.138991, ...

# --- 2. Compute TTR and uncertainty ------------------------------
TTR_uncertainty <- compute_recovery_time_uncertainty(
  raster_stack = r_stack_processed,
  years        = years,
  event_year   = 2000,
  thresholds   = c(0.7, 0.8, 0.9),
  persistence  = 1,
  mask_layer   = Fire_scars,
  return_band  = TRUE
)

print(TTR_uncertainty)
#> class       : SpatRaster 
#> dimensions  : 20, 20, 4  (nrow, ncol, nlyr)
#> resolution  : 18, 9  (x, y)
#> extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (CRS84) (OGC:CRS84) 
#> source(s)   : memory
#> names       : TTR_70, TTR_80, TTR_90, TTR_Thr~d_Range 
#> min values  :      1,      1,      1,               0 
#> max values  :      1,      1,      1,               0

# --- 3. Classify recovery typology -------------------------------
Recovery_results <- classify_recovery_typology(
  ttr_stack  = TTR_uncertainty,
  fast_TTR   = 3,
  unc_threshold = 2,
  plot_map   = FALSE,
  plot_stats = FALSE,
  area       = TRUE
)

print(Recovery_results$Stats)
#>                           Recovery_Type Count Percent Area_ha
#> 1                Fast & stable recovery     2     100  0.0324
#> 2 Fast but threshold-sensitive recovery     0       0  0.0000
#> 3                Slow & stable recovery     0       0  0.0000
#> 4   Slow & threshold-sensitive recovery     0       0  0.0000

Notes

The package uses a consistent year-indexing convention: event_year must be present in years, and pre-/post-disturbance windows are derived from it.
All pixel-wise operations are performed with terra and are memory-safe for large rasters.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.