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RecoverMetricsR  

Quantifying Post-Fire Spectral Recovery Metrics
View on CRAN: Click here


Download and install RecoverMetricsR package within the R console
Install from CRAN:
install.packages("RecoverMetricsR")

Install from Github:
library("remotes")
install_github("cran/RecoverMetricsR")

Install by package version:
library("remotes")
install_version("RecoverMetricsR", "0.1.0")



Attach the package and use:
library("RecoverMetricsR")
Maintained by
Hammadi Achour
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-10-15
Latest Update: 2026-10-15
Description:
Computes pixel-wise metrics describing post-disturbance spectral recovery from a yearly spectral-index time series. Metrics include disturbance magnitude (DM), an ordinary least squares (OLS) recovery slope, early- and late-period recovery rates, recovery-rate change, Relative Recovery Indicator (RRI), Ratio of Eighty Percent (R80P), and Year-on-Year recovery (YrYr). The RRI, R80P, and YrYr metrics are based on approaches described by Frazier et al. (2018) <doi:10.1016/j.rse.2017.11.007>. Threshold-based recovery-time approaches have been used to quantify post-fire vegetation recovery from spectral time series by Chompuchan and Lin (2017) <doi:10.1016/j.ecolind.2017.04.038>. Exponential recovery formulations have also been applied to post-fire vegetation time series by Gouveia et al. (2010) <doi:10.5194/nhess-10-673-2010>. Parametric recovery models, including exponential, logistic, and Gompertz formulations, are provided to characterize nonlinear post-disturbance recovery trajectories.
How to cite:
Hammadi Achour (2026). RecoverMetricsR: Quantifying Post-Fire Spectral Recovery Metrics. R package version 0.1.0, https://cran.r-project.org/web/packages/RecoverMetricsR. Accessed 25 Oct. 2026.
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