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  "Title": "Quantifying Post-Fire Spectral Recovery Metrics",
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  "Description": "Computes pixel-wise metrics describing post-disturbance\nspectral recovery from a yearly spectral-index time series.\nMetrics include disturbance magnitude (DM), an ordinary least\nsquares (OLS) recovery slope, early- and late-period recovery\nrates, recovery-rate change, Relative Recovery Indicator (RRI),\nRatio of Eighty Percent (R80P), and Year-on-Year recovery\n(YrYr). The RRI, R80P, and YrYr metrics are based on approaches\ndescribed by Frazier et al. (2018)\n<doi:10.1016/j.rse.2017.11.007>. Threshold-based recovery-time\napproaches have been used to quantify post-fire vegetation\nrecovery from spectral time series by Chompuchan and Lin (2017)\n<doi:10.1016/j.ecolind.2017.04.038>. Exponential recovery\nformulations have also been applied to post-fire vegetation\ntime series by Gouveia et al. (2010)\n<doi:10.5194/nhess-10-673-2010>. Parametric recovery models,\nincluding exponential, logistic, and Gompertz formulations, are\nprovided to characterize nonlinear post-disturbance recovery\ntrajectories.",
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      "page": "classify_disturbance_severity",
      "title": "Classify Spectral Disturbance and Recovery Across Post-Disturbance Years",
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      "page": "compute_recovery_metrics",
      "title": "Compute Pixel-wise Spectral Recovery Metrics",
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        "compute_recovery_metrics"
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      "title": "Compute Pixel-wise Recovery Time",
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      "title": "Assess Recovery-Time Threshold Sensitivity",
      "topics": [
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    },
    {
      "page": "detect_disturbance",
      "title": "Detect Disturbance Year from Spectral Time Series",
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    },
    {
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      "title": "Fit Spectral Recovery Model with Optional Model Fit",
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      "title": "Predict Spectral Recovery Raster",
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    },
    {
      "page": "preprocess_raster",
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      "title": "RecoverMetricsR: Post-Disturbance Vegetation Recovery Analysis",
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        "1. Recovery Metrics",
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