| name | environmental-time-series |
| description | Detects trends, breakpoints, and recovery trajectories in environmental time series data from remote sensing or field measurements. Use this skill when the user mentions time series analysis, NDVI/EVI/LST trends, Mann-Kendall tests, Sen slope, BFAST breakpoints, structural change detection, seasonal decomposition (STL), anomaly detection, recovery trajectories, regime shifts, or pixel-wise trend analysis. |
| skill_version | 1.0.0 |
Skill: environmental-time-series
Domain: Trend · Seasonality · Breakpoints · Anomalies · Recovery
Phase: 2 — Modeling
Used by: build-fire-risk-map, analyze-environmental-change
Purpose
Guides the agent through the analysis of environmental and ecological time series: trend detection, seasonal decomposition, structural breakpoint identification, anomaly detection, and post-disturbance recovery trajectory estimation.
When to Invoke
- Analysing NDVI, EVI, LST, rainfall, or any environmental time series
- Detecting trend direction and magnitude in ecological indicators
- Identifying regime shifts or abrupt changes in a time series
- Characterising anomalies relative to historical baseline
- Estimating recovery time after a disturbance event
Inputs
| Input | Format | Required |
|---|
| Time-indexed environmental variable | CSV or GeoTIFF time stack | Yes |
| Time series frequency (daily, monthly, annual) | Text | Yes |
| Disturbance event date (for recovery) | Date | Conditional |
| Historical baseline period | Date range | Recommended |
Outputs
| Output | Description |
|---|
trend_results.csv | Trend slope, magnitude, p-value per pixel or site |
seasonal_decomposition.png | STL decomposition plot |
breakpoints.csv | Detected breakpoint dates with confidence intervals |
anomaly_series.csv | Standardised anomaly per time step |
recovery_metrics.csv | Recovery rate and 80/100% recovery date |
timeseries_report.md | Analysis narrative |
Steps
1. Inspect and Clean the Series
- Plot raw series; identify obvious outliers, data gaps, sensor artifacts
- Interpolate short gaps (≤ 3 steps) if justified; document method
- Report temporal extent, frequency, and missing value rate
2. Trend Detection
- Mann-Kendall test for monotonic trend (non-parametric, handles non-normality)
- Sen's slope estimator for trend magnitude
- For raster: pixel-wise MK + Sen's slope
- Report τ (Kendall's tau), p-value, and slope in original units/year
3. Seasonal Decomposition
- STL decomposition (Seasonal-Trend decomposition using LOESS): robust to outliers
- Report seasonal amplitude, trend component, and remainder
- For annual data with no seasonality, skip this step
4. Breakpoint Detection
- BFAST (Breaks For Additive Season and Trend): simultaneous trend and breakpoint
strucchange (R) for structural change tests- Report: number of breakpoints, dates, 95% CIs
- Verify detected breakpoints against known events (fire, drought, deforestation)
5. Anomaly Detection
- Compute historical baseline (mean ± SD or percentiles) from reference period
- Standardised anomaly: z = (x − μ) / σ
- Flag values beyond ±2σ as anomalous
- For rainfall: SPI (Standardised Precipitation Index)
- For vegetation: VCI (Vegetation Condition Index) or zNDVI
6. Recovery Trajectory (post-disturbance)
- Define pre-disturbance baseline from N years before event
- Fit recovery curve: linear, exponential, or logistic
- Estimate: time to 80% recovery, time to full recovery
- Compute Recovery Indicator (RI) = (post − min) / (pre − min)
Decision Points
- Baseline period definition
- Gap interpolation method
- Seasonal decomposition method and parameters
- Breakpoint detection method and significance level
- Anomaly threshold (z-score cutoff)
- Recovery model type
Tools and Libraries
R: bfast, strucchange, trend, zoo, terra, ggplot2
Python: pymannkendall, ruptures, statsmodels.tsa, xarray
Remote sensing: Google Earth Engine (pixel-wise BFAST, trend)
Resources
resources/bfast-parameter-guide.md — how to set h, season, and type in BFASTresources/anomaly-indices-reference.md — SPI, VCI, zNDVI definitionsexamples/ — worked BFAST and recovery analysis example
Notes
- Autocorrelation in time series violates independence assumptions of standard tests; use MK which handles it
- BFAST requires at least 3 full seasonal cycles to be reliable
- For raster time series > 10 years monthly, consider GEE for computational efficiency
