PRO Production

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

## Workflow Narrative

Time-Series Change Analysis

### Problem It Solves

Where and when are structural shifts emerging in the time series, and how confident are those signals?

### Who It Is For

- Time-series monitoring teams and geospatial data science groups.

### Primary User

Regional planning programs, policy/compliance analytics groups, and EO product teams.

### What It Does

- Performs multi-date trend and breakpoint analysis from temporal stacks.
- Supports mode-dependent decomposition/segmentation behavior.
- Emits confidence-scored temporal change surfaces.

### How It Works

- Traverses each pixel time series and estimates trend response over the observation window.
- Uses algorithm_mode-specific breakpoint logic to detect structural shifts.
- Converts fit strength, breakpoint support, and observation sufficiency into confidence output.
- Indicative formula: confidence ~= f(|trend|, breakpoint_support, n_observations), bounded to [0, 1].

### Why It Wins

- Provides mode flexibility (fast/iterative/bfast) so users can balance throughput and analytical depth.

### Typical Buying Trigger

A monitoring program moves from two-date snapshots to sustained time-series surveillance.

### Typical Presets

- fast: throughput-oriented screening.
- iterative: stronger breakpoint refinement.
- bfast: decomposition-oriented analysis.

## Inputs

Parameter | Optional | Description
--- | --- | ---
input_stack (required) | no | Primary temporal raster stack used for trend and breakpoint analysis.
optional qa_stack | yes | Optional temporal QA stack used to weight or suppress low-quality observations.
algorithm_mode and thresholding controls | no | Time-series change algorithm mode and threshold controls.

## Outputs

Parameter | Type | Description
--- | --- | ---
trend_change | GeoTIFF | Primary change-intelligence raster showing direction and magnitude of detected change.
breakpoint_count | GeoTIFF | Per-pixel count of detected temporal breakpoints.
breakpoint_date | GeoTIFF | Estimated timing raster for dominant detected breakpoint events.
change_confidence | GeoTIFF | Confidence surface for the time-series change detection result.
summary | JSON | Machine-readable summary report containing run metadata, QA diagnostics, and key metrics.
html_report | HTML | Human-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

## Python Example

    import whitebox_workflows as wbw

    wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

    result = wbe.time_series_change_intelligence(
        input_stack="data/time_stack.tif",
        qa_stack="data/time_stack_qa.tif",
        algorithm_mode="bfast",
        output_prefix="output/ts_change",
    )

    print(result)

## License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

## Project Links

WbW Homepage User Manual Support WbW