RL-02 Case Study Live Research System

CFP Anomaly Tracker

A live spatial research system for tracking climate, vegetation, smoke, and water-quality anomalies across the California Floristic Province by comparing current observations against seasonal and historical baselines.

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Interface Studied

Climate anomaly × vegetation response × temporal baseline

Primary Domain

Climate anomalies, vegetation stress, smoke exposure, aquatic turbidity, ecological monitoring

Methods

Remote sensing, climate baselines, anomaly detection, GeoJSON APIs, spatial visualization

Case Study Thesis

An anomaly is only as meaningful as the baseline it disturbs.

Climate and vegetation conditions are not meaningful as isolated values. They become interpretable only when measured against a baseline specific enough to preserve seasonality, geography, historical variability, and sensor context.

The CFP Anomaly Tracker treats the California Floristic Province as a live environmental interface: a region where atmospheric stress, vegetation response, drought exposure, fire risk, and seasonal timing can diverge from expected patterns.

Research Question

Where are current climate and vegetation conditions departing from recent historical baselines, and how should those departures be read?

Analytical Frame

Environmental anomaly detection: comparing current spatial conditions against seasonal, ecological, and climatic expectations.

Status

The current application is a live research system. It is designed for environmental screening, anomaly detection, and methods demonstration, not as a final regulatory or causal determination. The system integrates finalized point observations, spatial climate anomaly layers, satellite-derived vegetation and moisture indicators, smoke context, aquatic turbidity screening, and public methods documentation.

Outputs

Interactive anomaly map
Finalized CIMIS observation status
PRISM spatial temperature anomaly layer
Landsat vegetation and moisture anomaly layers
Smoke and aquatic turbidity screening layers
CEQA air quality report output
Methods and data-source documentation

How to Read

From observation to anomaly.

Observe

Collect current climate and vegetation signals from spatial datasets.

Baseline

Compare current conditions against expected seasonal and historical ranges.

Detect

Identify where vegetation, moisture, heat, or drought indicators diverge.

Interpret

Read anomalies as screening signals, not final causal explanations.

Analytical Limits

The app should be read as a screening and interpretation system.

It identifies departures from historical baselines, but it does not by itself assign causality or determine regulatory significance.

Uncertainty remains important: sensor limits, cloud contamination, temporal mismatch, spatial resolution, station representativeness, and baseline-window choice can all affect interpretation. The interface therefore treats anomalies as signals requiring context, not as standalone conclusions.