Ecosystem Monitoring
Environmental Intelligence
Vegetation stress, fire risk, and ecosystem change detection through multi-signal satellite analysis.
Rather than predicting fires, the system identifies elevated fire risk based on early signals of vegetation stress and thermal anomalies — a distinction that matters for scientific integrity.
Fire Risk Module
Fire Risk Assessment Architecture
The fire risk module integrates five independent signal layers into a unified risk assessment. Each layer is computed independently before consensus integration.
The system identifies elevated fire risk conditions — it does not predict fire occurrence. All risk assessments are experimental and should not be used as the sole basis for operational fire management decisions.
Vegetation Analysis
Vegetation-Based Ecosystem Monitoring
Vegetation health is monitored through temporal analysis — tracking deviation from seasonal baselines to identify stress, decline, and recovery patterns.
Baseline Computation
Multi-year vegetation baselines are computed for each geographic region, accounting for seasonal variation. Anomaly detection operates relative to these baselines rather than absolute thresholds, reducing false positives from natural seasonal cycles.
Stress Classification
Vegetation deviation is classified into stress levels — mild, moderate, and severe — based on the degree of departure from established baselines. Classification thresholds are calibrated per biome type.
Temporal Trend Analysis
Beyond point-in-time anomaly detection, the system tracks vegetation trends over rolling time windows. Persistent decline over multiple observation periods is weighted more heavily than transient dips.
Recovery Detection
The system also identifies vegetation recovery following disturbance events. Recovery signals are tracked to assess ecosystem resilience and the effectiveness of natural regeneration processes.
Baseline Window
Prototype EvaluationMulti-Year
Vegetation baselines computed from multi-year historical averages, updated periodically.
Stress Detection
Experimental Module3 Levels
Mild, moderate, and severe stress classification with biome-specific thresholds.
Spatial Risk Modelling
Continuous Risk Surface
Point-based risk scores are extended to continuous spatial surfaces, enabling regional situational awareness beyond individual observation points.
Grid-Based Sampling
The region of interest is divided into a regular grid. Risk is computed at each grid point using the multi-signal pipeline. Grid resolution is adaptive — higher density in areas of elevated signal activity.
Spatial Interpolation
Spatial interpolation produces a smooth risk surface from discrete grid point estimates. Interpolation parameters are calibrated to balance local sensitivity against spatial smoothness.
Confidence-Weighted Rendering
Risk surface opacity is modulated by confidence score. Low-confidence regions are rendered with reduced opacity, communicating uncertainty directly in the visual representation.
Contour Detection
Risk contour boundaries are computed at defined threshold levels, delineating regions of moderate and elevated risk. Contours are smoothed to reduce artefacts from the underlying grid structure.
Use Cases
Environmental Monitoring Applications
Deforestation Detection
Persistent vegetation decline in forested regions, combined with spatial extent analysis, identifies deforestation events and tracks progression over time.
Fire Risk Early Warning
Vegetation stress combined with thermal anomaly signals provides early warning of elevated fire risk conditions before active fire events.
Drought Monitoring
Regional vegetation decline correlated with thermal anomaly patterns identifies drought stress across agricultural and natural ecosystems.
Ecosystem Recovery
Post-disturbance vegetation recovery tracking assesses ecosystem resilience and natural regeneration following fire, drought, or land-use change events.