Welfare Monitoring Using Satellite and Remote Technology

Emerging satellite and remote sensing technologies offer new possibilities for monitoring wild animal welfare at landscape scale, providing data that was previously impossible to collect non-invasively.

Key Facts

Satellite GPS collars now provide high-frequency location data enabling detailed movement and behaviour analysis
Accelerometer data from animal-borne tags can detect stress-associated movement patterns and activity budgets
Drone-based thermal imaging allows remote body condition assessment of wildlife populations
Satellite-derived vegetation and water availability maps can predict food stress at landscape scale
Automated classification of acoustic recordings can detect distress vocalisations in wildlife
Machine learning algorithms trained on known welfare states can classify remotely collected data
Remote sensing of habitat degradation provides early warning of welfare-compromising environmental change

Welfare Considerations

Remote and satellite monitoring technologies represent a transformative advance for wild animal welfare science. They enable continuous, non-invasive data collection across large spatial and temporal scales that were previously inaccessible to researchers. By integrating movement data, environmental conditions, and physiological indicators, researchers can build welfare assessment models applicable at population scale. These tools are particularly valuable for assessing welfare impacts of climate change, habitat loss, and human disturbance on wildlife populations.

What You Can Do

Support wildlife conservation organisations investing in non-invasive monitoring technology
Advocate for welfare science criteria to be included in satellite monitoring research programmes
Engage with open-access wildlife tracking data platforms that share non-invasive monitoring results
Encourage funding bodies to prioritise remote welfare monitoring in grant allocations
Follow research institutions developing AI-assisted welfare assessment from remote sensing data