Speaker
Description
National parks and protected areas are increasingly exposed to global change pressures derived from climate change, land-use dynamics, invasive species, and extreme events such as droughts, wildfires, and heatwaves. These pressures can alter the structure, ecosystem functioning, and species composition, often before visible degradation is detected on the ground. Effective conservation and adaptive management therefore require continuous, scalable, and timely monitoring systems capable of detecting ecological change across large and often inaccessible territories.
Remote sensing provides a unique opportunity to operationalize habitat monitoring by combining Earth observation data, environmental variables, and advanced analytics into an integrated system for detecting ecosystem change. Spectral information derived from satellite imagery enables repeated observation of vegetation condition, productivity, moisture stress, and land-cover dynamics at spatial and temporal scales relevant to conservation decision-making. When combined with robust data-ingestion pipelines and analytical models, these observations can be transformed into actionable indicators of habitat condition and resilience.
This pilot project is designed to demonstrate the value of AI-enabled environmental intelligence by developing an operational framework for monitoring habitats in national parks through remote sensing and automated analysis workflows. The project will generate habitat-level alerts based on changes in vegetation indices and related ecological indicators, enabling park managers and conservation authorities to identify early signs of degradation, disturbance, or recovery. By linking multi-source Earth observation data with habitat classification and ecological interpretation, we could support evidence-based conservation planning, rapid response, and long-term ecosystem stewardship.