Non-profit organization CTrees launched Land Use Change Alert (LUCA), the world's first global radar-based forest disturbance alert system.
Unique in its ability to cover all forest biomes, LUCA leverages data from the European Space Agency’s Sentinel 1 radar satellite system to fill a critical gap in global forest monitoring. It revolutionizes the understanding of forest disturbances — including all forms of deforestation and degradation — by providing near-real-time information on forests worldwide.
Available for free on the CTrees website, LUCA is set to become an indispensable resource for policymakers, law enforcement agencies, carbon market participants, academics, and think tanks — providing unparalleled speed and accuracy in identifying, addressing, and accounting for forest disturbances.
LUCA includes historical data dating back to January 2018 and employs state-of-the-art Machine Learning capabilities to ensure precise analysis. This enables previously unavailable trend evaluations, which is crucial for designing targeted policy interventions.
LUCA is a first-of-its-kind monitoring technology that offers clearer resolution, a global scope, and availability for all forest biomes, including tropical humid, tropical dry, temperate, and boreal forests. It also provides unparalleled alerts in near real-time to potential deforestation and degradation. LUCA also improves on prior monitoring approaches based on optical images which are sensitive to weather conditions and struggle to get cloud-free images across the tropics. LUCA counters this by utilizing synthetic aperture radar (SAR) data, ensuring alerts are generated in almost any weather condition. This extends its utility across a wide range of climates and conditions.
Image: Depositphotos
CTrees released LUCA on its website with continual updates and new features to be released over time. The team is committed to ongoing improvements, including integrating data from the joint NASA-ISRO NISAR mission, set to launch in early 2024, to further enhance LUCA's detection capacity and characterization of disturbances.
