GeoByte Webinar - January 26, 2024

LUCA: Monitoring Global Forests with Radar Satellites


LUCA: Monitoring Global Forests with Radar Satellites

Presenter Adugna Mullissa

Effective and timely monitoring of global forest disturbances is essential for understanding the dynamics of terrestrial ecosystems, assessing land use changes, and informing sustainable land management practices. Satellite-based remote sensing, especially synthetic aperture radar (SAR) images, offers a powerful tool for tracking these disturbances in near-real time on a global scale. In this webinar, Dr. Adugna Mullissa, a research scientist at nonprofit CTrees, will present on Land Use Change Alerts (LUCA), a forest disturbance alert system based on Sentinel-1 SAR images. Available for free in Google Earth Engine, LUCA alerts are generated globally starting in 2018 and refreshed approximately every 2 weeks. 


Dr. Adugna Mullissa is a research scientist at CTrees and a visiting researcher at UCLA. He is an expert in radar remote sensing and machine learning who specializes in near-real time forest change monitoring. At CTrees, Adugna leads the synthetic aperture radar (SAR) data product development and management. 

Adugna earned his Ph.D. in radar remote sensing in 2017 and conducted postdoctoral research from 2018-2019 at the University of Twente, Netherlands. During his postdoctoral research there he developed deep learning methods for crop monitoring using Polarimetric SAR data. Adugna continued his postdoctoral research from 2019-2023 at the Laboratory of Geo-information Science and Remote Sensing at Wageningen University, Netherlands, where he developed fundamental radar and deep learning methods for near-real time forest change monitoring applications. Adugna’s research interests revolve around leveraging radar remote sensing and machine learning techniques to enhance the accuracy and efficiency of environmental monitoring. Specifically, he focuses on image and signal processing and information extraction from SAR, PolSAR, and InSAR data using advanced machine learning such as deep learning. More details about Adugna’s research can be found on his personal website,


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1/26/2024 12:00 PM - 1:00 PM
Eastern Standard Time

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