{"id":80452,"date":"2026-03-16T15:03:50","date_gmt":"2026-03-16T14:03:50","guid":{"rendered":"https:\/\/www.he-arc.ch\/?post_type=he-arc_work&p=80452"},"modified":"2026-03-18T10:25:59","modified_gmt":"2026-03-18T09:25:59","slug":"kidane-millena","status":"publish","type":"he-arc_work","link":"https:\/\/www.he-arc.ch\/en\/projets-etudiants\/kidane-millena\/","title":{"rendered":"KIDANE Millena"},"content":{"rendered":"\n

This project aimed to monitor forest health using remote sensing data, focusing on Sentinel-2 satellite imagery from 2015 to 2024.<\/p>\n\n\n\n

By calculating the Normalized Difference Vegetation Index (NDVI), we detected changes in vegetation over time that could indicate stress or tree mortality.<\/p>\n\n\n\n

A LiDAR-derived forest mask was applied to limit analysis to vegetated areas.<\/p>\n\n\n\n

Different anomaly detection approaches were used – such as year-to-year differences, seasonal comparisons, and z-score standardization.<\/p>\n\n\n\n

Visual validation was performed using high-resolution orthophotos from 2017, 2019, 2020, and 2023, confirming the observed patterns.<\/p>\n\n\n\n

The method is scalable, reproducible, and offers valuable support for forest managers aiming to detect early signs of decline.<\/p>\n\n\n\n

While machine learning was not implemented due to time constraints, it is proposed as a future improvement for automated classification of tree health.<\/p>\n\n\n\n

<\/p>\n\n\n\n

Poster du travail de bachelor de Millena Kidane<\/a><\/p>\n\n\n\n

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