High-resolution mapping of tree mortality in European forests
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High-resolution mapping of tree mortality in European forests. / Cheng, Yan; Oehmcke, Stefan; Mosig, Clemens; Beloiu, Mirela; Kattenborn, Teja; Abel, Christin; Gominski, Dimitri Pierre Johannes; Nord-Larsen, Thomas; Fensholt, Rasmus; Horion, Stéphanie.
2024. Abstract from EGU General Assembly 2024, Vienna, Austria.Research output: Contribution to conference › Conference abstract for conference › Research › peer-review
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T1 - High-resolution mapping of tree mortality in European forests
AU - Cheng, Yan
AU - Oehmcke, Stefan
AU - Mosig, Clemens
AU - Beloiu, Mirela
AU - Kattenborn, Teja
AU - Abel, Christin
AU - Gominski, Dimitri Pierre Johannes
AU - Nord-Larsen, Thomas
AU - Fensholt, Rasmus
AU - Horion, Stéphanie
PY - 2024
Y1 - 2024
N2 - Tree mortality has escalated worldwide in recent years due to climate warming and unprecedented drought events. However, mapping tree mortality across forest ecosystems has not yet been achieved. Aerial photos provide opportunities to reveal the spatial and spectral characteristics of canopy death at local to landscape scales. In this work, we present a deep learning model for mapping tree mortality from aerial photos in various forested ecosystems across Europe. This model builds on a baseline model trained with data on dead tree canopies from California using sub-meter resolution aerial photos and allows the use of various spatial resolutions of the input images (ranging from 10 to 60 cm). By comparing our results to ground observations and/or state-of-the-art forest disturbance and loss products, we will discuss the advantages and limitations of aerial photo-based tree mortality mapping. The proposed framework can be used for large-scale mapping of tree mortality from multi-year aerial photos. The tree mortality maps provide detailed information that can help understand the mechanisms of tree mortality under climate change. Furthermore, aerial photo-based maps can serve as training labels for mapping pixel-level deadwood fractions from satellite images, which enables seamless spatial coverage and could be an essential step towards a global map of tree mortality.
AB - Tree mortality has escalated worldwide in recent years due to climate warming and unprecedented drought events. However, mapping tree mortality across forest ecosystems has not yet been achieved. Aerial photos provide opportunities to reveal the spatial and spectral characteristics of canopy death at local to landscape scales. In this work, we present a deep learning model for mapping tree mortality from aerial photos in various forested ecosystems across Europe. This model builds on a baseline model trained with data on dead tree canopies from California using sub-meter resolution aerial photos and allows the use of various spatial resolutions of the input images (ranging from 10 to 60 cm). By comparing our results to ground observations and/or state-of-the-art forest disturbance and loss products, we will discuss the advantages and limitations of aerial photo-based tree mortality mapping. The proposed framework can be used for large-scale mapping of tree mortality from multi-year aerial photos. The tree mortality maps provide detailed information that can help understand the mechanisms of tree mortality under climate change. Furthermore, aerial photo-based maps can serve as training labels for mapping pixel-level deadwood fractions from satellite images, which enables seamless spatial coverage and could be an essential step towards a global map of tree mortality.
U2 - 10.5194/egusphere-egu24-20213
DO - 10.5194/egusphere-egu24-20213
M3 - Conference abstract for conference
T2 - EGU General Assembly 2024
Y2 - 15 April 2024 through 19 April 2024
ER -
ID: 385223171