%0 Journal Article %1 mosig2024mapping %A Mosig, Clemens %A Mahecha, Miguel %A Montero, David %A Cheng, Yan %A Priego, Oscar Perez %A Beloiu, Mirela %A Volpi, Michele %A Horion, Stéphanie %A Latifi, Hooman %A Shafeian, Elham %A Fassnacht, Fabian %A Ganz, Selina %A Zielewska-Büttner, Katarzyna %A Laliberté, Etienne %A Cloutier, Myriam %A Schmehl, Marie-Therese %A Frick, Annett %A Müller-Landau, Helene %A Cushman, KC %A Hupy, Joseph %A Ma, Qin %A Su, Yanjun %A Khatri-Chhetri, Pratima %A Kruse, Stefan %A Frey, Julian %A Schiefer, Felix %A Junttila, Samuli %A Potts, Alastair %A Uhl, Andreas %A Rossi, Christian %A Kattenborn, Tej %D 2024 %K yaff %N EGU24-18230 %T Mapping Tree Mortality at Global Scale Using Sentinel-2 %X Excess tree mortality in the wake of climate extremes has been observed globally. However, we still lack precise data on mortality at global scale to understand respective drivers and spatiotemporal dynamics. The Sentinel-2 satellite fleet, equipped with the MultiSpectral Instrument (MSI), covers the entire earth on average every five days at spatial resolutions ranging from 10 m to 60 m. Mapping tree mortality from Sentinel-2 globally in diverse ecosystems requires equally diverse reference data. Using globally distributed high-resolution aerial orthoimagery reference data and artificial intelligence methods, we can translate spectral signatures of remote sensing into deadwood. Specifically, in this study we show how to predict the share of standing deadwood for a 10 m pixel in a specific year. The method takes into account temporal patterns, spatial context, as well as all Sentinel-2 spectral bands. This will enable us …

@article{mosig2024mapping, abstract = {Excess tree mortality in the wake of climate extremes has been observed globally. However, we still lack precise data on mortality at global scale to understand respective drivers and spatiotemporal dynamics. The Sentinel-2 satellite fleet, equipped with the MultiSpectral Instrument (MSI), covers the entire earth on average every five days at spatial resolutions ranging from 10 m to 60 m. Mapping tree mortality from Sentinel-2 globally in diverse ecosystems requires equally diverse reference data. Using globally distributed high-resolution aerial orthoimagery reference data and artificial intelligence methods, we can translate spectral signatures of remote sensing into deadwood. Specifically, in this study we show how to predict the share of standing deadwood for a 10 m pixel in a specific year. The method takes into account temporal patterns, spatial context, as well as all Sentinel-2 spectral bands. This will enable us …}, added-at = {2025-02-27T11:22:03.000+0100}, author = {Mosig, Clemens and Mahecha, Miguel and Montero, David and Cheng, Yan and Priego, Oscar Perez and Beloiu, Mirela and Volpi, Michele and Horion, Stéphanie and Latifi, Hooman and Shafeian, Elham and Fassnacht, Fabian and Ganz, Selina and Zielewska-Büttner, Katarzyna and Laliberté, Etienne and Cloutier, Myriam and Schmehl, Marie-Therese and Frick, Annett and Müller-Landau, Helene and Cushman, KC and Hupy, Joseph and Ma, Qin and Su, Yanjun and Khatri-Chhetri, Pratima and Kruse, Stefan and Frey, Julian and Schiefer, Felix and Junttila, Samuli and Potts, Alastair and Uhl, Andreas and Rossi, Christian and Kattenborn, Tej}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/28a9844c7305aaa6e1eda866bf830cb5c/scadsfct}, citation = {EGU24, 2024}, conference = {EGU24}, interhash = {efefb3706a9a404ae86c041e1471bf65}, intrahash = {8a9844c7305aaa6e1eda866bf830cb5c}, keywords = {yaff}, number = {EGU24-18230}, timestamp = {2025-02-27T11:22:03.000+0100}, title = {Mapping Tree Mortality at Global Scale Using Sentinel-2}, year = 2024 }