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1. Mapping Tree Mortality at Global Scale Using Sentinel-2

Author

Mosig, Clemens, Mahecha, Miguel, Montero, David, Cheng, Yan, Perez Priego, Oscar, Beloiu, Mirela, Volpi, Michele, Horion, Stéphanie, Latifi, Hooman, Shafeian, Elham, Fassnacht, Fabian, Ganz, Selina, Zielewska-Büttner, Katarzyna, Laliberté, Etienne, Cloutier, Myriam, Schmehl, Marie-Therese, Frick, Annett, Müller-Landau, Helene, Cushman, KC, Hupy, Joseph, Ma, Qin, Su, Yanjun, Khatri-Chhetri, Pratima, Kruse, Stefan, Frey, Julian, Schiefer, Felix, Junttila, Samuli, Potts, Alastair, Uhl, Andreas, Rossi, Christian, Kattenborn, Teja, Mosig, Clemens, Mahecha, Miguel, Montero, David, Cheng, Yan, Perez Priego, Oscar, Beloiu, Mirela, Volpi, Michele, Horion, Stéphanie, Latifi, Hooman, Shafeian, Elham, Fassnacht, Fabian, Ganz, Selina, Zielewska-Büttner, Katarzyna, Laliberté, Etienne, Cloutier, Myriam, Schmehl, Marie-Therese, Frick, Annett, Müller-Landau, Helene, Cushman, KC, Hupy, Joseph, Ma, Qin, Su, Yanjun, Khatri-Chhetri, Pratima, Kruse, Stefan, Frey, Julian, Schiefer, Felix, Junttila, Samuli, Potts, Alastair, Uhl, Andreas, Rossi, Christian, and Kattenborn, Teja

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 to map tree mortality globally at a new level of precision.

Published
2024

2. Delineating Standing Deadwood in High-Resolution RGB Drone Imagery

Author

Möhring, Jakobus, Mosig, Clemens, Cheng, Yan, Mahecha, Miguel, Priego, Oscar Perez, Beloiu, Mirela, Volpi, Michele, Horion, Stéphanie, Latifi, Hooman, Shafeian, Elham, Fassnacht, Fabian, Montero, David, Zielewska-Büttner, Katarzyna, Laliberté, Etienne, Cloutier, Myriam, Schmehl, Marie-Therese, Frick, Annett, Müller-Landau, Helene, Cushman, KC, Hupy, Joseph, Ma, Qin, Su, Yanjun, Khatri-Chhetri, Pratima, Kruse, Stefan, Frey, Julian, Schiefer, Felix, Junttila, Samuli, Potts, Alastair, Uhl, Andreas, Rossi, Christian, Kattenborn, Teja, Möhring, Jakobus, Mosig, Clemens, Cheng, Yan, Mahecha, Miguel, Priego, Oscar Perez, Beloiu, Mirela, Volpi, Michele, Horion, Stéphanie, Latifi, Hooman, Shafeian, Elham, Fassnacht, Fabian, Montero, David, Zielewska-Büttner, Katarzyna, Laliberté, Etienne, Cloutier, Myriam, Schmehl, Marie-Therese, Frick, Annett, Müller-Landau, Helene, Cushman, KC, Hupy, Joseph, Ma, Qin, Su, Yanjun, Khatri-Chhetri, Pratima, Kruse, Stefan, Frey, Julian, Schiefer, Felix, Junttila, Samuli, Potts, Alastair, Uhl, Andreas, Rossi, Christian, and Kattenborn, Teja

Abstract

We have observed tree die-offs in a variety of regions in the world. Understanding the diverse causes of tree mortality requires exact information about which trees are dying and where. With the increased user-friendliness of drones and the availability of airborne imagery, high-resolution imagery of forests is becoming widely available. Delineating standing deadwood in such aerial imagery has become a classic segmentation task and several models with varying accuracy have been developed. However, these machine-learning based models are not generic and limited to specific image resolutions, sensor characteristics, geographic regions, and forest ecosystems. The reason for this lack of generality is that previous models have been trained using only datasets representative of specific regions and obtained from a single source. In this study, we obtain a diverse dataset spanning more than a dozen countries across continents and implement a single convolutional neural network (CNN) model that is able to cope with most forest ecosystems, varying image quality, and spatial resolutions.

Published
2024

3. deadtrees.earth - An Open-Access and Interactive Database for Centimeter-Scale Aerial Imagery to Uncover Global Tree Mortality Dynamics

Author

Mosig, Clemens, Vajna-Jehle, Janusch, Mahecha, Miguel D., Cheng, Yan, Hartmann, Henrik, Montero, David, Junttila, Samuli, Horion, Stephanie, Adu-Bredu, Stephen, Al-Halbouni, Djamil, Allen, Matthew, Altman, Jan, Angiolini, Claudia, Astrup, Rasmus, Barrasso, Caterina, Bartholomeus, Harm, Brede, Benjamin, Buras, Allan, Carrieri, Erik, Chirici, Gherardo, Cloutier, Myriam, Cushman, KC, Dalling, James W., Dempewolf, Jan, Denter, Martin, Ecke, Simon, Eichel, Jana, Eltner, Anette, Fabi, Maximilian, Fassnacht, Fabian, Feirreira, Matheus Pinheiro, Frey, Julian, Frick, Annett, Ganz, Selina, Garbarino, Matteo, Garcia, Milton, Gassilloud, Matthias, Ghasemi, Marziye, Giannetti, Francesca, Gonzalez, Roy, Gosper, Carl, Greinwald, Konrad, Grieve, Stuart, Gutierrez, Jesus Aguirre, Goeritz, Anna, Hajek, Peter, Hedding, David, Hempel, Jan, Hernandez, Melvin, Heurich, Marco, Honkavaara, Eija, Jucker, Tommaso, Kalwij, Jesse M., Khatri-Chhetri, Pratima, Klemmt, Hans-Joachim, Koivumaeki, Niko, Korznikov, Kirill, Kruse, Stefan, Krueger, Robert, Laliberte, Etienne, Langan, Liam, Latifi, Hooman, Lehmann, Jan, Li, Linyuan, Lines, Emily, Lopatin, Javier, Lucieer, Arko, Ludwig, Marvin, Ludwig, Antonia, Lyytikaeinen-Saarenmaa, Paeivi, Ma, Qin, Marino, Giovanni, Maroschek, Michael, Meloni, Fabio, Menzel, Annette, Meyer, Hanna, Miraki, Mojdeh, Moreno-Fernandez, Daniel, Muller-Landau, Helene C., Maelicke, Mirko, Moehring, Jakobus, Muellerova, Jana, Neumeier, Paul, Naesi, Roope, Oppgenoorth, Lars, Palmer, Melanie, Paul, Thomas, Potts, Alastair, Prober, Suzanne, Puliti, Stefano, Perez-Priego, Oscar, Reudenbach, Chris, Rossi, Christian, Ruehr, Nadine Katrin, Ruiz-Benito, Paloma, Runge, Christian Mestre, Scherer-Lorenzen, Michael, Schiefer, Felix, Schladebach, Jacob, Schmehl, Marie-Therese, Schwarz, Selina, Schwenke, Mirela Beloiu, Seidl, Rupert, Shafeian, Elham, Simone, Leopoldo de, Sohrabi, Hormoz, Sotomayor, Laura, Sparrow, Ben, Steer, Benjamin S.C., Stenson, Matt, Stoeckigt, Benjamin, Su, Yanjun, Suomalainen, Juha, Torresani, Michele, Umlauft, Josefine, Vargas-Ramirez, Nicolas, Volpi, Michele, Vasquez, Vicente, Weinstein, Ben, Ximena, Tagle Casapia, Zdunic, Katherine, Zielewska-Buettner, Katarzyna, de Oliveira, Raquel Alves, van Wagtendonk, Liz, von Dosky, Vincent, Kattenborn, Teja, Mosig, Clemens, Vajna-Jehle, Janusch, Mahecha, Miguel D., Cheng, Yan, Hartmann, Henrik, Montero, David, Junttila, Samuli, Horion, Stephanie, Adu-Bredu, Stephen, Al-Halbouni, Djamil, Allen, Matthew, Altman, Jan, Angiolini, Claudia, Astrup, Rasmus, Barrasso, Caterina, Bartholomeus, Harm, Brede, Benjamin, Buras, Allan, Carrieri, Erik, Chirici, Gherardo, Cloutier, Myriam, Cushman, KC, Dalling, James W., Dempewolf, Jan, Denter, Martin, Ecke, Simon, Eichel, Jana, Eltner, Anette, Fabi, Maximilian, Fassnacht, Fabian, Feirreira, Matheus Pinheiro, Frey, Julian, Frick, Annett, Ganz, Selina, Garbarino, Matteo, Garcia, Milton, Gassilloud, Matthias, Ghasemi, Marziye, Giannetti, Francesca, Gonzalez, Roy, Gosper, Carl, Greinwald, Konrad, Grieve, Stuart, Gutierrez, Jesus Aguirre, Goeritz, Anna, Hajek, Peter, Hedding, David, Hempel, Jan, Hernandez, Melvin, Heurich, Marco, Honkavaara, Eija, Jucker, Tommaso, Kalwij, Jesse M., Khatri-Chhetri, Pratima, Klemmt, Hans-Joachim, Koivumaeki, Niko, Korznikov, Kirill, Kruse, Stefan, Krueger, Robert, Laliberte, Etienne, Langan, Liam, Latifi, Hooman, Lehmann, Jan, Li, Linyuan, Lines, Emily, Lopatin, Javier, Lucieer, Arko, Ludwig, Marvin, Ludwig, Antonia, Lyytikaeinen-Saarenmaa, Paeivi, Ma, Qin, Marino, Giovanni, Maroschek, Michael, Meloni, Fabio, Menzel, Annette, Meyer, Hanna, Miraki, Mojdeh, Moreno-Fernandez, Daniel, Muller-Landau, Helene C., Maelicke, Mirko, Moehring, Jakobus, Muellerova, Jana, Neumeier, Paul, Naesi, Roope, Oppgenoorth, Lars, Palmer, Melanie, Paul, Thomas, Potts, Alastair, Prober, Suzanne, Puliti, Stefano, Perez-Priego, Oscar, Reudenbach, Chris, Rossi, Christian, Ruehr, Nadine Katrin, Ruiz-Benito, Paloma, Runge, Christian Mestre, Scherer-Lorenzen, Michael, Schiefer, Felix, Schladebach, Jacob, Schmehl, Marie-Therese, Schwarz, Selina, Schwenke, Mirela Beloiu, Seidl, Rupert, Shafeian, Elham, Simone, Leopoldo de, Sohrabi, Hormoz, Sotomayor, Laura, Sparrow, Ben, Steer, Benjamin S.C., Stenson, Matt, Stoeckigt, Benjamin, Su, Yanjun, Suomalainen, Juha, Torresani, Michele, Umlauft, Josefine, Vargas-Ramirez, Nicolas, Volpi, Michele, Vasquez, Vicente, Weinstein, Ben, Ximena, Tagle Casapia, Zdunic, Katherine, Zielewska-Buettner, Katarzyna, de Oliveira, Raquel Alves, van Wagtendonk, Liz, von Dosky, Vincent, and Kattenborn, Teja

Abstract

Excessive tree mortality is a global concern and remains poorly understood as it is a complex phenomenon. We lack global and temporally continuous coverage on tree mortality data. Ground-based observations on tree mortality, e.g., derived from national inventories, are very sparse, not standardized and not spatially explicit. Earth observation data, combined with supervised machine learning, offer a promising approach to map tree mortality over time. However, global-scale machine learning requires broad training data covering a wide range of environmental settings and forest types. Drones provide a cost-effective source of training data by capturing high-resolution orthophotos of tree mortality events at sub-centimeter resolution. Here, we introduce deadtrees.earth, an open-access platform hosting more than a thousand centimeter-resolution orthophotos, covering already more than 300,000 ha, of which more than 58,000 ha are fully annotated. This community-sourced and rigorously curated dataset shall serve as a foundation for a global initiative to gather comprehensive reference data. In concert with Earth observation data and machine learning it will serve to uncover tree mortality patterns from local to global scales. This will provide the foundation to attribute tree mortality patterns to environmental changes or project tree mortality dynamics to the future. Thus, the open and interactive nature of deadtrees.earth together with the collective effort of the community is meant to continuously increase our capacity to uncover and understand tree mortality patterns.Competing Interest StatementThe authors have declared no competing interest.

Published
2024

4. deadtrees.earth - An Open-Access and Interactive Database for Centimeter-Scale Aerial Imagery to Uncover Global Tree Mortality Dynamics

Author

Mosig, Clemens, Vajna-Jehle, Janusch, Mahecha, Miguel D., Cheng, Yan, Hartmann, Henrik, Montero, David, Junttila, Samuli, Horion, Stephanie, Adu-Bredu, Stephen, Al-Halbouni, Djamil, Allen, Matthew, Altman, Jan, Angiolini, Claudia, Astrup, Rasmus, Barrasso, Caterina, Bartholomeus, Harm, Brede, Benjamin, Buras, Allan, Carrieri, Erik, Chirici, Gherardo, Cloutier, Myriam, Cushman, KC, Dalling, James W., Dempewolf, Jan, Denter, Martin, Ecke, Simon, Eichel, Jana, Eltner, Anette, Fabi, Maximilian, Fassnacht, Fabian, Feirreira, Matheus Pinheiro, Frey, Julian, Frick, Annett, Ganz, Selina, Garbarino, Matteo, Garcia, Milton, Gassilloud, Matthias, Ghasemi, Marziye, Giannetti, Francesca, Gonzalez, Roy, Gosper, Carl, Greinwald, Konrad, Grieve, Stuart, Gutierrez, Jesus Aguirre, Goeritz, Anna, Hajek, Peter, Hedding, David, Hempel, Jan, Hernandez, Melvin, Heurich, Marco, Honkavaara, Eija, Jucker, Tommaso, Kalwij, Jesse M., Khatri-Chhetri, Pratima, Klemmt, Hans-Joachim, Koivumaeki, Niko, Korznikov, Kirill, Kruse, Stefan, Krueger, Robert, Laliberte, Etienne, Langan, Liam, Latifi, Hooman, Lehmann, Jan, Li, Linyuan, Lines, Emily, Lopatin, Javier, Lucieer, Arko, Ludwig, Marvin, Ludwig, Antonia, Lyytikaeinen-Saarenmaa, Paeivi, Ma, Qin, Marino, Giovanni, Maroschek, Michael, Meloni, Fabio, Menzel, Annette, Meyer, Hanna, Miraki, Mojdeh, Moreno-Fernandez, Daniel, Muller-Landau, Helene C., Maelicke, Mirko, Moehring, Jakobus, Muellerova, Jana, Neumeier, Paul, Naesi, Roope, Oppgenoorth, Lars, Palmer, Melanie, Paul, Thomas, Potts, Alastair, Prober, Suzanne, Puliti, Stefano, Perez-Priego, Oscar, Reudenbach, Chris, Rossi, Christian, Ruehr, Nadine Katrin, Ruiz-Benito, Paloma, Runge, Christian Mestre, Scherer-Lorenzen, Michael, Schiefer, Felix, Schladebach, Jacob, Schmehl, Marie-Therese, Schwarz, Selina, Schwenke, Mirela Beloiu, Seidl, Rupert, Shafeian, Elham, Simone, Leopoldo de, Sohrabi, Hormoz, Sotomayor, Laura, Sparrow, Ben, Steer, Benjamin S.C., Stenson, Matt, Stoeckigt, Benjamin, Su, Yanjun, Suomalainen, Juha, Torresani, Michele, Umlauft, Josefine, Vargas-Ramirez, Nicolas, Volpi, Michele, Vasquez, Vicente, Weinstein, Ben, Ximena, Tagle Casapia, Zdunic, Katherine, Zielewska-Buettner, Katarzyna, de Oliveira, Raquel Alves, van Wagtendonk, Liz, von Dosky, Vincent, Kattenborn, Teja, Mosig, Clemens, Vajna-Jehle, Janusch, Mahecha, Miguel D., Cheng, Yan, Hartmann, Henrik, Montero, David, Junttila, Samuli, Horion, Stephanie, Adu-Bredu, Stephen, Al-Halbouni, Djamil, Allen, Matthew, Altman, Jan, Angiolini, Claudia, Astrup, Rasmus, Barrasso, Caterina, Bartholomeus, Harm, Brede, Benjamin, Buras, Allan, Carrieri, Erik, Chirici, Gherardo, Cloutier, Myriam, Cushman, KC, Dalling, James W., Dempewolf, Jan, Denter, Martin, Ecke, Simon, Eichel, Jana, Eltner, Anette, Fabi, Maximilian, Fassnacht, Fabian, Feirreira, Matheus Pinheiro, Frey, Julian, Frick, Annett, Ganz, Selina, Garbarino, Matteo, Garcia, Milton, Gassilloud, Matthias, Ghasemi, Marziye, Giannetti, Francesca, Gonzalez, Roy, Gosper, Carl, Greinwald, Konrad, Grieve, Stuart, Gutierrez, Jesus Aguirre, Goeritz, Anna, Hajek, Peter, Hedding, David, Hempel, Jan, Hernandez, Melvin, Heurich, Marco, Honkavaara, Eija, Jucker, Tommaso, Kalwij, Jesse M., Khatri-Chhetri, Pratima, Klemmt, Hans-Joachim, Koivumaeki, Niko, Korznikov, Kirill, Kruse, Stefan, Krueger, Robert, Laliberte, Etienne, Langan, Liam, Latifi, Hooman, Lehmann, Jan, Li, Linyuan, Lines, Emily, Lopatin, Javier, Lucieer, Arko, Ludwig, Marvin, Ludwig, Antonia, Lyytikaeinen-Saarenmaa, Paeivi, Ma, Qin, Marino, Giovanni, Maroschek, Michael, Meloni, Fabio, Menzel, Annette, Meyer, Hanna, Miraki, Mojdeh, Moreno-Fernandez, Daniel, Muller-Landau, Helene C., Maelicke, Mirko, Moehring, Jakobus, Muellerova, Jana, Neumeier, Paul, Naesi, Roope, Oppgenoorth, Lars, Palmer, Melanie, Paul, Thomas, Potts, Alastair, Prober, Suzanne, Puliti, Stefano, Perez-Priego, Oscar, Reudenbach, Chris, Rossi, Christian, Ruehr, Nadine Katrin, Ruiz-Benito, Paloma, Runge, Christian Mestre, Scherer-Lorenzen, Michael, Schiefer, Felix, Schladebach, Jacob, Schmehl, Marie-Therese, Schwarz, Selina, Schwenke, Mirela Beloiu, Seidl, Rupert, Shafeian, Elham, Simone, Leopoldo de, Sohrabi, Hormoz, Sotomayor, Laura, Sparrow, Ben, Steer, Benjamin S.C., Stenson, Matt, Stoeckigt, Benjamin, Su, Yanjun, Suomalainen, Juha, Torresani, Michele, Umlauft, Josefine, Vargas-Ramirez, Nicolas, Volpi, Michele, Vasquez, Vicente, Weinstein, Ben, Ximena, Tagle Casapia, Zdunic, Katherine, Zielewska-Buettner, Katarzyna, de Oliveira, Raquel Alves, van Wagtendonk, Liz, von Dosky, Vincent, and Kattenborn, Teja

Abstract

Excessive tree mortality is a global concern and remains poorly understood as it is a complex phenomenon. We lack global and temporally continuous coverage on tree mortality data. Ground-based observations on tree mortality, e.g., derived from national inventories, are very sparse, not standardized and not spatially explicit. Earth observation data, combined with supervised machine learning, offer a promising approach to map tree mortality over time. However, global-scale machine learning requires broad training data covering a wide range of environmental settings and forest types. Drones provide a cost-effective source of training data by capturing high-resolution orthophotos of tree mortality events at sub-centimeter resolution. Here, we introduce deadtrees.earth, an open-access platform hosting more than a thousand centimeter-resolution orthophotos, covering already more than 300,000 ha, of which more than 58,000 ha are fully annotated. This community-sourced and rigorously curated dataset shall serve as a foundation for a global initiative to gather comprehensive reference data. In concert with Earth observation data and machine learning it will serve to uncover tree mortality patterns from local to global scales. This will provide the foundation to attribute tree mortality patterns to environmental changes or project tree mortality dynamics to the future. Thus, the open and interactive nature of deadtrees.earth together with the collective effort of the community is meant to continuously increase our capacity to uncover and understand tree mortality patterns.Competing Interest StatementThe authors have declared no competing interest.

Published
2024

5. Delineating Standing Deadwood in High-Resolution RGB Drone Imagery

Author

Möhring, Jakobus, Mosig, Clemens, Cheng, Yan, Mahecha, Miguel, Priego, Oscar Perez, Beloiu, Mirela, Volpi, Michele, Horion, Stéphanie, Latifi, Hooman, Shafeian, Elham, Fassnacht, Fabian, Montero, David, Zielewska-Büttner, Katarzyna, Laliberté, Etienne, Cloutier, Myriam, Schmehl, Marie-Therese, Frick, Annett, Müller-Landau, Helene, Cushman, KC, Hupy, Joseph, Ma, Qin, Su, Yanjun, Khatri-Chhetri, Pratima, Kruse, Stefan, Frey, Julian, Schiefer, Felix, Junttila, Samuli, Potts, Alastair, Uhl, Andreas, Rossi, Christian, Kattenborn, Teja, Möhring, Jakobus, Mosig, Clemens, Cheng, Yan, Mahecha, Miguel, Priego, Oscar Perez, Beloiu, Mirela, Volpi, Michele, Horion, Stéphanie, Latifi, Hooman, Shafeian, Elham, Fassnacht, Fabian, Montero, David, Zielewska-Büttner, Katarzyna, Laliberté, Etienne, Cloutier, Myriam, Schmehl, Marie-Therese, Frick, Annett, Müller-Landau, Helene, Cushman, KC, Hupy, Joseph, Ma, Qin, Su, Yanjun, Khatri-Chhetri, Pratima, Kruse, Stefan, Frey, Julian, Schiefer, Felix, Junttila, Samuli, Potts, Alastair, Uhl, Andreas, Rossi, Christian, and Kattenborn, Teja

Abstract

We have observed tree die-offs in a variety of regions in the world. Understanding the diverse causes of tree mortality requires exact information about which trees are dying and where. With the increased user-friendliness of drones and the availability of airborne imagery, high-resolution imagery of forests is becoming widely available. Delineating standing deadwood in such aerial imagery has become a classic segmentation task and several models with varying accuracy have been developed. However, these machine-learning based models are not generic and limited to specific image resolutions, sensor characteristics, geographic regions, and forest ecosystems. The reason for this lack of generality is that previous models have been trained using only datasets representative of specific regions and obtained from a single source. In this study, we obtain a diverse dataset spanning more than a dozen countries across continents and implement a single convolutional neural network (CNN) model that is able to cope with most forest ecosystems, varying image quality, and spatial resolutions.

Published
2024

6. Mapping Tree Mortality at Global Scale Using Sentinel-2

Author

Mosig, Clemens, Mahecha, Miguel, Montero, David, Cheng, Yan, Perez Priego, Oscar, Beloiu, Mirela, Volpi, Michele, Horion, Stéphanie, Latifi, Hooman, Shafeian, Elham, Fassnacht, Fabian, Ganz, Selina, Zielewska-Büttner, Katarzyna, Laliberté, Etienne, Cloutier, Myriam, Schmehl, Marie-Therese, Frick, Annett, Müller-Landau, Helene, Cushman, KC, Hupy, Joseph, Ma, Qin, Su, Yanjun, Khatri-Chhetri, Pratima, Kruse, Stefan, Frey, Julian, Schiefer, Felix, Junttila, Samuli, Potts, Alastair, Uhl, Andreas, Rossi, Christian, Kattenborn, Teja, Mosig, Clemens, Mahecha, Miguel, Montero, David, Cheng, Yan, Perez Priego, Oscar, Beloiu, Mirela, Volpi, Michele, Horion, Stéphanie, Latifi, Hooman, Shafeian, Elham, Fassnacht, Fabian, Ganz, Selina, Zielewska-Büttner, Katarzyna, Laliberté, Etienne, Cloutier, Myriam, Schmehl, Marie-Therese, Frick, Annett, Müller-Landau, Helene, Cushman, KC, Hupy, Joseph, Ma, Qin, Su, Yanjun, Khatri-Chhetri, Pratima, Kruse, Stefan, Frey, Julian, Schiefer, Felix, Junttila, Samuli, Potts, Alastair, Uhl, Andreas, Rossi, Christian, and Kattenborn, Teja

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 to map tree mortality globally at a new level of precision.

Published
2024

7. Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

Author

Duncanson, L, Duncanson, L, Kellner, JR, Armston, J, Dubayah, R, Minor, DM, Hancock, S, Healey, SP, Patterson, PL, Saarela, S, Marselis, S, Silva, CE, Bruening, J, Goetz, SJ, Tang, H, Hofton, M, Blair, B, Luthcke, S, Fatoyinbo, L, Abernethy, K, Alonso, A, Andersen, HE, Aplin, P, Baker, TR, Barbier, N, Bastin, JF, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, PB, Boyd, DS, Burslem, DFRP, Calvo-Rodriguez, S, Chave, J, Chazdon, RL, Clark, DB, Clark, DA, Cohen, WB, Coomes, DA, Corona, P, Cushman, KC, Cutler, MEJ, Dalling, JW, Dalponte, M, Dash, J, de-Miguel, S, Deng, S, Ellis, PW, Erasmus, B, Fekety, PA, Fernandez-Landa, A, Ferraz, A, Fischer, R, Fisher, AG, García-Abril, A, Gobakken, T, Hacker, JM, Heurich, M, Hill, RA, Hopkinson, C, Huang, H, Hubbell, SP, Hudak, AT, Huth, A, Imbach, B, Jeffery, KJ, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, SL, Longo, M, Lucas, RM, Main, R, Manzanera, JA, Martínez, RV, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, AM, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O'Brien, M, Orwig, DA, Papathanassiou, K, Parker, G, Philipson, C, Phillips, OL, Pisek, J, Poulsen, JR, Pretzsch, H, Rüdiger, C, Duncanson, L, Duncanson, L, Kellner, JR, Armston, J, Dubayah, R, Minor, DM, Hancock, S, Healey, SP, Patterson, PL, Saarela, S, Marselis, S, Silva, CE, Bruening, J, Goetz, SJ, Tang, H, Hofton, M, Blair, B, Luthcke, S, Fatoyinbo, L, Abernethy, K, Alonso, A, Andersen, HE, Aplin, P, Baker, TR, Barbier, N, Bastin, JF, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, PB, Boyd, DS, Burslem, DFRP, Calvo-Rodriguez, S, Chave, J, Chazdon, RL, Clark, DB, Clark, DA, Cohen, WB, Coomes, DA, Corona, P, Cushman, KC, Cutler, MEJ, Dalling, JW, Dalponte, M, Dash, J, de-Miguel, S, Deng, S, Ellis, PW, Erasmus, B, Fekety, PA, Fernandez-Landa, A, Ferraz, A, Fischer, R, Fisher, AG, García-Abril, A, Gobakken, T, Hacker, JM, Heurich, M, Hill, RA, Hopkinson, C, Huang, H, Hubbell, SP, Hudak, AT, Huth, A, Imbach, B, Jeffery, KJ, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, SL, Longo, M, Lucas, RM, Main, R, Manzanera, JA, Martínez, RV, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, AM, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O'Brien, M, Orwig, DA, Papathanassiou, K, Parker, G, Philipson, C, Phillips, OL, Pisek, J, Poulsen, JR, Pretzsch, H, and Rüdiger, C

Abstract

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AG

Published
2022

8. Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

Author

Duncanson, L, Kellner, JR, Armston, J, Dubayah, R, Minor, DM, Hancock, S, Healey, SP, Patterson, PL, Saarela, S, Marselis, S, Silva, CE, Bruening, J, Goetz, SJ, Tang, H, Hofton, M, Blair, B, Luthcke, S, Fatoyinbo, L, Abernethy, K, Alonso, A, Andersen, HE, Aplin, P, Baker, TR, Barbier, N, Bastin, JF, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, PB, Boyd, DS, Burslem, DFRP, Calvo-Rodriguez, S, Chave, J, Chazdon, RL, Clark, DB, Clark, DA, Cohen, WB, Coomes, DA, Corona, P, Cushman, KC, Cutler, MEJ, Dalling, JW, Dalponte, M, Dash, J, de-Miguel, S, Deng, S, Ellis, PW, Erasmus, B, Fekety, PA, Fernandez-Landa, A, Ferraz, A, Fischer, R, Fisher, AG, García-Abril, A, Gobakken, T, Hacker, JM, Heurich, M, Hill, RA, Hopkinson, C, Huang, H, Hubbell, SP, Hudak, AT, Huth, A, Imbach, B, Jeffery, KJ, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, SL, Longo, M, Lucas, RM, Main, R, Manzanera, JA, Martínez, RV, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, AM, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O'Brien, M, Orwig, DA, Papathanassiou, K, Parker, G, Philipson, C, Phillips, OL, Pisek, J, Poulsen, JR, Pretzsch, H, Rüdiger, C, Duncanson, L, Kellner, JR, Armston, J, Dubayah, R, Minor, DM, Hancock, S, Healey, SP, Patterson, PL, Saarela, S, Marselis, S, Silva, CE, Bruening, J, Goetz, SJ, Tang, H, Hofton, M, Blair, B, Luthcke, S, Fatoyinbo, L, Abernethy, K, Alonso, A, Andersen, HE, Aplin, P, Baker, TR, Barbier, N, Bastin, JF, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, PB, Boyd, DS, Burslem, DFRP, Calvo-Rodriguez, S, Chave, J, Chazdon, RL, Clark, DB, Clark, DA, Cohen, WB, Coomes, DA, Corona, P, Cushman, KC, Cutler, MEJ, Dalling, JW, Dalponte, M, Dash, J, de-Miguel, S, Deng, S, Ellis, PW, Erasmus, B, Fekety, PA, Fernandez-Landa, A, Ferraz, A, Fischer, R, Fisher, AG, García-Abril, A, Gobakken, T, Hacker, JM, Heurich, M, Hill, RA, Hopkinson, C, Huang, H, Hubbell, SP, Hudak, AT, Huth, A, Imbach, B, Jeffery, KJ, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, SL, Longo, M, Lucas, RM, Main, R, Manzanera, JA, Martínez, RV, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, AM, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O'Brien, M, Orwig, DA, Papathanassiou, K, Parker, G, Philipson, C, Phillips, OL, Pisek, J, Poulsen, JR, Pretzsch, H, and Rüdiger, C

Published
2022

9. Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

Author

Duncanson, L, Duncanson, L, Kellner, JR, Armston, J, Dubayah, R, Minor, DM, Hancock, S, Healey, SP, Patterson, PL, Saarela, S, Marselis, S, Silva, CE, Bruening, J, Goetz, SJ, Tang, H, Hofton, M, Blair, B, Luthcke, S, Fatoyinbo, L, Abernethy, K, Alonso, A, Andersen, HE, Aplin, P, Baker, TR, Barbier, N, Bastin, JF, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, PB, Boyd, DS, Burslem, DFRP, Calvo-Rodriguez, S, Chave, J, Chazdon, RL, Clark, DB, Clark, DA, Cohen, WB, Coomes, DA, Corona, P, Cushman, KC, Cutler, MEJ, Dalling, JW, Dalponte, M, Dash, J, de-Miguel, S, Deng, S, Ellis, PW, Erasmus, B, Fekety, PA, Fernandez-Landa, A, Ferraz, A, Fischer, R, Fisher, AG, García-Abril, A, Gobakken, T, Hacker, JM, Heurich, M, Hill, RA, Hopkinson, C, Huang, H, Hubbell, SP, Hudak, AT, Huth, A, Imbach, B, Jeffery, KJ, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, SL, Longo, M, Lucas, RM, Main, R, Manzanera, JA, Martínez, RV, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, AM, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O'Brien, M, Orwig, DA, Papathanassiou, K, Parker, G, Philipson, C, Phillips, OL, Pisek, J, Poulsen, JR, Pretzsch, H, Rüdiger, C, Duncanson, L, Duncanson, L, Kellner, JR, Armston, J, Dubayah, R, Minor, DM, Hancock, S, Healey, SP, Patterson, PL, Saarela, S, Marselis, S, Silva, CE, Bruening, J, Goetz, SJ, Tang, H, Hofton, M, Blair, B, Luthcke, S, Fatoyinbo, L, Abernethy, K, Alonso, A, Andersen, HE, Aplin, P, Baker, TR, Barbier, N, Bastin, JF, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, PB, Boyd, DS, Burslem, DFRP, Calvo-Rodriguez, S, Chave, J, Chazdon, RL, Clark, DB, Clark, DA, Cohen, WB, Coomes, DA, Corona, P, Cushman, KC, Cutler, MEJ, Dalling, JW, Dalponte, M, Dash, J, de-Miguel, S, Deng, S, Ellis, PW, Erasmus, B, Fekety, PA, Fernandez-Landa, A, Ferraz, A, Fischer, R, Fisher, AG, García-Abril, A, Gobakken, T, Hacker, JM, Heurich, M, Hill, RA, Hopkinson, C, Huang, H, Hubbell, SP, Hudak, AT, Huth, A, Imbach, B, Jeffery, KJ, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, SL, Longo, M, Lucas, RM, Main, R, Manzanera, JA, Martínez, RV, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, AM, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O'Brien, M, Orwig, DA, Papathanassiou, K, Parker, G, Philipson, C, Phillips, OL, Pisek, J, Poulsen, JR, Pretzsch, H, and Rüdiger, C

Abstract

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AG

Published
2022

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