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1. Dominance and rarity in tree communities across the globe: Patterns, predictors and threats

Author: Hordijk, Iris, Bialic‐Murphy, Lalasia, Lauber, Thomas, Routh, Devin, Poorter, Lourens, Rivers, Malin C, Steege, Hans ter, Liang, Jingjing, Reich, Peter B, de‐Miguel, Sergio, Nabuurs, Gert‐Jan, Gamarra, Javier GP, Chen, Han YH, Zhou, Mo, Wiser, Susan K, Pretzsch, Hans, Paquette, Alain, Picard, Nicolas, Hérault, Bruno, Bastin, Jean‐Francois, Alberti, Giorgio, Abegg, Meinrad, Yao, Yves C Adou, Zambrano, Angelica M Almeyda, Alvarado, Braulio V, Alvarez‐Davila, Esteban, Alvarez‐Loayza, Patricia, Alves, Luciana F, Ammer, Christian, Antón‐Fernández, Clara, Araujo‐Murakami, Alejandro, Arroyo, Luzmila, Avitabile, Valerio, Corredor, Gerardo A Aymard, Baker, Timothy, Banki, Olaf, Barroso, Jorcely, Bastian, Meredith L, Birigazzi, Luca, Birnbaum, Philippe, Bitariho, Robert, Boeckx, Pascal, Bongers, Frans, Bouriaud, Olivier, Brancalion, Pedro HS, Brandl, Susanne, Brienen, Roel, Broadbent, Eben N, Bruelheide, Helge, Bussotti, Filippo, Gatti, Roberto Cazzolla, Cesar, Ricardo G, Cesljar, Goran, Chazdon, Robin, Chisholm, Chelsea, Cienciala, Emil, Clark, Connie J, Clar, David B, Colletta, Gabriel, Coomes, David, Valverde, Fernando Cornejo, Corral‐Rivas, Jose J, Crim, Philip, Cumming, Jonathan, Dayanandan, Selvadurai, de Gasper, André L, Decuyper, Mathieu, Derroire, Géraldine, DeVries, Ben, Djordjevic, Ilija, Iêda, Amaral, Dourdain, Aurélie, Dolezal, Jiri, Obiang, Nestor Laurier Engone, Enquist, Brian, Eyre, Teresa, Fandohan, Adandé Belarmain, Fayle, Tom M, Ferreira, Leandro V, Feldpausch, Ted R, Finér, Leena, Fischer, Markus, Fletcher, Christine, Frizzera, Lorenzo, Gianelle, Damiano, Glick, Henry B, Harris, David, Hector, Andrew, Hemp, Andreas, Hengeveld, Geerten, Herbohn, John, Hillers, Annika, Coronado, Eurídice N Honorio, Hui, Cang, Cho, Hyunkook, Ibanez, Thomas, Jung, Ilbin, Imai, Nobuo, Jagodzinski, Andrzej M, and Jaroszewicz, Bogdan
Subjects: Environmental Sciences, Ecological Applications, Ecology, Biological Sciences, Machine Learning and Artificial Intelligence, Life on Land, Life Below Water
Abstract: Aim: Ecological and anthropogenic factors shift the abundances of dominant and rare tree species within local forest communities, thus affecting species composition and ecosystem functioning. To inform forest and conservation management it is important to understand the drivers of dominance and rarity in local tree communities. We answer the following research questions: (1) What are the patterns of dominance and rarity in tree communities? (2) Which ecological and anthropogenic factors predict these patterns? And (3) what is the extinction risk of locally dominant and rare tree species?. Location: Global. Time period: 1990–2017. Major taxa studied: Trees. Methods: We used 1.2 million forest plots and quantified local tree dominance as the relative plot basal area of the single most dominant species and local rarity as the percentage of species that contribute together to the least 10% of plot basal area. We mapped global community dominance and rarity using machine learning models and evaluated the ecological and anthropogenic predictors with linear models. Extinction risk, for example threatened status, of geographically widespread dominant and rare species was evaluated. Results: Community dominance and rarity show contrasting latitudinal trends, with boreal forests having high levels of dominance and tropical forests having high levels of rarity. Increasing annual precipitation reduces community dominance, probably because precipitation is related to an increase in tree density and richness. Additionally, stand age is positively related to community dominance, due to stem diameter increase of the most dominant species. Surprisingly, we find that locally dominant and rare species, which are geographically widespread in our data, have an equally high rate of elevated extinction due to declining populations through large-scale land degradation. Main conclusions: By linking patterns and predictors of community dominance and rarity to extinction risk, our results suggest that also widespread species should be considered in large-scale management and conservation practices.
Published: 2024

2. Integrated global assessment of the natural forest carbon potential

Author: Mo, Lidong, Zohner, Constantin M, Reich, Peter B, Liang, Jingjing, de Miguel, Sergio, Nabuurs, Gert-Jan, Renner, Susanne S, van den Hoogen, Johan, Araza, Arnan, Herold, Martin, Mirzagholi, Leila, Ma, Haozhi, Averill, Colin, Phillips, Oliver L, Gamarra, Javier GP, Hordijk, Iris, Routh, Devin, Abegg, Meinrad, Adou Yao, Yves C, Alberti, Giorgio, Almeyda Zambrano, Angelica M, Alvarado, Braulio Vilchez, Alvarez-Dávila, Esteban, Alvarez-Loayza, Patricia, Alves, Luciana F, Amaral, Iêda, Ammer, Christian, Antón-Fernández, Clara, Araujo-Murakami, Alejandro, Arroyo, Luzmila, Avitabile, Valerio, Aymard, Gerardo A, Baker, Timothy R, Bałazy, Radomir, Banki, Olaf, Barroso, Jorcely G, Bastian, Meredith L, Bastin, Jean-Francois, Birigazzi, Luca, Birnbaum, Philippe, Bitariho, Robert, Boeckx, Pascal, Bongers, Frans, Bouriaud, Olivier, Brancalion, Pedro HS, Brandl, Susanne, Brearley, Francis Q, Brienen, Roel, Broadbent, Eben N, Bruelheide, Helge, Bussotti, Filippo, Cazzolla Gatti, Roberto, César, Ricardo G, Cesljar, Goran, Chazdon, Robin L, Chen, Han YH, Chisholm, Chelsea, Cho, Hyunkook, Cienciala, Emil, Clark, Connie, Clark, David, Colletta, Gabriel D, Coomes, David A, Cornejo Valverde, Fernando, Corral-Rivas, José J, Crim, Philip M, Cumming, Jonathan R, Dayanandan, Selvadurai, de Gasper, André L, Decuyper, Mathieu, Derroire, Géraldine, DeVries, Ben, Djordjevic, Ilija, Dolezal, Jiri, Dourdain, Aurélie, Engone Obiang, Nestor Laurier, Enquist, Brian J, Eyre, Teresa J, Fandohan, Adandé Belarmain, Fayle, Tom M, Feldpausch, Ted R, Ferreira, Leandro V, Finér, Leena, Fischer, Markus, Fletcher, Christine, Frizzera, Lorenzo, Gianelle, Damiano, Glick, Henry B, Harris, David J, Hector, Andrew, Hemp, Andreas, Hengeveld, Geerten, Hérault, Bruno, Herbohn, John L, Hillers, Annika, Honorio Coronado, Eurídice N, Hui, Cang, Ibanez, Thomas, Imai, Nobuo, and Jagodziński, Andrzej M
Subjects: Agricultural, Veterinary and Food Sciences, Ecological Applications, Environmental Sciences, Forestry Sciences, Life on Land, Humans, Ecosystem, Carbon, Conservation of Natural Resources, Forests, Biodiversity, Carbon Sequestration, General Science & Technology
Abstract: Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system1. Remote-sensing estimates to quantify carbon losses from global forests2-5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced6 and satellite-derived approaches2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151-363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.
Published: 2023

3. The global biogeography of tree leaf form and habit

Author: Ma, Haozhi, Crowther, Thomas W, Mo, Lidong, Maynard, Daniel S, Renner, Susanne S, van den Hoogen, Johan, Zou, Yibiao, Liang, Jingjing, de-Miguel, Sergio, Nabuurs, Gert-Jan, Reich, Peter B, Niinemets, Ülo, Abegg, Meinrad, Adou Yao, Yves C, Alberti, Giorgio, Almeyda Zambrano, Angelica M, Alvarado, Braulio Vilchez, Alvarez-Dávila, Esteban, Alvarez-Loayza, Patricia, Alves, Luciana F, Ammer, Christian, Antón-Fernández, Clara, Araujo-Murakami, Alejandro, Arroyo, Luzmila, Avitabile, Valerio, Aymard, Gerardo A, Baker, Timothy R, Bałazy, Radomir, Banki, Olaf, Barroso, Jorcely G, Bastian, Meredith L, Bastin, Jean-Francois, Birigazzi, Luca, Birnbaum, Philippe, Bitariho, Robert, Boeckx, Pascal, Bongers, Frans, Bouriaud, Olivier, Brancalion, Pedro HS, Brandl, Susanne, Brearley, Francis Q, Brienen, Roel, Broadbent, Eben N, Bruelheide, Helge, Bussotti, Filippo, Cazzolla Gatti, Roberto, César, Ricardo G, Cesljar, Goran, Chazdon, Robin, Chen, Han YH, Chisholm, Chelsea, Cho, Hyunkook, Cienciala, Emil, Clark, Connie, Clark, David, Colletta, Gabriel D, Coomes, David A, Valverde, Fernando Cornejo, Corral-Rivas, José J, Crim, Philip M, Cumming, Jonathan R, Dayanandan, Selvadurai, de Gasper, André L, Decuyper, Mathieu, Derroire, Géraldine, DeVries, Ben, Djordjevic, Ilija, Dolezal, Jiri, Dourdain, Aurélie, Engone Obiang, Nestor Laurier, Enquist, Brian J, Eyre, Teresa J, Fandohan, Adandé Belarmain, Fayle, Tom M, Feldpausch, Ted R, Ferreira, Leandro V, Finér, Leena, Fischer, Markus, Fletcher, Christine, Fridman, Jonas, Frizzera, Lorenzo, Gamarra, Javier GP, Gianelle, Damiano, Glick, Henry B, Harris, David J, Hector, Andrew, Hemp, Andreas, Hengeveld, Geerten, Hérault, Bruno, Herbohn, John L, Herold, Martin, Hillers, Annika, Honorio Coronado, Eurídice N, Hui, Cang, Ibanez, Thomas T, Amaral, Iêda, Imai, Nobuo, Jagodziński, Andrzej M, Jaroszewicz, Bogdan, and Johannsen, Vivian Kvist
Subjects: Biological Sciences, Ecology, Climate Action, Plant Biology, Crop and Pasture Production, Plant biology
Abstract: Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17-34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling.
Published: 2023

4. Author Correction: Native diversity buffers against severity of non-native tree invasions

Author: Delavaux, Camille S, Crowther, Thomas W, Zohner, Constantin M, Robmann, Niamh M, Lauber, Thomas, van den Hoogen, Johan, Kuebbing, Sara, Liang, Jingjing, de-Miguel, Sergio, Nabuurs, Gert-Jan, Reich, Peter B, Abegg, Meinrad, Adou Yao, Yves C, Alberti, Giorgio, Almeyda Zambrano, Angelica M, Alvarado, Braulio Vilchez, Alvarez-Dávila, Esteban, Alvarez-Loayza, Patricia, Alves, Luciana F, Ammer, Christian, Antón-Fernández, Clara, Araujo-Murakami, Alejandro, Arroyo, Luzmila, Avitabile, Valerio, Aymard, Gerardo A, Baker, Timothy R, Bałazy, Radomir, Banki, Olaf, Barroso, Jorcely G, Bastian, Meredith L, Bastin, Jean-Francois, Birigazzi, Luca, Birnbaum, Philippe, Bitariho, Robert, Boeckx, Pascal, Bongers, Frans, Bouriaud, Olivier, Brancalion, Pedro HS, Brandl, Susanne, Brienen, Roel, Broadbent, Eben N, Bruelheide, Helge, Bussotti, Filippo, Gatti, Roberto Cazzolla, César, Ricardo G, Cesljar, Goran, Chazdon, Robin, Chen, Han YH, Chisholm, Chelsea, Cho, Hyunkook, Cienciala, Emil, Clark, Connie, Clark, David, Colletta, Gabriel D, Coomes, David A, Cornejo Valverde, Fernando, Corral-Rivas, José J, Crim, Philip M, Cumming, Jonathan R, Dayanandan, Selvadurai, de Gasper, André L, Decuyper, Mathieu, Derroire, Géraldine, DeVries, Ben, Djordjevic, Ilija, Dolezal, Jiri, Dourdain, Aurélie, Engone Obiang, Nestor Laurier, Enquist, Brian J, Eyre, Teresa J, Fandohan, Adandé Belarmain, Fayle, Tom M, Feldpausch, Ted R, Ferreira, Leandro V, Fischer, Markus, Fletcher, Christine, Frizzera, Lorenzo, Gamarra, Javier GP, Gianelle, Damiano, Glick, Henry B, Harris, David J, Hector, Andrew, Hemp, Andreas, Hengeveld, Geerten, Hérault, Bruno, Herbohn, John L, Herold, Martin, Hillers, Annika, Honorio Coronado, Eurídice N, Hui, Cang, Ibanez, Thomas T, Amaral, Iêda, Imai, Nobuo, Jagodziński, Andrzej M, Jaroszewicz, Bogdan, Johannsen, Vivian Kvist, Joly, Carlos A, Jucker, Tommaso, Jung, Ilbin, and Karminov, Viktor
Subjects: General Science & Technology
Published: 2023

5. Native diversity buffers against severity of non-native tree invasions.

Author: Delavaux, Camille S, Crowther, Thomas W, Zohner, Constantin M, Robmann, Niamh M, Lauber, Thomas, van den Hoogen, Johan, Kuebbing, Sara, Liang, Jingjing, de-Miguel, Sergio, Nabuurs, Gert-Jan, Reich, Peter B, Abegg, Meinrad, Adou Yao, Yves C, Alberti, Giorgio, Almeyda Zambrano, Angelica M, Alvarado, Braulio Vilchez, Alvarez-Dávila, Esteban, Alvarez-Loayza, Patricia, Alves, Luciana F, Ammer, Christian, Antón-Fernández, Clara, Araujo-Murakami, Alejandro, Arroyo, Luzmila, Avitabile, Valerio, Aymard, Gerardo A, Baker, Timothy R, Bałazy, Radomir, Banki, Olaf, Barroso, Jorcely G, Bastian, Meredith L, Bastin, Jean-Francois, Birigazzi, Luca, Birnbaum, Philippe, Bitariho, Robert, Boeckx, Pascal, Bongers, Frans, Bouriaud, Olivier, Brancalion, Pedro HS, Brandl, Susanne, Brienen, Roel, Broadbent, Eben N, Bruelheide, Helge, Bussotti, Filippo, Gatti, Roberto Cazzolla, César, Ricardo G, Cesljar, Goran, Chazdon, Robin, Chen, Han YH, Chisholm, Chelsea, Cho, Hyunkook, Cienciala, Emil, Clark, Connie, Clark, David, Colletta, Gabriel D, Coomes, David A, Cornejo Valverde, Fernando, Corral-Rivas, José J, Crim, Philip M, Cumming, Jonathan R, Dayanandan, Selvadurai, de Gasper, André L, Decuyper, Mathieu, Derroire, Géraldine, DeVries, Ben, Djordjevic, Ilija, Dolezal, Jiri, Dourdain, Aurélie, Engone Obiang, Nestor Laurier, Enquist, Brian J, Eyre, Teresa J, Fandohan, Adandé Belarmain, Fayle, Tom M, Feldpausch, Ted R, Ferreira, Leandro V, Fischer, Markus, Fletcher, Christine, Frizzera, Lorenzo, Gamarra, Javier GP, Gianelle, Damiano, Glick, Henry B, Harris, David J, Hector, Andrew, Hemp, Andreas, Hengeveld, Geerten, Hérault, Bruno, Herbohn, John L, Herold, Martin, Hillers, Annika, Honorio Coronado, Eurídice N, Hui, Cang, Ibanez, Thomas T, Amaral, Iêda, Imai, Nobuo, Jagodziński, Andrzej M, Jaroszewicz, Bogdan, Johannsen, Vivian Kvist, Joly, Carlos A, Jucker, Tommaso, Jung, Ilbin, and Karminov, Viktor
Subjects: General Science & Technology
Abstract: Determining the drivers of non-native plant invasions is critical for managing native ecosystems and limiting the spread of invasive species1,2. Tree invasions in particular have been relatively overlooked, even though they have the potential to transform ecosystems and economies3,4. Here, leveraging global tree databases5-7, we explore how the phylogenetic and functional diversity of native tree communities, human pressure and the environment influence the establishment of non-native tree species and the subsequent invasion severity. We find that anthropogenic factors are key to predicting whether a location is invaded, but that invasion severity is underpinned by native diversity, with higher diversity predicting lower invasion severity. Temperature and precipitation emerge as strong predictors of invasion strategy, with non-native species invading successfully when they are similar to the native community in cold or dry extremes. Yet, despite the influence of these ecological forces in determining invasion strategy, we find evidence that these patterns can be obscured by human activity, with lower ecological signal in areas with higher proximity to shipping ports. Our global perspective of non-native tree invasion highlights that human drivers influence non-native tree presence, and that native phylogenetic and functional diversity have a critical role in the establishment and spread of subsequent invasions.
Published: 2023

6. Consistent patterns of common species across tropical tree communities

Author: Cooper, Declan L. M., Lewis, Simon L., Sullivan, Martin J. P., Prado, Paulo I., ter Steege, Hans, Barbier, Nicolas, Slik, Ferry, Sonké, Bonaventure, Ewango, Corneille E. N., Adu-Bredu, Stephen, Affum-Baffoe, Kofi, de Aguiar, Daniel P. P., Ahuite Reategui, Manuel Augusto, Aiba, Shin-Ichiro, Albuquerque, Bianca Weiss, de Almeida Matos, Francisca Dionízia, Alonso, Alfonso, Amani, Christian A., do Amaral, Dário Dantas, do Amaral, Iêda Leão, Andrade, Ana, de Andrade Miranda, Ires Paula, Angoboy, Ilondea B., Araujo-Murakami, Alejandro, Arboleda, Nicolás Castaño, Arroyo, Luzmila, Ashton, Peter, Aymard C, Gerardo A., Baider, Cláudia, Baker, Timothy R., Balinga, Michael Philippe Bessike, Balslev, Henrik, Banin, Lindsay F., Bánki, Olaf S., Baraloto, Chris, Barbosa, Edelcilio Marques, Barbosa, Flávia Rodrigues, Barlow, Jos, Bastin, Jean-Francois, Beeckman, Hans, Begne, Serge, Bengone, Natacha Nssi, Berenguer, Erika, Berry, Nicholas, Bitariho, Robert, Boeckx, Pascal, Bogaert, Jan, Bonyoma, Bernard, Boundja, Patrick, Bourland, Nils, Boyemba Bosela, Faustin, Brambach, Fabian, Brienen, Roel, Burslem, David F. R. P., Camargo, José Luís, Campelo, Wegliane, Cano, Angela, Cárdenas, Sasha, Cárdenas López, Dairon, de Sá Carpanedo, Rainiellen, Carrero Márquez, Yrma Andreina, Carvalho, Fernanda Antunes, Casas, Luisa Fernanda, Castellanos, Hernán, Castilho, Carolina V., Cerón, Carlos, Chapman, Colin A., Chave, Jerome, Chhang, Phourin, Chutipong, Wanlop, Chuyong, George B., Cintra, Bruno Barçante Ladvocat, Clark, Connie J., Coelho de Souza, Fernanda, Comiskey, James A., Coomes, David A., Cornejo Valverde, Fernando, Correa, Diego F., Costa, Flávia R. C., Costa, Janaina Barbosa Pedrosa, Couteron, Pierre, Culmsee, Heike, Cuni-Sanchez, Aida, Dallmeier, Francisco, Damasco, Gabriel, Dauby, Gilles, Dávila, Nállarett, Dávila Doza, Hilda Paulette, De Alban, Jose Don T., de Assis, Rafael L., De Canniere, Charles, De Haulleville, Thales, de Jesus Veiga Carim, Marcelo, Demarchi, Layon O., Dexter, Kyle G., Di Fiore, Anthony, Din, Hazimah Haji Mohammad, Disney, Mathias I., Djiofack, Brice Yannick, Djuikouo, Marie-Noël K., Do, Tran Van, Doucet, Jean-Louis, Draper, Freddie C., Droissart, Vincent, Duivenvoorden, Joost F., Engel, Julien, Estienne, Vittoria, Farfan-Rios, William, Fauset, Sophie, Feeley, Kenneth J., Feitosa, Yuri Oliveira, Feldpausch, Ted R., Ferreira, Cid, Ferreira, Joice, Ferreira, Leandro Valle, Fletcher, Christine D., Flores, Bernardo Monteiro, Fofanah, Alusine, Foli, Ernest G., Fonty, Émile, Fredriksson, Gabriella M., Fuentes, Alfredo, Galbraith, David, Gallardo Gonzales, George Pepe, Garcia-Cabrera, Karina, García-Villacorta, Roosevelt, Gomes, Vitor H. F., Gómez, Ricardo Zárate, Gonzales, Therany, Gribel, Rogerio, Guedes, Marcelino Carneiro, Guevara, Juan Ernesto, Hakeem, Khalid Rehman, Hall, Jefferson S., Hamer, Keith C., Hamilton, Alan C., Harris, David J., Harrison, Rhett D., Hart, Terese B., Hector, Andy, Henkel, Terry W., Herbohn, John, Hockemba, Mireille B. N., Hoffman, Bruce, Holmgren, Milena, Honorio Coronado, Euridice N., Huamantupa-Chuquimaco, Isau, Hubau, Wannes, Imai, Nobuo, Irume, Mariana Victória, Jansen, Patrick A., Jeffery, Kathryn J., Jimenez, Eliana M., Jucker, Tommaso, Junqueira, André Braga, Kalamandeen, Michelle, Kamdem, Narcisse G., Kartawinata, Kuswata, Kasongo Yakusu, Emmanuel, Katembo, John M., Kearsley, Elizabeth, Kenfack, David, Kessler, Michael, Khaing, Thiri Toe, Killeen, Timothy J., Kitayama, Kanehiro, Klitgaard, Bente, Labrière, Nicolas, Laumonier, Yves, Laurance, Susan G. W., Laurance, William F., Laurent, Félix, Le, Tinh Cong, Le, Trai Trong, Leal, Miguel E., Leão de Moraes Novo, Evlyn Márcia, Levesley, Aurora, Libalah, Moses B., Licona, Juan Carlos, Lima Filho, Diógenes de Andrade, Lindsell, Jeremy A., Lopes, Aline, Lopes, Maria Aparecida, Lovett, Jon C., Lowe, Richard, Lozada, José Rafael, Lu, Xinghui, Luambua, Nestor K., Luize, Bruno Garcia, Maas, Paul, Magalhães, José Leonardo Lima, Magnusson, William E., Mahayani, Ni Putu Diana, Makana, Jean-Remy, Malhi, Yadvinder, Maniguaje Rincón, Lorena, Mansor, Asyraf, Manzatto, Angelo Gilberto, Marimon, Beatriz S., Marimon-Junior, Ben Hur, Marshall, Andrew R, Martins, Maria Pires, Mbayu, Faustin M., de Medeiros, Marcelo Brilhante, Mesones, Italo, Metali, Faizah, Mihindou, Vianet, Millet, Jerome, Milliken, William, Mogollón, Hugo F., Molino, Jean-François, Mohd. Said, Mohd. Nizam, Monteagudo Mendoza, Abel, Montero, Juan Carlos, Moore, Sam, Mostacedo, Bonifacio, Mozombite Pinto, Linder Felipe, Mukul, Sharif Ahmed, Munishi, Pantaleo K. T., Nagamasu, Hidetoshi, Nascimento, Henrique Eduardo Mendonça, Nascimento, Marcelo Trindade, Neill, David, Nilus, Reuben, Noronha, Janaína Costa, Nsenga, Laurent, Núñez Vargas, Percy, Ojo, Lucas, Oliveira, Alexandre A., de Oliveira, Edmar Almeida, Ondo, Fidèle Evouna, Palacios Cuenca, Walter, Pansini, Susamar, Pansonato, Marcelo Petratti, Paredes, Marcos Ríos, Paudel, Ekananda, Pauletto, Daniela, Pearson, Richard G., Pena, José Luis Marcelo, Pennington, R. Toby, Peres, Carlos A., Permana, Andrea, Petronelli, Pascal, Peñuela Mora, Maria Cristina, Phillips, Juan Fernando, Phillips, Oliver L., Pickavance, Georgia, Piedade, Maria Teresa Fernandez, Pitman, Nigel C. A., Ploton, Pierre, Popelier, Andreas, Poulsen, John R., Prieto, Adriana, Primack, Richard B., Priyadi, Hari, Qie, Lan, Quaresma, Adriano Costa, de Queiroz, Helder Lima, Ramirez-Angulo, Hirma, Ramos, José Ferreira, Reis, Neidiane Farias Costa, Reitsma, Jan, Revilla, Juan David Cardenas, Riutta, Terhi, Rivas-Torres, Gonzalo, Robiansyah, Iyan, Rocha, Maira, Rodrigues, Domingos de Jesus, Rodriguez-Ronderos, M. Elizabeth, Rovero, Francesco, Rozak, Andes H., Rudas, Agustín, Rutishauser, Ervan, Sabatier, Daniel, Sagang, Le Bienfaiteur, Sampaio, Adeilza Felipe, Samsoedin, Ismayadi, Satdichanh, Manichanh, Schietti, Juliana, Schöngart, Jochen, Scudeller, Veridiana Vizoni, Seuaturien, Naret, Sheil, Douglas, Sierra, Rodrigo, Silman, Miles R., Silva, Thiago Sanna Freire, da Silva Guimarães, José Renan, Simo-Droissart, Murielle, Simon, Marcelo Fragomeni, Sist, Plinio, Sousa, Thaiane R., de Sousa Farias, Emanuelle, de Souza Coelho, Luiz, Spracklen, Dominick V., Stas, Suzanne M., Steinmetz, Robert, Stevenson, Pablo R., Stropp, Juliana, Sukri, Rahayu S., Sunderland, Terry C. H., Suzuki, Eizi, Swaine, Michael D., Tang, Jianwei, Taplin, James, Taylor, David M., Tello, J. Sebastián, Terborgh, John, Texier, Nicolas, Theilade, Ida, Thomas, Duncan W., Thomas, Raquel, Thomas, Sean C., Tirado, Milton, Toirambe, Benjamin, de Toledo, José Julio, Tomlinson, Kyle W., Torres-Lezama, Armando, Tran, Hieu Dang, Tshibamba Mukendi, John, Tumaneng, Roven D., Umaña, Maria Natalia, Umunay, Peter M., Urrego Giraldo, Ligia Estela, Valderrama Sandoval, Elvis H., Valenzuela Gamarra, Luis, Van Andel, Tinde R., van de Bult, Martin, van de Pol, Jaqueline, van der Heijden, Geertje, Vasquez, Rodolfo, Vela, César I. A., Venticinque, Eduardo Martins, Verbeeck, Hans, Veridiano, Rizza Karen A., Vicentini, Alberto, Vieira, Ima Célia Guimarães, Vilanova Torre, Emilio, Villarroel, Daniel, Villa Zegarra, Boris Eduardo, Vleminckx, Jason, von Hildebrand, Patricio, Vos, Vincent Antoine, Vriesendorp, Corine, Webb, Edward L., White, Lee J. T., Wich, Serge, Wittmann, Florian, Zagt, Roderick, Zang, Runguo, Zartman, Charles Eugene, Zemagho, Lise, Zent, Egleé L., and Zent, Stanford
Published: 2024
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7. Planetary computing for data-driven environmental policy-making

Author: Ferris, Patrick, Dales, Michael, Jaffer, Sadiq, Holcomb, Amelia, Scott, Eleanor Toye, Swinfield, Thomas, Eyres, Alison, Balmford, Andrew, Coomes, David, Keshav, Srinivasan, and Madhavapeddy, Anil
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing, D.0, D.4
Abstract: We make a case for "planetary computing" -- infrastructure to handle the ingestion, transformation, analysis and publication of global data products for furthering environmental science and enabling better informed policy-making. We draw on our experiences as a team of computer scientists working with environmental scientists on forest carbon and biodiversity preservation, and classify existing solutions by their flexibility in scalably processing geospatial data, and also how well they support building trust in the results via traceability and reproducibility. We identify research gaps in the intersection of computing and environmental science around how to handle continuously changing datasets that are often collected across decades and require careful access control rather than being fully open access.
Published: 2023

8. Evenness mediates the global relationship between forest productivity and richness

Author: Hordijk, Iris, Maynard, Daniel S, Hart, Simon P, Lidong, Mo, ter Steege, Hans, Liang, Jingjing, de‐Miguel, Sergio, Nabuurs, Gert‐Jan, Reich, Peter B, Abegg, Meinrad, Adou Yao, C Yves, Alberti, Giorgio, Almeyda Zambrano, Angelica M, Alvarado, Braulio V, Esteban, Alvarez‐Davila, Alvarez‐Loayza, Patricia, Alves, Luciana F, Ammer, Christian, Antón‐Fernández, Clara, Araujo‐Murakami, Alejandro, Arroyo, Luzmila, Avitabile, Valerio, Aymard C, Gerardo A, Baker, Timothy, Bałazy, Radomir, Banki, Olaf, Barroso, Jorcely, Bastian, Meredith L, Bastin, Jean‐Francois, Birigazzi, Luca, Birnbaum, Philippe, Bitariho, Robert, Boeckx, Pascal, Bongers, Frans, Bouriaud, Olivier, Brancalion, Pedro HS, Brandl, Susanne, Brienen, Roel, Broadbent, Eben N, Bruelheide, Helge, Bussotti, Filippo, Cazzolla Gatti, Roberto, César, Ricardo G, Cesljar, Goran, Chazdon, Robin, Chen, Han YH, Chisholm, Chelsea, Cienciala, Emil, Clark, Connie J, Clark, David B, Colletta, Gabriel, Coomes, David, Cornejo Valverde, Fernando, Corral‐Rivas, Jose J, Crim, Philip, Cumming, Jonathan, Dayanandan, Selvadurai, de Gasper, André L, Decuyper, Mathieu, Derroire, Géraldine, DeVries, Ben, Djordjevic, Ilija, Iêda, Amaral, Dourdain, Aurélie, Nestor Laurier, Engone Obiang, Enquist, Brian, Eyre, Teresa, Fandohan, Adandé Belarmain, Fayle, Tom M, Ferreira, Leandro V, Feldpausch, Ted R, Finér, Leena, Fischer, Markus, Fletcher, Christine, Frizzera, Lorenzo, Gamarra, Javier GP, Gianelle, Damiano, Glick, Henry B, Harris, David, Hector, Andrew, Hemp, Andreas, Hengeveld, Geerten, Hérault, Bruno, Herbohn, John, Hillers, Annika, Honorio Coronado, Eurídice N, Hui, Cang, Cho, Hyunkook, Ibanez, Thomas, Bin Jung, Il, Imai, Nobuo, Jagodzinski, Andrzej M, Jaroszewicz, Bogdan, Johanssen, Vivian, Joly, Carlos A, Jucker, Tommaso, Karminov, Viktor, Kartawinata, Kuswata, Kearsley, Elizabeth, and Kenfack, David
Subjects: Biological Sciences, Ecology, Life Below Water, diversity, ecosystem function and services, evenness, forests, global, productivity, species richness, Environmental Sciences, Agricultural and Veterinary Sciences
Abstract: 1. Biodiversity is an important component of natural ecosystems, with higher species richness often correlating with an increase in ecosystem productivity. Yet, this relationship varies substantially across environments, typically becoming less pronounced at high levels of species richness. However, species richness alone cannot reflect all important properties of a community, including community evenness, which may mediate the relationship between biodiversity and productivity. If the evenness of a community correlates negatively with richness across forests globally, then a greater number of species may not always increase overall diversity and productivity of the system. Theoretical work and local empirical studies have shown that the effect of evenness on ecosystem functioning may be especially strong at high richness levels, yet the consistency of this remains untested at a global scale. 2. Here, we used a dataset of forests from across the globe, which includes composition, biomass accumulation and net primary productivity, to explore whether productivity correlates with community evenness and richness in a way that evenness appears to buffer the effect of richness. Specifically, we evaluated whether low levels of evenness in speciose communities correlate with the attenuation of the richness–productivity relationship. 3. We found that tree species richness and evenness are negatively correlated across forests globally, with highly speciose forests typically comprising a few dominant and many rare species. Furthermore, we found that the correlation between diversity and productivity changes with evenness: at low richness, uneven communities are more productive, while at high richness, even communities are more productive. 4. Synthesis. Collectively, these results demonstrate that evenness is an integral component of the relationship between biodiversity and productivity, and that the attenuating effect of richness on forest productivity might be partly explained by low evenness in speciose communities. Productivity generally increases with species richness, until reduced evenness limits the overall increases in community diversity. Our research suggests that evenness is a fundamental component of biodiversity–ecosystem function relationships, and is of critical importance for guiding conservation and sustainable ecosystem management decisions.
Published: 2023

9. Tree species classification from hyperspectral data using graph-regularized neural networks

Author: Bandyopadhyay, Debmita, Mukherjee, Subhadip, Ball, James, Vincent, Grégoire, Coomes, David A., and Schönlieb, Carola-Bibiane
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: We propose a novel graph-regularized neural network (GRNN) algorithm for tree species classification. The proposed algorithm encompasses superpixel-based segmentation for graph construction, a pixel-wise neural network classifier, and the label propagation technique to generate an accurate and realistic (emulating tree crowns) classification map on a sparsely annotated data set. GRNN outperforms several state-of-the-art techniques not only for the standard Indian Pines HSI but also achieves a high classification accuracy (approx. 92%) on a new HSI data set collected over the heterogeneous forests of French Guiana (FG) when less than 1% of the pixels are labeled. We further show that GRNN is competitive with the state-of-the-art semi-supervised methods and exhibits a small deviation in accuracy for different numbers of training samples and over repeated trials with randomly sampled labeled pixels for training.
Published: 2022

10. Realizing the social value of impermanent carbon credits

Author: Balmford, Andrew, Keshav, Srinivasan, Venmans, Frank, Coomes, David, Groom, Ben, Madhavapeddy, Anil, and Swinfield, Tom
Published: 2023
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11. Unsupervised detection of ash dieback disease (Hymenoscyphus fraxineus) using diffusion-based hyperspectral image clustering

Author: Polk, Sam L., Chan, Aland H. Y., Cui, Kangning, Plemmons, Robert J., Coomes, David A., and Murphy, James M.
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Applications
Abstract: Ash dieback (Hymenoscyphus fraxineus) is an introduced fungal disease that is causing the widespread death of ash trees across Europe. Remote sensing hyperspectral images encode rich structure that has been exploited for the detection of dieback disease in ash trees using supervised machine learning techniques. However, to understand the state of forest health at landscape-scale, accurate unsupervised approaches are needed. This article investigates the use of the unsupervised Diffusion and VCA-Assisted Image Segmentation (D-VIS) clustering algorithm for the detection of ash dieback disease in a forest site near Cambridge, United Kingdom. The unsupervised clustering presented in this work has high overlap with the supervised classification of previous work on this scene (overall accuracy = 71%). Thus, unsupervised learning may be used for the remote detection of ash dieback disease without the need for expert labeling., Comment: (6 pages, 2 figures). Accepted to Proceedings of IEEE IGARSS 2022
Published: 2022

12. The global spectrum of plant form and function: enhanced species-level trait dataset.

Author: Díaz, Sandra, Kattge, Jens, Cornelissen, Johannes HC, Wright, Ian J, Lavorel, Sandra, Dray, Stéphane, Reu, Björn, Kleyer, Michael, Wirth, Christian, Prentice, I Colin, Garnier, Eric, Bönisch, Gerhard, Westoby, Mark, Poorter, Hendrik, Reich, Peter B, Moles, Angela T, Dickie, John, Zanne, Amy E, Chave, Jérôme, Wright, S Joseph, Sheremetiev, Serge N, Jactel, Hervé, Baraloto, Christopher, Cerabolini, Bruno EL, Pierce, Simon, Shipley, Bill, Casanoves, Fernando, Joswig, Julia S, Günther, Angela, Falczuk, Valeria, Rüger, Nadja, Mahecha, Miguel D, Gorné, Lucas D, Amiaud, Bernard, Atkin, Owen K, Bahn, Michael, Baldocchi, Dennis, Beckmann, Michael, Blonder, Benjamin, Bond, William, Bond-Lamberty, Ben, Brown, Kerry, Burrascano, Sabina, Byun, Chaeho, Campetella, Giandiego, Cavender-Bares, Jeannine, Chapin, F Stuart, Choat, Brendan, Coomes, David Anthony, Cornwell, William K, Craine, Joseph, Craven, Dylan, Dainese, Matteo, de Araujo, Alessandro Carioca, de Vries, Franciska T, Domingues, Tomas Ferreira, Enquist, Brian J, Fagúndez, Jaime, Fang, Jingyun, Fernández-Méndez, Fernando, Fernandez-Piedade, Maria T, Ford, Henry, Forey, Estelle, Freschet, Gregoire T, Gachet, Sophie, Gallagher, Rachael, Green, Walton, Guerin, Greg R, Gutiérrez, Alvaro G, Harrison, Sandy P, Hattingh, Wesley Neil, He, Tianhua, Hickler, Thomas, Higgins, Steven I, Higuchi, Pedro, Ilic, Jugo, Jackson, Robert B, Jalili, Adel, Jansen, Steven, Koike, Fumito, König, Christian, Kraft, Nathan, Kramer, Koen, Kreft, Holger, Kühn, Ingolf, Kurokawa, Hiroko, Lamb, Eric G, Laughlin, Daniel C, Leishman, Michelle, Lewis, Simon, Louault, Frédérique, Malhado, Ana CM, Manning, Peter, Meir, Patrick, Mencuccini, Maurizio, Messier, Julie, Miller, Regis, Minden, Vanessa, Molofsky, Jane, and Montgomery, Rebecca
Abstract: Here we provide the 'Global Spectrum of Plant Form and Function Dataset', containing species mean values for six vascular plant traits. Together, these traits -plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass - define the primary axes of variation in plant form and function. The dataset is based on ca. 1 million trait records received via the TRY database (representing ca. 2,500 original publications) and additional unpublished data. It provides 92,159 species mean values for the six traits, covering 46,047 species. The data are complemented by higher-level taxonomic classification and six categorical traits (woodiness, growth form, succulence, adaptation to terrestrial or aquatic habitats, nutrition type and leaf type). Data quality management is based on a probabilistic approach combined with comprehensive validation against expert knowledge and external information. Intense data acquisition and thorough quality control produced the largest and, to our knowledge, most accurate compilation of empirically observed vascular plant species mean traits to date.
Published: 2022

13. Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images

Author: Polk, Sam L., Cui, Kangning, Chan, Aland H. Y., Coomes, David A., Plemmons, Robert J., and Murphy, James M.
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Applications
Abstract: Hyperspectral images taken from aircraft or satellites contain information from hundreds of spectral bands, within which lie latent lower-dimensional structures that can be exploited for classifying vegetation and other materials. A disadvantage of working with hyperspectral images is that, due to an inherent trade-off between spectral and spatial resolution, they have a relatively coarse spatial scale, meaning that single pixels may correspond to spatial regions containing multiple materials. This article introduces the Diffusion and Volume maximization-based Image Clustering (D-VIC) algorithm for unsupervised material clustering to address this problem. By directly incorporating pixel purity into its labeling procedure, D-VIC gives greater weight to pixels that correspond to a spatial region containing just a single material. D-VIC is shown to outperform comparable state-of-the-art methods in extensive experiments on a range of hyperspectral images, including land-use maps and highly mixed forest health surveys (in the context of ash dieback disease), implying that it is well-equipped for unsupervised material clustering of spectrally-mixed hyperspectral datasets., Comment: 28 pages, 11 figures
Published: 2022
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14. Author Correction: Landscape-scale benefits of protected areas for tropical biodiversity

Author: Brodie, Jedediah F., Mohd-Azlan, Jayasilan, Chen, Cheng, Wearn, Oliver R., Deith, Mairin C. M., Ball, James G. C., Slade, Eleanor M., Burslem, David F. R. P., Teoh, Shu Woan, Williams, Peter J., Nguyen, An, Moore, Jonathan H., Goetz, Scott J., Burns, Patrick, Jantz, Patrick, Hakkenberg, Christopher R., Kaszta, Zaneta M., Cushman, Sam, Coomes, David, Helmy, Olga E., Reynolds, Glen, Rodríguez, Jon Paul, Jetz, Walter, and Luskin, Matthew Scott
Published: 2024
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15. Tallo: A global tree allometry and crown architecture database

Author: Jucker, Tommaso, Fischer, Fabian Jörg, Chave, Jérôme, Coomes, David A, Caspersen, John, Ali, Arshad, Panzou, Grace Jopaul Loubota, Feldpausch, Ted R, Falster, Daniel, Usoltsev, Vladimir A, Adu‐Bredu, Stephen, Alves, Luciana F, Aminpour, Mohammad, Angoboy, Ilondea B, Anten, Niels PR, Antin, Cécile, Askari, Yousef, Muñoz, Rodrigo, Ayyappan, Narayanan, Balvanera, Patricia, Banin, Lindsay, Barbier, Nicolas, Battles, John J, Beeckman, Hans, Bocko, Yannick E, Bond‐Lamberty, Ben, Bongers, Frans, Bowers, Samuel, Brade, Thomas, Breugel, Michiel, Chantrain, Arthur, Chaudhary, Rajeev, Dai, Jingyu, Dalponte, Michele, Dimobe, Kangbéni, Domec, Jean‐Christophe, Doucet, Jean‐Louis, Duursma, Remko A, Enríquez, Moisés, Ewijk, Karin Y, Farfán‐Rios, William, Fayolle, Adeline, Forni, Eric, Forrester, David I, Gilani, Hammad, Godlee, John L, Gourlet‐Fleury, Sylvie, Haeni, Matthias, Hall, Jefferson S, He, Jie‐Kun, Hemp, Andreas, Hernández‐Stefanoni, José L, Higgins, Steven I, Holdaway, Robert J, Hussain, Kiramat, Hutley, Lindsay B, Ichie, Tomoaki, Iida, Yoshiko, Jiang, Hai‐sheng, Joshi, Puspa Raj, Kaboli, Hasan, Larsary, Maryam Kazempour, Kenzo, Tanaka, Kloeppel, Brian D, Kohyama, Takashi, Kunwar, Suwash, Kuyah, Shem, Kvasnica, Jakub, Lin, Siliang, Lines, Emily R, Liu, Hongyan, Lorimer, Craig, Loumeto, Jean‐Joël, Malhi, Yadvinder, Marshall, Peter L, Mattsson, Eskil, Matula, Radim, Meave, Jorge A, Mensah, Sylvanus, Mi, Xiangcheng, Momo, Stéphane, Moncrieff, Glenn R, Mora, Francisco, Nissanka, Sarath P, O'Hara, Kevin L, Pearce, Steven, Pelissier, Raphaël, Peri, Pablo L, Ploton, Pierre, Poorter, Lourens, Pour, Mohsen Javanmiri, Pourbabaei, Hassan, Dupuy‐Rada, Juan Manuel, Ribeiro, Sabina C, Ryan, Casey, Sanaei, Anvar, Sanger, Jennifer, Schlund, Michael, Sellan, Giacomo, and Shenkin, Alexander
Subjects: Life on Land, Biomass, Carbon, Carbon Cycle, Ecosystem, Forests, Trees, allometric scaling, crown radius, forest biomass stocks, forest ecology, remote sensing, stem diameter, tree height, Environmental Sciences, Biological Sciences, Ecology
Abstract: Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research-from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology-from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.
Published: 2022

16. Repeat GEDI footprints measure the effects of tropical forest disturbances

Author: Holcomb, Amelia, Burns, Patrick, Keshav, Srinivasan, and Coomes, David A.
Published: 2024
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17. Native diversity buffers against severity of non-native tree invasions

Author: Delavaux, Camille S., Crowther, Thomas W., Zohner, Constantin M., Robmann, Niamh M., Lauber, Thomas, van den Hoogen, Johan, Kuebbing, Sara, Liang, Jingjing, de-Miguel, Sergio, Nabuurs, Gert-Jan, Reich, Peter B., Abegg, Meinrad, Adou Yao, Yves C., Alberti, Giorgio, Almeyda Zambrano, Angelica M., Alvarado, Braulio Vilchez, Alvarez-Dávila, Esteban, Alvarez-Loayza, Patricia, Alves, Luciana F., Ammer, Christian, Antón-Fernández, Clara, Araujo-Murakami, Alejandro, Arroyo, Luzmila, Avitabile, Valerio, Aymard, Gerardo A., Baker, Timothy R., Bałazy, Radomir, Banki, Olaf, Barroso, Jorcely G., Bastian, Meredith L., Bastin, Jean-Francois, Birigazzi, Luca, Birnbaum, Philippe, Bitariho, Robert, Boeckx, Pascal, Bongers, Frans, Bouriaud, Olivier, Brancalion, Pedro H. S., Brandl, Susanne, Brienen, Roel, Broadbent, Eben N., Bruelheide, Helge, Bussotti, Filippo, Gatti, Roberto Cazzolla, César, Ricardo G., Cesljar, Goran, Chazdon, Robin, Chen, Han Y. H., Chisholm, Chelsea, Cho, Hyunkook, Cienciala, Emil, Clark, Connie, Clark, David, Colletta, Gabriel D., Coomes, David A., Cornejo Valverde, Fernando, Corral-Rivas, José J., Crim, Philip M., Cumming, Jonathan R., Dayanandan, Selvadurai, de Gasper, André L., Decuyper, Mathieu, Derroire, Géraldine, DeVries, Ben, Djordjevic, Ilija, Dolezal, Jiri, Dourdain, Aurélie, Engone Obiang, Nestor Laurier, Enquist, Brian J., Eyre, Teresa J., Fandohan, Adandé Belarmain, Fayle, Tom M., Feldpausch, Ted R., Ferreira, Leandro V., Fischer, Markus, Fletcher, Christine, Frizzera, Lorenzo, Gamarra, Javier G. P., Gianelle, Damiano, Glick, Henry B., Harris, David J., Hector, Andrew, Hemp, Andreas, Hengeveld, Geerten, Hérault, Bruno, Herbohn, John L., Herold, Martin, Hillers, Annika, Honorio Coronado, Eurídice N., Hui, Cang, Ibanez, Thomas T., Amaral, Iêda, Imai, Nobuo, Jagodziński, Andrzej M., Jaroszewicz, Bogdan, Johannsen, Vivian Kvist, Joly, Carlos A., Jucker, Tommaso, Jung, Ilbin, Karminov, Viktor, Kartawinata, Kuswata, Kearsley, Elizabeth, Kenfack, David, Kennard, Deborah K., Kepfer-Rojas, Sebastian, Keppel, Gunnar, Khan, Mohammed Latif, Killeen, Timothy J., Kim, Hyun Seok, Kitayama, Kanehiro, Köhl, Michael, Korjus, Henn, Kraxner, Florian, Laarmann, Diana, Lang, Mait, Lewis, Simon L., Lu, Huicui, Lukina, Natalia V., Maitner, Brian S., Malhi, Yadvinder, Marcon, Eric, Marimon, Beatriz Schwantes, Marimon-Junior, Ben Hur, Marshall, Andrew R., Martin, Emanuel H., Martynenko, Olga, Meave, Jorge A., Melo-Cruz, Omar, Mendoza, Casimiro, Merow, Cory, Mendoza, Abel Monteagudo, Moreno, Vanessa S., Mukul, Sharif A., Mundhenk, Philip, Nava-Miranda, María Guadalupe, Neill, David, Neldner, Victor J., Nevenic, Radovan V., Ngugi, Michael R., Niklaus, Pascal A., Oleksyn, Jacek, Ontikov, Petr, Ortiz-Malavasi, Edgar, Pan, Yude, Paquette, Alain, Parada-Gutierrez, Alexander, Parfenova, Elena I., Park, Minjee, Parren, Marc, Parthasarathy, Narayanaswamy, Peri, Pablo L., Pfautsch, Sebastian, Phillips, Oliver L., Picard, Nicolas, Piedade, Maria Teresa T. F., Piotto, Daniel, Pitman, Nigel C. A., Polo, Irina, Poorter, Lourens, Poulsen, Axel D., Pretzsch, Hans, Ramirez Arevalo, Freddy, Restrepo-Correa, Zorayda, Rodeghiero, Mirco, Rolim, Samir G., Roopsind, Anand, Rovero, Francesco, Rutishauser, Ervan, Saikia, Purabi, Salas-Eljatib, Christian, Saner, Philippe, Schall, Peter, Schepaschenko, Dmitry, Scherer-Lorenzen, Michael, Schmid, Bernhard, Schöngart, Jochen, Searle, Eric B., Seben, Vladimír, Serra-Diaz, Josep M., Sheil, Douglas, Shvidenko, Anatoly Z., Silva-Espejo, Javier E., Silveira, Marcos, Singh, James, Sist, Plinio, Slik, Ferry, Sonké, Bonaventure, Souza, Alexandre F., Miscicki, Stanislaw, Stereńczak, Krzysztof J., Svenning, Jens-Christian, Svoboda, Miroslav, Swanepoel, Ben, Targhetta, Natalia, Tchebakova, Nadja, ter Steege, Hans, Thomas, Raquel, Tikhonova, Elena, Umunay, Peter M., Usoltsev, Vladimir A., Valencia, Renato, Valladares, Fernando, van der Plas, Fons, Do, Tran Van, van Nuland, Michael E., Vasquez, Rodolfo M., Verbeeck, Hans, Viana, Helder, Vibrans, Alexander C., Vieira, Simone, von Gadow, Klaus, Wang, Hua-Feng, Watson, James V., Werner, Gijsbert D. A., Wiser, Susan K., Wittmann, Florian, Woell, Hannsjoerg, Wortel, Verginia, Zagt, Roderik, Zawiła-Niedźwiecki, Tomasz, Zhang, Chunyu, Zhao, Xiuhai, Zhou, Mo, Zhu, Zhi-Xin, Zo-Bi, Irie C., and Maynard, Daniel S.
Published: 2023
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18. Limits to the accurate and generalizable use of soundscapes to monitor biodiversity

Author: Sethi, Sarab S., Bick, Avery, Ewers, Robert M., Klinck, Holger, Ramesh, Vijay, Tuanmu, Mao-Ning, and Coomes, David A.
Published: 2023
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19. Landscape-scale benefits of protected areas for tropical biodiversity

Author: Brodie, Jedediah F., Mohd-Azlan, Jayasilan, Chen, Cheng, Wearn, Oliver R., Deith, Mairin C. M., Ball, James G. C., Slade, Eleanor M., Burslem, David F. R. P., Teoh, Shu Woan, Williams, Peter J., Nguyen, An, Moore, Jonathan H., Goetz, Scott J., Burns, Patrick, Jantz, Patrick, Hakkenberg, Christopher R., Kaszta, Zaneta M., Cushman, Sam, Coomes, David, Helmy, Olga E., Reynolds, Glen, Rodríguez, Jon Paul, Jetz, Walter, and Luskin, Matthew Scott
Published: 2023
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20. Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission

Author: Duncanson, Laura, Kellner, James R, Armston, John, Dubayah, Ralph, Minor, David M, Hancock, Steven, Healey, Sean P, Patterson, Paul L, Saarela, Svetlana, Marselis, Suzanne, Silva, Carlos E, Bruening, Jamis, Goetz, Scott J, Tang, Hao, Hofton, Michelle, Blair, Bryan, Luthcke, Scott, Fatoyinbo, Lola, Abernethy, Katharine, Alonso, Alfonso, Andersen, Hans-Erik, Aplin, Paul, Baker, Timothy R, Barbier, Nicolas, Bastin, Jean Francois, Biber, Peter, Boeckx, Pascal, Bogaert, Jan, Boschetti, Luigi, Boucher, Peter Brehm, Boyd, Doreen S, Burslem, David FRP, Calvo-Rodriguez, Sofia, Chave, Jérôme, Chazdon, Robin L, Clark, David B, Clark, Deborah A, Cohen, Warren B, Coomes, David A, Corona, Piermaria, Cushman, KC, Cutler, Mark EJ, Dalling, James W, Dalponte, Michele, Dash, Jonathan, de-Miguel, Sergio, Deng, Songqiu, Ellis, Peter Woods, Erasmus, Barend, Fekety, Patrick A, Fernandez-Landa, Alfredo, Ferraz, Antonio, Fischer, Rico, Fisher, Adrian G, García-Abril, Antonio, Gobakken, Terje, Hacker, Jorg M, Heurich, Marco, Hill, Ross A, Hopkinson, Chris, Huang, Huabing, Hubbell, Stephen P, Hudak, Andrew T, Huth, Andreas, Imbach, Benedikt, Jeffery, Kathryn J, Katoh, Masato, Kearsley, Elizabeth, Kenfack, David, Kljun, Natascha, Knapp, Nikolai, Král, Kamil, Krůček, Martin, Labrière, Nicolas, Lewis, Simon L, Longo, Marcos, Lucas, Richard M, Main, Russell, Manzanera, Jose A, Martínez, Rodolfo Vásquez, Mathieu, Renaud, Memiaghe, Herve, Meyer, Victoria, Mendoza, Abel Monteagudo, Monerris, Alessandra, Montesano, Paul, Morsdorf, Felix, Næsset, Erik, Naidoo, Laven, Nilus, Reuben, O’Brien, Michael, Orwig, David A, Papathanassiou, Konstantinos, Parker, Geoffrey, Philipson, Christopher, Phillips, Oliver L, Pisek, Jan, Poulsen, John R, Pretzsch, Hans, and Rüdiger, Christoph
Subjects: Earth Sciences, LiDAR, GEDI, Waveform, Forest, Aboveground biomass, Modeling, Physical Geography and Environmental Geoscience, Geomatic Engineering, Geological & Geomatics Engineering, Earth sciences
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 AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.
Published: 2022

21. Enhancing the ecological value of oil palm agriculture through set-asides

Author: Bicknell, Jake E., O’Hanley, Jesse R., Armsworth, Paul R., Slade, Eleanor M., Deere, Nicolas J., Mitchell, Simon L., Hemprich-Bennett, David, Kemp, Victoria, Rossiter, Stephen J., Lewis, Owen T., Coomes, David A., Agama, Agnes L., Reynolds, Glen, Struebig, Matthew J., and Davies, Zoe G.
Published: 2023
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22. Publisher Correction: Landscape-scale benefits of protected areas for tropical biodiversity

Author: Brodie, Jedediah F., Mohd-Azlan, Jayasilan, Chen, Cheng, Wearn, Oliver R., Deith, Mairin C. M., Ball, James G. C., Slade, Eleanor M., Burslem, David F. R. P., Teoh, Shu Woan, Williams, Peter J., Nguyen, An, Moore, Jonathan H., Goetz, Scott J., Burns, Patrick, Jantz, Patrick, Hakkenberg, Christopher R., Kaszta, Zaneta M., Cushman, Sam, Coomes, David, Helmy, Olga E., Reynolds, Glen, Rodríguez, Jon Paul, Jetz, Walter, and Luskin, Matthew Scott
Published: 2024
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23. Frequency and Combination of Sequential Sexual Acts That May Lead to Sexually Transmitted Infections at Different Anatomic Sites Within the Same Person

Author: Khosropour, Christine M., Coomes, David M., and Barbee, Lindley A.
Published: 2023
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24. SLIC-UAV: A Method for monitoring recovery in tropical restoration projects through identification of signature species using UAVs

Author: Williams, Jonathan, Schönlieb, Carola-Bibiane, Swinfield, Tom, Irawan, Bambang, Achmad, Eva, Zudhi, Muhammad, Habibi, Gemita, Elva, and Coomes, David A.
Subjects: Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract: Logged forests cover four million square kilometres of the tropics and restoring these forests is essential if we are to avoid the worst impacts of climate change, yet monitoring recovery is challenging. Tracking the abundance of visually identifiable, early-successional species enables successional status and thereby restoration progress to be evaluated. Here we present a new pipeline, SLIC-UAV, for processing Unmanned Aerial Vehicle (UAV) imagery to map early-successional species in tropical forests. The pipeline is novel because it comprises: (a) a time-efficient approach for labelling crowns from UAV imagery; (b) machine learning of species based on spectral and textural features within individual tree crowns, and (c) automatic segmentation of orthomosaiced UAV imagery into 'superpixels', using Simple Linear Iterative Clustering (SLIC). Creating superpixels reduces the dataset's dimensionality and focuses prediction onto clusters of pixels, greatly improving accuracy. To demonstrate SLIC-UAV, support vector machines and random forests were used to predict the species of hand-labelled crowns in a restoration concession in Indonesia. Random forests were most accurate at discriminating species for whole crowns, with accuracy ranging from 79.3% when mapping five common species, to 90.5% when mapping the three most visually-distinctive species. In contrast, support vector machines proved better for labelling automatically segmented superpixels, with accuracy ranging from 74.3% to 91.7% for the same species. Models were extended to map species across 100 hectares of forest. The study demonstrates the power of SLIC-UAV for mapping characteristic early-successional tree species as an indicator of successional stage within tropical forest restoration areas. Continued effort is needed to develop easy-to-implement and low-cost technology to improve the affordability of project management.
Published: 2020

25. Arbuscular mycorrhizal trees influence the latitudinal beta-diversity gradient of tree communities in forests worldwide.

Author: Zhong, Yonglin, Chu, Chengjin, Myers, Jonathan A, Gilbert, Gregory S, Lutz, James A, Stillhard, Jonas, Zhu, Kai, Thompson, Jill, Baltzer, Jennifer L, He, Fangliang, LaManna, Joseph A, Davies, Stuart J, Aderson-Teixeira, Kristina J, Burslem, David FRP, Alonso, Alfonso, Chao, Kuo-Jung, Wang, Xugao, Gao, Lianming, Orwig, David A, Yin, Xue, Sui, Xinghua, Su, Zhiyao, Abiem, Iveren, Bissiengou, Pulchérie, Bourg, Norm, Butt, Nathalie, Cao, Min, Chang-Yang, Chia-Hao, Chao, Wei-Chun, Chapman, Hazel, Chen, Yu-Yun, Coomes, David A, Cordell, Susan, de Oliveira, Alexandre A, Du, Hu, Fang, Suqin, Giardina, Christian P, Hao, Zhanqing, Hector, Andrew, Hubbell, Stephen P, Janík, David, Jansen, Patrick A, Jiang, Mingxi, Jin, Guangze, Kenfack, David, Král, Kamil, Larson, Andrew J, Li, Buhang, Li, Xiankun, Li, Yide, Lian, Juyu, Lin, Luxiang, Liu, Feng, Liu, Yankun, Liu, Yu, Luan, Fuchen, Luo, Yahuang, Ma, Keping, Malhi, Yadvinder, McMahon, Sean M, McShea, William, Memiaghe, Hervé, Mi, Xiangcheng, Morecroft, Mike, Novotny, Vojtech, O'Brien, Michael J, Ouden, Jan den, Parker, Geoffrey G, Qiao, Xiujuan, Ren, Haibao, Reynolds, Glen, Samonil, Pavel, Sang, Weiguo, Shen, Guochun, Shen, Zhiqiang, Song, Guo-Zhang Michael, Sun, I-Fang, Tang, Hui, Tian, Songyan, Uowolo, Amanda L, Uriarte, María, Wang, Bin, Wang, Xihua, Wang, Youshi, Weiblen, George D, Wu, Zhihong, Xi, Nianxun, Xiang, Wusheng, Xu, Han, Xu, Kun, Ye, Wanhui, Yu, Mingjian, Zeng, Fuping, Zhang, Minhua, Zhang, Yingming, Zhu, Li, and Zimmerman, Jess K
Subjects: Mycorrhizae, Trees, Soil Microbiology, Biodiversity, Plant Dispersal, Forests, Host Microbial Interactions
Abstract: Arbuscular mycorrhizal (AM) and ectomycorrhizal (EcM) associations are critical for host-tree performance. However, how mycorrhizal associations correlate with the latitudinal tree beta-diversity remains untested. Using a global dataset of 45 forest plots representing 2,804,270 trees across 3840 species, we test how AM and EcM trees contribute to total beta-diversity and its components (turnover and nestedness) of all trees. We find AM rather than EcM trees predominantly contribute to decreasing total beta-diversity and turnover and increasing nestedness with increasing latitude, probably because wide distributions of EcM trees do not generate strong compositional differences among localities. Environmental variables, especially temperature and precipitation, are strongly correlated with beta-diversity patterns for both AM trees and all trees rather than EcM trees. Results support our hypotheses that latitudinal beta-diversity patterns and environmental effects on these patterns are highly dependent on mycorrhizal types. Our findings highlight the importance of AM-dominated forests for conserving global forest biodiversity.
Published: 2021

26. Carbon flux and forest dynamics: Increased deadwood decomposition in tropical rainforest tree‐fall canopy gaps

Author: Griffiths, Hannah M, Eggleton, Paul, Hemming‐Schroeder, Nicole, Swinfield, Tom, Woon, Joel S, Allison, Steven D, Coomes, David A, Ashton, Louise A, and Parr, Catherine L
Subjects: Biological Sciences, Ecology, Life on Land, Carbon, Carbon Cycle, Ecosystem, Forests, Rainforest, Trees, Tropical Climate, carbon cycling, carbon modelling, disturbance, global change, invertebrates, termites, tree mortality, Environmental Sciences, Biological sciences, Earth sciences, Environmental sciences
Abstract: Tree mortality rates are increasing within tropical rainforests as a result of global environmental change. When trees die, gaps are created in forest canopies and carbon is transferred from the living to deadwood pools. However, little is known about the effect of tree-fall canopy gaps on the activity of decomposer communities and the rate of deadwood decay in forests. This means that the accuracy of regional and global carbon budgets is uncertain, especially given ongoing changes to the structure of rainforest ecosystems. Therefore, to determine the effect of canopy openings on wood decay rates and regional carbon flux, we carried out the first assessment of deadwood mass loss within canopy gaps in old-growth rainforest. We used replicated canopy gaps paired with closed canopy sites in combination with macroinvertebrate accessible and inaccessible woodblocks to experimentally partition the relative contribution of microbes vs. termites to decomposition within contrasting understorey conditions. We show that over a 12 month period, wood mass loss increased by 63% in canopy gaps compared with closed canopy sites and that this increase was driven by termites. Using LiDAR data to quantify the proportion of canopy openings in the study region, we modelled the effect of observed changes in decomposition within gaps on regional carbon flux. Overall, we estimate that this accelerated decomposition increases regional wood decay rate by up to 18.2%, corresponding to a flux increase of 0.27 Mg C ha-1 year-1 that is not currently accounted for in regional carbon budgets. These results provide the first insights into how small-scale disturbances in rainforests can generate hotspots for decomposer activity and carbon fluxes. In doing so, we show that including canopy gap dynamics and their impacts on wood decomposition in forest ecosystems can help improve the predictive accuracy of the carbon cycle in land surface models.
Published: 2021

27. Computational tools for assessing forest recovery with GEDI shots and forest change maps

Author: Holcomb, Amelia, Mathis, Simon V., Coomes, David A., and Keshav, Srinivasan
Published: 2023
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28. Microclimate variation and recovery time in managed and old-growth temperate forests

Author: Máliš, František, Ujházy, Karol, Hederová, Lucia, Ujházyová, Mariana, Csölleová, Linda, Coomes, David A., and Zellweger, Florian
Published: 2023
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29. Pantropical variability in tree crown allometry

Author: Panzou, Grace Jopaul Loubota, Fayolle, Adeline, Jucker, Tommaso, Phillips, Oliver L, Bohlman, Stephanie, Banin, Lindsay F, Lewis, Simon L, Affum‐Baffoe, Kofi, Alves, Luciana F, Antin, Cécile, Arets, Eric, Arroyo, Luzmila, Baker, Timothy R, Barbier, Nicolas, Beeckman, Hans, Berger, Uta, Bocko, Yannick Enock, Bongers, Frans, Bowers, Sam, Brade, Thom, Brondizio, Eduardo S, Chantrain, Arthur, Chave, Jerome, Compaore, Halidou, Coomes, David, Diallo, Adama, Dias, Arildo S, Dimobe, Kangbéni, Djagbletey, Gloria Djaney, Domingues, Tomas, Doucet, Jean‐Louis, Drouet, Thomas, Forni, Eric, Godlee, John L, Goodman, Rosa C, Gourlet‐Fleury, Sylvie, Hien, Fidele, Iida, Yoshiko, Ilondea, Bhely Angoboy, Muledi, Jonathan Ilunga, Jacques, Pierre, Kuyah, Shem, López‐Portillo, Jorge, Loumeto, Jean Joël, Marimon‐Junior, Ben Hur, Marimon, Beatriz Schwantes, Mensah, Sylvanus, Mitchard, Edward TA, Moncrieff, Glenn R, Narayanan, Ayyappan, O’Brien, Sean T, Ouedraogo, Korotimi, Palace, Michael W, Pelissier, Raphael, Ploton, Pierre, Poorter, Lourens, Ryan, Casey M, Saiz, Gustavo, Santos, Karin, Schlund, Michael, Sellan, Giacomo, Sonke, Bonaventure, Sterck, Frank, Thibaut, Quentin, Van Hoef, Yorick, Veenendaal, Elmar, Vovides, Alejandra G, Xu, Yaozhan, Yao, Tze Leong, and Feldpausch, Ted R
Subjects: Environmental Sciences, Ecological Applications, Environmental Management, crown allometry, environment, forest, precipitation, savanna, soil, stand&#8208, level variable, tropical biomes, Ecology
Abstract: Aim: Tree crowns determine light interception, carbon and water exchange. Thus, understanding the factors causing tree crown allometry to vary at the tree and stand level matters greatly for the development of future vegetation modelling and for the calibration of remote sensing products. Nevertheless, we know little about large-scale variation and determinants in tropical tree crown allometry. In this study, we explored the continental variation in scaling exponents of site-specific crown allometry and assessed their relationships with environmental and stand-level variables in the tropics. Location: Global tropics. Time period: Early 21st century. Major taxa studied: Woody plants. Methods: Using a dataset of 87,737 trees distributed among 245 forest and savanna sites across the tropics, we fitted site-specific allometric relationships between crown dimensions (crown depth, diameter and volume) and stem diameter using power-law models. Stand-level and environmental drivers of crown allometric relationships were assessed at pantropical and continental scales. Results: The scaling exponents of allometric relationships between stem diameter and crown dimensions were higher in savannas than in forests. We identified that continental crown models were better than pantropical crown models and that continental differences in crown allometric relationships were driven by both stand-level (wood density) and environmental (precipitation, cation exchange capacity and soil texture) variables for both tropical biomes. For a given diameter, forest trees from Asia and savanna trees from Australia had smaller crown dimensions than trees in Africa and America, with crown volumes for some Asian forest trees being smaller than those of trees in African forests. Main conclusions: Our results provide new insight into geographical variability, with large continental differences in tropical tree crown allometry that were driven by stand-level and environmental variables. They have implications for the assessment of ecosystem function and for the monitoring of woody biomass by remote sensing techniques in the global tropics.
Published: 2021

30. Benchmarking airborne laser scanning tree segmentation algorithms in broadleaf forests shows high accuracy only for canopy trees

Author: Cao, Yujie, Ball, James G.C., Coomes, David A., Steinmeier, Leon, Knapp, Nikolai, Wilkes, Phil, Disney, Mathias, Calders, Kim, Burt, Andrew, Lin, Yi, and Jackson, Toby D.
Published: 2023
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31. Author Correction: Native diversity buffers against severity of non-native tree invasions

Author: Delavaux, Camille S., Crowther, Thomas W., Zohner, Constantin M., Robmann, Niamh M., Lauber, Thomas, van den Hoogen, Johan, Kuebbing, Sara, Liang, Jingjing, de-Miguel, Sergio, Nabuurs, Gert-Jan, Reich, Peter B., Abegg, Meinrad, Adou Yao, Yves C., Alberti, Giorgio, Almeyda Zambrano, Angelica M., Alvarado, Braulio Vilchez, Alvarez-Dávila, Esteban, Alvarez-Loayza, Patricia, Alves, Luciana F., Ammer, Christian, Antón-Fernández, Clara, Araujo-Murakami, Alejandro, Arroyo, Luzmila, Avitabile, Valerio, Aymard, Gerardo A., Baker, Timothy R., Bałazy, Radomir, Banki, Olaf, Barroso, Jorcely G., Bastian, Meredith L., Bastin, Jean-Francois, Birigazzi, Luca, Birnbaum, Philippe, Bitariho, Robert, Boeckx, Pascal, Bongers, Frans, Bouriaud, Olivier, Brancalion, Pedro H. S., Brandl, Susanne, Brienen, Roel, Broadbent, Eben N., Bruelheide, Helge, Bussotti, Filippo, Gatti, Roberto Cazzolla, César, Ricardo G., Cesljar, Goran, Chazdon, Robin, Chen, Han Y. H., Chisholm, Chelsea, Cho, Hyunkook, Cienciala, Emil, Clark, Connie, Clark, David, Colletta, Gabriel D., Coomes, David A., Cornejo Valverde, Fernando, Corral-Rivas, José J., Crim, Philip M., Cumming, Jonathan R., Dayanandan, Selvadurai, de Gasper, André L., Decuyper, Mathieu, Derroire, Géraldine, DeVries, Ben, Djordjevic, Ilija, Dolezal, Jiri, Dourdain, Aurélie, Engone Obiang, Nestor Laurier, Enquist, Brian J., Eyre, Teresa J., Fandohan, Adandé Belarmain, Fayle, Tom M., Feldpausch, Ted R., Ferreira, Leandro V., Fischer, Markus, Fletcher, Christine, Frizzera, Lorenzo, Gamarra, Javier G. P., Gianelle, Damiano, Glick, Henry B., Harris, David J., Hector, Andrew, Hemp, Andreas, Hengeveld, Geerten, Hérault, Bruno, Herbohn, John L., Herold, Martin, Hillers, Annika, Honorio Coronado, Eurídice N., Hui, Cang, Ibanez, Thomas T., Amaral, Iêda, Imai, Nobuo, Jagodziński, Andrzej M., Jaroszewicz, Bogdan, Johannsen, Vivian Kvist, Joly, Carlos A., Jucker, Tommaso, Jung, Ilbin, Karminov, Viktor, Kartawinata, Kuswata, Kearsley, Elizabeth, Kenfack, David, Kennard, Deborah K., Kepfer-Rojas, Sebastian, Keppel, Gunnar, Khan, Mohammed Latif, Killeen, Timothy J., Kim, Hyun Seok, Kitayama, Kanehiro, Köhl, Michael, Korjus, Henn, Kraxner, Florian, Laarmann, Diana, Lang, Mait, Lewis, Simon L., Lu, Huicui, Lukina, Natalia V., Maitner, Brian S., Malhi, Yadvinder, Marcon, Eric, Marimon, Beatriz Schwantes, Marimon-Junior, Ben Hur, Marshall, Andrew R., Martin, Emanuel H., Martynenko, Olga, Meave, Jorge A., Melo-Cruz, Omar, Mendoza, Casimiro, Merow, Cory, Mendoza, Abel Monteagudo, Moreno, Vanessa S., Mukul, Sharif A., Mundhenk, Philip, Nava-Miranda, María Guadalupe, Neill, David, Neldner, Victor J., Nevenic, Radovan V., Ngugi, Michael R., Niklaus, Pascal A., Oleksyn, Jacek, Ontikov, Petr, Ortiz-Malavasi, Edgar, Pan, Yude, Paquette, Alain, Parada-Gutierrez, Alexander, Parfenova, Elena I., Park, Minjee, Parren, Marc, Parthasarathy, Narayanaswamy, Peri, Pablo L., Pfautsch, Sebastian, Phillips, Oliver L., Picard, Nicolas, Piedade, Maria Teresa T. F., Piotto, Daniel, Pitman, Nigel C. A., Polo, Irina, Poorter, Lourens, Poulsen, Axel D., Pretzsch, Hans, Ramirez Arevalo, Freddy, Restrepo-Correa, Zorayda, Rodeghiero, Mirco, Rolim, Samir G., Roopsind, Anand, Rovero, Francesco, Rutishauser, Ervan, Saikia, Purabi, Salas-Eljatib, Christian, Saner, Philippe, Schall, Peter, Schepaschenko, Dmitry, Scherer-Lorenzen, Michael, Schmid, Bernhard, Schöngart, Jochen, Searle, Eric B., Seben, Vladimír, Serra-Diaz, Josep M., Sheil, Douglas, Shvidenko, Anatoly Z., Silva-Espejo, Javier E., Silveira, Marcos, Singh, James, Sist, Plinio, Slik, Ferry, Sonké, Bonaventure, Souza, Alexandre F., Miscicki, Stanislaw, Stereńczak, Krzysztof J., Svenning, Jens-Christian, Svoboda, Miroslav, Swanepoel, Ben, Targhetta, Natalia, Tchebakova, Nadja, ter Steege, Hans, Thomas, Raquel, Tikhonova, Elena, Umunay, Peter M., Usoltsev, Vladimir A., Valencia, Renato, Valladares, Fernando, van der Plas, Fons, Do, Tran Van, van Nuland, Michael E., Vasquez, Rodolfo M., Verbeeck, Hans, Viana, Helder, Vibrans, Alexander C., Vieira, Simone, von Gadow, Klaus, Wang, Hua-Feng, Watson, James V., Werner, Gijsbert D. A., Wiser, Susan K., Wittmann, Florian, Woell, Hannsjoerg, Wortel, Verginia, Zagt, Roderik, Zawiła-Niedźwiecki, Tomasz, Zhang, Chunyu, Zhao, Xiuhai, Zhou, Mo, Zhu, Zhi-Xin, Zo-Bi, Irie C., and Maynard, Daniel S.
Published: 2023
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32. Co-limitation towards lower latitudes shapes global forest diversity gradients

Author: Liang, Jingjing, Gamarra, Javier G. P., Picard, Nicolas, Zhou, Mo, Pijanowski, Bryan, Jacobs, Douglass F., Reich, Peter B., Crowther, Thomas W., Nabuurs, Gert-Jan, de-Miguel, Sergio, Fang, Jingyun, Woodall, Christopher W., Svenning, Jens-Christian, Jucker, Tommaso, Bastin, Jean-Francois, Wiser, Susan K., Slik, Ferry, Hérault, Bruno, Alberti, Giorgio, Keppel, Gunnar, Hengeveld, Geerten M., Ibisch, Pierre L., Silva, Carlos A., ter Steege, Hans, Peri, Pablo L., Coomes, David A., Searle, Eric B., von Gadow, Klaus, Jaroszewicz, Bogdan, Abbasi, Akane O., Abegg, Meinrad, Yao, Yves C. Adou, Aguirre-Gutiérrez, Jesús, Zambrano, Angelica M. Almeyda, Altman, Jan, Alvarez-Dávila, Esteban, Álvarez-González, Juan Gabriel, Alves, Luciana F., Amani, Bienvenu H. K., Amani, Christian A., Ammer, Christian, Ilondea, Bhely Angoboy, Antón-Fernández, Clara, Avitabile, Valerio, Aymard, Gerardo A., Azihou, Akomian F., Baard, Johan A., Baker, Timothy R., Balazy, Radomir, Bastian, Meredith L., Batumike, Rodrigue, Bauters, Marijn, Beeckman, Hans, Benu, Nithanel Mikael Hendrik, Bitariho, Robert, Boeckx, Pascal, Bogaert, Jan, Bongers, Frans, Bouriaud, Olivier, Brancalion, Pedro H. S., Brandl, Susanne, Brearley, Francis Q., Briseno-Reyes, Jaime, Broadbent, Eben N., Bruelheide, Helge, Bulte, Erwin, Catlin, Ann Christine, Cazzolla Gatti, Roberto, César, Ricardo G., Chen, Han Y. H., Chisholm, Chelsea, Cienciala, Emil, Colletta, Gabriel D., Corral-Rivas, José Javier, Cuchietti, Anibal, Cuni-Sanchez, Aida, Dar, Javid A., Dayanandan, Selvadurai, de Haulleville, Thales, Decuyper, Mathieu, Delabye, Sylvain, Derroire, Géraldine, DeVries, Ben, Diisi, John, Do, Tran Van, Dolezal, Jiri, Dourdain, Aurélie, Durrheim, Graham P., Obiang, Nestor Laurier Engone, Ewango, Corneille E. N., Eyre, Teresa J., Fayle, Tom M., Feunang, Lethicia Flavine N., Finér, Leena, Fischer, Markus, Fridman, Jonas, Frizzera, Lorenzo, de Gasper, André L., Gianelle, Damiano, Glick, Henry B., Gonzalez-Elizondo, Maria Socorro, Gorenstein, Lev, Habonayo, Richard, Hardy, Olivier J., Harris, David J., Hector, Andrew, Hemp, Andreas, Herold, Martin, Hillers, Annika, Hubau, Wannes, Ibanez, Thomas, Imai, Nobuo, Imani, Gerard, Jagodzinski, Andrzej M., Janecek, Stepan, Johannsen, Vivian Kvist, Joly, Carlos A., Jumbam, Blaise, Kabelong, Banoho L. P. R., Kahsay, Goytom Abraha, Karminov, Viktor, Kartawinata, Kuswata, Kassi, Justin N., Kearsley, Elizabeth, Kennard, Deborah K., Kepfer-Rojas, Sebastian, Khan, Mohammed Latif, Kigomo, John N., Kim, Hyun Seok, Klauberg, Carine, Klomberg, Yannick, Korjus, Henn, Kothandaraman, Subashree, Kraxner, Florian, Kumar, Amit, Kuswandi, Relawan, Lang, Mait, Lawes, Michael J., Leite, Rodrigo V., Lentner, Geoffrey, Lewis, Simon L., Libalah, Moses B., Lisingo, Janvier, López-Serrano, Pablito Marcelo, Lu, Huicui, Lukina, Natalia V., Lykke, Anne Mette, Maicher, Vincent, Maitner, Brian S., Marcon, Eric, Marshall, Andrew R., Martin, Emanuel H., Martynenko, Olga, Mbayu, Faustin M., Mbuvi, Musingo T. E., Meave, Jorge A., Merow, Cory, Miscicki, Stanislaw, Moreno, Vanessa S., Morera, Albert, Mukul, Sharif A., Müller, Jörg C., Murdjoko, Agustinus, Nava-Miranda, Maria Guadalupe, Ndive, Litonga Elias, Neldner, Victor J., Nevenic, Radovan V., Nforbelie, Louis N., Ngoh, Michael L., N’Guessan, Anny E., Ngugi, Michael R., Ngute, Alain S. K., Njila, Emile Narcisse N., Nyako, Melanie C., Ochuodho, Thomas O., Oleksyn, Jacek, Paquette, Alain, Parfenova, Elena I., Park, Minjee, Parren, Marc, Parthasarathy, Narayanaswamy, Pfautsch, Sebastian, Phillips, Oliver L., Piedade, Maria T. F., Piotto, Daniel, Pollastrini, Martina, Poorter, Lourens, Poulsen, John R., Poulsen, Axel Dalberg, Pretzsch, Hans, Rodeghiero, Mirco, Rolim, Samir G., Rovero, Francesco, Rutishauser, Ervan, Sagheb-Talebi, Khosro, Saikia, Purabi, Sainge, Moses Nsanyi, Salas-Eljatib, Christian, Salis, Antonello, Schall, Peter, Schepaschenko, Dmitry, Scherer-Lorenzen, Michael, Schmid, Bernhard, Schöngart, Jochen, Šebeň, Vladimír, Sellan, Giacomo, Selvi, Federico, Serra-Diaz, Josep M., Sheil, Douglas, Shvidenko, Anatoly Z., Sist, Plinio, Souza, Alexandre F., Stereńczak, Krzysztof J., Sullivan, Martin J. P., Sundarapandian, Somaiah, Svoboda, Miroslav, Swaine, Mike D., Targhetta, Natalia, Tchebakova, Nadja, Trethowan, Liam A., Tropek, Robert, Mukendi, John Tshibamba, Umunay, Peter Mbanda, Usoltsev, Vladimir A., Vaglio Laurin, Gaia, Valentini, Riccardo, Valladares, Fernando, van der Plas, Fons, Vega-Nieva, Daniel José, Verbeeck, Hans, Viana, Helder, Vibrans, Alexander C., Vieira, Simone A., Vleminckx, Jason, Waite, Catherine E., Wang, Hua-Feng, Wasingya, Eric Katembo, Wekesa, Chemuku, Westerlund, Bertil, Wittmann, Florian, Wortel, Verginia, Zawiła-Niedźwiecki, Tomasz, Zhang, Chunyu, Zhao, Xiuhai, Zhu, Jun, Zhu, Xiao, Zhu, Zhi-Xin, Zo-Bi, Irie C., and Hui, Cang
Published: 2022
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33. Three-dimensional Segmentation of Trees Through a Flexible Multi-Class Graph Cut Algorithm (MCGC)

Author: Williams, Jonathan, Schönlieb, Carola-Bibiane, Swinfield, Tom, Lee, Juheon, Cai, Xiaohao, Qie, Lan, and Coomes, David A.
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: Developing a robust algorithm for automatic individual tree crown (ITC) detection from laser scanning datasets is important for tracking the responses of trees to anthropogenic change. Such approaches allow the size, growth and mortality of individual trees to be measured, enabling forest carbon stocks and dynamics to be tracked and understood. Many algorithms exist for structurally simple forests including coniferous forests and plantations. Finding a robust solution for structurally complex, species-rich tropical forests remains a challenge; existing segmentation algorithms often perform less well than simple area-based approaches when estimating plot-level biomass. Here we describe a Multi-Class Graph Cut (MCGC) approach to tree crown delineation. This uses local three-dimensional geometry and density information, alongside knowledge of crown allometries, to segment individual tree crowns from LiDAR point clouds. Our approach robustly identifies trees in the top and intermediate layers of the canopy, but cannot recognise small trees. From these three-dimensional crowns, we are able to measure individual tree biomass. Comparing these estimates to those from permanent inventory plots, our algorithm is able to produce robust estimates of hectare-scale carbon density, demonstrating the power of ITC approaches in monitoring forests. The flexibility of our method to add additional dimensions of information, such as spectral reflectance, make this approach an obvious avenue for future development and extension to other sources of three-dimensional data, such as structure from motion datasets.
Published: 2019
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34. Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification

Author: Sellars, Philip, Aviles-Rivero, Angelica, Papadakis, Nicolas, Coomes, David, Faul, Anita, and Schönlieb, Carola-Bibane
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract: In this paper, we present a graph-based semi-supervised framework for hyperspectral image classification. We first introduce a novel superpixel algorithm based on the spectral covariance matrix representation of pixels to provide a better representation of our data. We then construct a superpixel graph, based on carefully considered feature vectors, before performing classification. We demonstrate, through a set of experimental results using two benchmarking datasets, that our approach outperforms three state-of-the-art classification frameworks, especially when an extremely small amount of labelled data is used., Comment: Four pages with two figures
Published: 2019

35. Surveying coconut trees using high-resolution satellite imagery in remote atolls of the Pacific Ocean

Author: Zheng, Juepeng, Yuan, Shuai, Wu, Wenzhao, Li, Weijia, Yu, Le, Fu, Haohuan, and Coomes, David
Published: 2023
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36. Functional susceptibility of tropical forests to climate change

Author: Aguirre‐Gutiérrez, Jesús, Berenguer, Erika, Oliveras Menor, Imma, Bauman, David, Corral-Rivas, Jose Javier, Nava-Miranda, Maria Guadalupe, Both, Sabine, Ndong, Josué Edzang, Ondo, Fidèle Evouna, Bengone, Natacha N’ssi, Mihinhou, Vianet, Dalling, James W., Heineman, Katherine, Figueiredo, Axa, González-M, Roy, Norden, Natalia, Hurtado-M, Ana Belén, González, Diego, Salgado-Negret, Beatriz, Reis, Simone Matias, Moraes de Seixas, Marina Maria, Farfan-Rios, William, Shenkin, Alexander, Riutta, Terhi, Girardin, Cécile A. J., Moore, Sam, Abernethy, Kate, Asner, Gregory P., Bentley, Lisa Patrick, Burslem, David F.R.P., Cernusak, Lucas A., Enquist, Brian J., Ewers, Robert M., Ferreira, Joice, Jeffery, Kathryn J., Joly, Carlos A., Marimon-Junior, Ben Hur, Martin, Roberta E., Morandi, Paulo S., Phillips, Oliver L., Bennett, Amy C., Lewis, Simon L., Quesada, Carlos A., Marimon, Beatriz Schwantes, Kissling, W. Daniel, Silman, Miles, Teh, Yit Arn, White, Lee J. T., Salinas, Norma, Coomes, David A., Barlow, Jos, Adu-Bredu, Stephen, and Malhi, Yadvinder
Published: 2022
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37. High Prevalence of Rectal Chlamydia and Gonorrhea Among Men Who Have Sex With Men Who Do Not Engage in Receptive Anal Sex

Author: Khosropour, Christine M., Coomes, David M., LeClair, Angela, Saechao, Farchung, Vorn, Seila, Soge, Olusegun O., and Barbee, Lindley A.
Published: 2023
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38. The rapid vegetation line shift in response to glacial dynamics and climate variability in Himalaya between 2000 and 2014

Author: Bandyopadhyay, Debmita, Mukherjee, Subhadip, Singh, Gulab, and Coomes, David
Published: 2023
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39. Sensitivity of estimates of the effectiveness of REDD+ projects to matching specifications and moving from pixels to polygons as the unit of analysis

Author: Guizar-Coutiño, Alejandro, primary, Coomes, David, additional, Swinfield, Tom, additional, and Jones, Julia PG, additional
Published: 2024
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40. Mitigating risk of credit non-delivery in nature-based climate solutions by optimally anticipating carbon release

Author: Rau, E-Ping, primary, Gross, James, additional, Coomes, David, additional, Swinfield, Thomas, additional, Madhavapeddy, Anil, additional, Balmford, Andrew, additional, and Keshav, Srinivasan, additional
Published: 2024
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41. Duodenal quantitative mucosal morphometry in children with environmental enteric dysfunction: a cross-sectional multi-country analysis

Author: Ehsan, Lubaina, primary, Coomes, David, additional, Kelly, Paul, additional, Greene, Adam R., additional, Ali, S. Asad, additional, Mulenga, Chola, additional, Denno, Donna M., additional, VanBuskirk, Kelley, additional, Raghib, Muhammad Faraz, additional, Mahfuz, Mustafa, additional, Moore, Sean R., additional, Hossain, M. Shabab, additional, Ahmed, Tahmeed, additional, Sullivan, Peter B., additional, Moskaluk, Christopher A., additional, Syed, Sana, additional, Ahmed, Kumail, additional, Ahmed, Sheraz, additional, Alam, Md. Ashraful, additional, Begum, S.M. Khodeza Nahar, additional, Das, Subhasish, additional, Denson, Lee A., additional, Fahim, Shah Mohammad, additional, Gazi, Md. Amran, additional, Hasan, Md. Mehedi, additional, Hotwani, Aneeta, additional, Iqbal, Junaid, additional, Jamil, Zehra, additional, Kabir, Furqan, additional, Liu, Ta-Chiang, additional, Mazumder, Ramendra Nath, additional, Ragahavan, Shyam S., additional, Rahman, Masudur, additional, Rahman, Najeeb, additional, Sadiq, Kamran, additional, Sarker, Shafiqul Alam, additional, Tarr, Phillip I., additional, Tearney, Guillermo J., additional, Umrani, Fayaz, additional, Umutesi, Grace, additional, and Yilmaz, Omer H., additional
Published: 2024
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42. Using repeat airborne LiDAR to map the growth of individual oil palms in Malaysian Borneo during the 2015–16 El Niño

Author: Beese, Lucy, Dalponte, Michele, Asner, Gregory P., Coomes, David A., and Jucker, Tommaso
Published: 2022
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43. Challenging the link between functional and spectral diversity with radiative transfer modeling and data

Author: Pacheco-Labrador, Javier, Migliavacca, Mirco, Ma, Xuanlong, Mahecha, Miguel D., Carvalhais, Nuno, Weber, Ulrich, Benavides, Raquel, Bouriaud, Olivier, Barnoaiea, Ionut, Coomes, David A., Bohn, Friedrich J., Kraemer, Guido, Heiden, Uta, Huth, Andreas, and Wirth, Christian
Published: 2022
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44. Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation

Author: Bungert, Leon, Coomes, David A., Ehrhardt, Matthias J., Rasch, Jennifer, Reisenhofer, Rafael, and Schönlieb, Carola-Bibiane
Subjects: Computer Science - Computer Vision and Pattern Recognition, Mathematics - Numerical Analysis, 49M37, 65K10, 90C30, 90C90
Abstract: Hyperspectral imaging is a cutting-edge type of remote sensing used for mapping vegetation properties, rock minerals and other materials. A major drawback of hyperspectral imaging devices is their intrinsic low spatial resolution. In this paper, we propose a method for increasing the spatial resolution of a hyperspectral image by fusing it with an image of higher spatial resolution that was obtained with a different imaging modality. This is accomplished by solving a variational problem in which the regularization functional is the directional total variation. To accommodate for possible mis-registrations between the two images, we consider a non-convex blind super-resolution problem where both a fused image and the corresponding convolution kernel are estimated. Using this approach, our model can realign the given images if needed. Our experimental results indicate that the non-convexity is negligible in practice and that reliable solutions can be computed using a variety of different optimization algorithms. Numerical results on real remote sensing data from plant sciences and urban monitoring show the potential of the proposed method and suggests that it is robust with respect to the regularization parameters, mis-registration and the shape of the kernel., Comment: 24 pages, 18 figures, published in Inverse Problems, typo corrected, figure added
Published: 2017
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45. Predicting leaf traits of temperate broadleaf deciduous trees from hyperspectral reflectance: can a general model be applied across a growing season?

Author: Chen, Litong, Zhang, Yi, Nunes, Matheus Henrique, Stoddart, Jaz, Khoury, Sacha, Chan, Aland H.Y., and Coomes, David A.
Published: 2022
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46. A regional model for estimating the aboveground carbon density of Borneo's tropical forests from airborne laser scanning

Author: Jucker, Tommaso, Asner, Gregory P., Dalponte, Michele, Brodrick, Philip, Philipson, Christopher D., Vaughn, Nick, Brelsford, Craig, Burslem, David F. R. P., Deere, Nicholas J., Ewers, Robert M., Kvasnica, Jakub, Lewis, Simon L., Malhi, Yadvinder, Milne, Sol, Nilus, Reuben, Pfeifer, Marion, Phillips, Oliver, Qie, Lan, Renneboog, Nathan, Reynolds, Glen, Riutta, Terhi, Struebig, Matthew J., Svátek, Martin, Teh, Yit Arn, Turner, Edgar C., and Coomes, David A.
Subjects: Quantitative Biology - Quantitative Methods
Abstract: Borneo contains some of the world's most biodiverse and carbon dense tropical forest, but this 750,000-km2 island has lost 62% of its old-growth forests within the last 40 years. Efforts to protect and restore the remaining forests of Borneo hinge on recognising the ecosystem services they provide, including their ability to store and sequester carbon. Airborne Laser Scanning (ALS) is a remote sensing technology that allows forest structural properties to be captured in great detail across vast geographic areas. In recent years ALS has been integrated into state-wide assessment of forest carbon in Neotropical and African regions, but not yet in Asia. For this to happen new regional models need to be developed for estimating carbon stocks from ALS in tropical Asia, as the forests of this region are structurally and compositionally distinct from those found elsewhere in the tropics. By combining ALS imagery with data from 173 permanent forest plots spanning the lowland rain forests of Sabah, on the island of Borneo, we develop a simple-yet-general model for estimating forest carbon stocks using ALS-derived canopy height and canopy cover as input metrics. An advanced feature of this new model is the propagation of uncertainty in both ALS- and ground-based data, allowing uncertainty in hectare-scale estimates of carbon stocks to be quantified robustly. We show that the model effectively captures variation in aboveground carbons stocks across extreme disturbance gradients spanning tall dipterocarp forests and heavily logged regions, and clearly outperforms existing ALS-based models calibrated for the tropics, as well as currently available satellite-derived products. Our model provides a simple, generalised and effective approach for mapping forest carbon stocks in Borneo, providing a key tool to support the protection and restoration of its tropical forests.
Published: 2017

47. A graph cut approach to 3D tree delineation, using integrated airborne LiDAR and hyperspectral imagery

Author: Lee, Juheon, Coomes, David, Schonlieb, Carola-Bibiane, Cai, Xiaohao, Lellmann, Jan, Dalponte, Michele, Malhi, Yadvinder, Butt, Nathalie, and Morecroft, Mike
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Recognising individual trees within remotely sensed imagery has important applications in forest ecology and management. Several algorithms for tree delineation have been suggested, mostly based on locating local maxima or inverted basins in raster canopy height models (CHMs) derived from Light Detection And Ranging (LiDAR) data or photographs. However, these algorithms often lead to inaccurate estimates of forest stand characteristics due to the limited information content of raster CHMs. Here we develop a 3D tree delineation method which uses graph cut to delineate trees from the full 3D LiDAR point cloud, and also makes use of any optical imagery available (hyperspectral imagery in our case). First, conventional methods are used to locate local maxima in the CHM and generate an initial map of trees. Second, a graph is built from the LiDAR point cloud, fused with the hyperspectral data. For computational efficiency, the feature space of hyperspectral imagery is reduced using robust PCA. Third, a multi-class normalised cut is applied to the graph, using the initial map of trees to constrain the number of clusters and their locations. Finally, recursive normalised cut is used to subdivide, if necessary, each of the clusters identified by the initial analysis. We call this approach Multiclass Cut followed by Recursive Cut (MCRC). The effectiveness of MCRC was tested using three datasets: i) NewFor, ii) a coniferous forest in the Italian Alps, and iii) a deciduous woodland in the UK. The performance of MCRC was usually superior to that of other delineation methods, and was further improved by including high-resolution optical imagery. Since MCRC delineates the entire LiDAR point cloud in 3D, it allows individual crown characteristics to be measured. By making full use of the data available, graph cut has the potential to considerably improve the accuracy of tree delineation.
Published: 2017

48. Maximizing the value of forest restoration for tropical mammals by detecting three-dimensional habitat associations

Author: Deere, Nicolas J., Guillera-Arroita, Gurutzeta, Swinfield, Tom, Milodowski, David T., Coomes, David A., Bernard, Henry, Reynolds, Glen, Davies, Zoe G., and Struebig, Matthew J.
Published: 2020

49. Insuring against variability in the performance of Nature-Based Climate Solutions

Author: Rau, E-Ping, primary, Gross, James, additional, Coomes, David, additional, Swinfield, Thomas, additional, Madhavapeddy, Anil, additional, Balmford, Andrew, additional, and Keshav, Srinivasan, additional
Published: 2024
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50. Fire traps in the wet subtropics: New perspectives from Hong Kong

Author: Chan, Aland H. Y., primary and Coomes, David A., additional
Published: 2024
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