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2. Allometric equations for integrating remote sensing imagery into forest monitoring programmes

Author

Jucker, Tommaso, Caspersen, John, Chave, Jérôme, Antin, Cécile, Barbier, Nicolas, Bongers, Frans, Dalponte, Michele, Ewijk, Karin Y, Forrester, David I, Haeni, Matthias, Higgins, Steven I, Holdaway, Robert J, Iida, Yoshiko, Lorimer, Craig, Marshall, Peter L, Momo, Stéphane, Moncrieff, Glenn R, Ploton, Pierre, Poorter, Lourens, Rahman, Kassim Abd, Schlund, Michael, Sonké, Bonaventure, Sterck, Frank J, Trugman, Anna T, Usoltsev, Vladimir A, Vanderwel, Mark C, Waldner, Peter, Wedeux, Beatrice MM, Wirth, Christian, Wöll, Hannsjörg, Woods, Murray, Xiang, Wenhua, Zimmermann, Niklaus E, and Coomes, David A

Subjects
Environmental Sciences, Life on Land, Biomass, Carbon, Carbon Cycle, Forests, Remote Sensing Technology, Trees, aboveground biomass, airborne laser scanning, carbon mapping, crown architecture, height-diameter allometry, stem diameter distributions, Biological Sciences, Ecology, Biological sciences, Earth sciences, Environmental sciences
Abstract

Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able - for the first time - to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed - specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the world's forests. These new allometric models provide an intuitive way of integrating remote sensing imagery into large-scale forest monitoring programmes and will be of key importance for parameterizing the next generation of dynamic vegetation models.

Published
2017

5. Advancing the application of remote sensing for forest information needs in Canada : lessons learned from a national collaboration of university, industrial and government stakeholders

Author

Coops, Nicholas C., Achim, Alexis, Arp, Paul, Bater, Christopher William, Caspersen, John P., Côté, Jean-François, Dech, Jeffery P., Dick, Adam R., Ewijk, Karin van, Fournier, Richard, Goodbody, Tristan R. H., Hennigar, Chris R., Leboeuf, Antoine, Van Lier, Olivier R., Luther, Joan E., MacLean, David A., McCartney, Grant, Pelletier, Gaetan, Prieur, Jean-Francois, Tompalski, Piotr, Treitz, , Paul M., White, Joanne C., Woods, Murray E., Coops, Nicholas C., Achim, Alexis, Arp, Paul, Bater, Christopher William, Caspersen, John P., Côté, Jean-François, Dech, Jeffery P., Dick, Adam R., Ewijk, Karin van, Fournier, Richard, Goodbody, Tristan R. H., Hennigar, Chris R., Leboeuf, Antoine, Van Lier, Olivier R., Luther, Joan E., MacLean, David A., McCartney, Grant, Pelletier, Gaetan, Prieur, Jean-Francois, Tompalski, Piotr, Treitz, , Paul M., White, Joanne C., and Woods, Murray E.

Abstract

The Canadian forest sector requires detailed information regarding the amount and characteristics of the forest resource. To address these needs, inventory systems that spatially quantify timber and other forest related ecosystem services are required, that are accurate, comprehensive and timely. The Assessment of Wood properties using Remote Sensing (AWARE) was a five-year project involving collaboration between seven Canadian universities, and seven forest companies with support provided by provincial and federal forestry agencies and other non-for-profit forestry focused organisations. AWARE provided methods and tools to enhance the characterization of forests at national, landscape and individual tree scales. The project supported 24 post-doctoral fellows, PhD and MSc students that examined the roles that advanced three-dimensional remote sensing technologies can play in the development of accurate forest inventory systems across Canada. In this review we examine the AWARE research project, review research highlights, key outcomes, future research needs, and provide an assessment of successes and challenges the project faced over its five-year lifetime., Le secteur forestier canadien a besoin d’information détaillée au sujet de la quantité et des caractéristiques des ressources forestières. Pour répondre à de tels besoins, des systèmes d’inventaire exacts, complets et opportuns qui quantifient spatialement le bois d’œuvre et les autres services écosystémiques liés aux forêts sont nécessaires. Le projet quinquennal AWARE (Assessment of Wood Attributes using Remote sEnsing [évaluation des attributs du bois à l’aide de la télédétection]) était une collaboration entre sept universités canadiennes et sept entreprises forestières soutenue par des organismes forestiers provinciaux et fédéraux et d’autres organismes sans but lucratif-axés sur la foresterie. AWARE a fourni des méthodes et des outils pour améliorer la caractérisation des forêts à une échelle nationale, du paysage et de l’arbre individuel. Vingt-quatre boursiers de recherches postdoctorales et étudiants au doctorat et à la maîtrise se sont associés au projet et ont examiné les rôles que les technologies de télédétection tridimensionnelle (3D) de pointe peuvent jouer dans la conception de systèmes d’inventaire forestier précis partout au Canada. Dans le présent article de revue, nous nous penchons sur le projet de recherche AWARE, les points saillants de la recherche, les résultats clés et les besoins futurs en recherche et présentons une évaluation des réussites et des défis auxquels le projet a été confronté au cours de ses cinq ans.

Published
2023

6. Automated characterization of forest canopy vertical layering for predicting forest inventory attributes by layer using airborne LiDAR data.

Author

Penner, Margaret, White, Joanne C, and Woods, Murray E

Subjects
FOREST surveys, FOREST canopies, FOREST resilience, FOREST management, LIDAR, FOREST productivity
Abstract

Forest canopy vertical layering influences stand development and yield and is critical information for forest management planning and wood supply analysis. It is also relevant for other applications including habitat modelling, forest fuels management and assessing forest resilience. Forest inventories that use coincident airborne Light Detection and Ranging (LiDAR) data and field plots (i.e. area-based approach) to predict forest attributes generally do not consider the multi-layer canopy structure that may be found in many natural and managed forest stands. With airborne LiDAR, it is possible to separate single-layer and multi-layer stands. This information can be used to allocate predictions of forest attributes such as timber volume (m3 ha−1), by canopy layer. In this study, we used single-photon LiDAR data to automate the mapping of vertical stand layering in a temperate mixedwood forest with a variety of forest types and vertical complexities. We first predicted whether each 25 × 25 m grid cell had one or two canopy layers, and then partitioned inventory attributes (e.g. basal area (BA), gross total stem volume (GTV)) by canopy layer. We compared two methods for estimating attributes by layer at the stand level using nine independent validation stands. Overall agreement between the reference and predicted structure for the calibration plots was 74% (n  = 266). At the grid-cell level, attributes were generally underestimated for the upper layer and overestimated for the lower layer. For the validation stands, the relative height of the lower layer was under-predicted compared to the reference data (46–52% versus 57%), while the proportion of BA and GTV in the lower layer were very similar to the reference values (17–19% versus 18% for BA and 12–15% versus 12% for GTV). Overall, the approach showed promise in distinguishing single- and two-layered stand conditions and partitioning estimates of inventory attributes such as BA and GTV by layer—both for grid cells and at the stand level. The inclusion of forest information by canopy layer enhances the utility of LiDAR-derived forest inventories for forest management in forest areas with complex, multi-layer stand conditions. [ABSTRACT FROM AUTHOR]

Published
2024
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7. sgsR: a structurally guided sampling toolbox for LiDAR-based forest inventories

Author

Goodbody, Tristan R H, primary, Coops, Nicholas C, additional, Queinnec, Martin, additional, White, Joanne C, additional, Tompalski, Piotr, additional, Hudak, Andrew T, additional, Auty, David, additional, Valbuena, Ruben, additional, LeBoeuf, Antoine, additional, Sinclair, Ian, additional, McCartney, Grant, additional, Prieur, Jean-Francois, additional, and Woods, Murray E, additional

Published
2023
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8. Assessing Site Productivity via Remote Sensing—Age-Independent Site Index Estimation in Even-Aged Forests.

Author

Penner, Margaret, Woods, Murray, and Bilyk, Alex

Subjects
FOREST productivity, REMOTE sensing, FOREST management, FOREST surveys, JACK pine, AUSTRIAN pine
Abstract

Forest productivity is a key driver of forest growth and yield and a critical information need for forest management and planning. Traditionally, this information has come from field plots, but these are expensive to measure and have limited coverage. Remote sensing, on the other hand, can provide forest inventory attributes on landscape scales and with a relatively low cost. A common predictor of forest productivity is site index (SI), traditionally estimated from age and height. In plantations, age can often be treated as a known quantity, but in natural-origin forests (of which Canada has vast swaths), age is often unknown and must be estimated, requiring expensive field work and resulting in a high level of error which, in turn, introduces error into SI estimates. The objective of this study is to generate estimates of SI from two successive LiDAR captures. The 99th percentiles (p99) of LiDAR returns from two successive captures 13 years apart were used along with species-specific SI curves to estimate SI. The results were compared to field-based estimates of SI for two major boreal species, jack pine and black spruce in managed and unmanaged conditions. Overall, the difference between the LiDAR-based SI and the field estimate was 2% with a relative mean squared error of 18%. For the few situations in which the height change was small or negative (less than 0.5%/year), SI was estimated from the average p99 and an assumed age of 100. The advantage of this method is that it does not require field sampling or estimates of age. Using two successive LiDAR captures, wall to wall estimates of SI can be generated at the grid cell level (e.g., 20 × 20 m), a level of detail generally not found in inventories. Overall, our results demonstrate the excellent potential for estimating SI from LiDAR alone, without age, to provide detailed productivity information for forest management and inventory that has been lacking in most large-scale inventories until now. [ABSTRACT FROM AUTHOR]

Published
2023
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9. Tallo: A global tree allometry and crown architecture database

Author

Jucker, Tommaso, primary, Fischer, Fabian Jörg, additional, Chave, Jérôme, additional, Coomes, David A., additional, Caspersen, John, additional, Ali, Arshad, additional, Loubota Panzou, Grace Jopaul, additional, Feldpausch, Ted R., additional, Falster, Daniel, additional, Usoltsev, Vladimir A., additional, Adu‐Bredu, Stephen, additional, Alves, Luciana F., additional, Aminpour, Mohammad, additional, Angoboy, Ilondea B., additional, Anten, Niels P. R., additional, Antin, Cécile, additional, Askari, Yousef, additional, Muñoz, Rodrigo, additional, Ayyappan, Narayanan, additional, Balvanera, Patricia, additional, Banin, Lindsay, additional, Barbier, Nicolas, additional, Battles, John J., additional, Beeckman, Hans, additional, Bocko, Yannick E., additional, Bond‐Lamberty, Ben, additional, Bongers, Frans, additional, Bowers, Samuel, additional, Brade, Thomas, additional, van Breugel, Michiel, additional, Chantrain, Arthur, additional, Chaudhary, Rajeev, additional, Dai, Jingyu, additional, Dalponte, Michele, additional, Dimobe, Kangbéni, additional, Domec, Jean‐Christophe, additional, Doucet, Jean‐Louis, additional, Duursma, Remko A., additional, Enríquez, Moisés, additional, van Ewijk, Karin Y., additional, Farfán‐Rios, William, additional, Fayolle, Adeline, additional, Forni, Eric, additional, Forrester, David I., additional, Gilani, Hammad, additional, Godlee, John L., additional, Gourlet‐Fleury, Sylvie, additional, Haeni, Matthias, additional, Hall, Jefferson S., additional, He, Jie‐Kun, additional, Hemp, Andreas, additional, Hernández‐Stefanoni, José L., additional, Higgins, Steven I., additional, Holdaway, Robert J., additional, Hussain, Kiramat, additional, Hutley, Lindsay B., additional, Ichie, Tomoaki, additional, Iida, Yoshiko, additional, Jiang, Hai‐sheng, additional, Joshi, Puspa Raj, additional, Kaboli, Hasan, additional, Larsary, Maryam Kazempour, additional, Kenzo, Tanaka, additional, Kloeppel, Brian D., additional, Kohyama, Takashi, additional, Kunwar, Suwash, additional, Kuyah, Shem, additional, Kvasnica, Jakub, additional, Lin, Siliang, additional, Lines, Emily R., additional, Liu, Hongyan, additional, Lorimer, Craig, additional, Loumeto, Jean‐Joël, additional, Malhi, Yadvinder, additional, Marshall, Peter L., additional, Mattsson, Eskil, additional, Matula, Radim, additional, Meave, Jorge A., additional, Mensah, Sylvanus, additional, Mi, Xiangcheng, additional, Momo, Stéphane, additional, Moncrieff, Glenn R., additional, Mora, Francisco, additional, Nissanka, Sarath P., additional, O'Hara, Kevin L., additional, Pearce, Steven, additional, Pelissier, Raphaël, additional, Peri, Pablo L., additional, Ploton, Pierre, additional, Poorter, Lourens, additional, Pour, Mohsen Javanmiri, additional, Pourbabaei, Hassan, additional, Dupuy‐Rada, Juan Manuel, additional, Ribeiro, Sabina C., additional, Ryan, Casey, additional, Sanaei, Anvar, additional, Sanger, Jennifer, additional, Schlund, Michael, additional, Sellan, Giacomo, additional, Shenkin, Alexander, additional, Sonké, Bonaventure, additional, Sterck, Frank J., additional, Svátek, Martin, additional, Takagi, Kentaro, additional, Trugman, Anna T., additional, Ullah, Farman, additional, Vadeboncoeur, Matthew A., additional, Valipour, Ahmad, additional, Vanderwel, Mark C., additional, Vovides, Alejandra G., additional, Wang, Weiwei, additional, Wang, Li‐Qiu, additional, Wirth, Christian, additional, Woods, Murray, additional, Xiang, Wenhua, additional, Ximenes, Fabiano de Aquino, additional, Xu, Yaozhan, additional, Yamada, Toshihiro, additional, and Zavala, Miguel A., additional

Published
2022
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10. Tallo: a global tree allometry and crown architecture database

Author

Jucker, Tommaso, Fischer, Fabian Jörg, Chave, Jérôme, Coomes, David A., Caspersen, John, Ali, Arshad, Loubota Panzou, Grace Jopaul, Feldpausch, Ted R., Falster, Daniel, Usoltsev, Vladimir A., Adu‐Bredu, Stephen, Alves, Luciana F., Aminpour, Mohammad, Angoboy, Ilondea B., Anten, Niels P.R., 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, van Breugel, Michiel, Chantrain, Arthur, Chaudhary, Rajeev, Dai, Jingyu, Dalponte, Michele, Dimobe, Kangbéni, Domec, Jean‐Christophe, Doucet, Jean‐Louis, Duursma, Remko A., Enríquez, Moisés, van 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, Shenkin, Alexander, Sonké, Bonaventure, Sterck, Frank J., Svátek, Martin, Takagi, Kentaro, Trugman, Anna T., Ullah, Farman, Vadeboncoeur, Matthew A., Valipour, Ahmad, Vanderwel, Mark C., Vovides, Alejandra G., Wang, Weiwei, Wang, Li‐Qiu, Wirth, Christian, Woods, Murray, Xiang, Wenhua, Ximenes, Fabiano de Aquino, Xu, Yaozhan, Yamada, Toshihiro, Zavala, Miguel A., Jucker, Tommaso, Fischer, Fabian Jörg, Chave, Jérôme, Coomes, David A., Caspersen, John, Ali, Arshad, Loubota Panzou, Grace Jopaul, Feldpausch, Ted R., Falster, Daniel, Usoltsev, Vladimir A., Adu‐Bredu, Stephen, Alves, Luciana F., Aminpour, Mohammad, Angoboy, Ilondea B., Anten, Niels P.R., 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, van Breugel, Michiel, Chantrain, Arthur, Chaudhary, Rajeev, Dai, Jingyu, Dalponte, Michele, Dimobe, Kangbéni, Domec, Jean‐Christophe, Doucet, Jean‐Louis, Duursma, Remko A., Enríquez, Moisés, van 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, Shenkin, Alexander, Sonké, Bonaventure, Sterck, Frank J., Svátek, Martin, Takagi, Kentaro, Trugman, Anna T., Ullah, Farman, Vadeboncoeur, Matthew A., Valipour, Ahmad, Vanderwel, Mark C., Vovides, Alejandra G., Wang, Weiwei, Wang, Li‐Qiu, Wirth, Christian, Woods, Murray, Xiang, Wenhua, Ximenes, Fabiano de Aquino, Xu, Yaozhan, Yamada, Toshihiro, and Zavala, Miguel A.

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

11. Tallo database

Author

Jucker, Tommaso, Fischer, Fabian Jörg, Chave, Jérôme, Coomes, David A., Caspersen, John, Ali, Arshad, Loubota Panzou, Grace Jopaul, Feldpausch, Ted R., Falster, Daniel, Usoltsev, Vladimir A., Adu-Bredu, Stephen, Alves, Luciana F., Aminpour, Mohammad, Angoboy, Ilondea B., Anten, Niels P.R., 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, van Breugel, Michiel, Chantrain, Arthur, Chaudhary, Rajeev, Dai, Jingyu, Dalponte, Michele, Dimobe, Kangbéni, Domec, Jean Christophe, Doucet, Jean Louis, Duursma, Remko A., Enríquez, Moisés, van 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, Shenkin, Alexander, Sonké, Bonaventure, Sterck, Frank J., Svátek, Martin, Takagi, Kentaro, Trugman, Anna T., Ullah, Farman, Vadeboncoeur, Matthew A., Valipour, Ahmad, Vanderwel, Mark C., Vovides, Alejandra G., Wang, Weiwei, Wang, Li Qiu, Wirth, Christian, Woods, Murray, Xiang, Wenhua, de Aquino Ximenes, Fabiano, Xu, Yaozhan, Yamada, Toshihiro, Zavala, Miguel A., Jucker, Tommaso, Fischer, Fabian Jörg, Chave, Jérôme, Coomes, David A., Caspersen, John, Ali, Arshad, Loubota Panzou, Grace Jopaul, Feldpausch, Ted R., Falster, Daniel, Usoltsev, Vladimir A., Adu-Bredu, Stephen, Alves, Luciana F., Aminpour, Mohammad, Angoboy, Ilondea B., Anten, Niels P.R., 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, van Breugel, Michiel, Chantrain, Arthur, Chaudhary, Rajeev, Dai, Jingyu, Dalponte, Michele, Dimobe, Kangbéni, Domec, Jean Christophe, Doucet, Jean Louis, Duursma, Remko A., Enríquez, Moisés, van 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, Shenkin, Alexander, Sonké, Bonaventure, Sterck, Frank J., Svátek, Martin, Takagi, Kentaro, Trugman, Anna T., Ullah, Farman, Vadeboncoeur, Matthew A., Valipour, Ahmad, Vanderwel, Mark C., Vovides, Alejandra G., Wang, Weiwei, Wang, Li Qiu, Wirth, Christian, Woods, Murray, Xiang, Wenhua, de Aquino Ximenes, Fabiano, Xu, Yaozhan, Yamada, Toshihiro, and Zavala, Miguel A.

Abstract

The Tallo database (v1.0.0) is 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. Data were compiled from 61,856 globally distributed sites and include measurements for 5,163 tree species., The Tallo database (v1.0.0) is 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. Data were compiled from 61,856 globally distributed sites and include measurements for 5,163 tree species. For a full description of the database, see: Jucker et al. (2022) Tallo – a global tree allometry and crown architecture database. Global Change Biology, https://doi.org/10.1111/gcb.16302. If using the Tallo database in your work please cite the original publication listed above, as well as this repository using the corresponding DOI (10.5281/zenodo.6637599).

Published
2022

13. Impact assessment for the Beetaloo GBA region. Geological and Bioregional Assessment Program: Stage 3 synthesis

Author

Huddlestone-Holmes, Cameron, Brandon, Clare, Crosbie, Russell, Davies, Phil, Doble, Rebecca, Kear, James, Holland, Kate, O'Grady, Anthony, Pavey, Chris, Peeters, Luk, Tetreault Campbell, Sally, Beiraghdar Aghbelagh, Yousef, Arjomand, Elaheh, Bailey, Adam, Barry, Karen, Donohue, Randall, Evans, Tim, Evenden, Chris, Flett, Dianne, Frery, Ema, Gonzalez, Dennis, Herr, Alexander, Kasperczyk, Dane, King, Darran, Lawrence, Emma, Lawson, Chris, Li, Lingtao, Macfarlane, Craig, Mokany, Karel, Mallants, Dirk, Markov, Jelena, Martinez, Cericia, Martinez, Jorge, Marvanek, Steve, McVicar, Tim, Merrin, Linda, Murray, Justine, Northover, Stacey, Raiber, Matthias, Reese, Bianca, Stewart, Stephen, Turnadge, Chris, Watson, Garth, Woods, Murray, and Pan, Zhejun

Published
2021
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14. Progrès dans l’application de la télédétection pour les besoins en matière d’information sur les forêts au Canada : leçons tirées d’une collaboration nationale d’intervenants universitaires, industriels et gouvernementaux

Author

Coops, Nicholas C., primary, Achim, Alexis, additional, Arp, Paul, additional, Bater, Christopher W., additional, Caspersen, John P., additional, Côté, Jean-François, additional, Dech, Jeffery P., additional, Dick, Adam R., additional, van Ewijk, Karin, additional, Fournier, Richard, additional, Goodbody, Tristan R. H., additional, Hennigar, Chris R., additional, Leboeuf, Antoine, additional, van Lier, Olivier R., additional, Luther, Joan E., additional, MacLean, David A., additional, McCartney, Grant, additional, Pelletier, Gaetan, additional, Prieur, Jean-Francois, additional, Tompalski, Piotr, additional, Treitz, Paul M., additional, White, Joanne C., additional, and Woods, Murray E., additional

Published
2021
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15. Advancing the application of remote sensing for forest information needs in Canada: Lessons learned from a national collaboration of university, industrial and government stakeholders

Author

Coops, Nicholas C., primary, Achim, Alexis, additional, Arp, Paul, additional, Bater, Christopher W., additional, Caspersen, John P., additional, Côté, Jean-François, additional, Dech, Jeffery P., additional, Dick, Adam R., additional, van Ewijk, Karin, additional, Fournier, Richard, additional, Goodbody, Tristan R. H., additional, Hennigar, Chris R., additional, Leboeuf, Antoine, additional, van Lier, Olivier R., additional, Luther, Joan E., additional, MacLean, David A., additional, McCartney, Grant, additional, Pelletier, Gaetan, additional, Prieur, Jean-Francois, additional, Tompalski, Piotr, additional, Treitz, Paul M., additional, White, Joanne C., additional, and Woods, Murray E., additional

Published
2021
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16. Snag dynamics in partially harvested and unmanaged northern hardwood forests (1)

Author

Vanderwel, Mark C., Caspersen, John P., and Woods, Murray E.

Subjects
Tree felling -- Measurement, Deciduous forests -- Observations -- Measurement, Earth sciences, Observations, Measurement
Abstract

Abstract: We used data from hardwood-dominated permanent sample plots in Ontario to estimate the probability of a tree falling during the 5 year period in which it dies ('tree fall'), [...]

Published
2006

18. Allometric equations for integrating remote sensing imagery into forest monitoring programmes

Author

Jucker, Tommaso, Caspersen, John, Chave, Jérôme, Antin, Cécile, Barbier, Nicolas, Bongers, Frans, Dalponte, Michele, van Ewijk, Karin Y., Forrester, David I., Haeni, Matthias, Higgins, Steven I., Holdaway, Robert J., Iida, Yoshiko, Lorimer, Craig, Marshall, Peter L., Momo, Stéphane, Moncrieff, Glenn R., Ploton, Pierre, Poorter, Lourens, Rahman, Kassim Abd, Schlund, Michael, Sonké, Bonaventure, Sterck, Frank J., Trugman, Anna T., Usoltsev, Vladimir A., Vanderwel, Mark C., Waldner, Peter, Wedeux, Beatrice M. M., Wirth, Christian, Wöll, Hannsjörg, Woods, Murray, Xiang, Wenhua, Zimmermann, Niklaus E., Coomes, David A., University of Cambridge [UK] (CAM), Evolution et Diversité Biologique (EDB), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD [France-Sud]), Institut Français de Pondichéry (IFP), Ministère de l'Europe et des Affaires étrangères (MEAE)-Centre National de la Recherche Scientifique (CNRS), Forest Ecology and Forest Management Group, Wageningen University and Research [Wageningen] (WUR), Manaaki Whenua – Landcare Research [Lincoln], Laboratoire de Botanique et Ecologie, Université de Yaoundé I, Universität Leipzig [Leipzig], Research Section of Forest Ecology, Central South University of Forestry and Technology, University of Toronto, Swiss Federal Research Institute, Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Fondazione Edmund Mach - Edmund Mach Foundation [Italie] (FEM), Queen's University [Kingston, Canada], University of Freiburg [Freiburg], University of Otago [Dunedin, Nouvelle-Zélande], Forestry and Forest Products Research Institute (FFPRI), University of Wisconsin-Madison, University of British Columbia (UBC), South African Environmental Observation Network [Pretoria] (SAEON), Friedrich-Schiller-Universität = Friedrich Schiller University Jena [Jena, Germany], Princeton University, University of Regina (UR), Universität Leipzig, German Centre for Integrative Biodiversity Research (iDiv), Central South University of Forestry and Technology (CSUFT ), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Ministère de l'Europe et des Affaires étrangères (MEAE), Wageningen University and Research Centre [Wageningen] (WUR), Landcare Research, Université de Yaoundé I [Yaoundé], Trugman, Anna T [0000-0002-7903-9711], and Apollo - University of Cambridge Repository

Subjects
Life on Land, AIRBORNE SENSING, airborne laser scanning, Forests, REMOTE SENSING, Carbon Cycle, Trees, HEIGHT, height-diameter allometry, carbon mapping, [SDV.SA.SF]Life Sciences [q-bio]/Agricultural sciences/Silviculture, forestry, Settore BIO/07 - ECOLOGIA, Primary Research Article, Biomass, CANOPY ARCHITECTURE, TREE, ALLOMETRY, stem diameter distributions, Ecology, crown architecture, DIAMETER, Biological Sciences, FOREST, Primary Research Articles, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Carbon, HEIGHT–DIAMETER ALLOMETRY, height–diameter allometry, MONITORING SYSTEM, Remote Sensing Technology, FOREST CANOPY, aboveground biomass, Environmental Sciences
Abstract

Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able – for the first time – to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed – specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the world's forests. These new allometric models provide an intuitive way of integrating remote sensing imagery into large-scale forest monitoring programmes and will be of key importance for parameterizing the next generation of dynamic vegetation models. © 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd. We thank the co-authors, field data collectors and funding agencies of the original data sources used in this study. We are also grateful to all curators of open-access databases from which we drew data for this study. In particular, we wish to thank Daniel Falster and Remko Duursma for compiling the biomass and allometry database (BAAD) for woody plants; Michael Keller and Maiza Nara for providing us access to data from the Sustainable Landscapes Brazil project; Kristina Anderson-Teixeira and her co-authors for archiving allometric data from the CTFS-ForestGEO forest dynamics plot at the Smithsonian Conservation Biology Institute (Virginia, USA); and KaDonna Randolph of the USDA forest service for her assistance in accessing the Forest Health Monitoring (FHM) database. We thank Bruno H?rault and an anonymous reviewer for their thoughtful and constructive comments on an earlier draft of our manuscript. T.J. was funded by NERC (grant number: NE/K016377/1). This work has benefited from ANR grants to J.C. (CEBA, ref. ANR-10-LABX-25-01 and TULIP, ref. ANR-10-LABX-0041). The Sustainable Landscapes Brazil project was supported by the Brazilian Agricultural Research Corporation (EMBRAPA), the US Forest Service, and USAID, and the US Department of State. Data collection for the UNECE ICP Forests PCC Collaborative Database was cofinanced by national or regional organizations and by the European Commission under regulations (EEC) No 2158/86, Forest Focus (EC) No 2152/200, FutMon (EC) LIFE07 ENV/D/218.

Published
2016
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32. Characterizing Forest Succession Stages for Wildlife Habitat Assessment Using Multispectral Airborne Imagery.

Author

Wen Zhang, Baoxin Hu, Woods, Murray, and Brown, Glen

Subjects
FOREST succession, AERIAL photography in forestry, FOREST canopies, ECOLOGICAL succession, FOREST dynamics
Abstract

In this study, we demonstrate the potential of using high spatial resolution airborne imagery to characterize the structural development stages of forest canopies. Four forest succession stages were adopted: stand initiation, young multistory, understory reinitiation, and old growth. Remote sensing metrics describing the spatial patterns of forest structures were derived and a Random Forest learning algorithm was used to classify forest succession stages. These metrics included texture variables from Gray Level Co-occurrence Measures (GLCM), range and sill from the semi-variogram, and the fraction of shadow and its spatial distribution. Among all the derived variables, shadow fractions and the GLCM variables of contrast, mean, and dissimilarity were the most important for characterizing the forest succession stages (classification accuracy of 89%). In addition, a LiDAR (Light Detection and Ranging) derived forest structural index (predicted Lorey’s height) was employed to validate the classification result. The classification using imagery spatial variables was shown to be consistent with the LiDAR derived variable (R2 = 0.68 and Root Mean Square Error (RMSE) = 2.39). This study demonstrates that high spatial resolution imagery was able to characterize forest succession stages with promising accuracy and may be considered an alternative to LiDAR data for this kind of application. Also, the results of stand development stages build a framework for future wildlife habitat mapping. [ABSTRACT FROM AUTHOR]

Published
2017
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34. Effect of assimilation of work experience on growth satisfaction: assessment of selected validity threats

Author

Bottger, Preston C., Chew, Irene K-H., and Woods, Murray A.

Subjects
Job satisfaction -- Research, Career development -- Psychological aspects, Human resource management -- Research, Social sciences
Abstract

Assimilation of work experience is a measure of an individual's responses to variations in work performance. Assimilation of work experiences explains variance in growth satisfaction independent of context and job scope satisfactions. The construct and internal validities of this finding are examined. The effect of assimilation of work experience is shown to be independent of leader behavior and role ambiguity. Assimilation of work experience has a random effect on satisfaction with the individual's supervisor.

Published
1988

40. The Business of Forest Enterprise / Urban Ecosystems

Author

Narolski, Steve, primary, Mei, Richard Bin, additional, De La Torre, Rafael, additional, Clutter, Mike, additional, Pait, John, additional, Kelly, Maggi, additional, Cubbage, Frederick, additional, MacDonagh, Patricio, additional, Balmelli, Gustavo, additional, Olmos, Virginia Morales, additional, Bussoni, Adriana, additional, Rubilar, Rafael, additional, Kotze, Heyn, additional, Muraro, Mauro, additional, Hoeflich, Vitor Afonso, additional, Hall, Peter, additional, Yao, Richard, additional, Lord, Roger, additional, Huang, Jin, additional, Monges, Elizabeth, additional, Perez, Carmelo Hernandez, additional, Wikle, Jeff, additional, Adams, Paul, additional, Gonzalez, Ronalds, additional, Carrero, Omar, additional, Abt, Robert, additional, Koesbanda, Sadharga, additional, Liang, Jingjing, additional, McCarter, James, additional, Perez-Garcia, John, additional, Sehnalek, Gerhard, additional, Zhang, Yanli, additional, McBroom, Matthew, additional, Sharma, Benktesh, additional, Wang, Jingxin, additional, Nepal, Prakash, additional, Ince, Peter, additional, Skog, Kenneth, additional, Chang, Sun, additional, Row, Clark, additional, Ford, Susan, additional, Jackson, Sam, additional, Rialsb, Timothy, additional, Williams, Hans, additional, McNamee, Rachel, additional, Unger, Daniel, additional, Hung, I-Kuai, additional, Farrish, Kenneth, additional, Siry, Jacek, additional, Harris, Tom, additional, Izlar, Bob, additional, Menard, Trae, additional, Lim, Kevin, additional, Pitt, Doug, additional, Woods, Murray, additional, Treitz, Paul, additional, Gralewicz, Nick, additional, Potter, Tiffany, additional, Dunster, Jeff, additional, Rothstein, Lewis, additional, Grala, Robert, additional, Grebner, Donald, additional, Munn, Ian, additional, Hussain, Anwar, additional, Jones, Mitchell, additional, Cushing, Tamara, additional, Wolfson, Alan, additional, Stewart, Tom, additional, Jones, Doug, additional, Allen, Steve, additional, Moore, Susan, additional, Moorman, Christopher, additional, Abdollahi, Kamran, additional, Ning, Zhu, additional, Davis, Jan, additional, Boughton, Jerry, additional, Leonhardt, Ken, additional, Loeb, Robert, additional, Bettinger, Pete, additional, Merry, Krista, additional, Bowker, J.M., additional, Gill, Samantha, additional, Thompson, Richard, additional, Mills, John, additional, Trueman-Madriaga, Teresa, additional, Lecaroz, Rosario, additional, Mojzak, Andrea, additional, Denovan, Rory, additional, Tsai, Wei-Lun, additional, Bruton, Candice, additional, Leung, Yu-Fai, additional, McHale, Melissa, additional, Baran, Perver, additional, Floyd, Myron, additional, Wade, Charles, additional, Wolf, Kathleen, additional, Romolini, Michele, additional, Brinkley, Weston, additional, Robbins, Alicia, additional, Blahna, Dale, additional, Daniels, Jean, additional, Cole, Michelle, additional, and Allen, Brenda, additional

Published
2011
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49. A Comparison of Three Airborne Laser Scanner Types for Species Identification of Individual Trees.

Author

Prieur, Jean-François, St-Onge, Benoît, Fournier, Richard A., Woods, Murray E., Rana, Parvez, and Kneeshaw, Daniel

Subjects
AIRBORNE lasers, OPTICAL scanners, MULTISPECTRAL imaging, SCANNING systems, FOREST surveys, SPECIES, CROWNS (Botany)
Abstract

Species identification is a critical factor for obtaining accurate forest inventories. This paper compares the same method of tree species identification (at the individual crown level) across three different types of airborne laser scanning systems (ALS): two linear lidar systems (monospectral and multispectral) and one single-photon lidar (SPL) system to ascertain whether current individual tree crown (ITC) species classification methods are applicable across all sensors. SPL is a new type of sensor that promises comparable point densities from higher flight altitudes, thereby increasing lidar coverage. Initial results indicate that the methods are indeed applicable across all of the three sensor types with broadly similar overall accuracies (Hardwood/Softwood, 83–90%; 12 species, 46–54%; 4 species, 68–79%), with SPL being slightly lower in all cases. The additional intensity features that are provided by multispectral ALS appear to be more beneficial to overall accuracy than the higher point density of SPL. We also demonstrate the potential contribution of lidar time-series data in improving classification accuracy (Hardwood/Softwood, 91%; 12 species, 58%; 4 species, 84%). Possible causes for lower SPL accuracy are (a) differences in the nature of the intensity features and (b) differences in first and second return distributions between the two linear systems and SPL. We also show that segmentation (and field-identified training crowns deriving from segmentation) that is performed on an initial dataset can be used on subsequent datasets with similar overall accuracy. To our knowledge, this is the first study to compare these three types of ALS systems for species identification at the individual tree level. [ABSTRACT FROM AUTHOR]

Published
2022
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50. Work and power in Australia : a study of power in work relationships

Author

Woods, Murray Angus

Subjects
Management -- Employee participation, Industrial relations -- Australia
Abstract

The alienation theme is now so large in the sociological literature that it has become an acceptable descriptive category for some condition of life in contemporary industrial society.

Published
1977

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