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10 results on '"Abbasi, Akane O."'

1. Spatial database of planted forests in East Asia

Author

Abbasi, Akane O., Tang, Xiaolu, Harris, Nancy L., Goldman, Elizabeth D., Gamarra, Javier G. P., Herold, Martin, Kim, Hyun Seok, Luo, Weixue, Silva, Carlos Alberto, Tchebakova, Nadezhda M., Mitra, Ankita, Finegold, Yelena, Jahanshahi, Mohammad Reza, Alvarez, Cesar Ivan, Kim, Tae Kyung, Ryu, Daun, and Liang, Jingjing

Published
2023
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2. 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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3. Mapping Planted Forests in the Korean Peninsula Using Artificial Intelligence.

Author

Mitra, Ankita, Alvarez, Cesar Ivan, Abbasi, Akane O., Harris, Nancy L., Shao, Guofan, Pijanowski, Bryan C., Jahanshahi, Mohammad Reza, Gamarra, Javier G. P., Kim, Hyun-Seok, Kim, Tae-Kyung, Ryu, Daun, and Liang, Jingjing

Subjects
MACHINE learning, ARTIFICIAL intelligence, CONVOLUTIONAL neural networks, DATA augmentation, FOREST plants, DEEP learning
Abstract

Forests are essential for maintaining the ecological balance of the planet and providing critical ecosystem services. Amidst an increasing rate of global forest loss due to various natural and anthropogenic factors, many countries are committed to battling forest loss by planting new forests. Despite the reported national statistics on the land area in plantations, accurately delineating boundaries of planted forests with remotely sensed data remains a great challenge. In this study, we explored several deep learning approaches based on Convolutional Neural Networks (CNNs) for mapping the extent of planted forests in the Korean Peninsula. Our methodology involved data preprocessing, the application of data augmentation techniques, and rigorous model training, with performance assessed using various evaluation metrics. To ensure robust performance and accuracy, we validated the model's predictions across the Korean Peninsula. Our analysis showed that the integration of the Near Infrared band from 10 m Sentinel-2 remote sensing images with the UNet deep learning model, incorporated with unfrozen ResNet-34 backbone architecture, produced the best model performance. With a recall of 64% and precision of 76.8%, the UNet model surpassed the other pixel-based deep learning models, including DeepLab and Pyramid Sense Parsing, in terms of classification accuracy. When compared to the ensemble-based Random Forest (RF) machine learning model, the RF approach demonstrates a significantly lower recall rate of 55.2% and greater precision of 92%. These findings highlight the unique strength of deep learning and machine learning approaches for mapping planted forests in diverse geographical regions on Earth. [ABSTRACT FROM AUTHOR]

Published
2024
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4. Forest types outpaced tree species in centroid-based range shifts under global change.

Author

Abbasi, Akane O., Woodall, Christopher W., Gamarra, Javier G. P., Hui, Cang, Picard, Nicolas, Ochuodho, Thomas, de-Miguel, Sergio, Sahay, Rajeev, Fei, Songlin, Paquette, Alain, Chen, Han Y. H., Catlin, Ann Christine, Liang, Jingjing, Lischke, Heike, and Noce, Sergio

Subjects
GLOBAL environmental change, FOREST conservation, FOREST management, FOREST surveys, K-means clustering
Abstract

Introduction: Mounting evidence suggests that geographic ranges of tree species worldwide are shifting under global environmental changes. Little is known, however, about if and how these species' range shifts may trigger the range shifts of various types of forests. Markowitz's portfolio theory of investment and its broad application in ecology suggest that the range shift of a forest type could differ substantially from the range shifts of its constituent tree species. Methods: Here, we tested this hypothesis by comparing the range shifts of forest types and the mean of their constituent species between 1970-1999 and 20002019 across Alaska, Canada, and the contiguous United States using continent- wide forest inventory data. We first identified forest types in each period using autoencoder neural networks and K-means cluster analysis. For each of the 43 forest types that were identified in both periods, we systematically compared historical range shifts of the forest type and the mean of its constituent tree species based on the geographic centroids of interpolated distribution maps. Results: We found that forest types shifted at 86.5 km-decade-1 on average, more than three times as fast as the average of constituent tree species (28.8 km-decade-1). We showed that a predominantly positive covariance of the species range and the change of species relative abundance triggers this marked difference. Discussion: Our findings provide an important scientific basis for adaptive forest management and conservation, which primarily depend on individual species assessment, in mitigating the impacts of rapid forest transformation under climate change. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Reviews and syntheses: Soil responses to manipulated precipitation changes – an assessment of meta-analyses

Author

Abbasi, Akane O., Salazar, Alejandro, Oh, Youmi, Reinsch, Sabine, del Rosario Uribe, Maria, Li, Jianghanyang, Rashid, Irfan, Dukes, Jeffrey S., Abbasi, Akane O., Salazar, Alejandro, Oh, Youmi, Reinsch, Sabine, del Rosario Uribe, Maria, Li, Jianghanyang, Rashid, Irfan, and Dukes, Jeffrey S.

Abstract

In the face of ongoing and projected climatic changes, precipitation manipulation experiments (PMEs) have produced a wealth of data about the effects of precipitation changes on soils. In response, researchers have undertaken a number of synthetic efforts. Several meta-analyses have been conducted, each revealing new aspects of soil responses to precipitation changes. Here, we conducted a comparative analysis of the findings of 16 meta-analyses focused on the effects of precipitation changes on 42 soil response variables, covering a wide range of soil processes. We examine responses of individual variables as well as more integrative responses of carbon and nitrogen cycles. We find strong agreement among meta-analyses that belowground carbon and nitrogen cycling accelerate under increased precipitation and slow under decreased precipitation, while bacterial and fungal communities are relatively resistant to decreased precipitation. Much attention has been paid to fluxes and pools in carbon, nitrogen, and phosphorus cycles, such as gas emissions, soil carbon, soil phosphorus, extractable nitrogen ions, and biomass. The rates of processes underlying these variables (e.g., mineralization, fixation, and (de)nitrification) are less frequently covered in meta-analytic studies, with the major exception of respiration rates. Shifting scientific attention to these less broadly evaluated processes would deepen the current understanding of the effects of precipitation changes on soil and provide new insights. By jointly evaluating meta-analyses focused on a wide range of variables, we provide here a holistic view of soil responses to changes in precipitation.

Published
2020

10. Soil responses to manipulated precipitation changes: A synthesis of meta-analyses.

Author

Abbasi, Akane O., Salazar, Alejandro, Youmi Oh, Reinsch, Sabine, del Rosario Uribe, Maria, Jianghanyang Li, Rashid, Irfan, and Dukes, Jeffrey S.

Subjects
PRECIPITATION (Chemistry), NITROGEN cycle, SOILS, CARBON cycle, PHOSPHORUS in soils
Abstract

In the face of ongoing and projected precipitation changes, precipitation manipulation experiments (PMEs) have produced a wealth of data about the effects of precipitation changes on soils. In response, researchers have undertaken a number of synthetic efforts. Several meta-analyses have been conducted, each revealing new aspects of soil responses to precipitation changes. We synthesize the findings of 16 meta-analyses focused on the effects of decreased and increased precipitation on 42 soil response variables, covering a wide range of soil processes and examining responses of individual variables as well as more integrative responses of carbon and nitrogen cycles. We found a strong agreement among meta-analyses that decreased and increased precipitation inhibits and promotes belowground carbon and nitrogen cycling, respectively, while microbial communities are relatively resistant to precipitation changes. Much attention has been paid to fluxes and pools in carbon, nitrogen, and phosphorus cycles, such as gas emissions, soil carbon, soil phosphorus, extractable nitrogen ions, and biomass, but the rates of processes underlying these variables are less frequently covered in meta-analytic studies (e.g., rates of mineralization, fixation, and de/nitrification). Shifting scientific attention to these "processes" would, therefore, deepen the current understanding of the effects of precipitation changes on soil and provide new insights. By comparing meta-analyses focused on different variables, we provide here a quantitative and holistic view of soil responses to changes in precipitation. [ABSTRACT FROM AUTHOR]

Published
2020
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