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Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps
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“However, deforestation still represents the main threat to the environment in the Amazon and the major source of greenhouse gas emissions (Baccini et al., 2012).”
From Transition in environmental governance in the Brazilian Amazon: emergence of a new pattern of socio-economic development and deforestation (2016) · cites this paper as Baccini et al., 2012
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“To address this challenge, ML can be used to predict the LiDAR's outcome from satellite imagery [48,599,693].From there, the learned estimator can perform predictions at scale.”
From Tackling climate change with machine learning (2023) · cites this paper as [48]
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
A large and persistent carbon sink in the world’s forests
Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., Kauppi, P. E., Kurz, W. A., Phillips, O. L., Shvidenko, A., Lewis, S. L., Canadell, J. G., et al. (2011) · Science · doi:10.1126/science.1201609 · Cited by 8,458
Self-citationTemperature sensitivity of soil carbon decomposition and feedbacks to climate change
Davidson, E. A., & Janssens, I. A. (2006) · Nature · doi:10.1038/nature04514 · Cited by 6,919
Tree allometry and improved estimation of carbon stocks and balance in tropical forests
Chave, J., Andalo, C., Brown, S., Cairns, M. A., Chambers, J. Q., Eamus, D., Fölster, H., Fromard, F., Higuchi, N., Kira, T., et al. (2005) · Oecologia · doi:10.1007/s00442-005-0100-x · Cited by 3,546
Benchmark map of forest carbon stocks in tropical regions across three continents
Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M. A., Mitchard, E. T. A., Salas, W., Zutta, B. R., Buermann, W., Lewis, S. L., Hagen, S. C., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1019576108 · Cited by 2,504
Trends in the sources and sinks of carbon dioxide
Quéré, C. L., Raupach, M., Canadell, J. G., Marland, G., Bopp, L., Ciais, P., Conway, T. J., Doney, S. C., Feely, R. A., Foster, P., et al. (2009) · Nature Geoscience · doi:10.1038/ngeo689 · Cited by 2,096
Self-citationCO2 emissions from forest loss
van der Werf, G. R., Morton, D. C., DeFries, R., Olivier, J. G. J., Kasibhatla, P. S., Jackson, R. B., Collatz, G. J., & Randerson, J. T. (2009) · Nature Geoscience · doi:10.1038/ngeo671 · Cited by 1,463
Aboveground Forest Biomass and the Global Carbon Balance
Houghton, R. A. (2005) · Global Change Biology · doi:10.1111/j.1365-2486.2005.00955.x · Cited by 1,273
Self-citationHoughton, R. A. (2003) · Tellus B · doi:10.3402/tellusb.v55i2.16764 · Cited by 1,186
Self-citationBalancing the Global Carbon Budget
Houghton, R. A. (2007) · Annual Review of Earth and Planetary Sciences · doi:10.1146/annurev.earth.35.031306.140057 · Cited by 1,165
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- DeFries, R. et al. Carbon emissions from tropical deforestation and regrowth based on satellite observations for the 1980s and 1990s. Proc. Natl Acad. Sci. USA 99, 14256 (2002). · DOI
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Items 901–1,000 of 1,000
Lopes, A. P., Dalagnol, R., Dutra, A. C., Silva, C. V. J., Graça, P. M. L. D. A., & Aragão, L. E. O. C. (2022) · Remote Sensing · doi:10.3390/rs14071545 · Cited by 19
Pindyck, R. S. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780197647349.001.0001 · Cited by 19
Pinagé, E. R., Keller, M., Peck, C. P., Longo, M., Duffy, P. A., & Csillik, O. (2023) · Carbon Balance and Management · doi:10.1186/s13021-023-00221-5 · Cited by 22
Gaboriau, D. M., Chaste, É., Girardin, M. P., Asselin, H., Ali, A. A., Bergeron, Y., & Hély, C. (2023) · iScience · doi:10.1016/j.isci.2023.106807 · Cited by 19
Wen, X., Hong, Y., Zhong, J., Li, L., Ma, Q., Hu, X., Han, X., Guo, W., Huang, Y., & Zhang, F. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.169906 · Cited by 19
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Fahrudin, R., Sakti, A. D., Komara, H. Y., Sumarga, E., Choiruddin, A., Hendrawan, V. S. A., Hati, T., Anna, Z., & Wikantika, K. (2024) · Trees Forests and People · doi:10.1016/j.tfp.2024.100700 · Cited by 19
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Dinerstein, E., Baccini, A., Anderson, M., Fiske, G. J., Wikramanayake, E., McLaughlin, D., Powell, G. V. N., Olson, D. M., & Joshi, A. R. (2014) · Conservation Letters · doi:10.1111/conl.12149 · Cited by 19
Self-citationShi, Y., Choi, S., Ni, X., Ganguly, S., Zhang, G., Duong, H. V., Lefsky, M. A., Simard, M., Saatchi, S. S., Lee, S., et al. (2013) · Remote Sensing · doi:10.3390/rs5010284 · Cited by 19
Urbazaev, M., Cremer, F., Migliavacca, M., Reichstein, M., Schmullius, C., & Thiel, C. (2018) · Remote Sensing · doi:10.3390/rs10081277 · Cited by 19
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Ceddia, M. G., Gunter, U., & Pazienza, P. (2019) · Forest Policy and Economics · doi:10.1016/j.forpol.2019.102001 · Cited by 21
Musthafa, M., & Singh, G. (2022) · Advances in Space Research · doi:10.1016/j.asr.2022.02.002 · Cited by 19
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Busch, J., & Grantham, H. S. (2013) · Environmental Research Letters · doi:10.1088/1748-9326/8/3/034028 · Cited by 19
Carlson, K. M., & Curran, L. M. (2013) · Carbon Management · doi:10.4155/cmt.13.39 · Cited by 19
He, Y., Bond‐Lamberty, B., Myers‐Pigg, A., Newcomer, M., Ladau, J., Holmquist, J. R., Brown, J., & Falco, N. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e30470 · Cited by 18
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Fu, L., Zhao, Y., Xu, Z. H., & Wu, B. (2014) · Journal of Soils and Sediments · doi:10.1007/s11368-014-1050-x · Cited by 18
Achille, L. S., Zhang, K., & Kouassi, C. J. A. (2021) · Open Journal of Ecology · doi:10.4236/oje.2021.115029 · Cited by 18
Continuous woody vegetation biomass estimation based on temporal modeling of Landsat data
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Ochiai, O., Poulter, B., Seifert, F. M., Ward, S., Jarvis, I., Whitcraft, A. K., Sahajpal, R., Gilliams, S., Herold, M., Carter, S., et al. (2023) · iScience · doi:10.1016/j.isci.2023.106489 · Cited by 18
Adinugroho, W. C., Prasetyo, L. B., Kusmana, C., & Krisnawati, H. (2019) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/399/1/012025 · Cited by 18
Zepharovich, E., Ceddia, M. G., & Rist, S. (2020) · Land Use Policy · doi:10.1016/j.landusepol.2020.105175 · Cited by 18
Miettinen, J., Carlier, S., Häme, L., Mäkelä, A., Minunno, F., Penttilä, J., Pišl, J., Rasinmäki, J., Rauste, Y., Seitsonen, L., et al. (2021) · International Journal of Remote Sensing · doi:10.1080/01431161.2021.1998715 · Cited by 18
Modeling direct above-ground carbon loss due to urban expansion in Zanzibar City Region, Tanzania
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do Nascimento, D. M., Sales, A. T., Souza, R. M. S., da Silva, A. S. A., Sampaio, E. V. D. S. B., & Menezes, R. S. C. (2022) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2022.100771 · Cited by 18
Choi, S., Ni, X., Shi, Y., Ganguly, S., Zhang, G., Duong, H. V., Lefsky, M. A., Simard, M., Saatchi, S. S., Lee, S., et al. (2013) · Remote Sensing · doi:10.3390/rs5010202 · Cited by 18
Sun, Q., Meyer, W. S., Koerber, G. R., & Marschner, P. (2017) · Agriculture Ecosystems & Environment · doi:10.1016/j.agee.2017.06.032 · Cited by 18
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Marlier, M. E., Voulgarakis, A., Shindell, D. T., Faluvegi, G., Henry, C. L., & Randerson, J. T. (2014) · Atmospheric Environment · doi:10.1016/j.atmosenv.2014.02.039 · Cited by 17
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Ota, T., Kajisa, T., Mizoue, N., Yoshida, S., Takao, G., Hirata, Y., Furuya, N., Sano, T., Ponce-Hernandez, R., Ahmed, O. S., et al. (2015) · Journal of Forest Research · doi:10.1007/s10310-015-0504-3 · Cited by 17
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Yue, C., Ciais, P., & Li, W. (2018) · Biogeosciences · doi:10.5194/bg-15-1185-2018 · Cited by 17
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Akita, N., & Ohe, Y. (2021) · Forests · doi:10.3390/f12081016 · Cited by 17
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Ni, X., Park, T., Choi, S., Shi, Y., Cao, C., Wang, X., Lefsky, M. A., Simard, M., & Myneni, R. B. (2014) · Remote Sensing · doi:10.3390/rs6053533 · Cited by 17
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dos Santos, C. O., Pinto, A. D. S., Silva, J. R., Parente, L., Mesquita, V. V., dos Santos, M. P., & Ferreira, L. G. (2022) · Land · doi:10.3390/land12010060 · Cited by 16
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Weisbin, C. R., Lincoln, W., & Saatchi, S. S. (2013) · Systems Engineering · doi:10.1002/sys.21275 · Cited by 15
Andersen, L. E., Doyle, A. S., del Granado, S., Ledezma, J. C., Medinaceli, A., Medinaceli, M. V., & Weinhold, D. M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0151241 · Cited by 15
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Metadata
- Authors
- Baccini, A., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun, M., Sulla-Menashe, D., Hackler, J., Beck, P. S. A., Dubayah, R., Friedl, M. A., Samanta, S., & Houghton, R. A.
- Affiliations
- Woodwell Climate Research Center (US)
- University of Maryland, College Park (US)
- Boston University (US)
- Corresponding author
- Baccini, A.
- ORCID
- Venue
- Nature Climate Change
- Published
- 2012-01-29
- Volume
- 2
- Issue
- 3
- Pages
- 182-185
- ISSN
- 1758-678X (ISSN-L)
- 1758-6798
- OpenAlex
- W2166394891
- Cited by
- 1,788
- References
- 28
- Citations by year
- 2026: 72 · 2025: 105 · 2024: 109 · 2023: 113 · 2022: 156 · 2021: 198 · 2020: 133 · 2019: 133 · 2018: 156 · 2017: 152 · 2016: 150 · 2015: 103 · 2014: 100 · 2013: 66 · 2012: 41
- FWCI
- 82.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Climate Change (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Forestry › Remote Sensing and LiDAR Applications
- Life Sciences › Agricultural and Biological Sciences › Forestry › Forest ecology and management
- Physical Sciences › Environmental Science › Global and Planetary Change › Conservation, Biodiversity, and Resource Management
- Keywords
- tropical deforestation, CO2 emissions, aboveground carbon density, carbon stocks, tropical forests, land use change, satellite remote sensing, Brazil, Indonesia
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