Articles | Volume 17, issue 23
https://doi.org/10.5194/bg-17-6185-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-17-6185-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Dec 2020
Research article |
| 09 Dec 2020
| 09 Dec 2020
Landsat near-infrared (NIR) band and ELM-FATES sensitivity to forest disturbances and regrowth in the Central Amazon
Robinson I. Negrón-Juárez
CORRESPONDING AUTHOR
Climate Sciences Department, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
Jennifer A. Holm
Climate Sciences Department, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
Boris Faybishenko
Climate Sciences Department, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
Daniel Magnabosco-Marra
Max Planck Institute for Biogeochemistry, Hans-Knoell Str. 10, 07745 Jena, Germany
National Institute of Amazonian Research (INPA), Av
André Araújo 2936, 690060-001, Manaus, Brazil
Rosie A. Fisher
National Center for Atmospheric Research (NCAR), 1850 Table Mesa Dr., Boulder, CO 80305, USA
Centre Européen de Recherche et de Formation Avencée en Calcul
Scientifique (CERFACS), Toulouse, France
Jacquelyn K. Shuman
National Center for Atmospheric Research (NCAR), 1850 Table Mesa Dr., Boulder, CO 80305, USA
Alessandro C. de Araujo
Embrapa Amazonia Oriental, Tv. Dr. Enéas Piheiro, s/n, Marco, CEP 66095-903, Caixa postal 48, Belem-Para, Brazil
William J. Riley
Climate Sciences Department, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
Jeffrey Q. Chambers
Climate Sciences Department, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
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Cited
12 citations as recorded by crossref.
- Modeling the topographic influence on aboveground biomass using a coupled model of hillslope hydrology and ecosystem dynamics Y. Fang et al. https://doi.org/10.5194/gmd-15-7879-2022
- Widespread forest disturbance from windthrow in central African rainforests R. Negron-Juarez et al. https://doi.org/10.1038/s44304-026-00172-0
- Windthrow characteristics and their regional association with rainfall, soil, and surface elevation in the Amazon R. Negron-Juarez et al. https://doi.org/10.1088/1748-9326/acaf10
- Assessing the Magnitude of the Amazonian Forest Blowdowns and Post-Disturbance Recovery Using Landsat-8 and Time Series of PlanetScope Satellite Constellation Data D. Ping et al. https://doi.org/10.3390/rs15123196
- Selective and clear-cut logging have varied imprints on tree community structure in a moist semi-deciduous forest in Ghana P. Addo-Fordjour et al. https://doi.org/10.1016/j.heliyon.2022.e11393
- Discrimination of Larch Needle Pest Severity Based on Sentinel-2 Super-Resolution and Spectral Derivatives—A Case Study of Erannis jacobsoni Djak G. Sun et al. https://doi.org/10.3390/f16010088
- DRMAT: A multivariate algorithm for detecting breakpoints in multispectral time series Y. Li et al. https://doi.org/10.1016/j.rse.2024.114402
- Sensitivity of Optical Satellites to Estimate Windthrow Tree-Mortality in a Central Amazon Forest L. Emmert et al. https://doi.org/10.3390/rs15164027
- Widespread windthrow in Southeast Asian tropical forests verified by satellite observations R. Negron-Juarez https://doi.org/10.1088/2515-7620/ae3f04
- Tracking Amazon forest succession after large-scale windthrow events J. David Urquiza-Muñoz et al. https://doi.org/10.1088/2752-664X/ae6784
- Functionally Assembled Terrestrial Ecosystem Simulator (FATES) for Hurricane Disturbance and Recovery M. Shi et al. https://doi.org/10.1029/2023MS003679
- Tree crown damage and its effects on forest carbon cycling in a tropical forest J. Needham et al. https://doi.org/10.1111/gcb.16318
12 citations as recorded by crossref.
- Modeling the topographic influence on aboveground biomass using a coupled model of hillslope hydrology and ecosystem dynamics Y. Fang et al. https://doi.org/10.5194/gmd-15-7879-2022
- Widespread forest disturbance from windthrow in central African rainforests R. Negron-Juarez et al. https://doi.org/10.1038/s44304-026-00172-0
- Windthrow characteristics and their regional association with rainfall, soil, and surface elevation in the Amazon R. Negron-Juarez et al. https://doi.org/10.1088/1748-9326/acaf10
- Assessing the Magnitude of the Amazonian Forest Blowdowns and Post-Disturbance Recovery Using Landsat-8 and Time Series of PlanetScope Satellite Constellation Data D. Ping et al. https://doi.org/10.3390/rs15123196
- Selective and clear-cut logging have varied imprints on tree community structure in a moist semi-deciduous forest in Ghana P. Addo-Fordjour et al. https://doi.org/10.1016/j.heliyon.2022.e11393
- Discrimination of Larch Needle Pest Severity Based on Sentinel-2 Super-Resolution and Spectral Derivatives—A Case Study of Erannis jacobsoni Djak G. Sun et al. https://doi.org/10.3390/f16010088
- DRMAT: A multivariate algorithm for detecting breakpoints in multispectral time series Y. Li et al. https://doi.org/10.1016/j.rse.2024.114402
- Sensitivity of Optical Satellites to Estimate Windthrow Tree-Mortality in a Central Amazon Forest L. Emmert et al. https://doi.org/10.3390/rs15164027
- Widespread windthrow in Southeast Asian tropical forests verified by satellite observations R. Negron-Juarez https://doi.org/10.1088/2515-7620/ae3f04
- Tracking Amazon forest succession after large-scale windthrow events J. David Urquiza-Muñoz et al. https://doi.org/10.1088/2752-664X/ae6784
- Functionally Assembled Terrestrial Ecosystem Simulator (FATES) for Hurricane Disturbance and Recovery M. Shi et al. https://doi.org/10.1029/2023MS003679
- Tree crown damage and its effects on forest carbon cycling in a tropical forest J. Needham et al. https://doi.org/10.1111/gcb.16318
Saved (final revised paper)
Latest update: 18 Sep 2026
Short summary
The temporal variability in the Landsat satellite near-infrared (NIR) band captured the dynamics of forest regrowth after disturbances in Central Amazon. This variability was represented by the dynamics of forest regrowth after disturbances were properly represented by the ELM-FATES model (Functionally Assembled Terrestrial Ecosystem Simulator (FATES) in the Energy Exascale Earth System Model (E3SM) Land Model (ELM)).
The temporal variability in the Landsat satellite near-infrared (NIR) band captured the dynamics...
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