Morton (CMS 2014) Project Profile   (updated 08-Jan-2020)
Project Title:Long-Term Carbon Consequences of Amazon Forest Degradation

Science Team
Members:

Douglas (Doug) Morton, NASA GSFC (Project Lead)

Project Duration: 2014 - 2017
Solicitation:NASA: Carbon Monitoring System (2014)
Abstract: Four decades of deforestation, forest degradation, and agricultural use have fundamentally altered remaining forest fragments along the arc of deforestation in southern Amazonia. Forest carbon stocks in these frontier forests remain poorly characterized by existing forest inventory data or moderate resolution (0.25-1 km2) satellite data products. Nonetheless, these frontier landscapes retain clues to historic forest carbon emissions and the legacy of forest degradation from logging and fire. Improving our understanding of the long-term carbon consequences of forest degradation is essential for efforts to Reduce Emissions from Deforestation and Forest Degradation and enhance forest carbon stocks (REDD+). The level of emphasis on forest degradation in monitoring, reporting, and verification (MRV) of REDD+ activities in Amazonia fundamentally depends on the magnitude of net carbon emissions from logging, fire, and forest fragmentation. We propose to conduct detailed analyses of forest carbon stocks and land cover transitions in three frontier forest regions in the Peruvian and Brazilian Amazon. The proposed study combines contemporary forest inventory data and extensive airborne lidar surveys with time series of Landsat data to evaluate landscape patterns of forest carbon stocks. Our major emphasis is the variety of forest carbon loss trajectories from different intensities and frequencies of forest degradation. We selected three frontier regions to evaluate the mosaic of forest ages and conditions from logging, fire, and forest fragmentation in old (Santarém, Pará, Brazil), established (Feliz Natal, Mato Grosso, Brazil), and young frontier forests (Colonel Portillo, Ucayali, Peru). Key research themes include 1) long-term changes in forest structure and carbon stocks from forest degradation; 2) lidar-biomass relationships in degraded forests; and 3) full carbon accounting of forest emissions, including deforestation, degradation, and secondary forest dynamics. The proposed research addresses the two priority areas in the Carbon Monitoring System (CMS) solicitation (A.7). Specifically, we will use airborne lidar data from commercial off-the-shelf sensors, collected under separate funding from USAID and the US Department of State, to characterize Amazon forest structure and biomass and prototype MRV capabilities for intact and degraded forest types. Improving estimates of carbon losses from forest degradation is a key priority for NASA CMS and SilvaCarbon (Peru is a SilvaCarbon country), and a major impediment to progress on REDD+. Research activities will further develop methodologies to combine field measurements, airborne scanning lidar data, and satellite observations in support of REDD+ MRV. Finally, study results will provide validation datasets for ICESat-2 and proposed lidar missions under NASA’s Earth Venture program (EVi-2 and EVs-2). The proposed effort leverages four sources of existing support. Field measurements and airborne lidar data for study sites in the Brazilian and Peruvian Amazon will be acquired under separate funding from USAID, US Department of State, SilvaCarbon, and the Brazilian Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). PI Morton is an unfunded collaborator on these existing projects, including his recent selection as a Ciência sem Fronteiras (Science Without Borders) Fellow by CNPq for 2014-2017. Additional funding for the proposed research through CMS would leverage these field and lidar data collections to address priority science areas for CMS and enhance the international impact of research activities supported by USAID and SilvaCarbon.
CMS Primary Theme:
  • Land Biomass
 CMS Science Theme(s):
  • Land Biomass
  • Land-Atmosphere Flux
  • Decision Support
  • MRV

Participants:

Christine Dragisic, U.S. Department of State
Douglas (Doug) Morton, NASA GSFC

Contact Support to request an email list of project participants.

Project URL(s): None provided.
 
Data
Products:
Product Title:  LiDAR Surveys over Selected Forest Research Sites, Brazilian Amazon, 2008-2018
Start Date:  01/2008      End Date:  12/2018     (June 2008-Aug 2018)
Description:  This dataset provides the complete catalog of point cloud data collected during LiDAR surveys over selected forest research sites across the Amazon rainforest in Brazil between 2008 and 2018 for the Sustainable Landscapes Brazil Project. Flight lines were selected to overfly key field research sites in the Brazilian states of Acre, Amazonas, Bahia, Goias, Mato Grosso, Para, Rondonia, Santa Catarina, and Sao Paulo. The point clouds have been georeferenced, noise-filtered, and corrected for misalignment of overlapping flight lines. They are provided in 1 km2 tiles. The data were collected to measure forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass and carbon balance, and forest recovery over time.
Status:  Archived
CMS Science Theme(s):  Land Biomass; Land-Atmosphere Flux; MRV
Keywords:  Ecosystem Composition and Structure
Spatial Extent:  Selected areas of the Amazon Basin and other regions in Brazil
Spatial Resolution:  ~ 10 points per m2 provided in 1 km2 tiles over key field research sites
Temporal Frequency:  single acquisition over most sites (multiple acquisition over some sites)
Input Data Products:  
Algorithm/Models Used:  
Evaluation:  
Intercomparison Efforts/Gaps:  
Uncertainty Estimates:  
Uncertainty Categories:  
Application Areas:  
Relevant Policies/Programs:  
Potential Users:  
Stakeholders:  
Current Application Readiness Level:  1
Start Application Readiness Level:  1
Target Application Readiness Level:  1
Future Developments:  
Limitations:  
Date When Product Available:  
Assigned Data Center:  ORNL DAAC
Metadata URL(s):

https://doi.org/10.3334/ORNLDAAC/1644
Data Server URL(s):

https://doi.org/10.3334/ORNLDAAC/1644
Archived Data Citation:  dos-Santos, M.N., M.M. Keller, and D.C. Morton. 2019. LiDAR Surveys over Selected Forest Research Sites, Brazilian Amazon, 2008-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1644

Bounding Coordinates:
West Longitude:-68.30000 East Longitude:-39.06000
North Latitude:-1.58000 South Latitude:-26.70000

Product Title:  Estimates of degradation carbon losses from logging and fire in Amazon forests Mato Grosso Brazil
Start Date:  01/1985      End Date:  12/2014     (1985-2014 ( lidar was collected between 2013 and 2015))
Description:  Here, we used a purposeful sample of high-density airborne lidar to capture a broad range of degraded and intact forest conditions in the southern Brazilian Amazon. For each forest stand, we combined degradation history information from annual time series of Landsat data with airborne lidar data to characterize canopy structure and estimate aboveground carbon density (ACD) using a lidar-biomass model specifically developed for frontier forests in the Brazilian Amazon (Longo et al 2016) Our study directly targets a lingering data gap for REDD+ (Andrade et al 2017) by quantifying the rates of ACD recovery over 1- to 15-year time horizons following a broad range of degradation pathways, including sequential impacts of logging and burning. These time-varying emissions estimates, or emissions factors, can be combined with activity data on the extent of forest degradation to establish REDD+ baselines; confirm the relative contributions from fire, logging, and regeneration to regional net forest carbon emissions; and estimate the consequences to mitigation targets if degradation remains omitted from greenhouse gas accounting. We combined Landsat time series and airborne lidar data to quantify variability in forest structure and ACD across gradients of degradation type, frequency, severity, and timing. Degradation history for areas with lidar coverage was characterized using a two-tiered classification approach
Status:  Public
CMS Science Theme(s):  Land Biomass; Land-Atmosphere Flux; MRV
Keywords:  Disturbance
Spatial Extent:  southern Mato Grosso, Brazil
Spatial Resolution:  30 m
Temporal Frequency:  Annually
Input Data Products:  Landsat time series
Algorithm/Models Used:  
Evaluation:  Multi-temporal lidar and field inventory data will be used to validate estimates of forest cover change
Intercomparison Efforts/Gaps:  
Uncertainty Estimates:  Uncertainty in the rates of deforestation and forest degradation will be assessed using high-resolution data
Uncertainty Categories:  data-data comparisons; model-data comparisons
Application Areas:  - MRV, REDD+; - Forest inventory; - GHG emissions inventory; - Land management
Relevant Policies/Programs:  REDD+, SilvaCarbon, Science Without Borders, Global Carbon Project, GFED
Potential Users:  INPE, Embrapa, USAID, US State Department, global carbon cycle community
Stakeholders:  National Wildlife Federation (Point of Contact: Barbara Bramble, bramble@nwf.org)
Current Application Readiness Level:  3
Start Application Readiness Level:  3
Target Application Readiness Level:  6
Future Developments:  
Limitations:  
Date When Product Available:  
Metadata URL(s):

https://iopscience.iop.org/article/10.1088/1748-9326/aac331/data

http://geoinfo.cnpm.embrapa.br/
Data Server URL(s):

https://iopscience.iop.org/article/10.1088/1748-9326/aac331/data

http://geoinfo.cnpm.embrapa.br/
Archived Data Citation:  
Bounding Coordinates:
West Longitude:-55.20000 East Longitude:-54.02000
North Latitude:-11.86700 South Latitude:-12.38000

Product Title:  Lidar-biomass models for intact, degraded, and secondary forests across the Brazilian Amazon
Start Date:  09/2011      End Date:  08/2015     (lidar was collected between 2012 and 2015 field data was collected between 2011 and 2015)
Description:  Here we investigated biomass variability in intact and degraded Amazon forest types using the largest integrated inventory plot and airborne lidar data set assembled to date for the Brazilian Amazon. Field samples and coincident lidar acquisitions specifically targeted degraded forest types in order to develop and calibrate a general model of carbon stocks for the Brazilian Amazon that captures different levels of forest degradation and recovery. This data product contains a summary with properties derived from all field plots, along with airborne-lidar derived metrics, the above-ground carbon density estimates from both field inventory plots and airborne lidar, and their uncertainties. A header is included in the file, and contains the variable description and units. Data are presented in a txt file with detailed documentation available in a pdf file.
Status:  Public
CMS Science Theme(s):  Land Biomass; MRV
Keywords:  Carbon Stocks (; terrestrial)
Spatial Extent:  18 study areas across the Brazilian Amazon
Spatial Resolution:  field inventory plots were approx 0.25 ha lidar metrics were computed on a 50 x 50 m grid
Temporal Frequency:  one time sampling for each study area
Input Data Products:  Commercial airborne Lidar, forest inventory data
Algorithm/Models Used:  Multivariate Regression
Evaluation:  Multi-temporal lidar and field inventory data will be used to validate regression models of carbon stocks in intact, degraded, and secondary forest types
Intercomparison Efforts/Gaps:  
Uncertainty Estimates:  Uncertainty in lidar-biomass relationships for degraded forests, effects of wood density variability in forest carbon stock estimates
Uncertainty Categories:  data-data comparisons; model-data comparisons
Application Areas:  - MRV, REDD+; - Forest inventory; - GHG emissions inventory; - Land management
Relevant Policies/Programs:  REDD+, SilvaCarbon, Science Without Borders, Global Carbon Project, GFED
Potential Users:  INPE, Embrapa, USAID, US State Department, global carbon cycle community
Stakeholders:  
Current Application Readiness Level:  3
Start Application Readiness Level:  3
Target Application Readiness Level:  6
Future Developments:  
Limitations:  
Date When Product Available:  
Metadata URL(s):

https://agupubs.onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2F2016GB005465&file=gbc20478-sup-0001-supplementary.pdf
Data Server URL(s):

https://agupubs.onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2F2016GB005465&file=gbc20478-sup-0002-supplementary.txt

https://www.paisagenslidar.cnptia.embrapa.br/webgis/
Archived Data Citation:  
Bounding Coordinates:
West Longitude:-67.98330 East Longitude:-46.83330
North Latitude:-1.61670 South Latitude:-13.08330

Product Title:  Secondary vegetation extent, age, and net carbon uptake in the Brazilian Amazon between 1985 and 2017.
Start Date:  01/1985      End Date:  12/2017     (1985-2017)
Description:  - Generate estimates of Annual Forest carbon emissions for each frontier landscape, including deforestation, degradation, and secondary Forest dynamics.
Status:  Public
CMS Science Theme(s):  Land Biomass; MRV
Keywords:  Carbon Stocks (; terrestrial)
Spatial Extent:  The Brazilian Amazon
Spatial Resolution:  30 m
Temporal Frequency:  Annually
Input Data Products:  Landsat time series
Algorithm/Models Used:  
Evaluation:  Multi-temporal lidar and field inventory data will be used to validate estimates of secondary forest extent
Intercomparison Efforts/Gaps:  
Uncertainty Estimates:  land cover validation
Uncertainty Categories:  data-data comparisons
Application Areas:  - MRV, REDD+; - Forest inventory; - GHG emissions inventory; - Land management
Relevant Policies/Programs:  REDD+, SilvaCarbon, Science Without Borders, Global Carbon Project, GFED
Potential Users:  INPE, Embrapa, USAID, US State Department, global carbon cycle community
Stakeholders:  National Wildlife Federation (Point of Contact: Barbara Bramble, bramble@nwf.org); Union of Concerned Scientists (Point of Contact: Sharon Smith)
Current Application Readiness Level:  3
Start Application Readiness Level:  3
Target Application Readiness Level:  6
Future Developments:  
Limitations:  
Date When Product Available:  2017
Metadata URL(s):

http://geoinfo.cnpm.embrapa.br/
Data Server URL(s):

https://floreser.users.earthengine.app/view/floreser
Archived Data Citation:  
Bounding Coordinates:
West Longitude:-68.00000 East Longitude:-46.00000
North Latitude:-1.00000 South Latitude:-13.00000

Product Title:  Forest Inventory and Biophysical Measurements, Brazilian Amazon, 2009-2018
Start Date:  01/2009      End Date:  12/2018     (2009-01-01 to 2018-12-31)
Description:  This dataset provides the complete catalog of forest inventory and biophysical measurements collected over selected forest research sites across the Amazon rainforest in Brazil between 2009 and 2018 for the Sustainable Landscapes Brazil Project. This dataset includes measurements for diameter at breast height (DBH), commercial tree height, and total tree height for forest inventories. Also included for each tree are the family, common and scientific names, coordinates, canopy position, crown radius, and for dead trees, the decomposition status. Aboveground biomass estimate is available for selected sites. The data are provided in comma-separated values (CSV) and shapefile formats. Sampling methodology for each site and year is described in companion files.
Status:  Archived
CMS Science Theme(s):  Land Biomass
Keywords:  Land Biomass
Spatial Extent:  Selected areas of the Amazon Basin and other regions in Brazil
Spatial Resolution:  Point data
Temporal Frequency:  Varies by site. Some sites were sampled once, and others were resampled in following years.
Input Data Products:  
Algorithm/Models Used:  
Evaluation:  
Intercomparison Efforts/Gaps:  
Uncertainty Estimates:  
Uncertainty Categories:  
Application Areas:  
Relevant Policies/Programs:  
Potential Users:  
Stakeholders:  
Current Application Readiness Level:  
Start Application Readiness Level:  
Target Application Readiness Level:  
Future Developments:  
Limitations:  
Date When Product Available:  
Assigned Data Center:  ORNL DAAC
Metadata URL(s):

https://doi.org/10.3334/ORNLDAAC/2007
Data Server URL(s):

https://doi.org/10.3334/ORNLDAAC/2007
Archived Data Citation:  dos-Santos, M.N., M.M. Keller, E.R. Pinage, and D.C. Morton. 2022. Forest Inventory and Biophysical Measurements, Brazilian Amazon, 2009-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/2007

Bounding Coordinates:
West Longitude:-67.98000 East Longitude:-46.83000
North Latitude:-1.50000 South Latitude:-13.09000

 
Publications: Andela, N., Morton, D. C., Giglio, L., Chen, Y., van der Werf, G. R., Kasibhatla, P. S., DeFries, R. S., Collatz, G. J., Hantson, S., Kloster, S., Bachelet, D., Forrest, M., Lasslop, G., Li, F., Mangeon, S., Melton, J. R., Yue, C., Randerson, J. T. 2017. A human-driven decline in global burned area. Science. 356(6345), 1356-1362. DOI: 10.1126/science.aal4108

Bustamante, M. M. C., Roitman, I., Aide, T. M., Alencar, A., Anderson, L. O., Aragao, L., Asner, G. P., Barlow, J., Berenguer, E., Chambers, J., Costa, M. H., Fanin, T., Ferreira, L. G., Ferreira, J., Keller, M., Magnusson, W. E., Morales-Barquero, L., Morton, D., Ometto, J. P. H. B., Palace, M., Peres, C. A., Silverio, D., Trumbore, S., Vieira, I. C. G. 2015. Toward an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity. Global Change Biology. 22(1), 92-109. DOI: 10.1111/gcb.13087

Eitel, J. U., Hofle, B., Vierling, L. A., Abellan, A., Asner, G. P., Deems, J. S., Glennie, C. L., Joerg, P. C., LeWinter, A. L., Magney, T. S., Mandlburger, G., Morton, D. C., Muller, J., Vierling, K. T. 2016. Beyond 3-D: The new spectrum of lidar applications for earth and ecological sciences. Remote Sensing of Environment. 186, 372-392. DOI: 10.1016/j.rse.2016.08.018

Leitold, V., Morton, D. C., Longo, M., dos-Santos, M. N., Keller, M., Scaranello, M. 2018. El Nino drought increased canopy turnover in Amazon forests. New Phytologist. 219(3), 959-971. DOI: 10.1111/nph.15110

Morton, D. C. 2016. A satellite perspective. Nature Climate Change. 6(4), 346-348. DOI: 10.1038/nclimate2978

Morton, D. C., Rubio, J., Cook, B. D., Gastellu-Etchegorry, J., Longo, M., Choi, H., Hunter, M., Keller, M. 2016. Amazon forest structure generates diurnal and seasonal variability in light utilization. Biogeosciences. 13(7), 2195-2206. DOI: 10.5194/bg-13-2195-2016

Noojipady, P., Morton, C. D., Macedo, N. M., Victoria, C. D., Huang, C., Gibbs, K. H., Bolfe, L. E. 2017. Forest carbon emissions from cropland expansion in the Brazilian Cerrado biome. Environmental Research Letters. 12(2), 025004. DOI: 10.1088/1748-9326/aa5986

Nunes, S., Oliveira, L., Siqueira, J., Morton, D. C., Souza, C. M. 2020. Unmasking secondary vegetation dynamics in the Brazilian Amazon. Environmental Research Letters. 15(3), 034057. DOI: 10.1088/1748-9326/ab76db

Rangel Pinage, E., Keller, M., Duffy, P., Longo, M., dos-Santos, M., Morton, D. 2019. Long-Term Impacts of Selective Logging on Amazon Forest Dynamics from Multi-Temporal Airborne LiDAR. Remote Sensing. 11(6), 709. DOI: 10.3390/rs11060709

Rappaport, D. I., Morton, D. C., Longo, M., Keller, M., Dubayah, R., dos-Santos, M. N. 2018. Quantifying long-term changes in carbon stocks and forest structure from Amazon forest degradation. Environmental Research Letters. 13(6), 065013. DOI: 10.1088/1748-9326/aac331

Longo, M., Keller, M., dos-Santos, M. N., Leitold, V., Pinage, E. R., Baccini, A., Saatchi, S., Nogueira, E. M., Batistella, M., Morton, D. C. 2016. Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon. Global Biogeochemical Cycles. 30(11), 1639-1660. DOI: 10.1002/2016GB005465

Archived Data Citations: dos-Santos, M.N., M.M. Keller, and D.C. Morton. 2019. LiDAR Surveys over Selected Forest Research Sites, Brazilian Amazon, 2008-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1644

dos-Santos, M.N., M.M. Keller, E.R. Pinage, and D.C. Morton. 2022. Forest Inventory and Biophysical Measurements, Brazilian Amazon, 2009-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/2007

2015 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)
  • G-LiHT: Multi-Sensor Airborne Image Data from Denali to the Yucatan   --   (Bruce Cook, Lawrence A Corp, Douglas Morton, Joel McCorkel)   [abstract]   [poster]