Brazilian tropical forests contain approximately one-third of the global carbon stock in above-ground tropical forest biomass. Deforestation has cleared about 15% of the extensive forest on the Brazilian Amazon frontier. Logging, and understory forest fires may have degraded a similar area of forest. In response to the potential climatic effects of deforestation, policy makers have suggested reductions in emissions through deforestation and forest degradation and enhanced forest carbon stocks (REDD+). Carbon accounting for REDD+ requires knowledge of deforestation, degradation, and associated changes in forest carbon stocks. Degradation is more difficult to detect than deforestation so SilvaCarbon, an US inter-agency effort, has set a priority to better characterize forest degradation effects on carbon loss.
We propose to quantify carbon stocks and changes and associated uncertainties in Paragominas, a jurisdiction in the eastern Brazilian Amazon with a high proportion of
logged and burned degraded forests where political change has opened the way for REDD+. We will build on a long history of research including our extensive studies of logging damage. In addition, we will use recent forest inventories and airborne lidar supported by USAID and managed by the US Forest Service and the Brazilian Corporation for Agricultural Research (EMBRAPA) under the Sustainable Landscapes Brazil program. Existing data will allow us to start analysis immediately and will also permit REDD+ relevant multi-temporal measurements of change during the brief three-year study period.
We plan to supplement the existing data by collection of additional ground-based forest inventory data contemporary with commercial airborne lidar (supported by USAID) and Landsat remote sensing data that will incorporate a novel use of time series data to estimate the structural properties of degraded forests using bidirectional reflectance information. We identify two objectives for forest carbon accounting at the jurisdictional level:
- Quantify spatially explicit above-ground carbon stocks and the changes in carbon stocks;
- Quantify spatially explicit uncertainties in above-ground carbon stocks and changes in carbon stocks
We will meet these objectives by employing innovative data assimilation methods. Our approach employs a hierarchical Bayesian modeling (HBM) framework where the assimilation of information from multiple sources is accomplished using a change of support (COS) technique. The COS problem formulation allows data from several spatial resolutions to be assimilated into an intermediate resolution. This approach provides a mechanism to assimilate information from multiple sources to produce spatially-explicit maps of carbon stocks and changes with corresponding spatially explicit maps of uncertainty. Importantly, this approach also provides a mechanism that can be used to assess the value of information from specific data products. Hence future data collection can be optimized in the context of the reduction of uncertainty. The spatially explicit quantification of uncertainties naturally provides insights into effective sampling designs. Members of the team used this statistical approach previously as part of prototyping efforts for the National Ecological Observatory Network.
The proposed work will add a new research dimension to the Sustainable Landscapes Brazil program, a direct outcome of the US-Brazil Memorandum of Understanding on climate change. Through that program, we have already successfully acquired airborne remote sensing data in Brazil and all requirements for international data collection have already been met. Because the proposed research is closely linked to an active program of international cooperation and capacity building, we will be in a unique position to transfer the results of our research to practitioners in the Brazilian government and in Brazilian civil society.
Relevant Policies/Programs: US-Brazil Memorandum of Understanding on Climate Change, Brazilian Forest Code, REDD+, NFMS, SilvaCarbon, Sustainable Landscapes Program Brazil
Potential Users: Municipality of Paragominas, State of Para, Brazilian Ministry of the Environment, Brazilian Space Agency, Instituto Floresta Tropical, Imazon
Stakeholders:
Current Application Readiness Level: 1
Start Application Readiness Level: 1
Target Application Readiness Level: 3
Future Developments:
Limitations: - Limited airborne lidar coverage: 30 strips x 1 km2, which comprise only ~0.15% of the jurisdictional area.
Relevant Policies/Programs: US-Brazil Memorandum of Understanding on Climate Change, Brazilian Forest Code, REDD+, NFMS, SilvaCarbon, Sustainable Landscapes Program Brazil
Potential Users: Municipality of Paragominas, State of Para, Brazilian Ministry of the Environment, Brazilian Space Agency, Instituto Floresta Tropical, Imazon
Stakeholders:
Current Application Readiness Level: 1
Start Application Readiness Level: 1
Target Application Readiness Level: 3
Future Developments:
Limitations: - Limited airborne lidar coverage: 30 strips x 1 km2, which comprise only ~0.15% of the jurisdictional area.
Description: This data set provides measurements for diameter at breast height (DBH), commercial tree height, and total tree height for forest inventories taken at the Fazenda Cauaxi and the Fazenda Nova Neonita, Paragominas municipality, Para, Brazil. Also included for each tree are the common, family, and scientific name, coordinates, canopy position, crown radius, and for dead trees the decomposition status. These biophysical measurements were made at Fazenda Cauaxi during 2012 and 2014 and at the Fazenda Nova Neonita during 2013.
Status: Archived
CMS Science Theme(s): Land Biomass
Keywords: Carbon Stocks (; terrestrial)
Spatial Extent: Fazenda Nova Neonita and Fazenda Cauaxi in the Paragominas municipality, Para, Brazil
Spatial Resolution: Plot sizes were 20 x 500 m with a 2 x 500-m subplot within a plot.
Temporal Frequency: Annual
Input Data Products: Field measurements
Algorithm/Models Used:
Evaluation: Data published by the Sustainable Landscapes Project undergo a strict process of quality control. Please refer to http://mapas.cnpm.embrapa.br/paisagenssustentaveis/ for additional information.
These data may be used to validate LiDAR data in a related data set (CMS: LiDAR Data for Forested Areas in Paragominas, Para, Brazil, 2012-2014) and in the quantification of carbon stocks, changes, and associated uncertainties in Paragominas, a jurisdiction in the eastern Brazilian Amazon with a high proportion of logged and burned degraded forests where political change has opened the way for REDD+.
Intercomparison Efforts/Gaps:
Uncertainty Estimates:
Uncertainty Categories:
Application Areas: - Quantification of carbon stocks, changes, and associated uncertainties; - MRV, REDD+; - Forest inventory; - Land management
Relevant Policies/Programs: US-Brazil Memorandum of Understanding on Climate Change, Brazilian Forest Code, REDD+, NFMS, SilvaCarbon, Sustainable Landscapes Program Brazil
Potential Users: Municipality of Paragominas, State of Para, Brazilian Ministry of the Environment, Brazilian Space Agency, Instituto Floresta Tropical, Imazon
Stakeholders:
Current Application Readiness Level: 3
Start Application Readiness Level: 1
Target Application Readiness Level: 3
Future Developments: This is an intermediate product, which will be used to develop other derived products.
Archived Data Citation: dos-Santos, M.N., and M.M. Keller. 2016. CMS: Forest Inventory and Biophysical Measurements, Para, Brazil, 2012-2014. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1301
Bounding Coordinates:
West Longitude:
-48.50000
East Longitude:
-47.51000
North Latitude:
-3.31000
South Latitude:
-3.76000
Product Title: CMS: LiDAR Data for Forested Areas in Paragominas, Para, Brazil, 2012-2014
Start Date: 07/2012End Date: 12/2014 (2012-2014)
Description: This data set provides raw LiDAR point cloud data and derived Digital Terrain Models (DTMs) for five forested areas in the municipality of Paragominas, Para, Brazil, for the years 2012, 2013, and 2014. Data are included for two areas in Paragominas for 2013 and 2014, two areas for the Fazenda Cauaxi for 2012 and 2014, and for the Fazenda Andiroba for 2014. Shapefiles showing the LiDAR/DTM coverage areas are also provided for each of the areas.
Status: Archived
CMS Science Theme(s): Land Biomass
Keywords: Carbon Stocks (; terrestrial)
Spatial Extent: Paragominas, Para, Brazil
Spatial Resolution: LiDAR Point Clouds: Overall, the resolution for the point cloud data is < 1 meter squared. Resolution for a particular flight may be as high as 0.1 meter squared. Digital Terrain Models (DTMs): 1 x 1m
Temporal Frequency: The LiDAR data were acquired on individual flights/days during 2012, 2013, and 2014.
Input Data Products: The data were collected and processed to point cloud *.las format files and corresponding DTM *.tif format files by commercial vendors under the Sustainable Landscapes project.
Algorithm/Models Used:
Evaluation: Data published by the Sustainable Landscapes Project undergo a strict process of quality control. Should a data set not fully meet these criteria, a new data collection is required from the vendor, therefore generating an entire new data set.
The coincident forest inventory and biophysical measurements data reported in the data set, dos-Santos, M.N.and M. Keller (2015), can be used for validation of LiDAR data.
Intercomparison Efforts/Gaps:
Uncertainty Estimates:
Uncertainty Categories:
Application Areas: - Quantification of carbon stocks, changes, and associated uncertainties; - MRV, REDD+; - Forest inventory; - Land management;
Relevant Policies/Programs: US-Brazil Memorandum of Understanding on Climate Change, Brazilian Forest Code, REDD+, NFMS, SilvaCarbon, Sustainable Landscapes Program Brazil
Potential Users: Municipality of Paragominas, State of Para, Brazilian Ministry of the Environment, Brazilian Space Agency, Instituto Floresta Tropical, Imazon
Stakeholders:
Current Application Readiness Level: 3
Start Application Readiness Level: 1
Target Application Readiness Level: 3
Future Developments: This is an intermediate product, which will be used to develop other derived products.
Archived Data Citation: dos-Santos, M.N., and M.M. Keller. 2016. CMS: LiDAR Data for Forested Areas in Paragominas, Para, Brazil, 2012-2014. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1302
Description: This dataset provides estimates of forest aboveground biomass for three study areas and the entire Paragominas municipality, in Para, Brazil, in 2012. Aboveground biomass (in megagrams of carbon per hectare) was measured for inventory plots within the study (focal) areas, and then assimilated and modeled with LiDAR and PALSAR metrics using gradient boosting machines (GBM) to predict spatially explicit forest aboveground biomass and uncertainties for the entire focal areas. The PALSAR data across the three focal areas was combined and used in a GBM model to predict forest aboveground biomass across the entire Paragominas municipality.
Status: Archived
CMS Science Theme(s): Land Biomass
Keywords:
Spatial Extent: Paragominas Municipality, Para, Brazil
Archived Data Citation: Keller, M.M., P. Duffy, and W. Barnett. 2019. LiDAR and PALSAR-Derived Forest Aboveground Biomass, Paragominas, Para, Brazil, 2012. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1648
Bounding Coordinates:
West Longitude:
-49.00000
East Longitude:
-46.00000
North Latitude:
-2.00000
South Latitude:
-4.01000
Publications:
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
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
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:
Keller, M.M., P. Duffy, and W. Barnett. 2019. LiDAR and PALSAR-Derived Forest Aboveground Biomass, Paragominas, Para, Brazil, 2012. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1648
dos-Santos, M.N., and M.M. Keller. 2016. CMS: LiDAR Data for Forested Areas in Paragominas, Para, Brazil, 2012-2014. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1302
dos-Santos, M.N., and M.M. Keller. 2016. CMS: Forest Inventory and Biophysical Measurements, Para, Brazil, 2012-2014. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1301