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Forest Inventory and Biophysical Measurements, Brazilian Amazon, 2009-2018

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Documentation Revision Date: 2022-10-12

Dataset Version: 1

Summary

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.

This data set contains 62 files, 31 in comma separate (*.csv) format and 31 in shapefile (*.shp) format contained in compressed (*.zip) files.

Figure 1. Locations in Brazil where forest inventory surveys were conducted.

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. https://doi.org/10.3334/ORNLDAAC/2007

Table of Contents

  1. Dataset Overview
  2. Data Characteristics
  3. Application and Derivation
  4. Quality Assessment
  5. Data Acquisition, Materials, and Methods
  6. Data Access
  7. References

Dataset Overview

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. Sampling methodology for each site and year is described in companion files.

Project: Carbon Monitoring System (CMS)

The CMS is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data.

Related Datasets:

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. https://doi.org/10.3334/ORNLDAAC/1644

  • LiDAR point cloud data collected across the many of the same forest research sites as this dataset from 2008-2018

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. https://doi.org/10.3334/ORNLDAAC/1301

  • Forest inventory measurements collected from forest research sites in Para, Brazil from 2012-2014

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. https://doi.org/10.3334/ORNLDAAC/1302

  • LiDAR data collected from forest research sites in Para, Brazil from 2012-2014

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. https://doi.org/10.3334/ORNLDAAC/1648

  • LiDAR point clouds and aboveground biomass estimates from Para, Brazil in 2012.

Acknowledgements:

Forest inventory measurements performed through the Sustainable Landscapes project were commissioned by the United States Forest Service in collaboration with the Brazilian Enterprise for Agricultural Research (EMBRAPA) (https://www.paisagenslidar.cnptia.embrapa.br/geonetwork/srv/por/catalog.search#/home) and are archived through the Carbon Monitoring System project funded by NASA.

Data Characteristics

Spatial Coverage: Selected areas of the Amazon Basin and other regions in Brazil

Spatial Resolution:  Point data

Temporal Coverage: 2009-01-01 to 2018-12-31

Temporal Resolution: Varies by site. Some sites were sampled once, and others were resampled in following years.

Study Area: (all latitude and longitudes given in decimal degrees)

Site Westernmost Longitude Easternmost Longitude Northernmost Latitude Southernmost Latitude
Brazil -67.982 -46.829 -1.50 -13.093

 

Data File Information

This data set contains 62 files, 31 in comma separate (*.csv) format and 31 in shapefile (*.shp) format contained in compressed (*.zip) files. CSV files contain forest inventory measurements, and shapefiles contain plot locations.

Files are named Site_Subsite_Year_inventory.csv or Site_Subsite_Year_inventory_plots.shp where Site and Subsite identify the forest inventory location and Year is the year sampling occurred. Please refer to the companion PDF metadata files for the definitions of the 3-character site codes and additional site descriptions. 

No data values are indicated by ‘NA’.

Survey techniques differed by site. Table 1 provides a summary of survey methodologies for each forest inventory site. Survey techniques (e.g. plot size, minimum recorded DBH, whether the plots were resampled the following year) were highly variable between locations, and in many cases, multiple survey techniques were employed at a given location. Please see the PDF companion files for detailed descriptions of sampling techniques.

Table 1. Forest inventory survey methodologies for each location and whether aboveground biomass (AGB) was estimated.

File Name
(Site_Subsite_Year)		 Survey Type Plot/Transect Size n Resampled Minimum DBH Subplot Size Subplot Minimum DBH  AGB 
ANA_A01_2015_2018 Plot 50 m x 50 m 32 Yes 10 cm 20 m x 50 m (2018) 10 cm No
AND_A01_2013_2018 Plot 50 m x 50 m 20 Yes 35 cm 5 m x 50 m (2013); 20 m x 50 m (2018) 10 cm No
BON_A01_2014 Plot 50 m x 50 m 10 No 35 cm 10 m x 50 m 10 cm No
CAU_A01_2014_2018 Plot 50 m x 50 m 88 Yes 35 cm 5 m x 50 m (2014); 20 m x 50 m (2018) 10 cm No
DUC_A01_2009_2011 Transect 500 m 5 Yes 5 cm NA NA No
DUC_A01_2016 Plot 50 m x 50 m 17 No 35 cm 20 m x 50 m 10 cm No
FN_A01_2015 Plot 50 m x 50 m 36 No 35 cm 5 m x 50 m 10 cm Yes
FNA_A01_2013 Plot 50 m x 50 m 20 No 5 cm NA NA Yes
FST_A01_2013 Plot 50 m x 50 m 20 No 35 cm 5 m x 50 m 10 cm No
HUM_A01_2014 Plot 50 m x 50 m 10 No 35 cm 20 m x 50 m 10 cm No
JAM_A01_2011 Transect 500 m 2 No 5 cm NA NA No
JAM_A02_2011 Transect 500 m 6 No 5 cm NA NA No
JAM_A02_2013 Plot 50 m x 50 m 24 No 35 cm 5 m x 50 m 10 cm Yes
JAM_A03_2013 Plot 50 m x 50 m 4 No 35 cm 5 m x 50 m 10 cm Yes
PAR_A01_2013_2018 Plot 20 m x 500 m 10 Yes 35 cm 2m x 500 m 10 cm No
PAR_A01_2018 Plot 50 m x 50 m 40 No 35 cm 20 m x 50 m 10 cm No
SAN_A01_2014_2016 Plot 50 m x 50 m 8 Yes 35 m 5 m x 50 m 10 cm No
SAN_A01b_2016_2018 Plot 50 m x 50 m 7 Yes 35 cm 5 m x 50 m (2016); 20 m x 50 m (2018) 10 cm No
SAN_A02_2014 Plot 50 m x 50 m 8 No 35 cm 5 m x 50 m 10 cm No
SFX_A01_2011 Plot 40 m x 40 m 9 No 10 cm NA NA Yes
SFX_A02_2012 Plot 40 m x 40 m 22 No 10 cm NA NA Yes
SFX_A03_2012 Plot 40 m x 40 m 8 No 10 cm NA NA Yes
TAC_A01_2014 Plot 30 m x 30 m 14 No 5 cm NA NA No
TAC_A01_2015 Plot 50 m x 50 m 13 No 10 cm 5 m x 50 m 5 cm No
TAL_A01_2014 Plot 50 m x 50 m 5 No 35 cm 10 m x 50 m 10 cm No
TAN_A01_2012 Plot 20 m x 500 m 10 No 35 cm 2 m x 500 m 10 cm Yes
TAP_A01_2009_2011 Transect 500 m 5 Yes 5 cm NA NA No
TAP_A01_2015_2018 Plot 50 m x 50 m 9 Yes 10 cm 20 m x 50 m 10 cm No
TAP_A03_2015_2018 Plot 50 m x 50 m 10 Yes 10 cm 20 m x 50 m 10 cm No
TAP_A04_2010_2011 Transect 500 m 4 Yes 5 cm NA NA No
TAP_A05_2010_2011 Transect 500 m 2 Yes 5 cm NA NA No

 

Companion File Information

The dataset contains three types of companion files:

  • Site_Subsite_Year_inventory.pdf
    • 31 files with specific survey methodology for a given site.
  • Site_Subsite_Year_inventory.kmz
    • 31 files holding plot locations from the shapefiles in KMZ format
  • Brazil_forest_inventory_data_dictionary.csv
    • The data dictionary for the Site_Subsite_Year_inventory.csv files

Application and Derivation

These forest inventory measurements characterize forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass, carbon balance, and forest recovery over time.

Quality Assessment

Uncertainty analysis was performed to height measurements only as described in Hunter et al. (2013). Field-measured tree height was compared to lidar-derived tree heights. The authors found that the precision of individual tree height measurements ranged from 3% to 20% of total height.

Data Acquisition, Materials, and Methods

Project Overview

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 due to deforestation and forest degradation and recommended increases in carbon sequestration by enhancing forest carbon stocks. Carbon accounting requires knowledge of deforestation, degradation, and associated changes in forest carbon stocks. 

Forest Inventory Measurements

Forest inventory surveys were conducted between 2009 and 2018 as part of the Sustainable Landscapes program. Sustainable Landscapes is supported by the United States Agency for International Development (USAID) and US Department of State. Surveys performed through the Sustainable Landscapes program were commissioned by the United States Forest Service in collaboration with the Brazilian Enterprise for Agricultural Research (EMBRAPA) and are archived through the Carbon Monitoring System project funded by NASA.

Forest surveys were employed across five Brazilian states (Table 2). Survey techniques  (e.g. plot size, minimum recorded DBH, whether plots were resampled the following year) were highly variable between locations, and in many cases, multiple survey techniques were employed at a given location. Please see the PDF companion files for detailed descriptions of sampling techniques.

EMBRAPA maintains a metadata portal for the Sustainable Landscapes project at: https://www.paisagenslidar.cnptia.embrapa.br/webgis/.

Table 2. Brazilian states where forest inventory sites are located.

File Name
(Site_Subsite_Year)		 State
ANA_A01_2015_2018 Para
AND_A01_2013_2018 Para
BON_A01_2014 Acre
CAU_A01_2014_2018 Para
DUC_A01_2009_2011 Amazonas
DUC_A01_2016 Amazonas
FN_A01_2015 Mato Grosso
FNA_A01_2013 Mato Grosso
FST_A01_2013 Para
HUM_A01_2014 Acre
JAM_A01_2011 Rondonia
JAM_A02_2011 Rondonia
JAM_A02_2013 Rondonia
JAM_A03_2013 Rondonia
PAR_A01_2013_2018 Para
PAR_A01_2018 Para
SAN_A01_2014_2016 Para
SAN_A01b_2016_2018 Para
SAN_A02_2014 Para
SFX_A01_2011 Para
SFX_A02_2012 Para
SFX_A03_2012 Para
TAC_A01_2014 Para
TAC_A01_2015 Para
TAL_A01_2014 Acre
TAN_A01_2012 Mato Grosso
TAP_A01_2009_2011 Para
TAP_A01_2015_2018 Para
TAP_A03_2015_2018 Para
TAP_A04_2010_2011 Para
TAP_A05_2010_2011 Para

Data Access

These data are available through the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Forest Inventory and Biophysical Measurements, Brazilian Amazon, 2009-2018

Contact for Data Center Access Information:

References

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. https://doi.org/10.3334/ORNLDAAC/1644

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. https://doi.org/10.3334/ORNLDAAC/1301

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. https://doi.org/10.3334/ORNLDAAC/130

Hunter, M.O., M. Keller, D. Victoria, and D.C. Morton. 2013. Tree height and tropical forest biomass estimation. Biogeosciences 10:8385–8399. https://doi.org/10.5194/bg-10-8385-2013

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. https://doi.org/10.3334/ORNLDAAC/1648