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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.

Product Summary

Platforms
FIELD SURVEYS
Instruments
VISUAL OBSERVATIONS
Projects
CMS
Spatial Extent

N: -1.5 S: -13.0928 E: -46.8288 W: -67.9824

Location
BRAZIL
Coordinate System
CARTESIAN
Granule Spatial Representation
CARTESIAN
Temporal Extent
2009-01-01 to 2018-12-31
Data Partner
THE OAK RIDGE NATIONAL LABORATORY (ORNL) DISTRIBUTED ACTIVE ARCHIVE CENTER (DAAC) (ORNL_DAAC)
Concept ID
C2515314177-ORNL_CLOUD
Data State
COMPLETE
Number of Files/Granules
93
Processing Level
2
Published
Updated
Science Keywords
Decomposition ,
Crown ,
Canopy Characteristics ,
Forests ,
Biomass

Citation

Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.

Copy Citation

dos-Santos, M. N., Keller, M. M., Pinage, E. R., & Morton, D. C. (2022). Forest Inventory and Biophysical Measurements, Brazilian Amazon, 2009-2018 (Version 1). ORNL Distributed Active Archive Center. https://doi.org/10.3334/ORNLDAAC/2007 Date Accessed: 2026-09-23

Documents

USER'S GUIDE

GENERAL DOCUMENTATION

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Publications Citing This Dataset

Filters
Title Authors Year Referenced Datasets
High-resolution aboveground biomass mapping: the benefits of biome-specific deep learning models
  • Perpinya-Valles, Marti, Cendagorta-Galarza, Daniel, Ameztegui, Aitor, Huertas, Claudia, Escorihuela, Maria Jose, Romero, Laia
Leveraging SAR and Optical Remote Sensing for Enhanced Biomass Estimation in the Amazon with Random Forest and XGBoost Models
  • Antunes, Rodrigo, Junior, Luiz, Costa, Gilson, Feitosa, Raul, Bias, Edilson de Souza, Cereda Junior, Abimael, Almeida, Catherine, La Rosa, Laura E. Cue, Happ, Patrick, Chiamulera, Leonardo
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