Amazon Forest Structure from 2016 Lidar Transects and ED2 Model Files

545 airborne lidar transects from the 2016 Amazon Biomass Estimation Project provide forest structure data aggregated into over 1,300,000 columns and 288 grid cells across the Brazilian Amazon. The dataset includes three file types for the Ecosystem Demography Model (ED2): site files for soil and terrain, patch files for location and biomass, and cohort files for individual stem characteristics. Data were compiled by ORNL_CLOUD and are hosted on multiple government platforms.

Use Cases

• Initializing the ED2 model with site-specific soil and terrain characteristics from *.sss files.
• Analyzing regional forest carbon storage using patch file columns for aboveground biomass (AGB), carbon density, and stem basal area.
• Studying vegetation structure and demography with cohort file data on diameter at breast height, plant height, and plant functional type.
• Mapping vertical distributions of stem density and leaf area index (LAI) from aggregated lidar-derived columns.
• Estimating carbon in litter and soil layers by integrating land use history with provided edaphic characteristics.

Strengths

• Derived from a large-scale 2016 airborne lidar campaign comprising 545 transects, each 300m by 12.5km.
• Contains detailed vertical distributions for over 1,300,000 individual 50x50 meter columns.
• Provides model-ready data structured into three complementary file types (site, patch, cohort) for the ED2 ecosystem model.

Limitations

• Conflicting last updated dates: one source states 2026-03-13, another states 2016-12-31.
• Specific column names, row counts, and total dataset size are not provided by any source.
• Soil carbon estimates are based on limited measurements and existing datasets, not direct comprehensive sampling.

Provenance

Source

ORNL_CLOUD, from the Amazon Biomass Estimation Project (EBA2016) campaign.

Collection Method

Airborne lidar transects processed to derive vegetation traits, aggregated with soil data from existing datasets.

Time Range

2016

Freshness

2026-03-13 15:44:30.097709

Geography

Brazilian Amazon