GEDI-FIA Fusion: Forest Attribute Lidar Training Data

GEDI-FIA Fusion provides interpolated cumulative lidar waveforms with uncertainties across Forest Inventory and Analysis field plots in the contiguous United States. The dataset from ORNL_CLOUD supports training linear regression models between GEDI lidar metrics and forest attributes, offering R scripts for data extraction and analysis. It contains predicted waveforms for the Global Ecosystem Dynamics Investigation instrument in RData and JSON formats, alongside a CSV table of FIA plot information.

Use Cases

• Training linear regression models to predict forest biomass using GEDI lidar metrics and FIA plot attributes.
• Analyzing forest canopy vertical structure and height from interpolated cumulative waveform data.
• Extracting plot-specific information for validation of large-scale forest carbon stock assessments.
• Studying the relationship between ground-measured forest inventory data and spaceborne lidar observations.

Strengths

• Provides predicted waveforms for the high-resolution GEDI instrument, enabling precise canopy structure measurements.
• Includes R scripts for user-selected plot data extraction and model training, facilitating analysis.
• Covers national FIA field plots across the contiguous United States, offering broad spatial coverage.

Limitations

• Key metadata such as row count, column names, and dataset size are unknown across all sources.
• There is a conflict in the last updated date, with sources reporting both 2023-03-01 and 2026-03-13.
• The specific license terms under 'other-license-specified' are not detailed.

Provenance

Source

ORNL_CLOUD

Collection Method

Interpolated cumulative waveforms predicted for GEDI instrument over FIA field plots.

Time Range

null

Freshness

2026-03-13 02:21:50.952662

Geography

Contiguous United States