Forest Aboveground Biomass for Maine at 10-Meter Resolution

2023 estimates of forest aboveground biomass (AGB) for the state of Maine, USA. The data were generated using airborne LiDAR from the USGS 3DEP project and a deep learning CNN model calibrated with USDA FIA plot inventory data. The dataset provides a high-resolution (10-m) raster map projected to the year 2023.

Use Cases

• Mapping forest carbon stocks for climate change mitigation reporting using the biomass density values.
• Validating regional or global biomass models with high-resolution, state-level LiDAR-derived estimates.
• Analyzing spatial patterns of forest structure and productivity across Maine's varied ecosystems.
• Informing sustainable forest management and conservation planning with spatially explicit biomass data.

Strengths

• High 10-meter spatial resolution provides detailed local biomass variation.
• Model combines multiple data sources: airborne LiDAR (USGS 3DEP), plot-level FIA inventory, and spectral indices for calibration.
• Estimates are temporally standardized to a single target year (2023) using biomass succession models.

Limitations

• The underlying LiDAR data were collected in different years, introducing potential temporal inconsistency in source inputs.
• Conflicting last updated dates exist between sources (2026-03-13 vs. 2023-12-31), creating uncertainty about versioning.
• Specific column names, row counts, and total dataset size are not provided by any source platform.

Provenance

Source

ORNL_CLOUD, utilizing USGS 3DEP LiDAR and USDA Forest Service FIA data.

Collection Method

Airborne LiDAR data processed with a deep learning convolutional neural network (CNN) model calibrated by plot-level forest inventory and spectral indices.

Time Range

2023

Freshness

2026-03-13 02:43:42.002720

Geography

Maine, USA