LiDAR-Derived Forest Biomass and Canopy Metrics for Maryland

NASA's CMS dataset provides 30-meter gridded estimates of aboveground biomass, canopy height, and canopy coverage for the state of Maryland in 2011. The models were built using 848 field sampling sites, leaf-off LiDAR data, and agricultural imagery, with predictions generated via random forests regression across three physiographic regions.

Use Cases

• Validating regional carbon stock estimates using the aboveground biomass (AGB) layer.
• Analyzing forest structure and vertical profiles with the canopy height layer.
• Mapping vegetation density and land cover using the canopy coverage estimates.
• Assessing model performance across different terrains using the three physiographic region models.

Strengths

• Model development is based on 848 field sampling sites for ground-truthing.
• Provides three distinct geospatial layers (AGB, height, cover) at 30-meter resolution.
• Methodology employs random forests regression, a established machine learning technique for remote sensing.

Limitations

• Data is from a single year (2011), limiting temporal analysis.
• Specific column names and row counts are not provided by any source.
• Conflicting 'last updated' dates exist between Data.gov (2026) and NASA Earthdata (2011).

Provenance

Source

National Aeronautics and Space Administration

Collection Method

Field-based biomass estimates related to LiDAR height and volume metrics via random forests regression models.

Time Range

2011

Freshness

2026-03-13 03:28:30.995105

Geography

State of Maryland, USA