LiDAR-Derived Forest Biomass and Canopy Maps for Mid-Atlantic States

Gridded 30-meter estimates of aboveground biomass, forest canopy height, and canopy coverage for Maryland, Pennsylvania, and Delaware in 2011. The dataset was produced by NASA using a model-based stratification of leaf-off LiDAR and agricultural imagery to select 848 field sampling sites, with random forest regression models relating field data to LiDAR metrics across three physiographic regions. Pixel-level spatial error estimates and validation against FIA plots and national maps were performed.

Use Cases

• Validating and calibrating national-scale biomass maps using high-resolution regional estimates.
• Modeling carbon storage and sequestration potential in Mid-Atlantic forests using the aboveground biomass layer.
• Analyzing forest structure and habitat characteristics via the canopy height and canopy coverage layers.
• Assessing the accuracy of LiDAR-derived biomass prediction models using the provided pixel-level spatial error estimates.

Strengths

• High spatial resolution of 30 meters provides detailed regional analysis.
• Model incorporates 848 field sampling sites for ground-truthing biomass estimates.
• Validation performed against the permanent FIA plot network and existing national maps.
• Pixel-level standard prediction intervals quantify spatial error for modeling accuracy.

Limitations

• Data is from a single year (2011), limiting temporal trend analysis.
• Conflicting last updated dates exist between sources (2014 vs 2026), suggesting potential metadata inconsistency.

Provenance

Source

National Aeronautics and Space Administration

Collection Method

Random forest regression models applied to field estimates and LiDAR height/volume metrics, following model-based stratification using leaf-off LiDAR and leaf-on agricultural imagery.

Time Range

2011

Freshness

2026-03-13 01:10:11.438845

Geography

Maryland, Pennsylvania, and Delaware, USA.