This dataset provides annual maps of aboveground biomass (AGB, Mg/ha) for forests in Washington, Oregon, Idaho, and western Montana, USA, for the years 2000-2016, at a spatial resolution of 30 meters. Tree measurements were summarized with the Fire and Fuels Extension of the Forest Vegetation Simulator (FFE-FVS) to estimate AGB in field plots contributed by stakeholders, then lidar was used to predict plot-level AGB using the Random Forests machine learning algorithm. The machine learning outputs were used to predict AGB from Landsat time series imagery processed through LandTrendr, climate metrics generated from 30-year climate normals, and topographic metrics generated from a 30-m Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). The non-forested pixels were masked using the PALSAR 2009 forest/nonforest mask.
N: 50.79 S: 39.81 E: -110.31 W: -127.52
Distribution: Not provided
|Temporal Extent:||Platform(s):||AIRCRAFT, Environmental Modeling|
|Data Center(s):||ORNL_DAAC||Instrument(s):||LIDAR, Computer|
Variables mapped on uniform space-time grid scales with completeness and consistency
|Project Short Name||Campaigns||Project Dates|
|CMS||No campaigns listed.||No dates provided.|
|Coverage Type||Zone Identifier||Geometry||Granule Representation|
ORNL DAAC User Services Office, P.O. Box 2008, MS 6407, Oak Ridge National Laboratory
Oak Ridge, Tennessee 37831-6407
There are no listed data contacts for this collection.
Format: Not provided
Format Type: Native