Description
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.
Resources
| Name | Format | Description | Link |
|---|---|---|---|
| Download this dataset | HTML | This link allows direct data access via Earthdata login | Open resource |
| Get a related visualization | PNG | Forest aboveground biomass in megagrams per hectare for the year 2016. Source: AGB_Forest_NW_USA_2016.tif | Open resource |
| View documentation related to this dataset | HTML | ORNL DAAC Data Set Documentation | Open resource |
| Download this dataset | HTML | This link allows direct data access via Earthdata login | Open resource |
| View documentation related to this dataset | CMS: Aboveground Forest Biomass Maps for Northwestern USA, 2000-2016: CMS_AGB_NW_USA.pdf | Open resource | |
| This dataset's landing page | HTML | Data set Landing Page DOI URL | Open resource |
| Google Scholar search results | HTML | Search results for publications that cite this dataset by its DOI. | Open resource |