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 |
|---|---|---|---|
| Original Metadata | Unknown | The metadata's original source. | Open resource |
| Data Information (Unknown Format) | Binary | Open resource | |
| Web Resource | Binary | ||
| Web Resource | Binary | ||
| PNG Image File | PNG | CMS_AGB_NW_USA_Fig1.png | Open resource |
| Data Information (PNG) | PNG | CMS_AGB_NW_USA_Fig1.png | Open resource |
| Web Resource | Binary | ||
| Data Information (PDF) | CMS_AGB_NW_USA.pdf | Open resource | |
| PDF File | CMS_AGB_NW_USA.pdf | Open resource | |
| Data Information (HTML) | HTML | Open resource | |
| Web Page | HTML | Open resource | |
| Data Information (HTML) | HTML | Open resource | |
| Web Resource | Binary | ||
| Web Resource | Binary | ||
| Web Resource | Binary | Open resource | |
| Data Information (Unknown Format) | Binary | Open resource |