Forest Health Protection Tree Species Metrics Basal Area (Image Service)
Basal Area (BA). 30 meter pixel resolution. Data represents forest conditions circa 2002.These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service’s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013–2027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
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"hasEmail": "mailto:SM.FS.data@usda.gov"
}
|
| description | <div style='text-align:Left;'><div><div><p><span>Basal Area (BA). 30 meter pixel resolution. Data represents forest conditions circa 2002.</span></p><p><span style='font-size:12pt'>These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service’s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013–2027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002</span></p><p><span /></p></div></div></div> |
| distribution |
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| identifier | https://www.arcgis.com/home/item.html?id=466d3043049245eea0e461e5bb64435b |
| issued | 2025-06-24 |
| keyword |
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|
| landingPage | https://data-usfs.hub.arcgis.com/datasets/usfs::forest-health-protection-tree-species-metrics-basal-area-image-service |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2025-09-03 |
| programCode |
[
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|
| progressCode | onGoing |
| publisher |
{
"name": "U.S. Forest Service",
"source": "Forest Health Technology Enterprise Team (FHTET)"
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|
| spatial | -171.1293,24.0720,-65.8925,69.6601 |
| theme |
[
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]
|
| title | Forest Health Protection Tree Species Metrics Basal Area (Image Service) |