Colorado Vegetation Model

Updated 22 March 2005

The goal of the Colorado Vegetation Model (CVM) is to provide a fine-grained, fine-classed, statewide land cover map for Colorado. Our research effort was originally designed to support the Colorado State Forest Service’s Wildland Fire Hazard Assessment Methodology. Our approach to developing a statewide land cover map was to refine the general NLCD classes using surrogate spatial data and knowledge of ecological processes that control the distribution of land cover types to produce a fine-grained (~1 ha minimum mapping unit) map of land cover types for Colorado.

Refinements from v1 to v8:

  1. Refined the moisture index to generate a Integrated Moisture Index (IMI). The IMI is a combination of hillshade (from sun’s zenith at equinox at 40 degree latitude), hillslope position, and average water holding capacity.
  2. A higher quality DEM (USGS NED, 30 m) was used to create the IMI and riparian mask.
  3. Refined the coniferous forest reclass tables based on updated IMI scores.

CVM v8.0 report only (PDF 1.6MB).

CVM v8.0 data and report, download zip file (~51 MB). Note that due to a problem with .lyr files, the old layer file included with the dataset can result in incorrect display during ID queries. The new layer file “cvm8.lyr” created 11 May 2004 has corrected this problem. Also, we recommend getting a custom script to copy and paste raster layers (http://arcscripts.esri.com/details.asp?dbid=13128).

Please send us a courtesy email letting us know how you are using the data, thanks!: davet@nrel.colostate.edu. Note that the data are in ESRI GRID data, with a .dbf file providing brief descriptions, and an ArcGIS v8 layer file.

 Please use the following to cite this work: Theobald, D.M., N. Peterson, and W. Romme. 2004. The Colorado Vegetation Model: Using National Land Cover Data and Ancillary Spatial Data to Produce a High Resolution, Fine-Classification Map of Colorado (v8). 4 February. Unpublished report, Natural Resource Ecology Lab, Colorado State University.