RMRS Raster Utility
Contact Information
  • RMRS Raster Utility
  • 200 E. Broadway
  • Missoula, MT 59807
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About the Raster Utility Project


In 2010, RMRS scientists began a research collaboration with Forest Service and Bureau of Land Management personnel to quantify landscape-level above ground biomass and the potential biomass flows resulting from restoration/fuel reduction treatments on the Uncompahgre Plateau in western Colorado. As part of the project, we developed a scalable software library of innovative GIS tools built around Environmental Systems Research Institute (ESRI) ArcGIS software. This system, referred to as the “RMRS Raster Utility”, consists of numerous user forms, multiple ESRI commands, and one ESRI toolbar.

A usage example is below.

The RMRS Raster Utility is a free tool, packaged as an ESRI add-in for easy installation and operation. RMRS Raster Utility requires ArcGIS Desktop 10 binaries and can be run under Engine or ArcView licensing levels.

Funding for the development of the RMRS Raster Utility, supporting documentation, and this website was provided by the Rocky Mountain Research Station Science Application and Integration Program, the National Fire Plan, and the Biomass Research and Development Initiative of the USDA National Institute of Food and Agriculture.


Key features of the RMRS Raster Utility toolbar include:

  • a friendly user interface that plugs into existing ESRI software and licensing
  • simplified data acquisition from GIS web services
  • significantly improved reading, writing, and processing of raster data
  • easy access to standard raster analysis techniques
  • aided raster surface sampling
  • simplified statistical modeling
  • automated first and second order texture surface creation
  • moving windows landscape metrics
  • function modeling, an easy to use modeling framework that allows users to combine multiple spatial operations into one function dataset

Usage Example

usage example
Using RMRS Raster Utility toolbar Function Modeling form (F1) we were able to quickly, easily, and efficiently combine and transform probabilistic raster datasets into multiple spatially explicit predictive surfaces. Next, using our Cluster Sample Raster form (F2), we were able to relate FIA sample data to those predictive surfaces via the spatial coordinates of the FIA plot locations. Finally, using our sampled surfaces and the Model Regression and Create Regression Surface(s) forms (F3 and F4, respectively), we were able to build a multivariate regression model and apply that model to our predictive surfaces to create 4 raster datasets depicting QMD (quadratic mean diameter), BAA (basal area per acre), TPA (trees per acre), and AGB (above ground biomass).