RMRS Raster Utility

RMRS Raster Utility is an object oriented coding library that facilitates a wide range of spatial and statistical analysis using our newly developed Function Modeling framework. Our library focuses on significantly reducing processing time and storage space associated with analyzing large datasets and has an easy to use graphical user interface, packaged as an ESRI add-in toolbar.

Overview and Applicability

The RMRS Raster Utility toolbar plugs directly into ESRI’s ArcMap and provides quick access to a variety of tools that streamline and simplify Data Acquisition, Sampling, Raster Analysis and Statistical Modeling.

Tools

Data Acquisition 

Data acquisition functionality can be used to seamlessly build or specify a file geodatabase and store data from a given web service. After specifying services and layers to download from web services, users can zoom to an area of interest and click Service Download or Tiled ImageServer Download to seamlessly download data.

Sampling

Sampling tools can be used to randomly select samples from a table given a specified statistical model.

• export tabular data
• explode sample location
• create random and stratified random samples
• sample raster values for a given location
• sample raster values around a given sample 

Raster Analysis

Most raster spatial analysis procedures can be access through the Raster Analysis menu.

Tools for each analysis work with multiband raster datasets and outputs are stored as function datasets with a pixel depth of 32 bit floating precision (with the exception of Convert Pixel Type).

Additional spatial analysis tools can be loaded into the toolbar using ArcMap's customization functionality.

Statistical Modeling

Statistical modeling can be used to build, design and run a wide variety of statistical and machine learning models for both raster and vector data. Examples of analyses that can be performed include:

• variance covariance
• T-Tests
• PCA
• K-means clustering
• linear
• multivariate
• logistic
• polytomous logistic regression
• accuracy assessment
• neural networks
• random forest

These tools integrate the Aforge.NET, Accord.NET, and ALGLIB libraries directly into ArcMap. Many of these tools need to be manually added to ArcMap via the customization tool. 

Function Modeling 

Function Modeling is a new raster processing methodology used to create and store transformations of raster datasets. It is an easy to use modeling framework that allows users to combine multiple spatial operations into one function dataset. This framework is accessible to users through one intuitive form (i.e. window) that can be used to view, sample, store, and manipulate datasets in a fraction of the time it takes to perform similar analysis using standard raster modeling techniques.

Function modeling uses our open source .net code library to store all the functions occurring to a source raster and dynamically build those datasets at run time. Function modeling significantly improves raster processing and substantially reduces storage associated with raster modeling. Benefits of function modeling include:

• no intermediate raster dataset creation
• immediate depiction of raster transformations
• faster processing
• reduced storage
• increased efficiency 

Frequently Asked Questions

Why do function models not display correctly?

Function models use statistics from the input datasets and make a guess at the output statistics. This can cause the surface to display incorrectly. To fix this simply rerun statics using the calculate statistic button or save the function model as a conventional dataset using the save raster button.

Why do some function model take a long time to display?

When displaying function models each cell must be processed. For most function datasets this is a relatively quick process but function dataset that are a composite of multiple functions can require additional time to display.

Why does stratified random sampling take a long time to finish?

Stratified random sampling can take a long time to finish when raster zones represent a small portion of the landscape.

What statistical libraries are being used?

Aforge.NET, Accord.NET, and ALGLIB.

I have installed the library. Why does it not show up in ArcMap?

You may need to adjust your security setting within ArcMap to allow unsigned add-ins.

Will RMRS Raster Utility work for ArcMap 10.1?

Yes.

Why do I get an error when I try to run batch files as a separate process within Windows 7?

Check your windows security setting. The process tool must be able to launch the RMRSBatchProcess.exe application.

How do I report bugs and errors?

Contact the team below. 

Background

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.

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.

A usage example is below.

Features

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

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).