The National Park Service contracted with GRS to perform a comprehensive landcover classification of Lassen Volcanic National Park (LAVO) in northern California. The National Park Service had dual goals in this project: the first goal was to create two detailed vegetation maps for LAVO - one based on photo-interpretation and the other on image classification; the second goal was to compare traditional aerial photo‐interpreted map data set with the map data set developed using the Discrete Classification Mapping Methodology (DCMM) developed by GRS. 
These two map data sets will be then evaluated to determine their map accuracies and evaluated to determine their relative utility as data sources for the various resource management based applications used by the National Park Service in their management of the Park.
GRS implemented our Discrete Classification Mapping Methodology that encompasses the entire vegetation mapping process from field data collection to map production. The selection of field data collection sites was based on spectral signatures and the diversity of the landscape features recognized throughout the Park. GRS field crews measured vegetation characteristics using the GRS Densitometer in combination with the field-data collection software TransIn. In addition, GRS integrated the Fire Monitoring Woody Debris Sampling protocols into our field sampling plan to estimate the fire fuels conditions present at each field site. GRS used our DCMM to maintain the plot-specific vegetation and fuels attributes through the spectral classification process. Raster/pixel data were aggregated into polygons based on the floristic similarity of the individual pixel data.
This methodology resulted in a highly detailed vegetation and landcover map that includes the assignment of categorical map values, such as National Vegetation Classification System names, as well as cover estimates for individual species that comprise the different mapped stands. Map data were developed in both a raster and vector format with no editing of any polygon boundaries.
We are currently in the final accuracy assessment and validation portion of this project.
