Growing international concern over the status of both global and regional forest resources has led to the implementation of numerous multi-agency projects to establish long term operational systems for land cover monitoring. Land cover change (i.e., location, extent and cause) is identified as the most important, and yet challenging research theme for many of the programs recently initiated by these agencies (e.g., Land Cover Land Use Change [LCLUC] Global Observation of Forest Cover [GOFC]). A key element in addressing this theme is involving of regional management authorities, (e.g., USGS and USDA Forest Service [FS]), in providing the necessary link between local/ municipal and national/ international land cover monitoring projects. These projects are increasingly using complex procedures that require the integration of remotely sensed data, state-of-the-art image processing approaches, collateral spatial data and georeferenced (GPS) field validation data within a Geographic Information System (GIS). To address the growing threat to forest and shrubland sustainability, caused by rapid and widespread land cover change in California, the FS and the California Department of Forestry and Fire Protection (CDF) are collaborating in a statewide land cover change monitoring program to improve monitoring data quality, data capability and minimize large area monitoring costs. Changes in forest, shrub and grassland cover types are the primary focus this approach, but changes in urban/suburban areas are also mapped. These change maps are required for regional interagency land management planning, fire and timber management, species habitat assessment and existing land cover map updating. This program requires an examination and comparison of the variety of remote sensing methods available, such as scene normalization, change feature extraction, classification, and accuracy assessment, in order to meet operational and standardization needs. Faced with this task, the monitoring program welcomed a research and experimentation component with San Diego State University (SDSU), as a way to increase efficiency through technology, by improving and automating change monitoring procedures. Specifically, this involves testing techniques that minimize time-consuming human interpretation and maximize automated procedures for large area mapping of land cover change. The long-term objective of this project is the application of its statewide proof of concept to the ongoing land cover mapping and monitoring program.