Synopsis Past research at Idaho State University (ISU)'s GIS Training and Research Center (GIS TReC) has found that changes to the rangelands of southeastern Idaho can be attributed to three principal agents: 1) fire, 2) invasive weeds, and 3) urbanization. However, Idaho has experienced severe to exceptional drought conditions since April 2001. The potential for drought to change rangelands is significant. Furthermore, without specific and purposeful monitoring of rangeland condition throughout this time period, observed changes could be misinterpreted. These changes to Idaho’s rangeland landscape –regardless of the cause (drought, fire, invasive weeds, or urbanization) tend to degrade the health, productivity, and sustainability of rangelands and thereby pose problems for ranchers, range managers, and the economy of many western states. We propose to use existing and new data (acquired both from the field and satellite remote sensing) to model rangeland sustainability relative to drought effects in southeastern Idaho at three study areas, the Big Desert (managed by the USDI BLM), the US Sheep Experiment Station (USSES) (managed by the USDA ARS), and the O'Neal Ecological Reserve (managed by Idaho State University). Scrutinizing these "problems" holistically, it is evident that drought, invasive weeds, fire, and urbanization alone do not drive change in rangelands. Drought, for instance is the result of a prolonged period of reduced precipitation. The real problem with a reduction of precipitation is the effect it has on water availability (soil moisture, stream flow, and aquifer recharge). Healthy rangelands (having effective hydrologic cycles and minimal bare ground exposure) may appear unaffected or less affected by drought whereas degraded rangelands may demonstrate substantial changes in biodiversity and biomass in response to drought. In the latter condition, drought is simply exacerbating an existing degraded rangeland condition created by the same “tools” used to manage rangelands 1) fire, 2) grazing and animal impact (e.g., trampling effects of large herbivores), and 3) technology (e.g., fences, wells, urbanization, roads, herbicides, etc.) and grazing systems (including the effect of partial or total "rest").

With over five years of research focusing upon temporal land cover change, scientists at ISU’s GIS TReC are poised to embark upon a new research project. The research proposed in this document continues the Temporal Land cover Change: Rangeland Remote Sensing Research Project. This three-year project will 1) examine specific drought effects relative to livestock grazing, rest treatments and bare earth exposure, 2) model and monitor rangeland condition as a function of hydrologic cycling, 3) forecast rangeland condition using cellular-automata/ Markov chain analysis and artificial neural network techniques, and 4) continue and advance the GIS TReC’s public outreach program.

The resulting GIS data and models will build upon the results from previous research in land cover change and rangeland health modeling and will relate these effects to rangeland ecosystems. These data and information will be invaluable in assessing the sustainability of our natural resources and specifically, livestock grazing, within a fire-influenced ecosystem. These data -- and the knowledge gained through our research -- will be made freely available to decision-makers, land managers, land stewards, and individual ranchers and farmers in the region using several community outreach tools including the Internet. These projects will also support graduate and undergraduate student education (including students in ISU’s growing MS in GIS program), which will produce peer-reviewed professional publications. This project addresses the enterprise mission of NASA's Office of Earth Science by developing and distributing GIS and remote sensing applications that "improve prediction of earth system change" on ecosystem dynamics in rangelands. This emphasis directly ties with NASA’s Earth Science Enterprise- Ecological Forecasting Program which encourages the use of Earth observation data and models to predict impacts of environmental change on the ecosystem.