Spatial Modeling of Land Use, Climate, and Environmental Consequences Active

Statement of Problem: Land-use and land-cover modeling is a critical component for analysis and potential mitigation of the consequences of landscape change on ecological processes.  Land managers and researchers require spatially and thematically consistent land-cover data to assess historical, current, and potential future interactions among land use, climate, and a host of ecologically and societally relevant processes. USGS provides historical and current land cover based on remote sensing databases, but only for a relatively short historical time period. Methodologies are needed to produce both historical and potential future land-cover maps that are consistent with remote sensing-based maps, facilitating long-term assessments of landscape, climate, and ecological interactions.

Why this Research is Important: Urban development, forestry, agriculture, mining, and other land uses can substantially alter the Earth's surface. Land use and the resultant change in land cover have important effects on ecological systems and processes. The use of current land-cover maps enables researchers and land managers to assess recent conditions and respond accordingly, based upon research and land management objectives. However, the availability of long-term historical land cover data and potential future land cover data enables researchers and land managers to move beyond a reactionary approach to an anticipatory approach. Historical records can be used to develop likely relationships between landscape change and the process of interest. That information can be used in conjunction with future scenarios of landscape change to visualize future outcomes and maximize societal, economic, or ecologic priorities, and to potentially mitigate any negative consequences before they even occur.

Objective(s): The objectives of this research are to develop state-of-the-art landscape modeling frameworks and apply them to problems of ecological, economic, and societal importance. We will:

• Continue development of the USGS's "FORE-SCE" model
• Investigate new modeling techniques, including continued improvements in our methodologies for mapping vegetation succession, fire and disturbance, and landscape response to climate change.
• Apply USGS land-cover models to produce spatially explicit, thematically detailed landscape maps / models that are consistent with widely used remote sensing-based land-cover data such as the National Land Cover Database (NLCD) or the Cropland Data Layer (CDL).
• Collaborate with USGS and other researchers on analyses of land-use and land-cover change effects on ecological and societal processes.

Methods: USGS EROS began development of a home-grown land-use and land-cover modeling framework in 2006.  Work continues to improve the capabilities of the Forecasting Scenarios of Land-use Change (FORE-SCE) model, a model which has gained acceptance in the scientific literature, and has been applied in the United States to produce both historical "backcasts" and future forecasts of land-use and land-cover change. Our work focuses on the continued development of the FORE-SCE modeling framework, the integration of other modeling frameworks, and the development of new modeling frameworks that improve USGS' ability to map and model land-use and land-cover change. 

Remote sensing provides an integral source of data for landscape modeling. Historical and current landscape information is used for scenario construction, model parameterization, and validation of model performance.  However, land-cover modeling is inherently an interdisciplinary activity. We rely on elements of socioeconomics, geography, hydrology, climate, and other disciplines to both ensure the production of realistic and useful landscape projections, and in the application of the resultant modeled projections to address issues of societal importance. Because of the high level of uncertainty associated with predicting future developments in complex socio-environmental systems, a scenario framework is used that enables land managers to anticipate and adapt to a wide range of plausible future conditions.