GC12C-01
Moving from pixels to parcels: Modeling agricultural scenarios in the northern Great Plains using a hybrid raster- and vector-based approach

Monday, 14 December 2015: 10:20
3005 (Moscone West)
Terry Sohl1, Steve Wika2, Jordan Dornbierer2, Kristi L Sayler3 and Robert Quenzer2, (1)USGS Earth Resources Observation and Science Center, Sioux Falls, SD, United States, (2)Stinger Ghaffarian Technologies (SGT, Inc.), Sioux Falls, SD, United States, (3)US Geological Survey, Sioux Falls, SD, United States
Abstract:
Policy and economic driving forces have resulted in a higher demand for biofuel feedstocks in recent years, resulting in substantial increases in cultivated cropland in the northern Great Plains. A cellulosic-based biofuel industry could potentially further impact the region, with grassland and marginal agricultural land converted to perennial grasses or other feedstocks. Scenarios of projected land-use change are needed to enable regional stakeholders to plan for the potential consequences of expanded agricultural activity. Land-use models used to produce spatially explicit scenarios are typically raster-based and are poor at representing ownership units on which land-use change is based. This work describes a hybrid raster/vector-based modeling approach for modeling scenarios of agricultural change in the northern Great Plains. Regional scenarios of agricultural change from 2012 to 2050 were constructed, based partly on the U.S. Department of Energy’s Billion Ton Update. Land-use data built from the 2012 Cropland Data Layer and the 2011 National Land Cover Database was used to establish initial conditions. Field boundaries from the U.S. Department of Agriculture’s Common Land Unit dataset were used to establish ownership units. A modified version of the U.S. Geological Survey’s Forecasting Scenarios of land-use (FORE-SCE) model was used to ingest vector-based field boundaries to facilitate the modeling of a farmer’s choice of land use for a given year, while patch-based raster methodologies were used to represent expansion of urban/developed lands and other land use conversions. All modeled data were merged to a common raster dataset representing annual land use from 2012 to 2050. The hybrid modeling approach enabled the use of traditional, raster-based methods while integrating vector-based data to represent agricultural fields and other ownership-based units upon which land-use decisions are typically made.