Spatially explicit land-use and land-cover scenarios for the Great Plains of the United States

Abstract

a b s t r a c tThe Great Plains of the United States has undergone extensive land-use and land-cover change in the past 150 years, with much of the once vast native grasslands and wetlands converted to agricultural crops, and much of the unbroken prairie now heavily grazed. Future land-use change in the region could have dramatic impacts on ecological resources and processes. A scenario-based modeling framework is needed to support the analysis of potential land-use change in an uncertain future, and to mitigate potentially negative future impacts on ecosystem processes. We developed a scenario-based modeling framework to analyze potential future land-use change in the Great Plains. A unique scenario construction process, using an integrated modeling framework, historical data, workshops, and expert knowledge, was used to develop quantitative demand for future land-use change for four IPCC scenarios at the ecoregion level. The FORE-SCE model ingested the scenario information and produced spatially explicit land-use maps for the region at relatively fine spatial and thematic resolutions. Spatial modeling of the four scenarios provided spatial patterns of land-use change consistent with underlying assumptions and processes associated with each scenario. Economically oriented scenarios were characterized by significant loss of natural land covers and expansion of agricultural and urban land uses. Environmentally oriented scenarios experienced modest declines in natural land covers to slight increases. Model results were assessed for quantity and allocation disagreement between each scenario pair. In conjunction with the U.S. Geological Survey's Biological Carbon Sequestration project, the scenario-based modeling framework used for the Great Plains is now being applied to the entire United States.Published by Elsevier B.V.

Quantifying climate change mitigation potential in the United States Great Plains wetlands for three greenhouse gas emission scenarios

“…Other geographic and thematic patterns of change are similar. However, it is important to note that this study only considered changes through the year 2050 and not the full IPCC-SRES projection period through 2100, where the A2 scenario experienced very high rates of agricultural intensification in the Great Plains Sohl et al 2012). It is likely that the large demand for agricultural in the latter half of the 21st century would lead to much larger declines in wetland carbon across all wetland carbon classes for A2 compared to the A1B scenario.…”

“…The USGS used a probabilistic LULC model, FOREcasting SCEnarios of land-use change (FORE-SCE) to distribute future regional LULC change on the landscape for each LULC change scenario (Sohl and Sayler 2008;Sohl et al 2012). The allocation was based on probabilities of occurrence determined by present-day LULC associations with biophysical and socioeconomic characteristics of the landscape, such as slope, elevation, soil carbon, climate and distance to roads and cities.…”

Forest Harvest Patterns on Private Lands in the Cascade Mountains, Washington, USA

“…), or to describe the suitability of the trees for harvest (where are the stands appropriate for logging?). We assembled 23 biophysical (soils, elevation, water), climatic (temperature and precipitation), cultural (transportation, population centers, housing density), and vegetation (greenness and stand age) parameters, most of which were adapted from [52] (Table 2). For the purposes of this study, we treated climatic parameters as static given their long-term influence on tree growth characteristics.…”

SCaMF–RM: A Fused High-Resolution Land Cover Product of the Rocky Mountains

“…Nevertheless, not all of them are detailed enough spatially (i.e., their spatial resolution is coarse-~1 km or coarser) to be applied in environmental studies at a regional scale [13,18]. Therefore, we use six LC products that include the region of interest and have a fine/moderate spatial resolution (i.e., 500 m or finer): National Land Cover Dataset 1992, hereafter referred to as NLCD92 [18,54]; National Land Cover Dataset, hereafter referred to as NLCD [7,14,55]; Collection 5 Moderate Resolution Imaging Spectroradiometer Global Land Cover Type, hereafter referred to as MODIS [6,56]; Enhanced Historical Land-Use and Land-Cover Data Sets of the USGS, hereafter referred to as Historical-USGS [57,58]; USGS Conterminous United States Projected Land-Use/Land-Cover Mosaics, referred to as BC-USGS (BC from BackCasting) [59][60][61][62][63]; and LANDFIRE Existing Vegetation Cover, hereafter referred to as LF [64]. These products are all derived from satellite imagery from different sources and have undergone different classification algorithms, sometimes including ancillary data such as surveying records.…”