The next generation of land-cover projections for the conterminous United States

Authors: Terry Sohl*, USGS EROS
Topics: Land Use and Land Cover Change, United States, Quantitative Methods
Keywords: land cover, land use, model, USGS, FORE-SCE, United States, scenario, projection
Session Type: Paper
Day: 4/4/2019
Start / End Time: 8:00 AM / 9:40 AM
Room: Buchanan, Marriott, Mezzanine Level
Presentation File: No File Uploaded


In 2014 the US Geological Survey published a unique suite of modeled land-cover data (historical and projected), with annual land cover maps for the conterminous United States from 1938 to 2100. While useful for a wide variety of applications, the spatial resolution, thematic resolution, and scenario characteristics were not optimal for some applications. Under the umbrella of the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative at USGS EROS, a new generation of high-resolution land-cover projections are being generated. Innovative tools using Landsat Analysis-Ready Data (ARD) are providing an unmatched representation of historical and current landscape condition, information that is used to parameterize a modified version of the Forecasting Scenarios of land use (FORE-SCE) model. Regional- to national-scale land cover is modeled at 30-meter resolution, matching the widely used National Land Cover Database (NLCD). Thematically, the land-cover projections now include NLCD classes as well as the most common crop classes by region, improving utility of the data for applications such as hydrology and biogeochemical assessments. A parcel-based modeling approach ensures fidelity of landscape pattern compared to older pixel-based methodologies. The response of both natural vegetation and anthropogenic land use to climate change and water availability is greatly improved, ensuring realistic representation of climate scenario impacts on landscapes. With a commitment to maintain consistency with existing USGS land-cover databases and “temporally extend” them backwards and forwards in time, we are facilitating the use of these projections in existing research and application workflows that depend upon data such as NLCD.

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