Synthesis of a Complete Land Use/Land Cover Dataset for the Conterminous United States
We present a new land cover dataset for the conterminous USA called “PEELo.” It is designed for climate change impact analysis based on land use change at 5 arc-minute resolution. The procedure described herein is adaptable for generating similar datasets for other regions and requirements. PEELo is derived from existing data products – the MODIS Land Cover Type (MLCT) and National Land Cover Database (NLCD) – but its design overcomes certain limitations that hinder their use for climate change impact analysis. First, while other products that focus on agriculture neglect non-agricultural land use/land cover (LULC) categories, PEELo contains eight distinct LULC classes in addition to a “crop” class. PEELo features subcell area fractions for each class, increasing its depth of information over traditional single-category LULC maps. Second, PEELo offers improved accuracy in characterizing cultivated lands, important for quantifying agriculturalactivity. PEELo provides a more accurate spatial distribution of cultivated lands over MLCT as compared to reference datasets and improved totals for cultivated land relative to USDA Major Land Uses census data. We present here landcover data for 2001 plus PEELos synthesis methodology, which combines information from multiple sources by establishing a common classification scheme at lower spatial resolution. PEELo was developed as an initialization dataset for a partial-equilibrium economic land use model (PEEL) that simulates land use/land cover change in response to exogenous agricultural prices and climate change scenarios. We anticipate that similar landcover data products will be of use to other modeling efforts worldwide.