@Article{Steckler_SciData_20250721,
 author		= {Morgan R. Steckler and Jitendra Kumar and Amy L. Breen and Tianqi Zhang and Forrest M. Hoffman and William W. Hargrove and Donald A. Walker and Aaron F. Wells and Amanda Droghini and Timm W. Nawrocki and Stan D. Wullschleger and Matthew J. Macander and Gerald V. Frost and Verity G. Salmon and David T. Barnett and Colleen M. Iversen},
 title		= {{PAVC}: The Foundation for a {P}an-{A}rctic {V}egetation {C}over Database},
 journal	= SciData,
 volume		= 12,
 number		= 1,
 pages		= 1271,
 doi		= {10.1038/s41597-025-05326-9},
 day		= 21,
 month		= jul,
 year		= 2025,
 abstract	= {Field-measured Arctic vegetation cover data is essential for creating accurate, high-quality vegetation structure and composition maps. Extrapolating field data into high-resolution cover maps provides detailed, function-specific information for use in Earth System Models, vegetation classifications, and monitoring vegetation change over time and space. However, field campaigns that collect plant cover vary substantially in scope, method, and purpose, which makes them difficult to unify across data stores, and they are often not designed to meet remote sensing needs. In this work, we synthesized and harmonized field-based fractional cover data from various data stores to create a high-quality, consistent repository schema for remote sensing-based vegetation cover mapping applications. We developed a reproducible workflow for synthesizing visual estimate and point-intercept fractional cover data. The resultant Pan-Arctic Vegetation Cover (PAVC) database contains synthesized fractional cover at both the species and plant functional type levels. The latter includes absolute foliar cover for deciduous shrubs and trees, evergreen shrubs and trees, forbs, graminoids, lichen, bryophytes, and ``other'' vegetation, as well as absolute cover for litter and top cover for water and bare ground.}
}