PAVC: The foundation for a Pan-Arctic Vegetation Cover database

Area of Science:

• Ecology
• Remote Sensing
• Geospatial Science

Background:

• Accurate Arctic vegetation cover data is crucial for mapping vegetation structure and composition.
• Existing field data collection methods vary, hindering unification for remote sensing applications.
• High-resolution vegetation maps are vital for Earth System Models and monitoring ecological change.

Purpose of the Study:

• To synthesize and harmonize diverse field-based fractional cover data from the Arctic.
• To establish a high-quality, consistent repository schema for remote sensing-based vegetation mapping.
• To develop a reproducible workflow for integrating various fractional cover data types.

Main Methods:

• Synthesized and harmonized field-based fractional cover data from multiple sources.
• Developed a reproducible workflow for combining visual estimate and point-intercept data.
• Created the Pan-Arctic Vegetation Cover (PAVC) database.

Main Results:

• The Pan-Arctic Vegetation Cover (PAVC) database contains synthesized fractional cover data.
• Data is available at both species and plant functional type levels.
• Includes absolute foliar cover for various vegetation types, litter, water, and bare ground.

Conclusions:

• The PAVC database provides a consistent resource for remote sensing-based vegetation mapping.
• This harmonized data facilitates improved Earth System Models and vegetation monitoring.
• The reproducible workflow enables future data integration and database expansion.