Spectral reflectance and associated photograph of boreal forest understory formation in interior Alaska

The Arctic and boreal regions have been experiencing a rapid warming in the 21st century. It is important to understand the dynamics of boreal forest at the continental scale under the climate and environmental changes. While the role of understory vegetation in boreal forest ecosystems on carbon and nutrient cycling cannot be ignored, they are still one of least understood components in boreal ecosystems. Spectroscopic measurements of vegetation are useful to identify species and their biochemical characteristics and to estimate the biophysical parameters such as understory leaf area index, above ground biomass. In this data paper, we present spectral reflectances of 44 typical understory formations and five 30-m long transects. The spectral reflectance covers the spectral region of visible, near infrared and shortwave infrared (350-2500 nm). For the transect measurements, we decided the length of transect at 30 m, similar to the scale of one pixel of a Landsat type satellite imagery. The photographs at all positions, where spectral reflectances were obtained, are included to understand the structure and status of each sample. The data set contains six dwarf shrubs (Bog bilberry (Vaccinium uliginosum), cranberry (Vaccinium vitis-idea), feltleaf willow (Salix alaxensis), young birch (Betula neoalaskana), young aspen (Pupulus tremuloides), and young black spruce (Picea mariana)), two herbaceous (cottongrass (Eriophorum vaginatum) and marsh Labrador tea (Ledum decumbens)), three mosses (Sphagnum moss, splendid feather moss (Hylocomium splendens), and polytrichum moss (Polytrichum commune)), and reindeer lichen (Cladonia rangiferina). Spectral reflectances from several non-vegetative such as snow, litter, and soil are also included. This spectral and photographic data set can be used for understanding the spectral characteristics of understory formations, designing newly planned spectral observations, and developing and validating the remote sensing methodology of large scale understory monitoring.