Using Hyperspectral Imagery to Study Meadows

Mount Rainier Science Brief

Aji John | [e-mail] | Janneke Hille Ris Lambers Lab, UW

Wildflowers in the Sunrise area at Mount Rainier.

Janneke Hille Ris Lambers

Conserving Wildflowers

Dramatic shifts in phenology in response to climate change have been observed for numerous species (CaraDonna et al., 2014). Montane ecosystems are particularly sensitive to climate warming (Chen et al., 2011). Alpine wildflowers that are dominant in these ecosystems are prone to local level extinction (Panetta et al., 2018) because of their sensitivity to spring and summer temperatures (Theobald et al., 2017). However, the effects of warming on these alpine wildflowers are difficult to quantify due to a paucity of spatially extensive and long-term studies. Traditionally, discernible phases of plant phenology have been collected by frequent local-scale field measurements by observers during the growing season. Although these monitoring data are highly valuable, this approach doesn’t cover large spatial scales and are resource-intensive to maintain over multiple years (Kobori et al., 2016).

Linking flowering to remotely sensed imagery

The advent of high spatial resolution CubeSat imagery, which provides daily 3 - 5m resolution imagery (e.g. Planet, Digital Globe, etc.) has recently opened up the possibility of accurately detecting multiple phenological changes (e.g. greenup, flowering) over large spatial and temporal scales (Cooley et al., 2017). However, on-the-ground validation of peak flowering in remote sensed imagery is generally lacking. We are addressing this very gap at Mt. Rainier National Park by using statistical models to link characteristics of CubeSat images to peak flowering states of meadows as quantified by MeadoWatch volunteers. Our project members plans to visit 25 locations ranging from 1485m to 2030m in elevation, adjacent to the MeadoWatch trials on the south and east side of Mt. Rainier National Park over summer of 2020. We will use hyperspectral camera to weekly capture multi-spectral images of flowering meadows. The measurements will allow us to correlate fine-resolution (3m) imagery with in-situ imagery. We will then assess whether estimates can predict wildflower phenology.