Cargando…

Extracting Plant Phenology Metrics in a Great Basin Watershed: Methods and Considerations for Quantifying Phenophases in a Cold Desert

Plant phenology is recognized as important for ecological dynamics. There has been a recent advent of phenology and camera networks worldwide. The established PhenoCam Network has sites in the United States, including the western states. However, there is a paucity of published research from semi-ar...

Descripción completa

Detalles Bibliográficos
Autores principales: Snyder, Keirith A., Wehan, Bryce L., Filippa, Gianluca, Huntington, Justin L., Stringham, Tamzen K., Snyder, Devon K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5134607/
https://www.ncbi.nlm.nih.gov/pubmed/27869752
http://dx.doi.org/10.3390/s16111948
Descripción
Sumario:Plant phenology is recognized as important for ecological dynamics. There has been a recent advent of phenology and camera networks worldwide. The established PhenoCam Network has sites in the United States, including the western states. However, there is a paucity of published research from semi-arid regions. In this study, we demonstrate the utility of camera-based repeat digital imagery and use of R statistical phenopix package to quantify plant phenology and phenophases in four plant communities in the semi-arid cold desert region of the Great Basin. We developed an automated variable snow/night filter for removing ephemeral snow events, which allowed fitting of phenophases with a double logistic algorithm. We were able to detect low amplitude seasonal variation in pinyon and juniper canopies and sagebrush steppe, and characterize wet and mesic meadows in area-averaged analyses. We used individual pixel-based spatial analyses to separate sagebrush shrub canopy pixels from interspace by determining differences in phenophases of sagebrush relative to interspace. The ability to monitor plant phenology with camera-based images fills spatial and temporal gaps in remotely sensed data and field based surveys, allowing species level relationships between environmental variables and phenology to be developed on a fine time scale thus providing powerful new tools for land management.