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Title: Sensor-based phenology from snowmelt experiment gradient, East River, Colorado, 2017 to 2020

Abstract

The timing of snowmelt is a critical cue for the initiation of growth in mountain meadow ecosystems and can also impact the duration and magnitude of plant production. High frequency observations of species-level phenology are time consuming and require a high degree of expertise, and publicly available remote sensing products lack the necessary temporal resolution to assess fine-scale growing season dynamics. Near-surface sensing methods provide a middle ground with high temporal frequency and tractable spatial scales (from sub-meter to hillslope scale). This data package includes csv files of Normalized Difference Vegetation Index (NDVI) timeseries (SM_NDVI_dailymax.csv) and phenological thresholds (SM_NDVI_summary.csv) for sub-plots (1m2) and Green Chromatic Coordinate (GCC) phenological thresholds (SM_GCC_summary.csv) at the plot scale (10m x 14m). Location IDs associated with this data package are: ER-LM, WG-UM, WG-LS, ER-US, and XX-AL. Related data packages include: “Microclimate observations associated with snowmelt experiment gradient sites, East River, Colorado, 2017 to 2020” and “Colorado Elevation Gradient Snowmelt Manipulation Plant Phenology 2017-2018”.

Authors:
ORCiD logo ; ORCiD logo ; ORCiD logo
  1. Fort Lewis College
  2. Rocky Mountain Biological Laboratory
  3. Colorado State University
Publication Date:
Other Number(s):
paf_739_742
Research Org.:
Environmental System Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) (United States)
Sponsoring Org.:
U.S. DOE > Office of Science > Biological and Environmental Research (BER)
Subject:
54 ENVIRONMENTAL SCIENCES
Keywords:
growing season; Green Chromatic Coordinate; Normalized Difference Vegetation Index; Phenology; NDVI
Geolocation:
39.034,-106.88|38.88,-106.88|38.88,-107.05|39.034,-107.05|39.034,-106.88
OSTI Identifier:
1842910
DOI:
https://doi.org/10.15485/1842910
Project Location:


Citation Formats

Steltzer, Heidi, Henderson, Amanda, and Wilmer, Chelsea. Sensor-based phenology from snowmelt experiment gradient, East River, Colorado, 2017 to 2020. United States: N. p., 2021. Web. doi:10.15485/1842910.
Steltzer, Heidi, Henderson, Amanda, & Wilmer, Chelsea. Sensor-based phenology from snowmelt experiment gradient, East River, Colorado, 2017 to 2020. United States. doi:https://doi.org/10.15485/1842910
Steltzer, Heidi, Henderson, Amanda, and Wilmer, Chelsea. 2021. "Sensor-based phenology from snowmelt experiment gradient, East River, Colorado, 2017 to 2020". United States. doi:https://doi.org/10.15485/1842910. https://www.osti.gov/servlets/purl/1842910. Pub date:Fri Jan 01 00:00:00 EST 2021
@article{osti_1842910,
title = {Sensor-based phenology from snowmelt experiment gradient, East River, Colorado, 2017 to 2020},
author = {Steltzer, Heidi and Henderson, Amanda and Wilmer, Chelsea},
abstractNote = {The timing of snowmelt is a critical cue for the initiation of growth in mountain meadow ecosystems and can also impact the duration and magnitude of plant production. High frequency observations of species-level phenology are time consuming and require a high degree of expertise, and publicly available remote sensing products lack the necessary temporal resolution to assess fine-scale growing season dynamics. Near-surface sensing methods provide a middle ground with high temporal frequency and tractable spatial scales (from sub-meter to hillslope scale). This data package includes csv files of Normalized Difference Vegetation Index (NDVI) timeseries (SM_NDVI_dailymax.csv) and phenological thresholds (SM_NDVI_summary.csv) for sub-plots (1m2) and Green Chromatic Coordinate (GCC) phenological thresholds (SM_GCC_summary.csv) at the plot scale (10m x 14m). Location IDs associated with this data package are: ER-LM, WG-UM, WG-LS, ER-US, and XX-AL. Related data packages include: “Microclimate observations associated with snowmelt experiment gradient sites, East River, Colorado, 2017 to 2020” and “Colorado Elevation Gradient Snowmelt Manipulation Plant Phenology 2017-2018”.},
doi = {10.15485/1842910},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2021},
month = {1}
}