Submitted by ORNL DAAC Staff on 2020-05-18
Vegetation point-intercept (VPI) any-hit cover metric data were combined with Landsat imagery to develop fractional maps of any-hit cover for four aggregated plant functional types (PFTs); shrubs bryophytes, lichen, and herbs for the upland tundra area of the Y-K Delta, Alaska. VPI data were collected from plots in areas burned in 1971, 1985, 2005, and 2015.
Ecological field data and maps of vegetation cover spanning gradients of fire history in upland tundra are available from the Arctic-Boreal Vulnerability Experiment (ABoVE).
Submitted by ORNL DAAC Staff on 2020-05-13
Each black dot on this digital elevation map (DEM) represents a surface weather station in continental North America providing Daymet data in 2010. Additional stations providing Daymet data are located in Puerto Rico and Hawaii.
Daymet derived annual average of daily minimum temperature, 1980 (left) and 2019 (right), for a subset of North America. Images are scaled from -20 to +20 degrees C.
Submitted by ORNL DAAC Staff on 2020-03-27
Trends in maximum annual NDVI for natural vegetation within the Greater Yellowstone Ecosystem (GYE) from 1989 to 2014 (Emmett et al. 2019).
Daymet and North American Carbon Program (NACP) data were used to study Normalized Difference Vegetation Index (NDVI) trends across the Greater Yellowstone Ecosystem.
Submitted by ORNL DAAC Staff on 2020-02-20
Study area included sites burned in 2014 and 2015 in the southeastern portion of the Northwest Territories and northern Alberta of Canada. The study area includes all 2014 and 2015 fires within a radius of approximately 300 km from Great Slave Lake.
Pre- and post-fire Landsat images were used to classify burn severity of soil organic matter across portions of arctic Canada.
Submitted by ORNL DAAC Staff on 2020-01-13
A global map of predicted annual soil respiration (Rs) at 1 km spatial resolution created by applying the QRF model to gridded covariates. Right is a plot of the latitudinal mean predicted annual Rs.
A machine learning approach was used to derive the predicted annual soil respiration and uncertainty at a 1 km spatial resolution.
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