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OBIS-USA brings together marine biological occurrence data – recorded observations of identifiable marine species at a known time and place, collected primarily from U.S. Waters or with U.S. funding. Coordinated by the Science Analytics and Synthesis (SAS) Program of the United States Geological Survey (USGS), OBIS-USA, strives to meet national data integration and dissemination needs for marine data about organisms and ecosystems. OBIS-USA is part of an international data sharing network (Ocean Biodiversity Information System, OBIS) coordinated by the Intergovernmental Oceanographic Commission, of UNESCO (United Nations Educational, Science and Cultural Organization) International Oceanographic Data and Information...
Note: This data release is currently under revision and is temporarily unavailable. Phenological dynamics of terrestrial ecosystems reflect the response of the Earth's vegetation canopy to changes in climate and hydrology and are thus important to monitor operationally. The Exotic Annual Grass (EAG) phenology in the western U.S. rangeland based on 30m near seamless Harmonized Landsat and Sentinel-2 (HLS) Normalized Difference Vegetation Index (NDVI) weekly composites between 2016 and 2021 (Dahal et al., 2022) were processed using these 3 methods: (1) NDVI threshold-based method, (2) manual phenological metrics, and (3) modeling and mapping. The EAG phenology model produced eight metrics identifying the sustainable...
Note: this data release has been depecrated. Find the updated version here: https://doi.org/10.5066/P942QL23 In June, 2022, the Mount Rainier Streamflow Permanence model was revised to replace monthly climatic covariates with seven-month summaries to address peer-review comments related to inclusion of correlated covariates into the model. Replacement of the monthly covariates resulted in changes to the model source code and model outputs, which are now annual probabilities of streamflow permanence for years 2018-2020. This data release contains spatially gridded geospatial data (rasters), R scripts, and supporting files to run Random Forest models to predict the probability of late summer surface flow in Mt....
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