U.S. Department of EnergyOffice of Scientific and Technical Information
Tool and Training Data for Cloud Detection in WorldView Satellite Imagery
Abstract
This repository includes the cloud detection algorithm used in the manuscript 'Topography drives variability in circumpolar permafrost thaw pond expansion' by Abolt et al. It also includes a demonstration of the algorithm's use at a survey area in northern Alaska and a demonstration of training the algorithm.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy?s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy?s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
- Publication Date:
- Other Number(s):
- NGA255
- DOE Contract Number:
- DE-AC05-00OR22725
- Research Org.:
- Next Generation Ecosystems Experiment - Arctic, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (US); NGEE Arctic, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Collaborations:
- ORNL
- Subject:
- 54 Environmental Sciences
- Keywords:
- Cloud area
- OSTI Identifier:
- 1834771
- DOI:
- https://doi.org/10.5440/1834771
Citation Formats
Rumpca, Collin, Abolt, Charles, Atchley, Adam, and Harp, Dylan. Tool and Training Data for Cloud Detection in WorldView Satellite Imagery. United States: N. p., 2020.
Web. doi:10.5440/1834771.
Rumpca, Collin, Abolt, Charles, Atchley, Adam, & Harp, Dylan. Tool and Training Data for Cloud Detection in WorldView Satellite Imagery. United States. doi:https://doi.org/10.5440/1834771
Rumpca, Collin, Abolt, Charles, Atchley, Adam, and Harp, Dylan. 2020.
"Tool and Training Data for Cloud Detection in WorldView Satellite Imagery". United States. doi:https://doi.org/10.5440/1834771. https://www.osti.gov/servlets/purl/1834771. Pub date:Mon Nov 02 00:00:00 EST 2020
@article{osti_1834771,
title = {Tool and Training Data for Cloud Detection in WorldView Satellite Imagery},
author = {Rumpca, Collin and Abolt, Charles and Atchley, Adam and Harp, Dylan},
abstractNote = {This repository includes the cloud detection algorithm used in the manuscript 'Topography drives variability in circumpolar permafrost thaw pond expansion' by Abolt et al. It also includes a demonstration of the algorithm's use at a survey area in northern Alaska and a demonstration of training the algorithm.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy?s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy?s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).},
doi = {10.5440/1834771},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {11}
}