A Concise Experiment Plan for the Arctic-Boreal Vulnerability Experiment

Description

This document presents the Concise Experiment Plan for NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) to serve as a guide to the Program as it identifies the research to be conducted under this study. Research for ABoVE will link field-based, process-level studies with geospatial data products derived from airborne and satellite remote sensing, providing a foundation for improving the analysis and modeling capabilities needed to understand and predict ecosystem responses and societal implications. The ABoVE Concise Experiment Plan (ACEP) outlines the conceptual basis for the Field Campaign and expresses the compelling rationale explaining the scientific and societal importance of the study. It presents both the science questions driving ABoVE research as well as the top-level requirements for a study design to address them.

Resources

Name Format Description Link
The dataset's project home page HTML ABoVE project site Open resource
Download this dataset HTML This link allows direct data access via Earthdata login Open resource
Get a related visualization PNG Figure 1: The Arctic-Boreal Vulnerability Experiment (ABoVE) is a NASA Terrestrial Ecology Program field campaign that will take place in Alaska and western Canada between 2016 and 2021. Open resource
View documentation related to this dataset HTML ORNL DAAC Data Set Documentation Open resource
View documentation related to this dataset PDF A Concise Experiment Plan for the Arctic-Boreal Vulnerability Experiment: ABoVE_Concise_Experiment_Plan.pdf Open resource
View documentation related to this dataset PDF A Concise Experiment Plan for the Arctic-Boreal Vulnerability Experiment: ACEP_Erratum.pdf Open resource
View documentation related to this dataset ZIP A Concise Experiment Plan for the Arctic-Boreal Vulnerability Experiment: ACEP_Figures.zip Open resource
This dataset's landing page HTML Data set Landing Page DOI URL Open resource
Google Scholar search results HTML Search results for publications that cite this dataset by its DOI. Open resource

Tags

Topics

Categories