Summary

To understand the global impact of climate change, we need long-term global data sets. Long- term remote sensing of earth systems has opened up new vistas in understanding planetary change (1). These data are limited, however, as automated classification is not possible for many applications – particularly when working with older sensors or features at the limit of a sensor’s detection (2). Small teams of researchers can only classify so much on their own. Citizen science fills a key gap in the use of remote sensing data to understand long-term, large scale shifts to biological systems across the planet. We will take the preliminary work we’ve done with Floating Forests and extend it to answer questions about the impact of climate change on giant kelp abundance at the global scale, use our project as a model to enable future citizen science projects using Landsat data, and enhance education about kelp forest biology at the high school and university level.

Student Involvement

Starting 2018 this project will implement the first phase of educational modules at the college level in MSCI 340. One undergraduate student will work on the outreach and education portion of this project.

Broader Significance

This work relates directly to the goals of Earth Science at NASA, particularly the focus area of Carbon Cycle & Ecosystems. Kelp forests are among the most productive ecosystems in the world, but this productivity will almost certainly be affected by climate change. This research will also directly aid NASA’s goal to support the activities of the U.S. Global Change Research Program and the research carried out on behalf of the National Ocean Council insofar as it will address questions regarding the impacts of climate change on coastal marine habitats that provide extensive ecosystem services to coastal communities. Furthermore, this project will contribute to NASA’s efforts to support crowdsourcing and citizen science projects. We will expand the scope of an already-successful citizen science program and develop a general framework for utilizing citizen science in the analysis of Landsat satellite imagery.