Landsat satellites, which have been regularly imaging Earth’s land surface since 1972, are a key contributor to the study of terrestrial ecosystems and ecosystem dynamics. Yet despite over 40 years of data collection, the use of Landsat imagery was historically limited by the high costs of acquiring imagery combined with limitations in computing capabilities. Following the opening of the Landsat archive for free public use in 2008, we are witnessing a fundamental change in how Landsat imagery is used for mapping and monitoring ecosystem dynamics. This webinar provides a general introduction to the Landsat series of satellites and Landsat time series analysis. Examples and case studies from various projects in Southern New England will be used illustrate how different types of ecosystem change, including abrupt shifts in cover types, long-term trends, and short-term changes in condition, can be characterized from time series of all available Landsat observations.
Dr. Valerie Pasquarella works at the intersection of remote sensing and ecology, using time series of satellite imagery to improve mapping and monitoring of landscape dynamics. She completed a BA/MA in Environmental Science and Environmental Remote Sensing and GIS, as well as a PhD in Geography at Boston University, and is currently a Postdoctoral Research Associate with the DOI Northeast Climate Science Center and the Department of Environmental Conservation at the University of Massachusetts Amherst. Having lived and worked in Southern New England for over a decade, Dr. Pasquarella actively collaborates with a number of local research groups and land management agencies, and her research has strong regional ties. Ongoing projects include using Landsat time series to improve forest composition mapping and near-real-time monitoring of gypsy moth defoliation. She is also interested in time series approaches to mapping early successional habitat and invasive plant distributions, with a long-term goal of utilizing all available Landsat observations to advance understanding of multi-scale multi-species interactions over large spatial extents.