How to recognize small trees from quite a long way away
Topics: Remote Sensing
, Natural Resources
, Global Change
Keywords: Remote Sensing, Disturbance, Succession, Landsat Time Series
Session Type: Virtual Paper
Day: Wednesday
Session Start / End Time: 4/7/2021 09:35 AM (Pacific Time (US & Canada)) - 4/7/2021 10:50 AM (Pacific Time (US & Canada))
Room: Virtual 9
Authors:
Kyle Rodman, University of Wisconsin-Madison
Robert Andrus, School of the Environment, Washington State University
Teresa Chapman, Colorado Field Office, The Nature Conservancy
Nathan Gill, Department of Natural Resources Management, Texas Tech University
Dominik Kulakowski, Department of Geography, Clark University
Thomas Veblen, Department of Geography, University of Colorado Boulder
Sarah Hart, Department of Forest and Rangeland Stewardship, Colorado State University
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Abstract
Climate-driven increases in disturbance activity and tree mortality have the potential to drive widespread changes in Earth's forested ecosystems, with critical implications for carbon storage, wildlife habitat, and other ecosystem services. But the extent to which disturbances will drive declines in forest cover is dependent upon ecosystem resilience, or the ability of systems to recover to a previously forested state. Remotely-sensed data will play an increasingly important role in monitoring forest resilience across broad areas, yet these data are constrained by factors such as cloud cover, spectral noise, and spatial grain sizes that are often coarser than the objects of interest. We used Landsat time series (LTS) and field surveys to assess conifer forest resilience following wildfire and bark beetle outbreak in the Southern Rocky Mountains, USA. In combination with field data, mapping efforts using LTS can help to isolate the signals of broad-leaf deciduous and evergreen conifer regrowth, furthering the understanding of post-disturbance landscape dynamics. We discuss the benefits and drawbacks of these techniques, as well as important areas for future research.
