Using spectral recovery, post-fire climate, and topography to predict vegetation dynamics following high severity wildfire

Award Period
to
Award Amount
$78,617
Agency Name
California Department of Forestry
Award Number
8GG23806
PI First Name
Leander
PI Last Name
Love-Anderegg
CO-PI
Joe Celebrezze
MSI People
Joe V. Celebrezze
Area/s of Research
Climate Change Science
Ecology and Evolution
Abstract

The primary research objectives of this project are to:

  1. Validate the use of spectral recovery metrics in sites impacted by high severity wildfire in the southern Sierra Nevadas by investigating relationships between various field measurements of post-fire community composition and spectral recovery metrics, as calculated using different multispectral indices.
  2. Understand the impacts of long-term drought on post-fire forest recovery and how future climate conditions could affect forest resilience.
  3. Formulate a robust spatial model which predicts which functional groups (i.e., grasses, herbs, shrubs, hardwood trees, and conifer trees; native vs. invasive species) recover after high severity wildfire using pre-fire community composition, spectral recovery metrics, and climatic and topographic data as predictors.
  4. Utilize the developed predictive model to produce a tool which guides application of the RAD framework.