Authors: Rowan Converse*, University of New Mexico
Topics: Remote Sensing, Arid Regions, Biogeography
Keywords: Remote sensing, MESMA, ecology, vegetation dynamics, grassland, shrubland, semiarid environments
Session Type: Poster
Presentation File: No File Uploaded
New Mexico experienced significant impacts of regional-scale drought from 2011-2014. Global climate change may make such events a new normal: drought events are expected to increase in both frequency and severity in the southwest. Many of the hardest-hit areas were in desert grass- and shrubland. While semiarid grasslands recover quickly from short-term drought, the cumulative impacts of climate change may reduce the resiliency of these systems over time. Remote sensing methods may allow efficient and cost-effective comparison of ecosystem recovery from drought events over time using long-running imagery systems like Landsat. We investigate the efficacy of using multi-endmember spectral mixture analysis to quantify the impacts of drought events on the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil. Field spectra of dominant vegetation species were collected at the Sevilleta National Wildlife Refuge over six field sessions from May – September 2019. Four endmember selection methods were tested to optimize the spectral library, as well as three thresholding methods to unmix Landsat imagery from 2009 (five years pre-drought), 2014 (final year of drought), and 2019 (five years post-drought). Our model successfully modeled fractions of NPV, GV and soil, with R2 values of 0.88, 0.76, and 0.86 respectively; however, the model was unreliable at modeling GV fractions <30%, problematic in this study area, where green vegetation averaged 12% in mixed cover reference plots. We see this research as a promising first step in building long-term maps of vegetation dynamics for the study area.
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