New Mexico experienced substantial impacts of regional-scale drought from 2011-2014. Global climate change may make such events a new normal for the southwest: drought events are expected to increase in both frequency and severity over the coming century. 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 can allow efficient and cost-effective comparison of ecosystem recovery from drought events over time using long-running imaging systems like Landsat. We investigate the efficacy of using multi-endmember spectral mixture analysis (MESMA) to quantify the impacts of drought events on the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil (S). Field spectra of dominant vegetation species were collected at the Sevilleta National Wildlife Refuge roughly monthly over six field sessions from May through September 2019. Four endmember selection methods were tested to optimize the spectral library, as well as three thresholding adjustments to unmix Landsat imagery from 2009 (five years pre-drought), 2014 (final year of drought), and 2019 (five years post-drought). The best fit model had high levels of agreement for all three classes with R2 values of 0.89 (NPV), 0.71 (GV), and 0.81 (S), respectively. Image differencing showed increases in S and decreases in NPV fractions throughout the study area that were unaccompanied by a return to baseline cover in the post-drought period, contradicting ground-based observations of full recovery of grassland vegetation.
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remote sensing, Sevilleta LTER, New Mexico
Converse, Rowan. "Assessing drought vegetation dynamics at the landscape scale in semiarid grass- and shrubland using MESMA." (2020). https://digitalrepository.unm.edu/geog_etds/51