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Drought intensity and duration are expected to increase over the coming century in the semiarid western United States due to anthropogenic climate change. Historic data indicate that megadroughts in this region have resulted in widespread ecosystem transitions. Landscape-scale monitoring with remote sensing can help land managers to track these changes. However, special considerations are required: traditional vegetation indices such as NDVI often underestimate vegetation cover in semiarid systems due to short and multimodal green pulses, extremely variable rainfall, and high soil fractions. Multi-endmember spectral mixture analysis (MESMA) may be more suitable, as it accounts for both green and non-photosynthetic soil fractions. To determine the suitability of MESMA for assessing drought vegetation dynamics in the western US, we test multiple endmember selection and model parameters for optimizing the classification of fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil (S) in semiarid grass- and shrubland in central New Mexico. Field spectra of dominant vegetation species were collected at the Sevilleta National Wildlife Refuge over six field sessions from May–September 2019. Landsat Thematic Mapper imagery from 2009 (two years pre-drought), and Landsat Operational Land Imager imagery from 2014 (final year of drought), and 2019 (five years post-drought) was unmixed. The best fit model had high levels of agreement with reference plots for all three classes, with R2 values of 0.85 (NPV), 0.67 (GV), and 0.74 (S) respectively. Reductions in NPV and increases in GV and S were observed on the landscape after the drought event, that persisted five years after a return to normal rainfall. Results indicate that MESMA can be successfully applied for monitoring changes in relative vegetation fractions in semiarid grass and shrubland systems in New Mexico. 

{"Abstract":["This dataset includes field-collected spectral reflectance of\n dominant vegetation species in grassland and shrubland at the\n Sevilleta National Wildlife Refuge collected monthly May \u2013 September\n 2019. A spectroradiometer was used to collect the percent spectral\n reflectance of electromagnetic radiation (range 400-2500nm) of a\n sample of dominant vegetation species ("spectra"),\n yielding a spectral curve for each species. At least ten individuals\n per species were sampled. These data form a spectral library which\n was used to calibrate a multiple-endmember spectral mixture analysis\n (MESMA) of satellite imagery of the Sevilleta NWR, as part of an\n ongoing collaboration between the LTER and the Center for the\n Advancement of Spatial Informatics Research and Education (ASPIRE).\n Ultimately, we aim to produce fractional images of green vegetation,\n non-photosynthetic vegetation, bare soil, and shade to form a\n synoptic thirty-year record of vegetation dynamics at the Refuge.\n The spectral library can be referenced by future researchers using\n remote sensing methods to examine vegetation dynamics at the\n Sevilleta NWR."]}