Abstract
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.
Highlights
An increase in drought events driven by anthropogenic climate change has been observed globally [1] and is likely to have profound ecosystem impacts in semiarid lands.For example, a state of megadrought persisting in the western United States over the past 20 years has been attributed to climate change [2], and drought events in the region are anticipated to increase in frequency and severity over the century [2,3]
A state of megadrought persisting in the western United States over the past 20 years has been attributed to climate change [2], and drought events in the region are anticipated to increase in frequency and severity over the century [2,3]
Because count-based endmember selection (CoB) weights the spectra that capture the highest amount of variability within a class, spectra that modeled the full range of phenological variation from May through September may not have been appropriate for modeling vegetation spectra in June
Summary
An increase in drought events driven by anthropogenic climate change has been observed globally [1] and is likely to have profound ecosystem impacts in semiarid lands. A state of megadrought persisting in the western United States over the past 20 years has been attributed to climate change [2], and drought events in the region are anticipated to increase in frequency and severity over the century [2,3]. A drought event in New Mexico during this period led to growing season declines of up to 40%, with significant impacts on vegetation observed [4]. Using remote sensing methodologies to monitor vegetation in arid and semiarid environments requires special considerations when compared to more mesic systems. Low concentrations of both green and dormant/non-photosynthetic
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