Seasonal and interannual drought responses of vegetation in a California urbanized area measured using complementary remote sensing indices
The effects of drought can manifest in vegetation across an array of physiological responses and time scales. In metropolitan areas, vegetation provides shading and cooling during hot and dry conditions, but these benefits can be reduced with drought. While many studies have evaluated interannual vegetation drought responses, these responses to drought can be expressed diversely across seasons, especially in cities that regularly experience seasonal drought (e.g., in Mediterranean climates). Here, we evaluated seasonal and interannual drought responses across the dominant types of urban trees and grasses in the Santa Barbara, California, USA metropolitan area, primarily using Landsat imagery acquired from 2010 to 2019 as well as repeat Airborne Visible Infrared Imaging Spectrometer - Classic (AVIRIS-C) imagery acquired 2013–2015. To track vegetation types, we produced a random forest classification from 4 m AVIRIS-Next Generation (AVIRIS-NG) imagery acquired in June 2014 (overall accuracy = 86%; kappa = 0.85), thresholding to >90% pure pixels for most vegetation types in the coarser time series imagery. We monitored drought response from Landsat imagery using the Normalized Difference Vegetation Index (NDVI) and the difference in land surface temperature (ΔLST) between vegetation and developed/impervious surfaces. We used AVIRIS-C to measure equivalent water thickness (EWT), comparing it with NDVI. During drought years, NDVI was lower and ΔLST was closer to zero. Changes in EWT revealed seasonal adjustments by vegetation that were not readily apparent in the NDVI. To show how drought response expression in vegetation can vary by season, drought duration, and urban vegetation type, we examined the correlations of both NDVI and ΔLST to the Standardized Precipitation Evapotranspiration Index (SPEI) calculated over a range of time spans. For most vegetation types, the strongest correlations of NDVI to SPEI and ΔLST to SPEI were during the summer, except for annual grass and turfgrass NDVI, which had the strongest correlations in the winter. In general, NDVI and ΔLST for annual grass were most often correlated with SPEI at spans <12 months, particularly for NDVI. By contrast, NDVI and ΔLST for trees and turfgrass were commonly also correlated with SPEI at spans ≥12 months, in addition to seasonal time spans of <12 months. This study demonstrates the benefits of using functionally and seasonally distinctive remote sensing variables (NDVI, ΔLST, and EWT) together to quantify changes in vegetation canopy condition during droughts.