In cities, microclimates are created by local mixtures of vegetation, constructed materials, vertical structure, and moisture, with significant consequences for human health, air quality, and resource use. Vegetation can moderate microclimates through evapotranspiration, however this function is dependent on local conditions so its effect may vary over space and time. This dissertation used hyperspectral and thermal remote sensing imagery to derive key observations of urban physical and biophysical properties and model urban microclimates across the megacity of Los Angeles. In Chapter 1, I used Multiple Endmember Spectral Mixture Analysis (MESMA) to map sub-pixel fractions of different vegetation types, as well as other types of urban cover, at 4 m and 18 m resolution over Santa Barbara, California (Wetherley et al., 2017). Fractional estimates correlated with validation fractions at both scales (mean R2 = 0.84 at 4 m and R2 = 0.76 at 18 m), with accuracy affected by image spatial resolution, endmember spatial resolution, and class spectral (dis)similarity. Accuracy was improved by using endmembers measured at multiple spatial resolutions, likely because they incorporated additional spectral variability that occurred across spatial scales. In Chapter 2, I applied this methodology to derive sub-pixel cover for the greater Los Angeles metropolitan area (4,466 km2) (Wetherley et al., 2018). Further improvement in quantifying sub-pixel vegetation types was achieved by modifying the MESMA shade parameter. Land surface temperature (LST), derived from thermal imagery, was used to model temperature change along vegetation fractional gradients, with slopes of LST change showing significant differences between trees and turfgrass (p < 0.001). Expected per-pixel LST was derived from these gradients based on sub-pixel composition, and when compared to measured LST was found to deviate with a standard deviation of 3.5 °C across the scene. These deviations were negatively related to irrigation and income, while building density was observed to affect tree LST more than it affected turfgrass LST. In Chapter 3, I used the map of Los Angeles landcover, along with data from LiDAR, GIS, and WRF climate variables, to parameterize an urban climate model (Surface Urban Energy and Water Balance Scheme: SUEWS) for 2,123 neighborhoods (each 1 km2) across Los Angeles. Modeled latent fluxes were correlated with remote sensing LST (R2 = 0.39) collected over a period of 5 hours, with an overall diurnal pattern modified by irrigation timing. Spatial variability across the study area was related to local landcover, with albedo and vegetation fraction strongly influencing latent and sensible fluxes. A strong regional climatic gradient was observed to affect latent fluxes based on coastal proximity. Overall, this dissertation quantifies the key drivers of urban vegetation function in a large city, and further demonstrates the potential of hyperspectral and thermal imagery for observing city scale surface and microclimate variability.
|Advisor:||McFadden, Joseph P., Roberts, Dar A.|
|Commitee:||Quattrochi, Dale A., Tague, C. Naomi|
|School:||University of California, Santa Barbara|
|School Location:||United States -- California|
|Source:||DAI-B 80/07(E), Dissertation Abstracts International|
|Subjects:||Urban Forestry, Ecology, Remote sensing|
|Keywords:||Hyperspectral, Los Angeles, Spectral mixture analysis, Thermal, Urban energy balance model, Urban vegetation|
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