Atmospheric mixing exerts a large control over the concentrations of atmospheric pollutants. For example, pollutants are diluted during the growth of the daytime boundary layer in the morning and also carried with horizontal winds. To understand the impact of these mixing processes on air quality in the polluted city of Los Angeles, California, I use in situ observations of atmospheric vertical profiles collected onboard the NASA C-23 Sherpa and remote-sensing observations of the horizontal distribution of NO2 made by the GeoTASO instrument from the NASA UC-12 across the LA basin on 26 June 2017. I focus on chemical and meteorological data from the C-23 Sherpa collected during four missed approaches at LAX airport to interpret boundary layer growth throughout the morning. I take advantage of the fact that during missed approaches over LAX, the Sherpa descended over land and ascended over the ocean, which produced distinct vertical profiles for comparison. I use these vertical profiles and the GeoTASO data to investigate land-sea breeze effects, finding a very steep land-ocean gradient in the early morning.

Commercial aviation, with a predicted carrier passenger growth rate of 1.9 percent per year over the next twenty years in the U.S., will continue to be one of the most dominant forms of transportation in the sector. It is becoming increasingly important to understand the engine emissions from aircraft at altitude and throughout the landing and takeoff cycle (LTO) at airports. This study uses a new, top-down approach to measure the total airport emission rates of the Los Angeles International Airport (LAX), as well as predictions for other major airports. Since 2012, the NASA Student Airborne Research Program has made in-situ measurements of carbon dioxide and the oxides of nitrogen (NO_x) during airborne missed approaches at multiple airports within the Los Angeles Basin. We show that observationally derived NO_x emission indices (grams of NOx emitted per kilogram of jet fuel consumed) from individual emission plumes sampled at low altitude along the runway are consistent with the values listed in the ICAO Aircraft Engine Emissions Databank. The total airport emissions rates determined by the top-down approach are compared to bottom-up aircraft emission rate estimations.

Atmospheric nitrogen dioxide (NO₂) plays a role in the formation of hazardous compounds such as tropospheric ozone, aerosols, and acid rain. To better understand these processes, we need precise and accurate measurements of atmospheric NO₂ that capture variability across space and time relevant for the atmospheric lifetime of NO₂. The Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument is a test bed for the Tropospheric Emissions Monitoring of Pollution (TEMPO) and Geostationary Environment Monitoring Spectrometer (GEMS) satellites. This study compares the NO₂ differential slant column data obtained from the GeoTASO instrument aboard the UC-12B King Air with the slant column data from six ground-based Pandora spectrometers during the June 27^th and 28^th, 2017 SARP flights over the Los Angeles Basin. The GeoTASO data were used to retrieve differential slant column densities of NO₂ using Differential Optical Absorption Spectroscopy (DOAS) from a 435-460 nm window. This work evaluates how well these differential slant columns compare to the more well-established retrievals from Pandora. The collected coincident points for each of the five recorded flights show a strong correlation between the two instruments. Additionally, the NO₂ column densities at each Pandora site are investigated relative to the spatial distribution mapped by GeoTASO and compared to the more long-term dataset from Pandora collected over the month surrounding the SARP flight days. Results from this study will assist in evaluating the differential slant column retrievals from GeoTASO and show results on the spatial and temporal changes in NO₂ in the LA Basin for the first time with these two instruments.

NO₂ is a reactive gas directly linked to ambient air quality through its role as a precursor to tropospheric ozone. Natural emissions of NO₂ are dominated by biomass burning and lightning strikes. Production of NO₂ through these means is challenging to constrain due to the highly variable nature of each phenomena. To better understand natural NO₂ production, this study focused on emissions generated by a lightning ignited forest fire located in Sequoia National Park known as the Schaeffer Fire in June 2017. NO₂ columns were collected by GeoTASO onboard the NASA UC-12 King Air over the fire and the resulting smoke plume. Lightning data was obtained from the Earth Networks Total Lightning Network to determine the storm severity leading up to ignition of the fire in addition to measuring the lightning flash rate required for modeling of NO₂ production. An empirical relationship was adapted from previously published work to estimate NO₂ emissions from the fire with remote sensing data obtained from the MODIS instrument onboard the Aqua and Terra satellites for application in the model. Results for the Schaeffer Fire, which would have been too small to be visible by current satellite NO₂ instruments, were compared to results derived from a global study.

In the U.S., poor air quality greatly affects the health of people living in cities. However, research has consistently shown that air pollution burdens are highest for low income and communities of color. Though, these studies often use spatially-averaged concentrations measured by surface monitors for whole communities, which may potentially lead to bias in exposure estimates. To address this issue, this study quantifies income and race-based patterns in distribution of the pollutant nitrogen dioxide (NO₂) in Los Angeles, California utilizing NO₂ slant columns measured by the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument onboard the NASA UC-12B King Air on 26 June and 27 2017. GeoTASO observations were combined with income and demographic data at the census tract spatial scale provided by the California Environmental Protection Agency Environmental Health Screening Report. This analysis found higher NO₂ associated with greater poverty and nonwhite communities. For example, NO₂ concentrations were higher for the Hispanic community experiencing poverty. Communities that were 60-80% Hispanic with 60-80% in poverty were exposed to statistically significantly more NO₂ than a community with 60-80% White at the same poverty level. It was generally the case that White, more wealthy communities had lower mean NO₂ value. Poor Hispanic communities had the strongest association with NO₂ exposure, i.e. as percent Hispanic increased and percent in poverty increased the mean NO₂ also increased, displaying an R² of .90-.95 over the two days of the study. A similar trend was found for Asian Americans, and, in certain cases, for African Americans. Second, I compare the results obtained with the high-resolution GeoTASO dataset to results derived from an identical analysis using interpolated surface monitor NO₂ data. I find the surface monitor observations underestimate the association between the presence of NO₂ with poverty compared to GeoTASO.

Los Angeles has historically experienced the highest ozone in the U.S. Strategies to reduce high ozone have included reduction of emissions of both nitrogen oxides (NOx) and volatile organic compounds (VOC), the two main precursors to ozone. Observations of ozone on weekdays and weekends, as well as from many intensive atmospheric experiments in LA, have indicated that for decades, ozone production chemistry in LA has remained sensitive to VOC rather than NOx. However, very recent literature has suggested that LA ozone chemistry may be transitioning to a chemical regime that is sensitive to NOx, which would mean that LA air quality is positioned to benefit from NOx emission control on diesel truck traffic that are currently in the implementation phase. This study investigates this possible chemical-regime transition by first looking at the temporal trends in NOx concentrations at nine monitoring stations throughout LA. I find that there is spatial variability in the observed rate of decrease of NOx, suggesting that ozone has been affected differently throughout the LA basin. To investigate this potential spatial variability in the impacts of NOx changes, and, potentially, the change in ozone chemistry sensitivity to NOx, I use data gathered from the Geostationary Trace Gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument on board the NASA UC12-B King Air on June 26^th and 27^th, 2017. I use an analytical model to simulate ozone production in LA to identify the sensitivity of ozone production to NOx. Understanding the dependence of ozone production on NOx is critical for developing effective control strategies in this area.

The California San Joaquin Valley (SJV) has recently (2012–2015) experienced the worst drought in recorded history. The SJV is also the location of some of the worst ozone pollution in the U.S. Ozone concentrations are a function of atmospheric mixing, the ozone lifetime, and ozone chemical production, and each of these terms are expected to vary with drought conditions. As a result, it is difficult to predict the impacts of drought on high ozone in the SJV, which is important, as global climate change is anticipated to alter drought frequency and severity. Tropospheric ozone production results from complex, nonlinear chemistry combining nitrogen oxides (NOx), the atmospheric oxidant the hydroxyl radical (OH), and volatile organic compounds (VOCs). One ozone-forming VOC is isoprene, a highly reactive biogenic VOC emitted from plants through the stomata. In the daytime, the concentration of isoprene in the atmosphere is determined by the rate of emissions and the abundance of OH. This study takes advantage of the short lifetime of isoprene (~10 minutes on a sunny day), before and during drought conditions (2009–2016), to test how the chemical sensitivity of OH and ozone production has changed as a result of the drought. To do this, I use isoprene and ozone data collected by the U.S. EPA. I also compare isoprene observations to emissions used to constrain the widely-used chemical transport model GEOS-Chem. Understanding these results is an integral step in making effective controls and policies for tropospheric ozone pollution.

Agriculture is the largest source of anthropogenic nitrous oxide (N₂O) in the U.S. While it is generally known which processes produce N₂O, there is considerable uncertainty in controls over N₂O emissions. Factors that determine N₂O fluxes, such as soil properties and manure management, are highly variable in space and time, and, as a result, it has proven difficult to upscale chamber-derived soil flux measurements to regional spatial scales. Aircraft observations provide a regional picture of the N₂O spatial distribution, but, because N₂O is very long-lived, it is challenging to attribute measured concentrations of N₂O to distinct local sources, especially over areas with complex and integrated land use. This study takes advantage of a novel aircraft N₂O dataset collected onboard the low-flying, slow-moving NASA C-23 Sherpa in the San Joaquin Valley (SJV) of California, a region with a variety of N₂O sources, including dairies, feedlots, fertilized cropland, and industrial facilities. With these measurements, we link observed N₂O enhancements to specific sources at sub-inventory spatial scales. We compare our results with area-weighted emission profiles obtained by integrating detailed emission inventory data, agricultural statistics, and GIS source mapping.

Between 2012 and 2016, the state of California experienced one of the most severe multiyear droughts in nearly 120 years, causing a drastic reduction of freshwater flow to the San Francisco Estuary (SFE). During this period, the lack of winter rains and spring snow melt, coupled with high retention by dams, led to roughly a third less water reaching the SFE. Decreased freshwater flow to the bay alters the ecology of the SFE, for example by advancing the seasonal timing of phytoplankton blooms (Raimonet et al., 2016), and has been linked to phytoplankton plumes of different, and often more toxic, species. Phytoplankton functional type (PFT) methods, such as PHYDOtax (Palacios et al., 2015), enable the measurement  of community composition, and has been validated in SFE. We will test the accuracy of the PHYDOtax algorithm during the drought period in SFE using matchups between in situ pigment measurements and remotely sensed reflectance spectra from the AVIRIS airborne sensor. We will present time series of salinity and phytoplankton composition in the SFE and evaluate the effects of the drought on the estuarine phytoplankton composition. California is expected to experience increased frequency of extreme weather events, such as drought, as a consequence of climate change. We evaluate the consequences of the drought on phytoplankton community composition to understand how future extreme weather events may alter the ecology or toxicity of SFE.

The area in the lee of the Hawaiian Islands is conducive to mesoscale eddy formation due to the interaction between the trade winds and the islands. Cyclonic activity promotes upwelling of deeper, nutrient-rich waters and has been shown to fuel phytoplankton blooms. In situ ocean measurements of a cyclonic mesoscale eddy, Cyclone Opal, were gathered in 2005 to investigate the physical properties of eddies in this region. Ocean color remote sensing has indicated increased chlorophyll-a concentration corresponding to Opal’s known position and dates. However, oceanic productivity and export are dependent on size-class and physiology of the phytoplankton community, and whether the heightened chlorophyll resulted in a phytoplankton community shift is unknown. Numerous methods to distinguish phytoplankton types have been validated in oceanic waters. Here we present a time series of MODIS chlorophyll-a images and the results of an abundance-based method for measuring phytoplankton composition (Hirata et al., 2011) to describe changes in community structure during mesoscale eddy propagation.

Certain species of cyanobacteria (blue-green algae) have been found to produce a hepatotoxin known as microcystin, which is linked to liver cancer and disease in humans (Kudela et al., 2014). Humans are exposed to microcystin by various mechanisms: ingesting tap water from areas of cyanobacterial harmful algal blooms (CyanoHABs), accidental ingestion during recreational activities, and through the consumption of seafood exposed to microcystin. Satellite and airborne sensing platforms are useful for detecting cyanobacterial blooms, but since only certain species of cyanobacteria produce microcystin toxin, an algorithm targeting phycocyanin pigment is used to differentiate between toxic and non-toxic blooms. High concentrations of phycocyanins, associated with CyanoHABs are detectable from remote sensing data using wavelengths 615-630 nm (Ogashawara et al., 2013). Frequency of CyanoHABs detected with the MERIS satellite platform has been linked to incidence of nonalcoholic liver disease (Zhang et al., 2015), but many inland water bodies conducive to CyanoHAB events are too small to be detected using legacy platforms like MERIS. This study evaluates the potential to measure phycocyanin in smaller water bodies using the newly operational OLCI sensor aboard Sentinel-3. We investigate the occurrence of non-alcoholic liver disease in counties with low estimated CyanoHAB events previously measured by legacy platforms to test potential benefits from improved spatial resolution to CyanoHAB monitoring for human health.

About 8 million tons of plastic are dumped into the ocean annually. Many plastics break down into smaller pieces, or microplastics, which release chemical contaminants into the water are consumed by marine wildlife. These microplastics are long-lived, but difficult to quantify. Obtaining an accurate estimation of the quantity of microplastics in the world’s oceans is difficult because in-situ plastic surveys are expensive, cover small areas, and cannot sample at the frequency required to monitor global plastic accumulation in the oceans. A method enabling ocean remote sensing of marine debris would allow the evaluation and monitoring of plastic pollution throughout the global ocean and the ability to track changes through time, but so far, no robust tool for measuring plastics in the oceans exists. Laboratory measurements of submerged microplastics suggest that high plastic concentrations may correspond to an elevated near-infrared signal in water-leaving radiances (Estess 2010). Here we evaluate the remote sensing spectra of oceanic microplastics by comparing non-aerosol-corrected satellite retrievals (MODIS aqua) with microplastic concentrations measured in the North Pacific subtropical gyre during a Sea Education Association research cruise. By comparing MODIS reflectance data with observed in-situ concentration of plastics, We test the feasibility of remotely sensing the presence of plastics in what has been called the “Pacific Garbage Patch,” where plastics concentrate in the Gyre. This would enable satellite remote sensing to be used on large scales to detect and quantify plastics abundance in open ocean water.

Giant kelp (Macrocystis pyrifera) is a foundation species found in temperate coasts, forming thick forests, which provide shelter and food to numerous organisms. This makes them a vital component in food webs and structures of many coastal ecosystems, which in turn support commercial fisheries and food production. The Santa Barbara Channel, an upwelling ecosystem, hosts numerous patches of kelp forests where they thrive in the cool, nutrient-rich waters. A multiyear north Pacific warm anomaly followed by one of the strongest ENSO events on record heightened temperatures in the Santa Barbara channel and led to prolonged, decreased nutrient levels. Here we show a time series of kelp canopy measured using the Normalized Difference Vegetation Index from Landsat 5 and 7 imagery during the extended warm anomaly and subsequent El Niño. We examine how the extended period of heightened sea surface temperatures and decreased nutrients affected giant kelp canopy in the Santa Barbara Channel, and we test canopy cover against multiple basin-scale oscillations to evaluate their combined impacts on kelp survival and recruitment.

Bull kelp (Nereocystis luetkeana), a species of canopy-forming brown macroalga dominant in the coastal US Pacific Northwest, provides critical ecological services such as habitat for a diverse array of marine species, nutrient regulation, photosynthesis, and regional marine carbon cycling. Starting around 2014, annual aerial surveys of bull kelp forests along California’s northern coastline conducted by the California Department of Fish and Wildlife (CDFW) have reported a sudden 93% reduction in bull kelp canopy area. Remote sensing using satellite imagery is a robust, highly accurate tool for detecting and quantifying the abundance of the canopy-forming giant kelp, Macrocystis pyrifera; however, it has not been successfully applied to measuring northern bull kelp forests. One of the main difficulties associated with bull kelp detection via satellite is the small surface area of bull kelp canopies. As a result, bull kelp beds often only constitute part of a satellite pixel, making it difficult to obtain a kelp reflectance signal significantly different than water’s reflectance signal. Here we test a novel method for detecting and quantifying bull kelp canopy area using a multiple endmember spectral mixing analysis (MESMA) applied to Landsat 5 and Landsat 8 imagery from 2003-2016. Water and kelp spectral endmembers are selected along the northern California coastline from Havens Neck cape to Point Arena. MESMA results are ground truthed with the CDFW aerial multispectral imagery data. This project will present a satellite-based time series of bull kelp canopy area and evaluate canopy change in a northern California kelp ecosystem.

The 2013-2015 warm water anomaly, “The Blob,” was an atmospheric and oceanic phenomenon that resulted in shifts of the dynamics and biology of the Northeastern Pacific Ocean. This anomaly, caused by an area of persistent high pressure, stretched from the Gulf of Alaska to Baja California and impacted the phytoplankton productivity in the Santa Barbara Channel. As ocean temperatures fluctuate, anomalous warm conditions provide an opportunity to understand how phytoplankton might respond to future changes in oceanic and atmospheric coupling, such as nutrient availability via curl-driven or wind-driven upwelling. Because phytoplankton are the base of the oceanic food chain, changes in their abundance and composition alter the structure of higher trophic levels. We present sea surface temperature anomalies, surface wind fields, and chlorophyll-a anomalies during the Blob, and assess how physical shifts in an upwelling ecosystem alter phytoplankton productivity. This project will evaluate the effects of the 2014-15 high-pressure atmospheric anomaly on the sea surface wind fields across the upwelling-driven Santa Barbara channel ecosystem to understand how future weather anomalies could change the structure of the food chain in similar ecosystems.

Since the mid-twentieth century the California Current System has experienced a long-term rise in water column temperature, along with recent signals of increased Chlorophyll-a (Chl-a) and dinoflagellate concentrations. Ocean color remote sensing enables the study of sea surface temperature and phytoplankton on a large scale, although coastal systems remain a challenge due to their optical complexity. The Santa Barbara Channel lies in the lee of Point Conception, which partially shelters the region from strong equatorward winds that flow along the central California coastline. We examine the impact of changing ocean temperatures on phytoplankton communities in this complex coastal system using a remote sensing abundance-based approach that partitions Chl-a concentrations by phytoplankton size class to estimate the underlying biomass composition in the Santa Barbara Channel (Hirata et al., 2011). We validate our remote sensing phytoplankton classification method using matchups with in situ time series of phytoplankton abundance, and perform a regional spatial analysis of Chl-a and phytoplankton composition in the Santa Barbara Channel to test how phytoplankton may respond to future ocean temperature shifts in coastal upwelling ecosystems.

Riparian forests are groundwater-dependent ecosystems that are highly sensitive to changes in the water table. As climate change continues, droughts are likely to become more frequent and severe in Southern California, threatening the persistence of these ecosystems. From 2012 to 2017, California experienced the most severe drought in the past century, providing a case study to assess drought impacts. Using imagery collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) from 2013 and 2016, we evaluated changes in riparian forest health in a central section of the Santa Clara River Basin, a multi-use river located just north of Los Angeles. We used Iterative Endmember Selection (IES) to select image endmembers of green vegetation (GV), non-photosynthetic vegetation (NPV), and rock/soil. We then used Multiple Endmember Spectral Mixture Analysis to estimate endmember fractions. We assessed changes in GV and NPV cover and canopy water content at key monitoring sites using cover fractions, the Normalized Difference Water Index, and the Water Band Index. Results showed a decrease in fractional cover of GV and an increase in fractional cover of NPV in riparian forests. Individual forest stands reacted differently to the drought, where larger drops in water table correlated with less fractional cover of GV. These results demonstrate the potential for severe droughts to decimate riparian forests, urging the creation of more sustainable groundwater management plans that consider the health of riparian forests.

The recent drought in California from 2012 to 2017 was the most severe drought event in the last century, driven by record high temperatures and significantly reduced precipitation. Such prolonged, intense drought conditions can have adverse impacts on plant health and can destabilize ecosystem functioning. Airborne imaging spectrometer data provide the opportunity to develop spatially detailed assessments of the impacts of drought on terrestrial vegetation at regional scales through repeated acquisitions. This imagery data can be used to quantify biophysical properties of the land surface, providing a greater understanding of ecosystem processes and improving ecological modeling methods. In this study, we analyzed vegetation cover change in a region of Santa Barbara County by comparing vegetation cover in 2011 (pre-drought) and in 2017 (post-drought). We used reflectance data in 7.5 meter pixels from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) to create a post-drought plant species classification map to compare with an existing 2011 classification dataset. A spectral library was built based on the unique spectral profile of each plant species in the imagery and was optimized using Iterative Endmember Selection (IES). We used spectral mixture analysis to generate a map of the 16 dominant plant species and rock and soil classes, as well as a fractional cover map to identify green vegetation (GV), non-photosynthetic vegetation (NPV), and soil. The results showed pronounced changes in the spatial distribution and appearance of plant species throughout the study area, and changes in fractional cover of GV and NPV due to the drought were observed. This study emphasizes the need for further analysis to quantify land cover changes and identify species most impacted by severe drought conditions. Examining vegetation changes in response to drought can provide valuable information for managing natural resources and can provide greater insights into the effects of drought on terrestrial ecosystems.

Wildfires can have dramatic and lasting effects on vegetation at landscape scales. With fire frequency increasing due to drying climatic conditions in places such as California, it is becoming increasingly important to understand the spatial impacts of wildfires over time. Remote sensing datasets can show land cover composition and changes that are time-intensive and difficult to assess from the ground at the large scales needed for wildfires. This study examines differences in burn severity and its relationship to pre-fire vegetation cover for the 2016 Rey Fire near Santa Barbara, California. We developed a pre-fire fuels map of vegetation divided by similar resource usage and other properties as classified by plant functional types (such as evergreen broadleaf trees). Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) imagery was spectrally unmixed with Multiple Endmember Spectral Mixture Analysis (MESMA) using a spectral library optimized by Iterative Endmember Selection (IES). The differenced Normalized Burn Ratio (dNBR) was derived from Landsat 8 Operational Land Imager (OLI) imagery to assess the immediate post-fire burn severity. Burn severity was also assessed on the same imagery by post-fire land cover class fractions (green vegetation (GV), soil, non-photosynthetic vegetation (NPV) and ash) as determined from MESMA using a spectral library optimized by Constrained Reference Endmember Selection (CRES). Within the fire perimeter, dNBR values ranged from -200, indicating plant growth in an unburnt region, to 1100, indicating extreme burn severity, but more typical sections of the burn scar dNBR values ranged from 100 to 600. Initial analyses showed that evergreen broadleaf and needleleaf shrubs were more intensely burned relative to evergreen broadleaf trees or annual herbs.

Wildfire is both an important ecological process and a dangerous natural threat that humans face. In situ measurements of wildfire temperature are notoriously difficult to collect due to dangerous conditions. Imaging spectrometry data has the potential to provide some of the most accurate and highest temporally-resolved active fire temperature retrieval information for monitoring and modeling. Recent studies on fire temperature retrieval have used have used Multiple Endmember Spectral Mixture Analysis (MESMA) applied to Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) bands to model fire temperatures within the regions marked to contain fire. However, these methods are less effective at coarser spatial resolutions as linear mixing methods are degraded by saturation within the pixel. The assumption of a distribution of temperatures within pixels allows us to model pixels with an effective maximum and likely minimum temperature. This assumption allows a more robust approach to modeling temperature at different spatial scales. In this study, instrument-corrected radiance is forward-modeled for different ranges of temperatures, with weighted temperatures from an effective maximum temperature to a likely minimum temperature contributing to the total radiance of the modeled pixel. Effective maximum fire temperature is estimated by minimizing the Root Mean Square Error (RMSE) between modeled and measured fires. The model was tested using AVIRIS data collected over the 2016 Sherpa Fire in and the 2007 Zaca Fire, both in Santa Barbara County, California. While only in situ experimentation would be able to confirm active fire temperatures, the fit of the data to modeled radiance can be assessed, as well as the similarity in temperature distributions estimated at different spatial resolutions. Results show that this model improves upon current modeling methods in producing similar effective temperatures at multiple spatial scales as well as a similar modeled area distribution of those temperatures.

Forest fires play a key role in shaping ecosystems.  The risk to vegetation depends on the fire regime, fuel conditions (age and amount), fire temperature, and plant adaptations such as bark thickness and stem diameter.  The 2007 Zaca Fire (24 km NE of Buellton, Santa Barbara County, California) burned 826.4 km² over the course of 2 months.  In this study, we used a time series of Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) imagery to evaluate plant burn severity and post fire recovery as defined into classes of above average recovery, normal recovery, and below average recovery.  We spectrally unmixed the images into green vegetation (GV), non-photosynthetic vegetation (NPV), soil surface (SOIL), and ash with a spectral library developed using Constrained Reference Endmember Selection (CRES).  We delineated the fire perimeter using the differenced Normalized Burn Ratio (dNBR) and evaluated changes in this index and the Normalized Difference Vegetation Index (NDVI) through time.  The results showed an immediate decline in GV and NPV fractions, with a rise in soil and ash fractions directly following the fire and a slow recovery in GV fraction and a loss of bare soil cover at later dates.  There was a sharp increase in the ash fraction following the fire and gradual decrease in the year after.  Most areas have recovered as of 2017, with prominent recovery around the perimeter of the fire scar and reduced recovery in the center.  These results indicate how post-fire vegetation varies based on initial burn severity and pre-fire GV and NPV fractions.

Cities are predicted to house 61% of the world’s population within the next 30 years and their ecological footprints extend much farther than the extent of developed land. They trap 15-30% more radiation than their rural counterparts making urban areas 0.6 – 1.3° C warmer. In this study, we looked at the thermal effects and pollution mitigation potential of tree species in Santa Barbara, California using data from the Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) on June 3, 2014. We mapped the 33 most common tree species in the city area in addition to non-photosynthetic vegetation (NPV), soil, and impervious surfaces using Multiple Endmember Spectral Mixture Analysis (MESMA). We developed a spectral library using image endmembers from reference polygons, and optimized the library using Iterative Endmember Selection (IES). We compared the urban tree map with land surface temperature data acquired on June 4, 2014 from the MODIS/ASTER airborne simulator (MASTER) to evaluate patterns between dominant tree species and temperature reduction. We found that increases in the fractional cover of urban tree species such as coast live oak (Quercus agrifolia), blue gum eucalyptus (Eucalyptus globulus), California sycamore (Platanus racemose), and avocado (Persea americana) were correlated with temperature reductions in surrounding areas. In addition, we compared species cover and air pollution reduction using literature values by species and plant functional type to provide examples of the direct effects of urban vegetation on the ecology of cities.

California’s San Joaquin Valley is an extremely agriculturally productive region of the country, and understanding the state of soils in this region is an important factor in maintaining this high productivity. In this study, we quantified changing soil cover during the drought and analyzed spatial changes in salinity, organic matter, and moisture using unique soil spectral characteristics. We used data from the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) from Hyperspectral Infrared Imager (HyspIRI) campaign flights in 2013 and 2014 over the San Joaquin Valley. A mixture model was applied to both images that identified non-photosynthetic vegetation (NPV), green vegetation (GV), and soil cover fractions through image endmembers. We optimized the spectral library used to identify these classes with Iterative Endmember Selection (IES), and the images were unmixed using Multiple Endmember Spectral Mixture Analysis (MESMA). Maps of soil electrical conductivity, organic matter, soil saturated moisture, and field moisture were generated for the San Joaquin Valley based on indices developed by Ben-Dor et al. (2002). Representative polygons were chosen to quantify changes between years. Maps of spectrally distinct soils were also generated for 2013 and 2014 to determine their spatial distribution and temporal dynamics between years. We estimated that soil cover increased by 16% from 2013 to 2014. Six spectrally distinct soil types were identified for the region, and we determined that the distribution of these soil types was not constant for most areas between 2013 and 2014. We observed similar changes in soil pH, electrical conductivity, and soil moisture in the region between 2013 and 2014.

Widespread plant mortality has been observed following the California drought of 2012-2017, and many conifer trees experienced massive delayed mortality events due to a gradual decline in water available in deep soils. Bishop Pine (Pinus muricata) is a drought-intolerant species endemic to the western coast of the USA that has been particularly affected by the most recent drought. In this study, we sought to map the extent of Bishop Pine mortality on Santa Cruz Island to better understand mortality patterns of this rare species in a unique maritime ecosystem. Using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data from the HyspIRI campaign, and Multiple Endmember Spectral Mixture Analysis (MESMA), we mapped the change in the spatial range of living and dead Bishop Pine between 2014 and 2016. Results showed extensive mortality in Bishop Pine stands across Santa Cruz Island, even in areas previously thought to be better able to withstand the effects of drought. The maritime environment of Santa Cruz Island provides a buffer against summer drought, but the prevailing climatic conditions were likely insufficient to prevent widespread die-off during the prolonged drought. These results suggest that future droughts have the potential to cause widespread mortality in mainland stands of Bishop Pine further north, regardless of climatic buffers present.

https://youtu.be/2VJ99U9dFgA

The San Joaquin Valley (SJV) in California is a major contributor to the state’s oil and natural gas (ONG) production and to United States agriculture (dairy and crops). Despite these sectors’ economic success, they emit volatile organic compounds (VOCs) which indirectly help to produce tropospheric ozone (O3) that is harmful to human health and vegetation. The SJV is a nonattainment area and had 112 exceedances for national O3 standards in 2016. Thus, these sources and location merit further study of local contribution to tropospheric O3. Whole air samples collected aboard the NASA C-23 Sherpa during the Student Airborne Research Program (SARP) were analyzed for various VOC mixing ratios and used to estimate potential O3 formation by calculating hydroxyl (OH) reactivity. We assume the emissions from Tulare and Kings County are similar because both are agriculturally analogous and don’t have many active ONG wells whereas an i-pentane/n-pentane ratio of ~1 is indicative of ONG influence which separates Kern County (1.43 ± 0.45) from the two. The calculated OH reactivity (ROH) for agriculture was ROH = 1.77 s-1 ± 0.19 s-1 and for ONG was ROH = 1.95 s-1 ± 0.63 s-1. In addition, agricultural contribution to OH reactivity was 36% VOCs, 17% methane, and 47% carbon monoxide, while ONG contribution was 48% VOCs, 16% methane, and 36% carbon monoxide, suggesting that the primary source of O3 in the SJV varies greatly with wind transport from north to south as supported by the NOAA HYSPLIT wind trajectories. Quantifying OH reactivity will illuminate the local source O3 production throughout the Central Valley and can provide insight into future mitigation strategies to improve air quality within this region.

Dimethyl Sulfide (DMS) is a sulfur-containing compound that leads to the formation of aerosols which contribute to the formation of haze and fog. Whole air samples were collected on board the NASA C-23 Sherpa aircraft during the 2017 Student Airborne Research Program (SARP) over dairies and agricultural fields in the San Joaquin Valley. Analysis of the samples indicate average DMS concentrations of 23 +/- 9 pptv, with a maximum concentration of 49 pptv. When compared with DMS concentrations from previous SARP missions (2009-2016), 2017 by far had the highest frequency of elevated DMS in this region. For this study, agricultural productivity of this region was analyzed to determine whether land use could be contributing to the elevated DMS. Top down and bottom up analysis of agriculture and dairies were used to determine emission rates of DMS in the San Joaquin Valley. Correlations to methane and ethanol were used to determine that DMS emissions were strongly linked to dairies, and resulted in R² values of 0.61 and 0.43, respectively. These values indicate a strong correlation between dairies and DMS emissions. Combined with NOAA HySPLIT back trajectories and analysis of air samples collected on the research aircraft and on the ground, results suggest that the contribution of dairies to annual DMS emissions in the San Joaquin Valley exceeds those from DMS-emitting crops.

Dimethyl sulfide (DMS) is primarily emitted to the atmosphere by marine environments, agriculture, and dairy farms, and is important to study because its oxidation has been shown to contribute to the formation of sulfate aerosols. Whole air samples were collected in Southern California during the 2017 NASA Student Airborne Research Program and an average DMS concentration of 12 ± 11 pptv was observed during flights over land. However, elevated DMS concentrations as high as 63 pptv were observed over an area of dense agriculture in the northern area of the Coachella Valley. Wind trajectories originating from the Los Angeles Basin were determined using the NOAA HYSPLIT model. Since DMS has a short lifetime of 1-2 days, results indicate that despite HYSPLIT data originating from LA, the DMS was emitted locally. Comparisons to local marine trace gases imply that the elevated DMS was not solely emitted from the nearby Salton Sea. Correlations with acetone and ethyl nitrate suggests that the elevated DMS was likely also a result of various terrestrial sources. Through analyzing DMS and methane correlations, dairies were excluded as a potential source. However, investigations into potential land use change in the region since the 2014 NASA SARP campaign confirmed an increase in crop acreage for citrus plants, grapes, and aquaculture. A notable increase was also observed in DMS concentrations, from 10 ± 8 pptv in 2014 to 30 ± 20 pptv in 2017. Further analysis using ground sample comparisons between DMS with alpha-pinene and beta-pinene also exclude citrus farms in the area as a potential source. Findings confirm that the elevated DMS was influenced by both the Salton Sea and the local agriculture. Ruling out the unlikely sources provided a better understanding of terrestrial emissions, which gives insight into the understanding of the impact of sulfate aerosol formation and its effect on the radiative forcing budget of Earth’s climate.

Joshua Tree’s ozone levels have risen above the EPA’s daily maximum standard of 65 ppb, 55 times in 2017, so far. It is imperative to understand why the park is so polluted and from where the pollution is originating to preserve our National Parks and improve local air quality. During the 2017 Student Airborne Research Program (SARP), students collected in situ measurements at different locations throughout the park. Whole air samples were collected and analyzed in an attempt to understand the pollution affecting Joshua Tree. Maximum ozone measurements on the western side of the park were reported as 84 ± 9 ppb, compared to 59 ± 6 ppb on the eastern side of the park on the day that in-situ measurements were taken. A gradient of pollution has been seen to form in the park from west to east. Time-series of ground samples at Black Rock and Keys View show that as the day progresses, alkane concentration decreases, and alkyl-nitrate concentrations increase, indicating photolysis may occur throughout the day. Additionally, non-methane hydrocarbon OH reactivity was calculated for the ground samples across the park. Oxygenates were calculated to account for 54% of the total OH reactivity in the park. Biogenics were calculated for 30% of total OH reactivity in the park, indicating that the park could be affected by an agricultural area. HySPLIT models validate these assumptions with wind backtrack trajectories leading to the Salton Sea area.

In an effort to reduce California’s carbon footprint from electricity generation, natural gas has become a more prevalent fossil fuel. Los Angeles is an urban area that uses a significant amount of oil and natural gas. To study the emissions in the city and the contribution from oil and natural gas, whole air samples (WAS) were collected onboard the NASA C-23 Sherpa during the 2017 Student Airborne Research Program (SARP) mission on June 26, 2017. Within the boundary layer in the Los Angeles International Airport (LAX) region, the average methane mixing ratio (2100 ± 200 ppbv) and ethane mixing ratio (6 ± 5 ppbv) were significantly higher than their background concentrations, 1800 ± 100 ppbv and 1 ± 2 ppbv, respectively. This suggests that natural gas is leaking into the atmosphere from the Los Angeles basin. Additionally, the area around LAX yielded an i/n pentane ratio of 2.5 ± 0.1, similar to that of urban areas in California. Although there are urban emissions in the area, natural gas leakage in the LAX region is significant enough to alter the urban trace gas composition. Using the California Air Resources Board (CARB) carbon monoxide estimations for 2015, a top-down approach was conducted for the LA basin and determined that 0.2 ± 0.3 Tg/y of natural gas leaks in the LA basin (2 ± 3% of the total natural gas delivered annually and twenty times higher than the leakage rate reported by SoCalGas).

The growing interest in hydrogen (H2) fuel cell vehicles has created a need to study the atmospheric H2 budget. While there is resounding agreement that hydrogen would escape into the atmosphere due to fuel transport/storage processes, there is disagreement over the amount that would be leaked in a hydrogen fuel economy. Leakage rate estimates range from 2% to 10% for total hydrogen production and transport. A hydrogen based energy infrastructure seems a viable clean alternative to oil because, theoretically, the only waste products are H2O and heat.  However, hydrogen leads to the formation of water vapor, polar stratospheric clouds, and a decrease in stratospheric temperatures, which contribute to the depletion of stratospheric ozone. Whole air samples (WAS) collected aboard the NASA Sherpa C-23 during the Student Airborne Research Program (SARP) showed elevated concentrations of hydrogen near LAX (950 ± 110 ppbv) compared to global average concentrations of 560 ± 20 ppbv. Trace gas analysis along with wind trajectories obtained with the NOAA HySPLIT models indicate that the source of elevated mixing ratios was leakage from H2 fuel stations in the surrounding areas. Correlation and ratio analyses eliminate the potential for common photochemical sources of H2 in the LAX area. This project could elucidate new and potential factors that contribute to the global atmospheric hydrogen budget.

Methyl Bromide (CH3Br) is a volatile organic compound (VOC) that is highly regulated due to its toxicity and its photolysis that leads to the formation of bromine; which is known to cause mass degradation of stratospheric ozone at a rate that is 100 time more efficient than chlorine. Whole air samples containing elevated levels of methyl bromide were collected over the Los Angeles International Airport (LAX) region. Over the runway, 31 ± 2 pptv of methyl bromide was observed, compared to background concentrations, 7 ± 2 pptv. Methyl bromide showed correlations to the following gases that can be used to identify specific sources: polybromomethanes, CO2, CO, and CH3CCl3. LAX is surrounded by both anthropogenic and biogenic sources of methyl bromide; but, from the correlations observed three likely sources are responsible for elevating CH3Br concentrations above the background levels. The three sources are saltwater marshes, biomass burning, and the Hyperion water treatment plant. Additional sources of methyl bromide in the LAX area are marine, which hosts major micro/macro algal blooms that produce CH3Br, and fumigation of crop fields. However, crop field fumigation leading to a spike in methyl bromide levels seem unlikely as HySPLIT trajectories indicate there would be considerable dilution of air masses coming from the nearest sites of possible fumigation. LAX is surrounded by multiple methyl bromide sources, but large fluxes from saltwater marshes, Hyperion water treatment processes, and biomass burning have the potential to cause the variation in methyl bromide concentrations observed over LAX.

Methyl nitrate (CH3ONO2) is an important atmospheric trace gas that plays a significant role in the reactive nitrogen budget and ozone production. It is primarily produced through two main mechanisms: outgassing of ocean surface waters and atmospheric photochemistry. During the 2017 NASA Student Airborne Research Program (SARP), whole air samples were collected onboard the NASA C-23 Sherpa over California’s Central Valley and LA Basin. Samples collected over the LAX region during missed approaches had elevated concentrations of methyl nitrate, up to 20 pptv. Correlations between methyl nitrate concentrations and other marine trace gases throughout the day suggest that marine emissions played a role in the observed enhancements of methyl nitrate near LAX. In the summer of 2016, the first leg of the Atmospheric Tomography Mission (ATom 1) flew around the world to study the impacts of anthropogenic pollution on the remote environment. Global methyl nitrate concentrations were analyzed using whole air samples, and average background concentrations measured over deep Pacific waters (6 ± 1 pptv) were found to be much lower than those examined in the coastal marine regions of Southern California during SARP (13 ± 4 pptv). The vast disparity between measured methyl nitrate concentrations over deep ocean waters and coastal marine regions indicate that measurements near coastal environments overestimate background methyl nitrate concentrations, which can potentially result in inaccurate models, studies, and errors in the reactive nitrogen budget. This study will provide insight into these discrepancies to further improve global monitoring of marine trace gases and the understanding of the reactive nitrogen budget.