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By NASA
1 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
Dr. Misty Davies receives the prestigious AIAA Fellowship in May 2024 for her contributions to aerospace safety and autonomous systems, recognized at a ceremony in Washington, DC.NASA In May 2024, Dr. Misty Davies joined the American Institute of Aeronautics and Astronautics (AIAA) Class of 2024 Fellows at a ceremony in Washington, DC. The AIAA website states that, “AIAA confers Fellow upon individuals in recognition of their notable and valuable contributions to the arts, sciences or technology of aeronautics and astronautics.” The first AIAA Fellows were elected in 1934; since then only 2064 people have been selected for the honor. Dr. Davies has focused her career at NASA Ames Research Center on developing tools and techniques that enable the safety assurance of increasingly autonomous systems. She currently serves as the Associate Chief for Aeronautics Systems in the Intelligent Systems Division at NASA Ames and is the Aerospace Operations and Safety Program (AOSP) Technical Advisor for Assurance and Safety. More information on AIAA Fellows is at https://www.aiaa.org/news/news/2024/02/08/aiaa-announces-class-of-2024-honorary-fellows-and-fellows
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Last Updated Nov 22, 2024 Related Terms
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8 min read SARP East 2024 Ocean Remote Sensing Group
Article 52 mins ago 10 min read SARP East 2024 Atmospheric Science Group
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By NASA
8 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
Return to 2024 SARP Closeout Faculty Advisors:
Dr. Tom Bell, Woods Hole Oceanographic Institution
Dr. Kelsey Bisson, NASA Headquarters Science Mission Directorate
Graduate Mentor:
Kelby Kramer, Massachusetts Institute of Technology
Kelby Kramer, Graduate Mentor
Kelby Kramer, graduate mentor for the 2024 SARP Ocean Remote Sensing group, provides an introduction for each of the group members and shares behind-the scenes moments from the internship.
Lucas DiSilvestro
Shallow Water Benthic Cover Type Classification using Hyperspectral Imagery in Kaneohe Bay, Oahu, Hawaii
Lucas DiSilvestro
Quantifying the changing structure and extent of benthic coral communities is essential for informing restoration efforts and identifying stressed regions of coral. Accurate classification of shallow-water benthic coral communities requires high spectral and spatial resolution, currently not available on spaceborne sensors, to observe the seafloor through an optically complex seawater column. Here we create a shallow water benthic cover type map of Kaneohe Bay, Oahu, Hawaii using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) without requiring in-situ data as inputs. We first run the AVIRIS data through a semi-analytical inversion model to derive color dissolved organic matter, chlorophyll concentration, bottom albedo, suspended sediment, and depth parameters for each pixel, which are then matched to a Hydrolight simulated water column. Pure reflectance for coral, algae, and sand are then projected through each water column to create spectral endmembers for each pixel. Multiple Endmember Spectral Mixture Analysis (MESMA) provides fractional cover of each benthic class on a per-pixel basis. We demonstrate the efficacy of using simulated water columns to create surface reflectance spectral endmembers as Hydrolight-derived in-situ endmember spectra strongly match AVIRIS surface reflectance for corresponding locations (average R = 0.96). This study highlights the capabilities of using medium-fine resolution hyperspectral imagery to identify fractional cover type of localized coral communities and lays the groundwork for future spaceborne hyperspectral monitoring of global coral communities.
Atticus Cummings
Quantifying Uncertainty In Kelp Canopy Remote Sensing Using the Harmonized Landsat Sentinel-2 Dataset
Atticus Cummings
California’s giant kelp forests serve as a major foundation for the region’s rich marine biodiversity and provide recreational and economic value to the State of California. With the rising frequency of marine heatwaves and extreme weather onset by climate change, it has become increasingly important to study these vital ecosystems. Kelp forests are highly dynamic, changing across several timescales; seasonally due to nutrient concentrations, waves, and predator populations, weekly with typical growth and decay, and hourly with the tides and currents. Previous remote sensing of kelp canopies has relied on Landsat imagery taken with a eight-day interval, limiting the ability to quantify more rapid changes. This project aims to address uncertainty in kelp canopy detection using the Harmonized Landsat and Sentinel-2 (HLS) dataset’s zero to five-day revisit period. A random forest classifier was used to identify pixels that contain kelp, on which Multiple Endmember Spectral Mixture Analysis (MESMA) was then run to quantify intrapixel kelp density. Processed multispectral satellite images taken within 3 days of one another were paired for comparison. The relationship between fluctuations in kelp canopy density with tides and currents was assessed using in situ data from an acoustic doppler current profiler (ADCP) at the Santa Barbara Long Term Ecological Research site (LTER) and a NOAA tidal buoy. Preliminary results show that current and tidal trends cannot be accurately correlated with canopy detection due to other sources of error. We found that under cloud-free conditions, canopy detection between paired images varied on average by 42%. Standardized image processing suggests that this uncertainty is not created within the image processing step, but likely arises due to exterior factors such as sensor signal noise, atmospheric conditions, and sea state. Ultimately, these errors could lead to misinterpretation of remotely sensed kelp ecosystems, highlighting the need for further research to identify and account for uncertainties in remote sensing of kelp canopies.
Jasmine Sirvent
Kelp Us!: A Methods Analysis for Predicting Kelp Pigment Concentrations from Hyperspectral Reflectance
Jasmine Sirvent
Ocean color remote sensing enables researchers to assess the quantity and physiology of life in the ocean, which is imperative to understanding ecosystem health and formulating accurate predictions. However, without proper methods to analyze hyperspectral data, correlations between spectral reflectance and physiological traits cannot be accurately derived. In this study, I explored different methods—single variable regression, partial least squares regressions (PLSR), and derivatives—in analyzing in situ Macrocystis pyrifera (giant kelp) off the coast of Santa Barbara, California in order to predict pigment concentrations from AVIRIS hyperspectral reflectance. With derivatives as a spectral diagnostic tool, there is evidence suggesting high versus low pigment concentrations could be diagnosed; however, the fluctuations were within 10 nm of resolution, thus AVIRIS would be unable to reliably detect them. Exploring a different method, I plotted in situ pigment measurements — chlorophyll a, fucoxanthin, and the ratio of fucoxanthin to chlorophyll a—against hyperspectral reflectance that was resampled to AVIRIS bands. PLSR proved to be a more successful model because of its hyperdimensional analysis capabilities in accounting for multiple wavelength bands, reaching R2 values of 0.67. Using this information, I constructed a model that predicts kelp pigments from simulated AVIRIS reflectance using a spatial time series of laboratory spectral measurements and photosynthetic pigment concentrations. These results have implications, not only for kelp, but many other photosynthetic organisms detectable by hyperspectral airborne or satellite sensors. With these findings, airborne optical data could possibly predict a plethora of other biogeochemical traits. Potentially, this research would permit scientists to acquire data analogous to in situ measurements about floating matters that cannot financially and pragmatically be accessed by anything other than a remote sensor.
Isabelle Cobb
Correlations Between SSHa and Chl-a Concentrations in the Northern South China Sea
Isabelle Cobb
Sea surface height anomalies (SSHa)–variations in sea surface height from climatological averages–occur on seasonal timescales due to coastal upwelling and El Niño-Southern Oscillation (ENSO) cycles. These anomalies are heightened when upwelling plumes bring cold, nutrient-rich water to the surface, and are particularly strong along continental shelves in the Northern South China Sea (NSCS). This linkage between SSHa and nutrient availability has interesting implications for changing chlorophyll-a (chl-a) concentrations, a prominent indicator of phytoplankton biomass that is essential to the health of marine ecosystems. Here, we evaluate the long-term (15 years) relationship between SSHa and chl-a, in both satellite remote sensing data and in situ measurements. Level 3 SSHa data from Jason 1/2/3 satellites and chl-a data from MODIS Aqua were acquired and binned to monthly resolution. We found a significant inverse correlation between SSHa and chl-a during upwelling months in both the remote sensing (Spearman’s R=-0.57) and in situ data, with higher resolution in situ data from ORAS4 (an assimilation of buoy observations from 2003-2017) showing stronger correlations (Spearman’s R=-0.75). In addition, the data reveal that the magnitude of SSH increases with time during instances of high correlation, possibly indicating a trend of increased SSH associated with reduced seasonal chl-a concentrations. Thus, this relationship may inform future work predicting nutrient availability and threats to marine ecosystems as climate change continues to affect coastal sea surface heights.
Alyssa Tou
Exploring Coastal Sea Surface Temperature Anomalies and their effect on Coastal Fog through analyzing Plant Phenology
Alyssa Tou
Marine heat waves (MHW) have been increasing in frequency, duration and intensity, giving them substantial potential to influence ecosystems. Do these MHWs sufficiently enhance coastal precipitation such that plant growth is impacted? Recently, the Northeast Pacific experienced a long, intense MHW in 2014/2015, and another short, less intense MHW in 2019/2020. Here we investigate how the intensity and duration of MHWs influence the intensity and seasonal cycle of three different land cover types (‘grass’, ‘trees’, and a combination of both ‘combined’’) to analyze plant phenology trends in Big Sur, California. We hypothesize that longer intense MHWs decrease the ocean’s evaporative capacity, decreasing fog, thus lowering plant productivity, as measured by Normalized Difference Vegetation Index (NDVI). Sea surface temperature (SST) and NDVI data were collected from the NOAA Coral Reef Watch, and NASA MODIS/Terra Vegetation Indices 16-Day L3 Global 250m products respectively. Preliminary results show no correlation (R2=0.02) between SSTa and combined NDVI values and no correlation (R2=0.01) between SST and NDVI. This suggests that years with anomalously high SST do not significantly impact plant phenology. During the intense and long 2014/2015 MHW, peak NDVI values for ‘grass’ and ‘combined’ pixels were 2.0 and 1.7 standard deviations above the climatological average, while the shorter 2019/2020 MHW saw higher peaks of 3.2 and 2.4 standard deviations. However, the ‘grass’, ‘tree’ and ‘combined’ NDVI anomalies were statistically insignificant during both MHWs, showing that although NDVI appeared to increase during the shorter and less intense MHW, these values may be attributed to other factors. The data qualitatively suggest that MHW’s don’t impact the peak NDVI date, but more data at higher temporal resolution are necessary. Further research will involve analyzing fog indices and exploring confounding variables impacting NDVI, such as plant physiology, anthropogenic disturbance, and wildfires. In addition, it’s important to understand to what extent changes in NDVI are attributed to the driving factors of MHWs or the MHWs themselves. Ultimately, mechanistically understanding the impacts MHW intensity and duration have on terrestrial ecosystems will better inform coastal community resilience.
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Last Updated Nov 22, 2024 Related Terms
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10 min read SARP East 2024 Atmospheric Science Group
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Article 21 mins ago 11 min read SARP East 2024 Terrestrial Fluxes Group
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By NASA
10 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
Return to 2024 SARP Closeout Faculty Advisors:
Dr. Guanyu Huang, Stony Brook University
Graduate Mentor:
Ryan Schmedding, McGill University
Ryan Schmedding, Graduate Mentor
Ryan Schmedding, graduate mentor for the 2024 SARP Atmospheric Science group, provides an introduction for each of the group members and shares behind-the scenes moments from the internship.
Danielle Jones
Remote sensing of poor air quality in mountains: A case study in Kathmandu, Nepal
Danielle Jones
Urban activity produces particulate matter in the atmosphere known as aerosol particles. These aerosols can negatively affect human health and cause changes to the climate system. Measures for aerosols include surface level PM2.5 concentration and aerosol optical depth (AOD). Kathmandu, Nepal is an urban area that rests in a valley on the edge of the Himalayas and is home to over three million people. Despite the prevailing easterly winds, local aerosols are mostly concentrated in the valley from the residential burning of coal followed by industry. Exposure to PM2.5 has caused an estimated ≥8.6% of deaths annually in Nepal. We paired NASA satellite AOD and elevation data, model meteorological data, and local AirNow PM2.5 and air quality index (AQI) data to determine causes of variation in pollutant measurement during 2023, with increased emphasis on the post-monsoon season (Oct. 1 – Dec. 31). We see the seasonality of meteorological data related to PM2.5 and AQI. During periods of low temperature, low wind speed, and high pressure, PM2.5 and AQI data slightly diverge. This may indicate that temperature inversions increase surface level concentrations of aerosols but have little effect on the total air column. The individual measurements of surface pressure, surface temperature, and wind speed had no observable correlation to AOD (which was less variable than PM2.5 and AQI over the entire year). Elevation was found to have no observable effect on AOD during the period of study. Future research should focus on the relative contributions of different pollutants to the AQI to test if little atmospheric mixing causes the formation of low-altitude secondary pollutants in addition to PM2.5 leading to the observed divergence in AQI and PM2.5.
Madison Holland
Analyzing the Transport and Impact of June 2023 Canadian Wildfire Smoke on Surface PM2.5 Levels in Allentown, Pennsylvania
Madison Holland
The 2023 wildfire season in Canada was unparalleled in its severity. Over 17 million hectares burned, the largest area ever burned in a single season. The smoke from these wildfires spread thousands of kilometers, causing a large population to be exposed to air pollution. Wildfires can release a variety of air pollutants, including fine particulate matter (PM2.5). PM2.5 directly affects human health – exposure to wildfire-related PM2.5 has been associated with respiratory issues such as the exacerbation of asthma and chronic obstructive pulmonary disease. In June 2023, smoke from the Canadian wildfires drifted southward into the United States. The northeastern United States reported unhealthy levels of air quality due to the transportation of the smoke. In particular, Pennsylvania reported that Canadian wildfires caused portions of the state to have “Hazardous” air quality. Our research focused on how Allentown, PA experienced hazardous levels of air quality from this event. To analyze the concentrations of PM2.5 at the surface level, NASA’s Hazardous Air Quality Ensemble System (HAQES) and the EPA’s Air Quality System (AQS) ground-based site data were utilized. By comparing HAQES’s forecast of hazardous air quality events with recorded daily average PM2.5 with the EPA’s AQS, we were able to compare how well the ensemble system was at predicting total PM2.5 during unhealthy air quality days. NOAA’s Hybrid Single-Particle Lagrangian Integrated Trajectory model, pyrsig, and the Canadian National Fire Database were used. These datasets revealed the trajectory of aerosols from the wildfires to Allentown, Pennsylvania, identified the densest regions of the smoke plumes, and provided a map of wildfire locations in southeastern Canada. By integrating these datasets, we traced how wildfire smoke transported aerosols from the source at the ground level.
Michele Iraci
Trends and Transport of Tropospheric Ozone From New York City to Connecticut in the Summer of 2023
Michele Iraci
Tropospheric Ozone, or O₃, is a criteria pollutant contributing to most of Connecticut and New York City’s poor air quality days. It has adverse effects on human health, particularly for high-risk individuals. Ozone is produced by nitrogen oxides and volatile organic compounds from fuel combustion reacting with sunlight. The Ozone Transport Region (OTR) is a collection of states in the Northeast and Mid-Atlantic United States that experience cross-state pollution of O₃. Connecticut has multiple days a year where O₃ values exceed the National Ambient Air Quality Standards requiring the implementation of additional monitoring and standards because it falls in the OTR. Partially due to upstream transport from New York City, Connecticut experiences increases in O₃ concentrations in the summer months. Connecticut has seen declines in poor air quality days from O₃ every year due to the regulations on ozone and its precursors. We use ground-based Lidar, Air Quality System data, and a back-trajectory model to examine a case of ozone enhancement in Connecticut caused by air pollutants from New York between June and August 2023. In this time period, Connecticut’s ozone enhancement was caused by air pollutants from New York City. As a result, New York City and Connecticut saw similar O₃ spikes and decline trends. High-temperature days increase O₃ in both places, and wind out of the southwest may transport O₃ to Connecticut. Production and transport of O₃ from New York City help contribute to Connecticut’s poor air quality days, resulting in the need for interstate agreements on pollution management.
Stefan Sundin
Correlations Between the Planetary Boundary Layer Height and the Lifting Condensation Level
Stefan Sundin
The Planetary Boundary Layer (PBL) characterizes the lowest layer in the atmosphere that is coupled with diurnal heating at the surface. The PBL grows during the day as solar heating causes pockets of air near the surface to rise and mix with cooler air above. Depending on the type of terrain and surface albedo that receives solar heating, the depth of the PBL can vary to a great extent. This makes PBL height (PBLH) a difficult variable to quantify spatially and temporally. While several methods have been used to obtain the PBLH such as wind profilers and lidar techniques, there is still a level of uncertainty associated with PBLH. One method of predicting seasonal PBLH fluctuation and potentially lessening uncertainty that will be discussed in this study is recognizing a correlation in PBLH with the lifting condensation level (LCL). Like the PBL, the LCL is used as a convective parameter when analyzing upper air data, and classifies the height in the atmosphere at which a parcel becomes saturated when lifted by a forcing mechanism, such as a frontal boundary, localized convergence, or orographic lifting. A reason to believe that PBLH and LCL are interconnected is their dependency on both the amount of surface heating and moisture that is present in the environment. These thermodynamic properties are of interest in heavily populated metropolitan areas within the Great Plains, as they are more susceptible to severe weather outbreaks and associated economic losses. Correlations between PBLH and LCL over the Minneapolis-St. Paul metropolitan statistical area during the summer months of 2019-2023 will be discussed.
Angelica Kusen
Coupling of Chlorophyll-a Concentrations and Aerosol Optical Depth in the Subantarctic Southern Ocean and South China Sea (2019-2021)
Angelica Kusen
Air-sea interactions form a complex feedback mechanism, whereby aerosols impact physical and biogeochemical processes in marine environments, which, in turn, alter aerosol properties. One key indicator of these interactions is chlorophyll-a (Chl-a), a pigment common to all phytoplankton and a widely used proxy for primary productivity in marine ecosystems. Phytoplankton require soluble nutrients and trace metals for growth, which typically come from oceanic processes such as upwelling. These nutrients can also be supplied via wet and dry deposition, where atmospheric aerosols are removed from the atmosphere and deposited into the ocean. To explore this interaction, we analyze the spatial and temporal variations of satellite-derived chl-a and AOD, their correlations, and their relationship with wind patterns in the Subantarctic Southern Ocean and the South China Sea from 2019 to 2021, two regions with contrasting environmental conditions.
In the Subantarctic Southern Ocean, a positive correlation (r²= 0.26) between AOD and Chl-a was found, likely due to dust storms following Austrian wildfires. Winds deposit dust aerosols rich in nutrients, such as iron, to the iron-limited ocean, enhancing phytoplankton photosynthesis and increasing chl-a. In contrast, the South China Sea showed no notable correlation (r² = -0.02) between AOD and chl-a. Decreased emissions due to COVID-19 and stricter pollution controls likely reduced the total AOD load and shifted the composition of aerosols from anthropogenic to more natural sources.
These findings highlight the complex interrelationship between oceanic biological activity and the chemical composition of the atmosphere, emphasizing that atmospheric delivery of essential nutrients, such as iron and phosphorus, promotes phytoplankton growth. Finally, NASA’s recently launched PACE mission will contribute observations of phytoplankton community composition at unprecedented scale, possibly enabling attribution of AOD levels to particular groups of phytoplankton.
Chris Hautman
Estimating CO₂ Emission from Rocket Plumes Using in Situ Data from Low Earth Atmosphere
Chris Hautman
Rocket emissions in the lower atmosphere are becoming an increasing environmental concern as space exploration and commercial satellite launches have increased exponentially in recent years. Rocket plumes are one of the few known sources of anthropogenic emissions directly into the upper atmosphere. Emissions in the lower atmosphere may also be of interest due to their impacts on human health and the environment, in particular, ground level pollutants transported over wildlife protected zones, such as the Everglades, or population centers near launch sites. While rockets are a known source of atmospheric pollution, the study of rocket exhaust is an ongoing task. Rocket exhaust can have a variety of compositions depending on the type of engine, the propellants used, including fuels, oxidizers, and monopropellants, the stoichiometry of the combustion itself also plays a role. In addition, there has been increasing research into compounds being vaporized in atmospheric reentry. These emissions, while relatively minimal compared to other methods of travel, pose an increasing threat to atmospheric stability and environmental health with the increase in human space activity. This study attempts to create a method for estimating the total amount of carbon dioxide released by the first stage of a rocket launch relative to the mass flow of RP-1, a highly refined kerosene (C₁₂H₂₆)), and liquid oxygen (LOX) propellants. Particularly, this study will focus on relating in situ CO₂ emission data from a Delta II rocket launch from Vandenberg Air Force Base on April 15, 1999, to CO₂ emissions from popular modern rockets, such as the Falcon 9 (SpaceX) and Soyuz variants (Russia). The findings indicate that the CO₂ density of any RP-1/LOX rocket is 6.9E-7 times the mass flow of the sum of all engines on the first stage. The total mass of CO₂ emitted can be further estimated by modeling the volume of the plume as cylindrical. Therefore, the total mass can be calculated as a function of mass flow and first stage main engine cutoff. Future CO₂ emissions on an annual basis are calculated based on these estimations and anticipated increases in launch frequency.
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Last Updated Nov 22, 2024 Related Terms
General Explore More
8 min read SARP East 2024 Ocean Remote Sensing Group
Article 21 mins ago 10 min read SARP East 2024 Hydroecology Group
Article 21 mins ago 11 min read SARP East 2024 Terrestrial Fluxes Group
Article 22 mins ago View the full article
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By NASA
11 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
Return to 2024 SARP Closeout Faculty Advisors:
Dr. Lisa Haber, Virginia Commonwealth University
Dr. Brandon Alveshere, Virginia Commonwealth University
Dr. Chris Gough, Virginia Commonwealth University
Graduate Mentor:
Mindy Priddy, Virginia Commonwealth University
Mindy Priddy, Graduate Mentor
Mindy Priddy, graduate mentor for the 2024 SARP Terrestrial Fluxes group, provides an introduction for each of the group members and shares behind-the scenes moments from the internship.
Angelina De La Torre
Using NDVI as a Proxy for GPP to Predict Carbon Dioxide Fluxes
Angelina De La Torre
Climate change, driven primarily by greenhouse gases, poses a threat to the future of our planet. Among these gases is carbon dioxide (CO₂), which has a much longer atmospheric residence time compared to other greenhouse gases. One potential factor in reducing atmospheric CO₂ enrichment is plant productivity. Gross Primary Productivity (GPP) estimates the amount of CO₂ fixed during photosynthesis. The Normalized Difference Vegetation Index (NDVI) provides insight into the health of an ecosystem by measuring the density and greenness of vegetation. Therefore, it can be inferred that there is a relationship between NDVI and GPP, as greener plants are likely more productive. In this study, we used NDVI as a proxy for GPP and analyzed the effect NDVI had on CO₂ fluxes during California’s wet season between January and March 2023 in a restored tidal freshwater wetland. GPP and CO₂ flux data were obtained from the Dutch Slough AmeriFlux tower in Oakley, California. Landsat data were used to calculate the average NDVI. The influence of NDVI on GPP was assessed using linear regression. A second linear regression was then performed using NDVI and CO₂ flux, of which GPP is one component. We anticipate that wetlands with greater vegetation density will have lower CO₂ emissions.
Because Landsat data scans in 16-day intervals, daily variation in NDVI could not be observed. This translates to a frequency discrepancy between the Landsat and AmeriFlux data, as AmeriFlux towers measure in half-hour intervals. Additionally, the wet season represented was limited by data availability, as the data before 2023 were unavailable. Despite data limitations in this study, the outlined process could be repeated in various wetland and climate classifications for further analysis of a larger sample size. This study could assist in developing strategies to increase CO₂ sequestration in an attempt to slow the effects of climate change.
Samarth Jayadev
Using Machine Learning to Assess Relationships between NDVI and Net Carbon Exchange During the COVID-19 Pandemic
Samarth Jayadev
Understanding the movement of carbon between Earth’s land surface and atmosphere is essential for ecosystem monitoring, creating climate change mitigation strategies, and assessing the carbon budget on national to global scales. Measures of greenness serve as indicators of processes such as photosynthesis that control carbon exchange and are vital in modeling of carbon fluxes. NASA’s Orbiting Carbon Observatory (OCO-2) provides high quality measurements of column-averaged CO₂ concentrations that can be used to derive net carbon exchange (NCE), a measure of CO₂ flux between terrestrial ecosystems and the atmosphere.
From OCO-2, NCE data collected at the land nadir, land glint satellite position combined with in situ sampling can provide accurate measurements on a 1°x1° scale suitable for carbon flux characterization across the contiguous United States (CONUS). Normalized difference vegetation index (NDVI), which ranges from -1 to +1, measures the greenness of vegetation, serving as an indicator of plant density and health. This can help to understand ecosystem to carbon-cycle interactions and be leveraged for determining patterns with NCE. We examined the relationship between NDVI and NCE across CONUS during 2020 using Gradient Boosting Decision Trees (GBDT) which specialize in classifying and predicting non-linear relationships. This algorithm takes multiple weak learners (decision trees) and combines their predictions in an iterative ensemble method to improve prediction accuracy. Feature and permutation importance tests found that January and August (trough and peak NDVI, respectively) were the highest weighted predictor variables related to NCE. The dataset was split in a 90% training 10% test ratio across latitude/longitude grid cells to assess and verify model performance. Using the mean squared error loss function and hyperparameters with optimal estimators, tree depth, sample split, and learning rate the algorithm was able to converge the test predictions to match the deviance of the training data. The gradient boosting model can be applied to different months and years of NDVI/NCE to further explore these relationships or a multitude of research questions. Further studies should consider integrating land use and land cover change variables such as bare land and urbanization to improve predictions of NCE.
Makai Ogoshi
Deep-learning Derived Spaceborne Canopy Structural Metrics Predict Forest Carbon Fluxes
Makai Ogoshi
Terrestrial and airborne lidar data products describing canopy structure are potent predictors of forest carbon fluxes, but whether satellite data products produce similarly robust indicators of canopy structure is not known. The assessment of contemporary spaceborne lidar and other remote sensing data products as predictors of carbon fluxes is crucial to next generation instrument and data product design and large-spatial scale modeling. We investigated relationships between deciduous broadleaf forest canopy structure, derived from deep-learning models created with lidar data from GEDI and optical imagery from Sentinel-2, and forest carbon exchange. These included comparisons to in-situ continuous net ecosystem exchange (NEE), gross primary production (GPP), and net primary production (NPP). We find that the mean canopy height from the gridded spaceborne product has a strong correlation with forest NPP, similar to prior analysis with ground-based lidar (portable canopy lidar; PCL). For comparison to NPP, heights taken from the gridded spaceborne product were compared by overlapping the product with nine terrestrial forest sites from the National Ecological Observatory Network (NEON). We used standard deviation of canopy height as a measure of canopy structural complexity. Complexity derived from the gridded spaceborne product does not show the same strong correlation with NPP as found when using PCL. Mean annual GPP and NEE across five years were compared to the gridded spaceborne product at six Fluxnet2015-tower sites with continuous, gap-filled carbon flux data. When compared to in-situ flux tower data, neither mean canopy height nor structural complexity strongly correlate to annual NEE or GPP. Primarily, the finding that derived spaceborne products exhibit a strong correlation between forest canopy height and NPP will advance global-scale application of forest-carbon flux predictions. Secondarily, a variety of limitations highlight shortcomings in the current terrestrial flux data network. A small number of available study sites, both spatially and temporally, and lack of resolution in vertical complexity of canopy structure both contribute to uncertainty in assessing the relationships to NEE and GPP.
Sebastian Reed
Porewater Methane Concentrations Vary Significantly Across A Freshwater Tidal Wetland
Sebastian Reed
Methane is a potent greenhouse gas that is over 80 times more powerful than CO₂ at trapping heat and accounts for an estimated 30% of global temperature rise associated with climate change. The largest natural source of methane worldwide is wetlands. Despite the role of methane in driving climate change, the magnitude of global annual wetland methane flux remains highly uncertain. This study analyzes the effects of greenness (assessed using Normalized Difference Vegetation Index; NDVI), plant species composition, rooting depth, atmospheric methane concentration, and plant longevity on porewater methane concentration at the Kimages Rice Rivers Center tidal freshwater wetland. Samples for atmospheric and porewater concentrations were conducted in situ in June 2024. For each sampling location (n = 23) we collected whole air samples (WAS) 2m above the marsh surface and porewater samples 5cm below the marsh surface. We visually assessed species composition at each sample location, with 12 species of wetland plants present overall. We used the TRY plant database to find the rooting depth, leaf nitrogen content, and lifespan of each species. Drone multispectral data from 2023 was used to estimate NDVI values. These variables were compared to the pore water methane concentration via stepwise linear regression. Leaf N content, NDVI, plant species, and WAS sampling did not show statistically significant correlation to porewater methane concentration. Rooting depth showed a slight positive correlation with porewater methane (alpha = 0.1, p = 0.08, R^2 = 0.1). Samples with only perennial plants (as opposed to annual plants) had a higher mean value of porewater methane (p = 0.1). Analyzing porewater methane provides insight as to what wetland components affect methanogenesis and methane release, which aids in assessing which plant functional traits are most responsible for driving or mitigating climate change. Results from this study and future research in this area has the potential to more accurately assess how methane cycles through wetlands to the atmosphere.
Nohemi Rodarte
Understanding the vertical profile of CO₂ concentration: How carbon dioxide levels change with altitude
Nohemi Rodarte
Carbon dioxide (CO₂) is one of the main greenhouse gasses that contribute to global warming.While the relationship between CO₂ concentrations and land cover types, such as forests and urban areas, is well documented, there is limited knowledge of how CO₂ concentrations vary with altitude at fine spatial scales. Guided by our hypothesis that CO₂ levels vary with altitude and increase with elevation, we used airborne data collected from the B200 aircraft, which flew at different altitudes (400 to 1200 feet) above the urban area of Hopewell, Virginia, between 9:40 AM and 10:40 AM. We analyzed the CO₂ concentrations recorded by the flight to obtain the median and range for each 100 feet of altitude. Our results reveal that carbon dioxide concentrations varied significantly across the range of altitudes investigated. Within the area studied, CO₂ concentrations were found to range between 410 and 470 ppm. The distribution of these concentrations along the altitude gradient shows a bimodal pattern, with notable peaks at altitudes of 700 to 800 feet and 1100 to 1200 feet. Although CO₂ levels were present at all measured altitudes, there was a noticeable drop in the mean concentration at 800 feet,which then stabilized until reaching 1,000 feet before rising again. This pattern indicates that the concentrations of this greenhouse gas are not uniformly distributed with altitude, but rather vary significantly, showing higher concentrations at certain elevations and lower concentrations at others. The CO₂ distribution fluctuates with altitude, showing higher or lower levels at specific heights rather than a smooth gradient, indicating that altitude impacts CO₂ concentrations. While we did not identify the drivers of this change, future studies could evaluate how factors such as surface emissions, atmospheric mixing, and local conditions may contribute to vertical CO₂ profiles, since the altitudes we considered in this research are within the troposphere.
Camille Shaw
Linking NDVI with CO₂ and CH₄ Fluxes: Insights into Vegetation and Urban Source-Sink Dynamics in the Great Dismal Swamp
Camille Shaw
In recent years, carbon dioxide, methane, and other greenhouse gases have gained attention because of their contribution to the rise in Earth’s global mean temperature. Methane and carbon dioxide have various sources and sinks, but an expanding array of sources have created a need to assess ongoing change in carbon balance. This study aims to quantify the relationship between Normalized Difference Vegetation Index, or NDVI, and methane and carbon dioxide fluxes. We measured carbon dioxide and methane concentrations within the boundary layer using the PICARRO instrument, focusing on the Great Dismal Swamp, a forested wetland, and surrounding areas in the Eastern Mid-Atlantic Region. Data collection occurred at various times of day and along different flight paths in 2016, 2017, and 2024, with each year representing data from a single season, either spring or fall, for temporal analysis. We calculated methane and carbon dioxide fluxes along the flight paths using airborne eddy covariance, a method for capturing accurate flux measurements while accounting for the mixing of gases in the boundary layer caused by heat. Additionally, we calculated NDVI for this area using NASA’s Landsat 8 and 9 satellite imagery. Analysis of the afternoon flight data revealed a negative linear correlation between NDVI and carbon dioxide flux. Urban areas, characterized by low NDVI, exhibit a positive carbon dioxide flux as a consequence of emissions from vehicles, while forested areas, with high NDVI, show a negative carbon dioxide flux because of photosynthesis. In contrast, methane flux shows minimal correlation with NDVI. The lack of correlation arises because forested wetlands, with high NDVI, emit substantial amounts of methane, while urban areas, despite having low NDVI, still produce significant methane emissions from landfills and industrial activities. Future research could further investigate how seasonal and diurnal variations influence the correlations between NDVI and greenhouse gases by collecting comprehensive data across all seasons within a given year and at various times of the day.
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