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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
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
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
10 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
Return to 2024 SARP Closeout Faculty Advisors:
Dr. Dom Ciruzzi, College of William & Mary
Graduate Mentor:
Marley Majetic, Pennsylvania State University
Marley Majetic, Graduate Mentor
Marley Majetic, graduate mentor for the 2024 SARP Hydroecology group, provides an introduction for each of the group members and shares behind-the scenes moments from the internship.
Jordan DiPrima
How are different land cover types affected by land subsidence on the U.S. Atlantic Coast?
Jordan DiPrima
Land subsidence is a frequently overlooked geologic hazard that is caused by natural processes and, more recently, anthropogenic stressors. The goal of this study is to observe subsidence trends and hotspots among land cover types on Virginia’s Eastern Shore and Long Island, New York. This study utilizes interferometric synthetic aperture radar, or InSAR, satellite data from Sentinel-1 to map vertical land motion from 2017 to 2023. Land cover data were sourced from Landsat 8 satellite imagery. Subsidence was mapped within the following land cover types on the Eastern Shore: urban, wetland, cropland, temperate or sub-polar grassland, temperate or sub-polar shrubland, mixed forest, and temperate or subpolar needleleaf forest. These land cover types have mean vertical velocities ranging from -0.2 mm/yr to -5.2 mm/yr. Results suggest that land subsidence is most severe in cropland areas on the Eastern Shore, with a mean vertical velocity of -5.2 mm/yr. In contrast, wetlands display the most subsidence on Long Island with a mean vertical velocity of -2.1 mm/yr. Long Island lacked distinct trends among land cover types and instead showed evidence of subsidence hotspots. These hotspots exist in the following land cover types: temperate or sub-polar grassland, barren lands, wetland, cropland, and temperate or sub-polar broadleaf deciduous forest. Overall, Eastern Shore croplands and Long Island wetlands were determined to be the most susceptible land cover types. These findings highlight regions at risk of sea level rise, flooding, and coastal erosion as a result of subsidence. With further research, we can map subsiding landscapes on a global scale to improve resource allocation and mitigation techniques.
Isabelle Peterson
Total Thermokarst Lake Changes on the Seward Peninsula, Alaska: 2016 to 2024
Isabelle Peterson
Thermokarst landscapes have and will continue to change as the arctic landscape warms due to climate change. Permafrost underlies much of these arctic landscapes, and as it melts, thermokarst landscapes are left behind. The Seward Peninsula in Alaska has an abundance of these landscapes, and thermokarst lakes are present in the northernmost portion. Several lakes have come and gone, but with increasing climate instability and warming of the area, there is a possibility of more permafrost melting, creating more of these lakes. To capture these changes, Harmonized Landsat Sentinel-2 (HLS) imagery were used to create annual lake maps of the northern portion of the Seward Peninsula from 2016 to 2024. Much of the methodology was informed from Jones et al. (2011); however, their study used eCognition, while the present study used ArcGIS Pro. This caused some differences in results likely due to the differences in software, satellite imagery, and the proposed study area. Lake number changes were observed annually. From this annual change, several 10 to 40 ha lakes disappeared and reappeared within the study period, along with smaller lakes filling in where larger lakes once were. Thermokarst lake drainage is a process described by Jones and Arp (2015) which has devastating geomorphological impacts on the surrounding area, creating large drainage troughs which diminish surrounding permafrost in a quick time frame. To capture these events and overall changes, satellite imagery is essential. This is especially true in remote regions which are hard to reach by foot and require flight missions to be scheduled over the area for aerial photography. However, LVIS and other higher resolution aerial instruments would provide higher accuracy when identifying smaller lakes, as satellite imagery does not accurately capture lakes below 1 ha in the study area. This assertion is made due to conflicting results compared to Jones et al (2011). While the methodologies of this study have been executed manually, Qin, Zhang, and Lu (2023) have proposed the idea of using Sentinel-2 imagery to map thermokarst lakes through automatic methods. While automatization has not yet been perfected, the potential is there and can be used to analyze thermokarst areas effectively. With more satellite imagery, annual, monthly, and potentially daily changes can be captured in favorable months to monitor changing landscapes in arctic regions. Thermokarst lakes have been changing, and monitoring them can help in the process of understanding the changing climate in arctic areas, especially through the lens melting permafrost.
Emmanelle Cuasay
Finding Refuge in Climate Crisis: Analyzing the Differences between Refugia and Non-Refugia in the Northern Philippines Using Remote Sensing
Emmanelle Cuasay
Refugia are areas that are characterized by stable environmental conditions that can act as a refuge for species as Earth’s climate warms. In this study, fourteen Harmonized Landsat Sentinel-2 images from February 2014 – March 2024 of the northern Philippines region were used. The region of interest is the terrestrial biome by Lake Taal. Normalized Difference Vegetation Index (NDVI) maps were created from all fourteen images to determine the NDVI 25th highest quartiles of the long-term average NDVI images and of a dry and wet year NDVI image. These values were then used to create refugia and non-refugia maps using ArcGIS Pro. Land cover data from Sentinel-2 and a digital elevation model (DEM), using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), were plotted in ArcGIS Pro to determine the slope and aspect of the area. Global Ecosystems Dynamics Investigation (GEDI) data were used to look at forest height of the study area, and the distribution of forest height, slope, aspect, and elevation were plotted to determine their probability densities in refugia and non-refugia areas. Results of this study show increased biomass in refugia areas. This suggests that conservation practices are crucial to aid in the preservation of biodiversity and biomass within these refugia areas.
Jayce Crayne
Site-Based Observations of a Saharan Dust Storm’s Impacts on Evapotranspiration in North-Central Florida
Jayce Crayne
Saharan dust storms serve an important role in the western Atlantic’s climate in their contribution to Earth’s radiation budget, modulating sea surface temperatures (SSTs), fertilizing ecosystems, and suppressing cloud and precipitation patterns (Yuan et al., 2020). However, Saharan dust storms are expected to become less frequent in this region as SSTs continue to rise (Yuan et al., 2020). Predicting the climate response to this change requires a keen understanding of how the presence of these storms affect evapotranspiration (ET) and its indicators. This study utilizes site-based observational data from an AmeriFlux tower near Gainesville, FL recorded during a large dust storm in late June 2020. The storm’s progression was documented using satellite imagery from Aqua and Terra and aerosol optical depth (AOD) measurements from an Aerosol Robotic Network (AERONET) station co-located with the AmeriFlux tower. Indicators of ET such as surface air temperature, vapor pressure deficit, photosynthetic photon flux density, and net radiation were analyzed. Findings were compared to modeled ET and latent energy flux reanalysis data provided by the Global Land Data Assimilation System (GLDAS). Both model simulations and on-site observations support that ET decreased during the days dust concentrations were heaviest and for a short time thereafter. Cloud cover data adopted from meteorological aerodrome reports (METARs) provided by an automated surface observing system (ASOS) located in Gainesville showed that clouds were not a major contributor in decreasing ET during the days of heaviest dust. The results of this study show a considerable decrease in ET as a result of dust aerosols. Further research is necessary to determine whether changes in ET due to Saharan dust storms are significant enough to alter climates in the western Atlantic and, if so, what the climate response will be if the frequency of storms decreases.
Brandon Wilson
Predicting 2025 and 2028 dNBR and dNDIV for Csarf Smith River Complex / Evaluating the Effects of 2019 California Wildfire Fund
Brandon Wilson
Biodiverse regions across California remain vulnerable to harmful wildfires year round. Quantifying and measuring these regions’ wildfire resilience is necessary for understanding where/how to allocate environmental resources. Several ecological wildfire studies have been conducted utilizing artificial intelligence and remote sensing to analyze and predict biodiversity damage across wildfire prone regions, including Northern Algeria and Arkansas, USA. The current case study aims to analyze biodiversity damage from the 2023 Csarf Smith River Complex Fire in Six Rivers National Forest, California and predict the difference in Normalized Burn Ratio (dNBR) and difference in Normalized Difference Vegetation Index (dNDVI) for 2025 and 2028 using remote-sensing-based random forest (RF) regression. Furthermore, to observe, holistically, a practical method California has implemented to address state-wide wildfire damage, the 2019 California Wildfire Fund (AB 1054 and AB 111) was evaluated using the synthetic control method (SCM). For this case study, remote sensing data from the United States Geological Survey (USGS) and NASA (Landsat 9 Satellite C2 L2, TerraClimate and the Land Data Assimilation System) were utilized for processing relevant spectral indexes for the RF. Data from NOAA, Energy Information Agency, International Monetary Fund and Bureau of Economic Analysis were utilized as synthetic control datasets to evaluate the effects of the 2019 California Wildfire Fund. Elevated topography in this study area is susceptible to high severity burn effects, while less elevated topography burns less. This result affected dNBR and dNDVI predictions as elevated areas seemingly did not have strong resilience to rampant burns. This demonstrates a direct correlation to potential lower transpiration rates for elevated areas, warranting further analysis. Results of low variance, post-treatment, between the treated unit and the synthetic control unit, poses concern for the positive effect of the 2019 Wildfire Fund.
Carrie Hashimoto
Describing changes in evapotranspiration following the 2020 Creek Fire in the southern Sierra Nevada
Carrie Hashimoto
Climatic warming and high tree density have caused larger and more severe wildfires to occur in western United States forests over time. Wildfires affect both the hydrology and ecology of forests via alterations to the water balance (e.g., evapotranspiration, streamflow, infiltration, and more) and could shift vegetation communities and subsequent ecosystem structure and function. This project explores ecological characteristics of a landscape that predict the extent to which the Creek Fire in the southern Sierra Nevada has affected evapotranspiration. Strides in understanding of consequential evapotranspiration changes can create pathways to address emerging forest health challenges posed by similar western fires. For analysis, various remote sensing and modeled data were collected from OpenET, the North American Land Data Assimilation System, TerraClimate, Harmonized LandSat Sentinel-2 data, and the Shuttle Radar Topography Mission. Multiple linear regression and generalized additive models were constructed. Relative change in evapotranspiration served as the response variable. Model covariates included average temperature, total precipitation in the preceding months, average soil moisture, elevation, slope, aspect, northness, latitude, pre-fire normalized difference vegetation index (NDVI), and post-fire change in normalized burn ratio (dNBR). Best subset selection with cross validation demonstrated minimization of cross-validation error with a 7-covariate model. This reduced model yields lower complexity and more interpretability while sustaining an adjusted R2 of 0.626, compared to the full model’s adjusted R2 of 0.663. A reduced generalized additive model (GAM) with interaction terms drawn from the linear model variable selection demonstrated an adjusted R2 of 0.695, indicating a better fit that comes at the cost of reduced interpretability and higher computational requirements than the linear models. The goal of this work is to disentangle environmental indicators of post-fire evapotranspiration change, such that predictive modeling of future wildfire impacts on evapotranspiration can be achieved.
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