Spring 2024 - Project Abstracts

Amaya Sathurusinghe - Temporal analysis of forest cover dynamics of Wilpattu Forest Complex in Sri Lanka from 1997 to 2015

Wilpattu Forest Complex comprises Sri Lanka’s largest protected area, Wilpattu National Park and the adjoining forested areas. This ecologically significant ecosystem faces many anthropogenic threats due to forest clearance for large-scale settlements and small-scale agricultural expansion. Our study aims to evaluate the forest cover change in Wilpattu Forest Complex between 1997 to 2015. Using ENVI software, we performed a supervised classification for two LandSAT images obtained from 1997 and 2015. We identified the extent of forested and non forested areas each year and then used change detection techniques to evaluate the changes in land cover. We found that forested areas of Wilpattu declined by 108 km2 between 1997 and 2015. Forest loss was concentrated in the northwestern part and the northeastern edge of the forest complex. Forest loss in the northwestern region aligns with the field observations of largescale resettlements. Our findings provide an overview of the land cover changes in Wilpattu Forest Complex across 18 years.

Andres Mauricio Garcia-Chacon - Examining the Impact of Storm Surge from Major Hurricanes in Florida Through Remote Sensing Imagery

In October 2018, Hurricane Michael struck the Florida Panhandle as a Category 5 hurricane dealing significant devastation and damage to coastal communities through intense wind, rainfall, and storm surge. This project used PlanetScope imagery with a spatial resolution of 3 meters in combination with both supervised and unsupervised machine learning algorithms to understand the extent of damage caused by Hurricane Michael’s storm surge specifically. Supervised classification algorithms found a significant increase in collapsed structures and debris and a decrease in vegetation and buildings/asphalt. Principal component analysis was also performed on a stacked image consisting of 8 total bands that resulted in new gray scale images with loadings that reflected the highest amount of variation between the two images. PCA revealed that there was a significant decrease in building structures, decrease in vegetation, and increase in sand pushed onto shore.

Becca Chausse - Remotely Sensed Assessments of Food Insecurity in South Sudan Using Normalized Burn Ratio

This study builds from a previous study utilizing the same methods, but instead investigates the applicability and interoperability of utilization of Normalized Burn Ratio in detecting changes to food or conflict related variables during harvest season. Moreover, the prior study that this project builds upon was one of the first to investigate the possibility, development, and application related to the standardization of remote sensing methods for detecting acute food insecurity in conflict settings. The area of interest for this project includes one county in South Sudan, Rumbek Centre. Multispectral imagery was collected from Sentinel 2-L2A between the years of 2017 to 2023 where a Normalized Burn Ratio was applied to detect likely burned areas, hereafter referred to as observed thermal activity.2,3 After converting from pixel to area in square-kilometers, spearman rank correlations were run between observed thermal activity, average U.S. dollar price of cereal and tubers, average number of fatalities, and average number of conflict events to test the associations between these variables. The results of this study include a strong negative correlation between observed thermal activity and conflict events. Conversely a strong positive correlation was detected between fatalities and observed thermal activity. More research must be done to understand if this phenomenon is seen across South Sudan and between other countries that may have similar contexts.

Botao Zhao - Are People Farming on Steeper Lands? Analysis of Hydraulic Facilities Construction and Land Use Change in the Jinsha River Basin

The Jinsha River, the upper stream of the Yangtze River, flows through the mountainous Hengduan region, home to ethnic minorities like the Yi, Naxi, and Tibetans, who rely on farming and grazing. This ecologically significant area has undergone notable land use changes due to climate change, deforestation, urban expansion, and infrastructure development. The construction of large reservoirs, like the Xiangjiaba Hydropower Station, has transformed the landscape, submerging fertile croplands and pushing agricultural activities to higher, steeper slopes. This study examines land use and land cover changes in the Jinsha River Basin before and after the construction of the Xiangjiaba Dam in 2012, focusing on the transformation of valley farmlands, particularly the increase in farmland area and its shift to higher elevations. Using Landsat satellite imagery from 2009 and 2022, land use classifications were conducted through supervised classification and NDVI extraction. Topographic data from ASTER DEM was analyzed to categorize slopes, and spatial statistics in ArcGIS were used to assess changes in cropland distribution and slope. Significant land use changes were observed, with forest areas decreasing and settlements, water bodies, and bare soil expanding. Despite the submersion of valley croplands, the overall farmland area increased due to new reclamation on higher slopes. In 2009, 67.04 km² of farmland was on slopes greater than 25 degrees, which increased to 86.87 km² by 2022. The construction of the Xiangjiaba Dam has led to substantial land use changes, with a notable increase in farmland on steeper slopes. This poses challenges for sustainable agriculture due to difficulties in mechanization and water access. Maintaining or increasing arable land does not ensure stable crop yields or livelihoods, necessitating further research into soil fertility, water availability, and conservation practices. Ethnographic studies and interviews are essential for a deeper understanding of the socio-economic impacts of these changes.

Caitlin Chung - Landsat 8 Remote Sensing for Oyster Growth Sites on the Connecticut Shoreline

Following the introduction of Landsat-8’s OLI and TIRS sensors, more attention has been paid to the possibilities of analyzing Landsat data for aquatic science. Compared to satellites with a longer history of being used over water, Landsat-8 boasts compelling advantages– higher resolution than MODIS, currently operational and supported unlike SeaWIFS. I used a simple ecophysiological model for oyster growth based on data collected using Landsat-8. From Level 2 atmospherically corrected data, I used Band 10 to calculate Sea Surface Temperature (SST), atmospheric reflectance from Bands 1 and 2 for Chl-a, and bands 1 and 3 to derive turbidity. I then sorted these values into ‘poor’, ‘moderate’, and ‘good’ values for oyster growth based on past in-situ studies conducted by the Connecticut Department of Agriculture. I then weighted these values by importance in order to generate an Oyster Sustainability index (OSI). This method shows promise as an affordable and quick tool to improve site selection for individuals or CT government programs looking to farm oysters. However, collecting in situ data would allow researchers to more comprehensively evaluate the possible strengths and weaknesses of using Landsat. Furthermore, more advanced and localized models of turbidity and chlorophyll could be generated in comparison to in-situ data.

Carl Philip Dybwad - Exploring Urban Colorscapes: Satellite Analysis of City Hues and Their Impact on Well-being and Climate Resilience

Recent scholarship has highlighted a “graying” of both the objects and the built environment that surrounds us on a daily basis. However, color has been scientifically proven to provide a multitude of benefits, from well-being and mental health to combating the urban heat island. Various cities seem to have different hues and different shapes based on lived experiences. From a first-person perspective, yet we question whether remote sensing could provide beneficial insights to power a data-driven resource on the topic. We know that color impacts us, yet there have been no scientific studies examining a possible correlation between the geospatial spread of colors and the aforementioned topics. Do cities have a unique colorscape? If yes, how does that colorscape correlate to climate change (measured through the poxy of land-surface temperature) and to well being (measured through a life-evaluation index)? Do the happiest cities have the same mean color (Arithmetic Mean), or do we see that cities with one hue have lower surface temperatures? This study programmatically collects satellite imagery from 120 different cities from all continents, then finds the arithmetic mean of all the pixels within the given image to calculate a new HEX code, which later was used to examine correlations between well-being and LST. The study did not find any correlation between the average mean of a city (using the given methodology) and well being while providing inconclusive results on the correlation on LST. There is a clear understanding of the limitations of the study, and one sees the possibility of performing a similar analysis with greater precision by isolating the city instead of using a random sampling method to find the plots of land used for the study. As a preliminary and first-of-its-kind study, one hopes that it can serve as a foundation for research on the impact color has on our lives and the way we may develop climate resiliency through new and innovative means, such as color, in future urban development.

Catie Fenstermaker - Using Remote Sensing to Evaluate Sediment Migration and Barrier Island Erosion in Georgia’s Cumberland Island

This study utilizes remote sensing techniques to analyze the dynamics of sediment migration and barrier island erosion on Georgia’s Cumberland Island, a critical component of the Sea Islands barrier chain. The research focuses on assessing changes in island morphology, migration patterns, and sediment and turbidity variations over a twenty-two-year period, from 2001 to 2023. Employing Landsat imagery, robust image classification was performed and spectral indices were calculated to quantitatively measure these changes. This comprehensive temporal analysis reveals significant insights into the sedimentological behavior of Cumberland Island, highlighting the impacts of natural processes and human activities on barrier island ecosystems. The outcomes contribute to a deeper understanding of coastal dynamics and provide valuable information for coastal management and conservation efforts.

Chris Santiago - Mapping Urban Spaces: Texas subdivision Housing Blueprinting Mass Suburbanization

This study investigates the evolving patterns of suburban housing developments in Texas, employing a robust methodological framework to explore cross-cultural architectural influences and their socio-economic ramifications. Utilizing high-resolution satellite imagery from Planet Scope and Landsat TM 8, complemented by geographic information systems (GIS) from multiple states, this research maps and analyzes the geometric configurations of new subdivision housing. The data, processed through ENVI software, underwent rigorous georeferencing and image warping to ensure precise alignment with real-world coordinates, facilitating detailed comparative analyses across diverse landscapes. Our findings reveal that while Texas exhibits novel subdivision designs, these are not merely adaptations from Mexican cities such as Mexico City and Acapulco Guerrero, contrary to initial hypotheses. Instead, the distinctive geometric shapes in areas like Puebla and La Costa Dorada are products of new housing developments that are gentrifying existing neighborhoods, driven by an influx of tourists-turned-landowners. This dynamic, characterized by an importation of American land-use ideologies, suggests that Texas is setting, rather than following, trends in suburban architecture. This study highlights the role of international tourism and economic exploitation in shaping residential spaces, thereby influencing urban planning and development practices across borders. The implications of such crucial, as they reshape not only cityscapes but also socio-economic structures within communities.

Christina Lee - Synthetic Aperture Radar for Wet Snow Classification in the Sierra Nevada

Snowpack in the Sierra Nevada mountains is a critical form of water storage that determines California’s water supply in the dry summer months. Understanding snowpack extent and snowmelt initiation provides important insights for water management and conservation, particularly as severe droughts and storms are both predicted to intensify due to climate change. This study aims to use Synthetic Aperture Radar (SAR) data from Sentinel-1 images to classify snowpack and snowmelt dynamics in Northern California over the Sierra Nevada, primarily using a sequence of images from December 2022 to July 2023. This sequence tracked a notably heavy storm season, with supplementary analysis from the dry 2020-21 season for comparison. Sensitivity of cross-polarized backscatter (VH) to snowpack depth and intensity was used to classify snow, with additional moderation for topographic and angle effects by utilizing co-polarized channels (VV) and comparison to snow-free images. Area statistics were used to plot pixels classified as snow over time from the beginning of the storm season into the summer. Overall, the results indicated a distinct signature of wet snowpack changes over time that aligned with recorded patterns of snowpack buildup and snowmelt. Future research should examine accuracy of snowpack classification on smaller scales.

Disha Shidham - Detecting Long-Lived Aircraft-Produced Condensation Cirrus Clouds by Fine-tuning an ML Model on Human-Labeled GOES-16 ABI Imagery

Utilizing convolutional neural networks (CNNs) and the OpenContrails dataset generated by Google, a segmentation model was developed to accurately detect contrails in satellite imagery. Despite the inherent limitations of coarse spatial resolution, long-lasting contrails, which are the primary contributors to radiative forcing phenomena, remain discernible in GOES-16 Advanced Baseline Imager (ABI) imagery. The application of an ash false-color RGB product derived from longwave infrared data enhances the visibility of contrails. By leveraging both the U Net and MaxViT architectures, the model captures both local features and global context, while temporal context is accounted for by concatenating consecutive satellite images. Evaluation of the model demonstrates a promising F1 score of 0.68894. Challenges encountered in this project include discrepancies in ground truth annotations and constraints imposed by the resolution of GOES-16 imagery. Future research should explore custom loss functions, pseudo labeling techniques, alternative spectral bands, models that incorporate temporal dependencies, and higher resolution imagery. This study represents a significant advancement in contrail detection methodologies, thereby contributing to the broader field of atmospheric research and environmental monitoring.

Hanna Winter - Quantifying the Shrinking of the Dead Sea Detecting Changes in the Dead Sea Surface Area Utilizing Satellite Imagery

Sea level fall and a decrease in water volume has been observed in the Dead Sea due to a reduced water influx contribution of the Jordan River, because of increased upstream water use for agricultural and commercial purposes, as well as increasing evaporation due to the already arid climate warming further with anthropogenic climate change (Asmar et al., 1999). Using Landsat 5 and 9 imagery, this paper quantifies the changes in sea surface area between 1985, 2003, and 2021 by classifying the area of interest into the water and non-water class. A total sea surface area reduction of 88.6788 km2, roughly 13% of the initial area in 1985, has been observed over the total 36 year interval, with an average rate of -2.46 km2/yr. An increased rate of change of -3.0889 km2/yr. has been observed for the recent interval between 2003 and 2021.

Ian French - Evaluating the Changing Landscape of the Hunters Point Naval Shipyard During Site Cleanup: 1989-2023

This analysis tracks the changing landscape of the Hunters Point Naval Shipyard, an active Superfund site that has undergone diverse cleanup operations since 1989. Unfortunately, the cleanup has been surrounded by lack of transparency and scandal, further contributing to the marginalization of the surrounding Bayview Hunters Point community. Therefore, this analysis adopts a framework of “remote sensing for accountability.” First, it uses two Landsat images - one from 1989 and one from 2023 - to track changing land cover throughout the shipyard across four classes: bare soil, water, concrete, and vegetation. Results demonstrate significant increases in vegetation, aligning with development at uncontaminated sites, and expansion of bare soil in the active cleanup zones on highly contaminated sites. Next, this analysis begins a preliminary exploration of remote sensing techniques for the identification of heavy metal soil contamination. Comparison of bare soil samples across the shipyard in 1989 reveals a different spectral signal between relatively uncontaminated sites and highly contaminated sites.

Jamila Jaxaliyeva - 2024 Russia-Kazakhstan Floods

This study used ENVI remote sensing technology to evaluate the extent of the 2024 Russia Kazakhstan floods. The catastrophic floods in April 2024 represented the most severe flooding in the Russia-Kazakhstan border region in eighty years. Employing Sentinel-2 satellite imagery from April 11th, 2023, and April 11th, 2024, this study undertakes a comparative analysis on flooding patterns in Orenburg, Russia. Methodologically, image preprocessing, cloud masking, supervised classification, change detection analysis, and spectral indices calculation were executed using ENVI, ArcGIS and ArcMap software. Results indicate a substantial increase in flooded areas, notably impacting residential zones. Recommendations include zoning regulations to curb residential construction in flood-prone areas and the adoption of Planetscope imagery for real-time flood monitoring. Visualizations incorporating absolute differences between flooded and non-flooded areas are suggested for enhanced analysis and risk assessment
in future flood events.

Jeremy Pustilnik - Detecting African wildlife trails from space: A six year time series analysis

Ecosystem engineers can change the structure of landscapes. Trailblazing by megafauna through forests or across grasslands is one form of ecosystem engineering and is visually apparent. But how visually apparent? Using 12 high resolution (3-meter) remotely-sensed satellite images from PlanetScope from 2018 to 2023, I explored whether trails made by migratory megafauna in the East African Serengeti could be detected, classified, and quantified in ENVI. Trails were visible from space at this resolution, and unsupervised classification did a relatively good job of detecting patterns of geometry, while supervised classification allowed for some degree of trail area quantification. However, neither unsupervised nor supervised classification could accurately measure trail area within a reasonable amount of certainty. Vector polyline analysis revealed patterns of trail length and orientation that corroborated known routes of migratory ungulates, however. The results from this investigation indicate that satellite remote sensing is a powerful and useful tool to detect the tracks and signs of animals, but that more advanced machine algorithms are needed for better classification and quantification of trails.

Josh Chang - Deciphering Spatio-Temporal Variation of Snow Cover Trends in the Northeastern U.S. Using Neural Network Classification

As a result of global warming, the climate in the northeastern United States is changing, especially in regards to snowfall. However, these changes are not always predictable or intuitive. This project examined and compared regional trends in snow cover in the Northeast over the past25 years. Four discrete locations were chosen for study throughout the Northeast, each representing a different set of climatic conditions — New Haven, Connecticut; Mount Washington, New Hampshire; Buffalo, New York; and Acadia National Park, Maine. A per-pixel classification model was programmed using an artificial neural network machine learning architecture. The model was trained on a number of manually labeled regions of interest across twelve Landsat 8 images, with each ROI classified as either snow or no snow. The model was then tasked with classifying each pixel in a 10 km square centered at each of the four locations for every available Landsat 7 (2000-2013) or Landsat 8 (2013-2024) image taken in meteorological winter. The percentage of pixels classified as snow was averaged across all images for each winter. While the resulting data was noisy, it was found that the snow cover percentage was decreasing most rapidly in New Haven in comparison to the other locations. This suggests that New Haven’s warmer, urban climate may be on the border between rain and snow more often, with global warming tipping the scale towards rain and increased melting, while it might still be cold enough to support snow elsewhere.

Kumba Jammeh - Analysis of vegetation Restoration in The Gambia’s Most deforested regions (Central and Lower River Region) for the period of 24years (1998-2022) Using Remote Sensing

This study investigates vegetation restoration in the Central and Lower River Regions of The Gambia, which have suffered from significant deforestation and degradation due to climatic and non-climatic factors. The primary drivers of forest loss include agricultural expansion, infrastructural development, and recurring forest fires. To assess the effectiveness of restoration efforts, Landsat images from 1998 and 2022 were analyzed using remote sensing techniques and GIS software. The analysis revealed a general reduction in land cover classes, except for forest and settlement areas, which increased over the 24-year period. Forest areas expanded from 14,037 hectares in 1998 to 15,489 hectares in 2022, while settlements grew from 21,165.57 hectares to 22,796.46 hectares. This increase in forest cover supports the hypothesis that restoration strategies implemented by NGOs and other organizations have been effective. However, the study also noted challenges in differentiating between settlements and bare soil due to the low resolution of the satellite images. The results highlight the ongoing urbanization trends and underscore the need for sustainable urban planning to protect and restore forest cover. Despite some positive outcomes, the study could not conclusively attribute the increase in forest cover to specific factors due to the complexity of the socio-environmental dynamics involved. Future research should include ground truth data and more detailed monitoring to provide a clearer understanding of the long-term impacts of restoration projects on forest ecosystems.

Madeleine Tran - Impact of Urban Tree Canopy on Temperature in Bridgeport, CT

This study investigates the impact of urban tree canopy on temperature in Bridgeport, CT, addressing the urban heat island effect caused by heat-absorbing infrastructure. Using satellite imagery from Google Earth and Landsat 9, the research maps tree canopy and surface temperatures across the city. Bridgeport, known for its low tree canopy percentages and significant disparity between low- and high-income neighborhoods, serves as the focal point. The analysis reveals that areas with higher tree canopy percentages exhibit lower land surface temperatures. However, no clear relationship is found between median household income and either tree canopy or surface temperature. The study underscores the cooling benefits of urban tree canopies and suggests future research using higher resolution imagery to enhance accuracy. The findings aim to guide city planners in identifying high-temperature zones for strategic tree planting and canopy cover initiatives to mitigate urban heat risks exacerbated by climate change.

Murphy Tu - Ice Jam into Desert: Monitoring the Development of New Wetlands after the Implementation of “Channeling Ice Jam of Yellow River into Desert” Project in Kubuqi Desert near Hanggin Banner, China

Kubuqi Desert, located in Haggin Banner of Inner Mongolia, China, suffered from desertification and ice jam flooding over the years. The implementation of ice-jam channeling project created new wetlands in the north-eastern part of Kubuqi Desert since 2015. This report explores the land cover change around the in Kubuqi Desert and the impact to the local environment. Data is drawn from Landsat 8 OLI and TIRS, and Landsat 9 TIRS in 2014, 2017, and 2022 to detect land cover changes concerning waterbodies from MNDWI, vegetation from NDVI, and annual temperature from time-series analysis. The report argues that: 1) There is a significant increase in waterbody between pre- and post-implementation seasons; 2) there is a slow increase in vegetation cover between pre- and earlier post implementation seasons; 3) There is a significant difference in annual temperature in wetland area from the desert area after the implementation of the project, while no significant difference is observed from 2013-2014 season.

Nicole Israel-Meyer - Analyzing the Effectiveness of Brazil’s 2012 Forest Code in Northeast Mato Grosso

Brazil’s 2012 Forest Code was passed with the promise to halt illegal deforestation. A key mechanism for compliance with the Code is the Rural Environmental Registry (CAR in Portuguese), which requires private landholders to register their lands to be eligible for certain tax breaks and subsidies. The Code operates under the assumption that registered land is less likely to be deforested than non-registered land. This study analyzed this CAR hypothesis in 10 municipalities in northeastern Mato Grosso. To do so, the study conducted an NDVI and supervised classification temporal analysis to compare changes in forest cover between 2011(pre-Forest Code) and 2023. The preliminary results from this study are promising. There were significantly more pixels with decreased NDVI in non registered areas than registered areas, likely indicating deforestation. Non-registered areas had more pronounced trends in the classification change detection. Noticeably, while the non registered areas reported a decrease in human dominated pixels, the registered areas reported an increase in human-dominated pixels. Non registered areas also had a significant increase in the Cerrado classification. While it is difficult to draw more substantial conclusions without further study to increase confidence in the classification schemes and NDVI values, these initial results support the Forest Code’s assumption that registering private and public lands will reduce illegal deforestation.

Parker Chang - Evaluating Changes in Coverage of Residential Area and Built Environments in Maui Over Two Decades

The population on Maui Island in Hawaiʻi has increased by 24.2% between 2001 and 2022, which has put stress on Maui’s housing availability and affordability. Such housing issues have become particularly important in the past year as the 2023 Lahaina wildfire destroyed many homes and forced many families to relocate. The objectives of this project were to 1) characterize changes in coverage of residential area and built environments over the past two decades on Maui Island, 2) analyze changes in built environment by region, and 3) characterize land change in Upcountry Maui by distance to major highways. Such results served to provide a comprehensive overview of residential development on the island and quantitative statistics concerning land use change. The two images used were a Landsat 7 image from January 6, 2001 and a Landsat 8 image from January 8, 2022. After being cropped and cloud masked, a maximum likelihood supervised classification was performed on each image, assigning built environment pixels to either an “urban” or “suburban/rural” classification. These classification maps revealed overall increases in area for both built environment classes, though the accuracy of the classification maps, particularly in areas surrounding the masked-out clouds, was less than ideal. Change detection visualizations and statistics were obtained for the island as a whole in addition to four major population centers: Central, South, West, and Upcountry Maui. Two different patterns of development were observed, with Central, South, and West Maui undergoing notable processes of urbanization while Upcountry Maui had notable increases only in the suburban/rural class. This suburban/rural growth in Upcountry Maui was revealed to be focused in areas close to major highways rather than being an expansion of built environment to the east or west. Future studies might build upon the results of this project by identifying when built environment in Upcountry Maui switches to the urban classification (like the other regions of the island). Other future studies could also use remote sensing to quantify changes in urban land in West Maui before and after the 2023 wildfire.

Regina Sung - Vegetation Density, Socioeconomic Factors, and Urban Sustainability: Remote Sensing Analysis of Lawn Irrigation Practices in Las Vegas Valley

This paper investigates the relationship between vegetation density, surface temperatures, and socioeconomic factors in the Las Vegas Valley, employing a combination of remote sensing data and socioeconomic datasets. Utilizing Landsat 8 imagery and data from the US Census Bureau integrated into ArcMap, this study focuses on the Normalized Difference Vegetation Index (NDVI) values and land surface temperatures across different residential zones characterized by varying socioeconomic statuses. The project methodology included the detailed mapping of land use through the consolidation of zoning information from multiple cities and the analysis of NDVI and temperature data corresponding to vegetation types within these zones. The research findings indicate a strong correlation between higher NDVI values and lower poverty rates, suggesting that wealthier neighborhoods maintain denser vegetation which potentially mitigates the Urban Heat Island (UHI) effect. The study contributes to urban ecological assessments by highlighting how socioeconomic factors influence environmental quality and underscores the necessity of incorporating these elements into urban planning to enhance urban resilience and sustainability.

Sarah Cheung - Tracking the Effects on War on the Environment in Gaza

This study focuses on the Gaza Strip, where recent military actions have resulted in profound environmental disruptions. Through the application of remote sensing techniques, we investigate the multifaceted impact of war on the environment, including changes in vegetation cover, patterns of displacement, and the extent of infrastructure destruction. By analyzing Sentinel-2 satellite imagery, we aim to provide comprehensive insights into the environmental consequences of conflict in Gaza.

Seb Wang Gaouette - Raven Frog // Eagle Fish: Indigenous Sovereignty and Coastal Climate Change on the Haida Gwaii Archipelago

The Haida Gwaii archipelago off the coast of northern British Columbia has been inhabited by the Haida people since at least the early Holocene (ca. 9000 BC); the archaeological record shows that throughout that time, marine resources like fish and mollusks played an important role in the Haida diet. In 2010, after decades of pushback from the Haida Nation against commercial fishing throughout the Haida islands, the waters surrounding the southern half of the archipelago were placed under provisional protections by the federal government of Canada; in 2018, the so called Land Sea People Management Plan (Gina ‘Waadlux̱an KilG̱ uhlG̱ a) went into effect, banning fishing entirely in several parts of the provisionally protected areas. This study uses changes in seasonally averaged sea surface temperatures (SST) and chlorophyll-a concentrations between 2008 and 2023 to investigate changes in the ecological health of 24 Haida fishery sites, all of which are known from archaeological evidence to have been highly productive during the last two millennia. Results indicate that while changes in SST for all sites tend to mirror global trends, regardless of a site’s conservation status, sites where fishing has been outlawed by the Land Sea People Plan seem to be less vulnerable to potentially harmful algal blooms.

Serena Yang - Detecting Atmospheric Rivers in Satellite Observations: A Case Study of the 2021 Pacific Northwest Heatwave

Atmospheric rivers (ARs) are long, narrow corridors of intense horizontal water vapor transport in the lower atmosphere. When these storms make landfall, they can cause extreme precipitation, flooding, and many others hazards, and they exert significant control on the water resources of many regions. In addition, ARs have been linked to extreme heat events in the Arctic and midlatitudes, such as the 2021 Pacific Northwest heatwave. While ARs are usually identified and studied in reanalysis datasets, the Atmospheric Infrared Sounder (AIRS) instrument on the NASA Aqua satellite provides a potential resource for native identification of ARs in satellite data. In this paper I use AIRS Level 3 data to calculate integrated vapor transport (IVT) on the west coast of North America on June 25th, 2021. This calculation is performed by using 3D humidity data and approximating the wind field from satellite-derived geopotential heights, assuming geostrophic balance. The satellite derived IVT field are then used to identify the AR associated with the 2021 heatwave, using the Lora v2 Global AR identification algorithm. I compare the resulting AR identified from satellite data to the same event in the MERRA-2 reanalysis dataset for validation. I find that the general shape and structure of the AR feature is similar between the satellite data and reanalysis, but there are some significant discrepancies between the two, likely due to the geostrophic approximation. In addition, I also examine the outgoing longwave radiation (OLR) observed from AIRS during the event, and discuss potential improvements to satellite-based AR identification methods that could be made in the future.

Sooman Han - Exploring the Interplay of Sea Surface Temperature and Height: Insights into ENSO and PDO Dynamics in the Northern Pacific Ocean

This study examines the relationship between sea surface temperature (SST) and sea surface height (SSH) to understand the dynamics of the El Niño-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) in the Northern Pacific Ocean. Utilizing 30 years of Level 4 composite satellite data from the Copernicus Climate Data Store, the analysis focuses on SST and SSH anomalies from 1993 to 2022. The findings highlight the distinct yet interconnected nature of ENSO and PDO phenomena, with ENSO showing interannual variability and PDO exhibiting interdecadal cycles. The study identifies significant correlations between SST and SSH anomalies during notable El Niño and La Niña events, illustrating the impact of trade winds and ocean wave movements, particularly through coastal Kelvin waves. The results underscore the importance of continuous satellite monitoring to refine climate models and predict global climatic impacts, emphasizing the need for further research into the interplay of oceanic patterns and their broader implications.

Stefan Oliva - Quantifying and Predicting Methane Emissions Using Remote Sensing

“Beef “[c]attle production is the most important agricultural industry in the United States, consistently accounting for the largest share of total cash receipts for agricultural commodities,” according to data from the United States Department of Agriculture (USDA).1 Unfortunately, cattle are ruminants, which means that their stomachs contain four compartments that developed to ferment their feed during digestion; this is an issue because that process, called enteric fermentation, produces methane, a greenhouse gas which is “shorter lived than carbon dioxide but 28 times more potent in warming the atmosphere.”2 For that reason, experts often consider cattle to be the least climate-friendly of all commercially-reared animals but simultaneously consider cattle to be a tantalizing opportunity to limit a fast-acting source of greenhouse gasses. While the cattle industry also produces carbon dioxide (CO2) and nitrous oxide, we will focus on methane in this report due to its unique attributes and growing potential to monitor and regulate via remote sensing.

Yeim We - Assessment of Sentinel-2 Vegetation Indices for Estimating Aboveground Biomass and Soil Carbon in Brazilian Atlantic Forest and Eucalyptus Plantations

This study evaluates the effectiveness of various vegetation indices derived from Sentinel-2 imagery in estimating aboveground biomass (AGB) and soil carbon (C) in two contrasting ecosystems: the Atlantic Forest and eucalyptus plantations in Bahia, Brazil. Despite the extensive cultivation of eucalyptus influenced by high economic stakes and its environmental impact on the biodiverse Atlantic Forest, detailed assessments using remote sensing for accurate estimation of AGB and soil C remain underexplored in these areas. Results indicated that traditional indices like NDVI and GNDVI strongly correlate with AGB in the Atlantic Forest, whereas in eucalyptus plantations, these indices showed minimal correlation. This research highlights the differential response of vegetation indices in varying forest types and suggests a nuanced approach to employing these indices in forest management and conservation strategies. Through a comprehensive analysis involving field data and satellite imagery, the study underscores the critical need for tailored remote sensing applications to enhance forest C stock assessments and management practices in tropical regions.

Yike Chen - An analysis of the annual and seasonal distribution of methane mass mixing ratio of China in 2022

AIRS (The Atmospheric Infrared Sounder) provide timely measurements of methane concentrations in the mid to upper troposphere. In this study, patterns of annual and seasonal variation of methane mass mixing ratio in China was visualized and the possible contributors analyzed. The results were also compared against a similar study but with different observation spectrums and thus, drastically different results. This workflow is an example of how to conduct a simple time series analysis of atmospheric methane concentration that feeds into the “top-down” component of building a regional methane budget.