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Ryan Hammock

Publications and source records attributed to Ryan Hammock.

18 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Ellicott City Disasters II - Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.

Erika Munshi↗
NASA NTRS2020

Ellicott City Disasters III: Building a Real-Time Statistical Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.

Erika Munshi↗
NASA NTRSDate not supplied

Building a Real-Time Flood Prediction Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the local government of Howard County, Maryland, to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a statistical model capable of hindcasting the two severe flash flood events that devastated Ellicott City and transitioned to a ‘Long Short-Term Memory’ based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using Nash-Sutcliffe Efficiency. The final product, the Sequentially Trained Real-time EstimAted Model (STREAM) predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

NASA DEVELOP↗
NASA NTRSDate not supplied

Establishing an Urban Heat Exposure Severity Index for Infrastructure Prioritization in Tempe, Arizona, Using NASA Earth Observations and LiDAR

Located on the banks of the Salt River in the Sonoran Desert, Tempe, Arizona, features a semi-arid climate with summer daily maximum temperatures regularly exceeding 37.8°C. Tempe is also subject to the southwestern monsoon season from July-September and the humidity exacerbates the high temperatures. Furthermore, the rapid urbanization experienced in Tempe has resulted in an intensification of the urban heat island. The summer of 2020 shattered the previous record of days exceeding 43.4°C, leading to higher energy and water costs, lower comfort, and increased risk of heat stroke for residents. Recognizing the impacts of extreme heat, the City of Tempe partnered with the Healthy Urban Environments initiative and NASA DEVELOP to identify census tracts that experience a higher mean land surface temperature than the city average. The NASA DEVELOP team used remotely sensed land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), and albedo data calculated from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) instruments from 2015 to 2020 to create heat hazard and exposure maps. LiDAR point cloud data, provided by the United States Geological Survey through Arizona State University’s Map and Geospatial Hub, were used to derive 3D buildings, building footprints, and tree point data for a shading analysis of walking paths, roads, and buildings at the census tract level. In situ meteorological measurements including air temperature and humidity were used to compare the macro-scale temperature measurements. The team worked with the City of Tempe to develop a methodology to process available data and identify areas of highest concern for urban heat effects within the city. With these insights, Tempe, Arizona can better address these issues with data-driven information to make decisions regarding heat mitigation and adaptation efforts.

John Dialesandro↗
NASA NTRSDate not supplied

Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the Howard County government in Maryland to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a prediction model capable of hindcasting the two severe flash flood events that devastated Ellicott City, and transitioned to an Long Short-Term Memory (LSTM) based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using the Nash-Sutcliffe Efficiency (NSE). The final product, called the Sequentially Trained Real-time EstimAted Model (STREAM), predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh (HRRR) model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate the OEM’s emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

Ryan Hammock↗
NASA NTRSDate not supplied

NASA DEVELOP’s Approach to Experiential Learning

The DEVELOP Program, part of NASA’s Applied Sciences’ Capacity Building Program, connects decision makers and early career individuals through rapid 10-week feasibility studies. These projects utilize Earth observation assets to address environmental issues and are conducted by small, interdisciplinary teams who work autonomously under the guidance of a cohort of science advisors. The teams tailor their research to create end products that support partner decision making processes. The connection to real-world decision making and direct communication with partner organizations drives meaningful experiences for DEVELOP participants that are directly applicable to future work in the geosciences. This poster will share DEVELOP’s model for offering meaningful experiences to its participants, address lessons learned over the past 20+ years, and highlight a variety of testimonials.

Capacity Building↗
NASA NTRSDate not supplied

Integrating Earth Observations and Socioeconomic Data to Identify Vulnerable Populations and Support Environmental Justice Initiatives

Part of NASA’s Earth Applied Sciences, DEVELOP is a dual capacity building program that introduces communities to applications of NASA Earth observation data to help inform environmental decision making. These projects are conducted by small teams of students, recent graduates, and early and transitioning career professionals and are tailored specifically to end-user decision making processes. Since 2018, a collaboration between NASA DEVELOP, Groundwork USA and its network of trusts, the AGU Thriving Earth Exchange, and local communities such as the City of Cambridge and the City of San Diego, has culminated in a campaign of 14 projects focused on assessing vulnerability to environmental threats such as extreme heat and flooding. These projects have explored the integration of satellite data and socio-economic data to discern populations at risk of being negatively impacted and align with Groundwork’s Climate Safe Neighborhoods Initiative that explores the relationship between historical race-based housing segregation and current and predicted impacts of climate change. This presentation will introduce an overview of the DEVELOP projects using Earth observations to support vulnerabilities assessments and community resilience, the collaboration with Groundwork USA, and future plans for environmental justice efforts at NASA.

Capacity Building↗
NASA NTRSDate not supplied

The Application of Remote Sensing using NASA Earth Observations paired with Sociodemographic Indicators to Identify Communities Most Susceptible to Urban Heat Exposure in Austin, Texas

In recent years, Austin, Texas has experienced an increase in population and urban development. Additionally, the City’s climate—already characterized by periods of extreme heat and drought—continues to change. As temperatures and demand for utilities and cooling resources rise, the number of heat-related deaths and illnesses in socially vulnerable populations (e.g., older, lower-income populations) is expected to increase. The City of Austin, The University of Texas at Austin (UT Austin), and The University of Texas Health Science Center at Houston (UT Health) partnered with NASA DEVELOP to examine the distribution of urban heat throughout the City. This project used land surface temperature, greenness, plant water content, and urban surface material analysis parameters derived from NASA Earth observations from Landsat 8 Operational Land Imager (OLI), Landsat 8 Thermal Infrared Sensor (TIRS), and Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS). The project team produced three different indices to create a spatial analysis for the study area including a social vulnerability index (SoVI), heat exposure index (HEI), and an overall heat priority index (HPI) score. This overall score was determined with a weighted analysis of heat-related environmental variables from NASA Earth observations and socioeconomic data from the 2019 American Community Survey. Based on the HPI score, the project team identified 121 census block groups out of 605 total that are designated as being most at risk of adverse impacts from extreme heat events. To test the sensitivity of the HPI, the team used a Monte Carlo analysis using different approaches for geographic scale, variable inclusion, census uncertainty, and index aggregation. Based on the sensitivity analysis, the resulting HPI score showed the metric was consistent with the baseline HPI. This provided increased confidence the score can be used as a tool to make informed infrastructure improvement plans in targeted areas (e.g., siting of cooling centers) and ensure equitable sustainable development.

Ryan Hammock↗
NASA NTRSDate not supplied

The Application of Remote Sensing and Machine Learning to Improve Early Warning Systems for Harmful Algal Events in the Highland Lake Chain, TX

Beginning in 2019, harmful algal events have caused canine deaths in both Lady Bird Lake and Lake Travis located near Austin, Texas. These two reservoirs are part of the larger Highland Lakes chain, managed by the City of Austin Department of Watershed Protection (COA DWP) and the Lower Colorado River Authority (LCRA), which fulfill municipal, commercial, and agricultural water demands. Given the recent increase in favorable environmental conditions for algal events in central Texas, NASA DEVELOP partnered with LCRA and COA DWP to improve monitoring and early detection of algal events, utilizing satellite remote sensing and machine learning. Spatially and temporally varied chlorophyll a concentrations, cyanobacteria detections, turbidity, and water surface temperature products are used as environmental proxies. Landsat 8 Operational Land Imager (OLI) and the Sentinel-2 MultiSpectral Instrument (MSI) data were used to provide a combined revisit time of up to ~3 days and < 30 m per pixel products. Chlorophyll a concentrations were estimated using a pre-trained Mixture Density Network, and cyanobacteria detection was accomplished using the Broad Wavelength Algae Index, which can differentiate algal blooms from algal proliferations (mats). In situ data were used to validate remotely sensed measurements and quantify uncertainties. Preliminary results show a good fit between the modeled output and in situ observations, suggesting that remote sensing data can be used to retrieve biogeochemical properties and/or inherent optical properties (IOPs) of water columns in these inland human-made lakes. Uncertainties were introduced from the sensitivity to atmospheric correction, inherent mismatch between satellite and sampling data, and a relatively lower signal-to-noise ratio over water. The resulting products enable near real-time monitoring of environmental proxies relevant to algal event presence in the Highland Lakes chain, and will ultimately support water management, decision making, and risk communication.

Shuyu Chang↗
NASA NTRSDate not supplied

Utilizing Earth Observations to Model Probable Coastal Wetland Extent, Sea-Level Rise Inundation Risk, and Assess Impacts on Historic Hawaiian Lands

Climate induced sea-level rise poses a risk to coastal areas on the Island of Hawai’i, and many of the island’s historic cultural lands are in danger of becoming overtaken by wetlands or inundation. In partnership with the County of Hawai’i, State of Hawai’i Department of Land and Natural Resources, and Arizona State University, NASA DEVELOP mapped wetland extent and short-term sea-level rise inundation risk. We utilized Earth observations over a 10-year span (2013 – 2022) that included the NASA MEaSUREs Gridded Sea Surface Height Anomalies and MEaSUREs Group for High Resolution Sea Surface Temperature datasets, United States Geological Survey (USGS) Hawaii Digital Elevation Models (DEM), and in situ tidal gauge data. Flood risk index values were acquired for 5 known Hawai’i flood events between 2019 – 2021 from the Global Flood Mapper tool on Google Earth Engine. We used a random forest model to predict short-term sea-level rise inundation risk along the entire coast of Hawai’i. Current wetland extents and probabilistic locations of new wetlands were modeled with the most recently available data from PlanetScope Surface Reflectance optical imagery (2022), USGS 3D Elevation Program (3DEP) 10m DEM (2020), temperature and precipitation data from the Hawai’i Climate Atlas, and soils data from the Hawai’i Soil Atlas (2014) using the Wetland Intrinsic Potential tool. Results indicated locations that had the highest probability of wetland creation. The end products aimed to help the partners prioritize efforts to meeting regulation requirements for wetlands protection, evaluate the inundation risk to historical features, and support decision-making for their Shoreline Setback and Climate Adaption plans.

Lisa Tanh↗
NASA NTRSDate not supplied

Localizing Earth Science: The NASA DEVELOP Model of Community Science & Engagement

NASA’s DEVELOP Program conducts rapid feasibility studies that apply Earth observations to address community priorities and needs and enable informed decision making. Part of NASA’s Applied Sciences’ Capacity Building Program, DEVELOP builds skills in both participants (students, recent graduates, and early/transitioning career professionals) who conduct projects on small teams, and partner organizations (state and local governments, federal agencies, non-profit and for-profit organizations, and international organizations) that work closely with DEVELOP to design the project. While only a subset of DEVELOP partner organizations are local municipalities or community-led non-profits, the DEVELOP model is an effective engagement tool and often used by NASA as an introduction for communities to engage in Earth science research and familiarization with satellite remote sensing. This presentation will highlight the DEVELOP model of co-production and community collaboration, lessons learned in partnering with local communities, and evaluation activities that look at the program’s reach and efforts.

Robert Cecil Byles↗
NASA NTRSDate not supplied

Employing NASA Earth Observations and Socioeconomic Data to Conduct Site Suitability Analyses on Residential Tree Planting Initiatives in Phoenix, Arizona

Phoenix, Arizona is the hottest large city in the United States with an average summer daytime temperature of 106°F. Temperatures in Phoenix continue to climb due to increasing global greenhouse gas concentrations and regional urbanization. The impacts of high temperatures, including heat-related illnesses and deaths, are disproportionately concentrated in low-income neighborhoods often characterized by little tree canopy, lack of green space, and insufficient access to shade. The City of Phoenix’s Office of Heat Response and Mitigation and Arizona State University’s Urban Climate Research Center, partnered with NASA DEVELOP to identify residential neighborhoods and parcels within qualified census tracts (QCTs) to be prioritized for tree planting initiatives using funding from the American Rescue Plan Act (ARPA). This project conducted analyses using NASA Earth observations, socioeconomic data from the 2019 American Community Survey, and local tree canopy and mobility data. For Earth observations, daytime land surface temperature, vegetation, and land cover were obtained from the Landsat 8 Thermal Infrared Sensor (TIRS) and Operational Land Imager (OLI). The project team incorporated these data into a heat vulnerability index (HVI) with an emphasis on tree canopy and social vulnerability to rank block groups within QCTs and focused on the resulting top 25 block group HVI scores. These top 25 most vulnerable block groups were then processed through a parcel analysis to determine the feasibility of residential tree planting based on building footprints on each parcel. Within the top 25 block groups, 2,411 parcels were analyzed, and 3,133 existing trees were identified averaging 1.3 trees per parcel with 90% of parcels having 3 trees or less. Homes with 2 trees or fewer were considered high priority for future planting efforts. Based on the City’s goals for increased tree canopy the project team determined that <10,000 additional trees would need to be planted within the most vulnerable 25 block groups. The project findings have helped initiate community engagement efforts and have contributed to the approval of tree planting funds in Phoenix.

Ryan Hammock↗