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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

NASA IMAGESEER: NASA IMAGEs for Science, Education, Experimentation and Research

A number of web-accessible databases, including medical, military or other image data, offer universities and other users the ability to teach or research new Image Processing techniques on relevant and well-documented data. However, NASA images have traditionally been difficult for researchers to find, are often only available in hard-to-use formats, and do not always provide sufficient context and background for a non-NASA Scientist user to understand their content. The new IMAGESEER (IMAGEs for Science, Education, Experimentation and Research) database seeks to address these issues. Through a graphically-rich web site for browsing and downloading all of the selected datasets, benchmarks, and tutorials, IMAGESEER provides a widely accessible database of NASA-centric, easy to read, image data for teaching or validating new Image Processing algorithms. As such, IMAGESEER fosters collaboration between NASA and research organizations while simultaneously encouraging development of new and enhanced Image Processing algorithms. The first prototype includes a representative sampling of NASA multispectral and hyperspectral images from several Earth Science instruments, along with a few small tutorials. Image processing techniques are currently represented with cloud detection, image registration, and map cover/classification. For each technique, corresponding data are selected from four different geographic regions, i.e., mountains, urban, water coastal, and agriculture areas. Satellite images have been collected from several instruments - Landsat-5 and -7 Thematic Mappers, Earth Observing-1 (EO-1) Advanced Land Imager (ALI) and Hyperion, and the Moderate Resolution Imaging Spectroradiometer (MODIS). After geo-registration, these images are available in simple common formats such as GeoTIFF and raw formats, along with associated benchmark data.

Le Moigne, Jacqueline↗
NASA NTRSDate not supplied

Deep Learning-Based Negotiation Strategy Selection for Cooperative Conflict Resolution in Urban Air Mobility

This paper presents a collaborative conflict resolution technique using deep neural network-based intelligent search of the solution space. This approach offers a rapid convergence to a mutually acceptable solution for real-time conflict resolution, suitable for urban air mobility operations. Furthermore, the presented technique allows operational flexibility to the urban air mobility agents where these agents can collaboratively devise the solution via integrative negotiation, based on their local utility functions, as long as such a solution does not violate the global safety thresholds. The presented machine-to-machine negotiation method is built on our prior work on holistic assessment of the airspace and potential conflict detection implemented at-the-edge, onboard the unmanned aircraft systems. This paper extends the prior work to augment decision-making at-the-edge, thereby, promising a true distributed control architecture for urban air mobility. In this approach, each agent (a) builds a potential in-flight conflict map, (b) identifies the conflicting agents, (c) dynamically prepares a list of alternatives based on its current utility functions, (d) negotiates with the conflicting agents to pick one of these alternatives, and (e) implements the negotiated alternative to mutually resolve the conflict. Note that such an approach does not require a contingency plan to be made pre-flight, as the conflict resolution strategies are decided and negotiated in real time based on the present state of the agent. The contingency plan, if available, can serve as an input to the real-time conflict resolution strategy formulation, and also can be used as a fallback plan in case the negotiation fails and the impacted agents need to switch to a rule-based/supervisory resolution mode from the discussed distributed resolution mode. The presented collaborative negotiation-based conflict resolution technique incorporates a time-dependent reward function to catalyze collaborative resolution by incentivizing the agents with local and global rewards beneficial to their business operations.

Advanced Air Mobility↗
NASA NTRS2023

InVEST Urban Development: Incorporating Earth Observation Data into the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model Python API

Urban flooding poses as one of the biggest issues for cities today as its impacts are amplified by both climate change and urbanization. The Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation (UFRM) model, which benefits from its simplicity and robustness, is commonly used in NASA DEVELOP projects for disaster mitigation, urban planning, and environmental justice issues. While the InVEST UFRM model was able to produce the surface water runoff and retention map sufficient for the scopes of past projects, the model’s accuracy and spatial variability need improvement. Since the current InVEST UFRM model employs constant rainfall depth for all pixels in the area of interest (AOI), the model suffers from inaccurately estimating rainfall depth, runoff volume, and flood depth. Therefore, we adapted the model so that satellite-based precipitation raster datasets (i.e., Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [GPM IMERG]) can be used instead of a single constant value. We simulated the flood events on August 21st and August 22nd, 2017, in Wyandotte County, Kansas using both our modified and the original InVEST UFRM model and then compared the results after incorporating rainfall raster into the model. Areas with developed land on the land use map predicted moderate to high flood volume in the original volume regardless of the actual amount of precipitation. The modified model considered the rainfall depth’s spatial variation achieving less overestimation of flood runoff and volume at low-to-moderate rainfall area.

Urban flooding↗
NASA NTRS2023

Youngstown and Warren Disasters: Mapping Flood Susceptibility, Vulnerability, and Risk and Tree Canopy Coverage in Northern Ohio to Inform Stormwater Management and Flood Mitigation Efforts

Both pluvial and fluvial flooding events pose direct challenges on urban infrastructure and communities across the United States. Heavy rainfall events oversaturate the ground, overflow waterbodies, and overwhelm stormwater infrastructure. Vulnerable areas receive heavy damage from flooding events due to physical factors like increased impervious surfaces, poor stormwater systems, and limited greenspaces. These vulnerable neighborhoods are comprised of aging populations, minority communities, and lower income levels. Lack of data in these communities have made it difficult to implement policymaking and flood mitigation strategies. Using the Urban Flood Risk Mitigation model (InVEST) and the PlanetScope satellite constellation, the team visualized historical flooding and tree canopy coverage as a measure of flood susceptibility. The team also used the Arc-Malstrom model to provide further insight into where flooding accumulates via surface elevation depressions in the study area. To validate these models, the team explored the spatial variation of rainfall events using NASA’s Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG). The resulting maps highlight areas surrounding the cities of Youngstown and Warren as being the most flood susceptible and socially vulnerable, while the city centers contain the lowest tree canopy coverage. The DEVELOP team collaborated with the Environmental Collaborative of Ohio (ECO) to create products for end users within the City of Warren’s Water Pollution Control Department, the Eastgate Regional Council of Governments and the Healthy Community Partnership of Mahoning Valley. These products help identify target areas for preventative flood mitigation measures as well as areas ideal for green infrastructure intervention.

InVEST Urban Flood Risk Mitigation Model↗
NASA NTRS2020

Cambridge Urban Development - Quantifying Changes in Urban Albedo with NASA Earth Observations to Reduce Urban Heat Island Effect in Cambridge, Massachusetts

The urban heat island (UHI) effect occurs when urban areas have temperatures that are warmer on average than the surrounding suburban and rural regions. Low albedo surfaces traditionally found in urban landscapes, such as dark asphalt and rooftops, absorb solar irradiance and reemit heat, which contributes to the UHI effect. Cambridge,Massachusetts,part of the Boston metropolitan area,expects severe impacts on human health,infrastructure, and local environmental features due to increasing urban heat. The Massachusetts – Boston NASA DEVELOP team partnered with the City of Cambridge Community Development Department and the American Geophysical Union’s Thriving Earth Exchange to inform ongoing planning and heat mitigation efforts. High Resolution Orthoimagery (HRO) and National Agriculture Imagery Program (NAIP) scenes were analyzed in conjunction with ancillary datasets to assess changes in rooftop albedo between 2008 and 2018. Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to determine nighttime land surface temperature (LST) between 2003 and 2019. Lastly, temperature anomalies were calculated using seasonally averaged nighttime LST values obtained from Aqua MODIS to display ‘hot spots’ for summers between 2004 and 2019. On average, albedo increased by 0.12 across the city, with the majority of positive change in commercial areas. Analysis of mean summer nighttime LST between 2003 and 2019 revealed a non-significant increase in temperature, although temperature anomaly maps demonstrated that Cambridge was warmer on average than rural areas. These results were incorporated in the interactive Cambridge Urban Heat Dashboard, which allows users to explore temporal trends in albedo.

Nicole Ramberg-Pihl↗
NASA NTRS2020

Cambridge Urban Development: Quantifying Changes in Urban Albedo with NASA Earth Observations to Reduce Urban Heat Island Effect in Cambridge, Massachusetts

The urban heat island (UHI) effect occurs when urban areas have temperatures that are warmer on average than the surrounding suburban and rural regions. Low albedo surfaces traditionally found in urban landscapes, such as dark asphalt and rooftops, absorb solar irradiance and reemit heat, which contributes to the UHI effect. Cambridge, Massachusetts, part of the Boston metropolitan area, expects severe impacts on human health, infrastructure, and local environmental features due to increasing urban heat. The Massachusetts – Boston NASA DEVELOP team partnered with the City of Cambridge Community Development Department and the American Geophysical Union’s Thriving Earth Exchange to inform ongoing planning and heat mitigation efforts. High Resolution Orthoimagery (HRO) and National Agriculture Imagery Program (NAIP) scenes were analyzed in conjunction with ancillary datasets to assess changes in rooftop albedo between 2008 and 2018. Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to determine nighttime land surface temperature (LST) between 2003 and 2019. Lastly, temperature anomalies were calculated using seasonally averaged nighttime LST values obtained from Aqua MODIS to display ‘hot spots’ for summers between 2004 and 2019. On average, albedo increased by 0.12 across the city, with the majority of positive change in commercial areas. Analysis of mean summer nighttime LST between 2003 and 2019 revealed a non-significant increase in temperature, although temperature anomaly maps demonstrated that Cambridge was warmer on average than rural areas. These results were incorporated in the interactive Cambridge Urban Heat Dashboard, which allows users to explore temporal trends in albedo.

Nicole Ramberg-Pihl↗
NASA NTRS2016

Texas Disasters II: Utilizing NASA Earth Observations to Assist the Texas Forest Service in Mapping and Analyzing Fuel Loads and Phenology in Texas Grasslands

The risk of severe wildfires in Texas has been related to weather phenomena such as climate change and recent urban expansion into wild land areas. During recent years, Texas wild land areas have experienced sequences of wet and dry years that have contributed to increased wildfire risk and frequency. To prevent and contain wildfires, the Texas Forest Service (TFS) is tasked with evaluating and reducing potential fire risk to better manage and distribute resources. This task is made more difficult due to the vast and varied landscape of Texas. The TFS assesses fire risk by understanding vegetative fuel types and fuel loads. To better assist the TFS, NASA Earth observations, including Landsat and Moderate Resolution Imaging Specrtoradiometer (MODIS) data, were analyzed to produce maps of vegetation type and specific vegetation phenology as it related to potential wildfire fuel loads. Fuel maps from 2010-2011 and 2014-2015 fire seasons, created by the Texas Disasters I project, were used and provided alternating, complementary map indicators of wildfire risk in Texas. The TFS will utilize the end products and capabilities to evaluate and better understand wildfire risk across Texas.

Brooke, Michael↗
NASA NTRS1998

Change Detection Analysis in Urban and Suburban Areas Using Landsat Thematic Mapper data: Case of Huntsville, Alabama

Two Landsat Thematic Mapper (TM) images, from July 1984 and July 1992, were used to identify land use/cover changes in the urban and suburban fringe of the city of Huntsville, Alabama. Image difference was the technique used to quantify the change between the two dates. The eight-year period showed a 16% change, mainly from agricultural lands to urban areas generated by the settlement of industrial, commercial, and residential areas. Visual analysis of the change map (i.e., difference image) supported this phenomenon by showing that most changes were occurring in the vicinity of the major roads and highways across the city.

Kuan, Dana↗
NASA NTRS1975

Photomorphic mapping for land-use planning

A comparison of different land types based on their physical and environmental characteristics is seen as a useful, if not vital, element of land-use planning decisions. The use of the photomorphic-mapping technique is described in order to delineate and compare the different land types in Boulder County, Colorado, according to their constraints and values for agricultural and urban uses. Employing high-altitude color infrared aerial photography of Boulder County at a scale of 1:100,000, photomorphic areas were delineated according to similarities in pattern, tone, and texture on the photographs. The boundaries of the areas were checked and adjusted using information from thematic maps and sampling data. Constraints on specific land uses in the county could then be described on a regional basis using the photomorphic areas as a framework.

Nichol, J. E.↗
NASA NTRS2012

An Effort to Map and Monitor Baldcypress Forest Areas in Coastal Louisiana, Using Landsat, MODIS, and ASTER Satellite Data

This presentation discusses a collaborative project to develop, test, and demonstrate baldcypress forest mapping and monitoring products for aiding forest conservation and restoration in coastal Louisiana. Low lying coastal forests in the region are being negatively impacted by multiple factors, including subsidence, salt water intrusion, sea level rise, persistent flooding, hydrologic modification, annual insect-induced forest defoliation, timber harvesting, and conversion to urban land uses. Coastal baldcypress forests provide invaluable ecological services in terms of wildlife habitat, forest products, storm buffers, and water quality benefits. Before this project, current maps of baldcypress forest concentrations and change did not exist or were out of date. In response, this project was initiated to produce: 1) current maps showing the extent and location of baldcypress dominated forests; and 2) wetland forest change maps showing temporary and persistent disturbance and loss since the early 1970s. Project products are being developed collaboratively with multiple state and federal agencies. Products are being validated using available reference data from aerial, satellite, and field survey data. Results include Landsat TM- based classifications of baldcypress in terms of cover type and percent canopy cover. Landsat MSS data was employed to compute a circa 1972 classification of swamp and bottomland hardwood forest types. Landsat data for 1972-2010 was used to compute wetland forest change products. MODIS-based change products were applied to view and assess insect-induced swamp forest defoliation. MODIS, Landsat, and ASTER satellite data products were used to help assess hurricane and flood impacts to coastal wetland forests in the region.

Spruce, Joseph P.↗
DOE OSTI2020

URBANopt: An Open-Source Software Development Kit for Community and Urban District Energy Modeling: Preprint

Urban building modeling tools are developing rapidly; these tools use emerging simulation workflows for specific urban environmental design tasks, such as assessing the impacts of energy efficiency technologies at a district scale. However, with the emergence of new environmental design tasks, addressing all possible use cases and tasks is challenging and cannot be covered by a single tool. Urban-scale analysis at this level of complexity often requires linking multiple emerging tools, rather than using a single tool, to adequately evaluate a variety of possible fields in urban environmental design. To achieve this, flexible platforms are needed to support multiple input formats (e.g., geometric and non-geometric building properties), enabling the mapping of such inputs to underlying simulation engines. This paper provides an overview of the open-source URBANopt Software Development Kit (SDK) for modeling high-performance buildings and energy systems at a district scale. URBANopt's flexible SDK is composed of several modules that can be customized to integrate with other tools and generate new workflows to perform urban environmental design tasks, such as capturing interactions between individual buildings, district energy systems, distributed energy resources, and the electric distribution grid. We describe the functionality of the core SDK modules in URBANopt (called Core Gem, GeoJSON Gem, and Scenario Gem) and discuss the flexibility of these modules as a means of integration with a variety of tools. We also document and demonstrate technical details of writing and combining new modules to create customized workflows. Finally, we present a case study that uses the URBANopt SDK to model a hypothetical mixed-use urban project and simulate various scenarios to meet district energy performance goals.

buildings↗
NASA NTRS1980

Indentifying environmental features for land management decisions

The wetlands and water-related land use in the Uinta Basin were classified and mapped using photointerpretation of U-2 infrared photography and digital LANDSAT data. The digital maps were used to augment photointerpretations. A highly effective diagnostic tool emerged when the LANDSAT digital print was photoreduced to a film positive at the same scale as the U-2 film and overlain on the U-2 color film. As a result of this merging technique, cover types can be identified more accurately and probablistic statements can be made about the relative amounts of water being consumed in one pasture vs. another. The hazards to urban development on sensitive and unstable land in the foothills of Davis County were studied using NASA U-2 photography. Shoreline fluctuations were mapped in the Farmington Bay using LANDSAT digital data.

Source record↗
NASA NTRS1982

The role of multispectral scanners as data sources for EPA hydrologic models

An estimated cost savings of 30% to 50% was realized from using LANDSAT-derived data as input into a program which simulates hydrologic and water quality processes in natural and man-made water systems. Data from the satellite were used in conjunction with EPA's 11-channel multispectral scanner to obtain maps for characterizing the distribution of turbidity plumes in Flathead Lake and to predict the effect of increasing urbanization in Montana's Flathead River Basin on the lake's trophic state. Multispectral data are also being studied as a possible source of the parameters needed to model the buffering capability of lakes in an effort to evaluate the effect of acid rain in the Adirondacks. Water quality in Lake Champlain, Vermont is being classified using data from the LANDSAT and the EPA MSS. Both contact-sensed and MSS data are being used with multivariate statistical analysis to classify the trophic status of 145 lakes in Illinois and to identify water sampling sites in Appalachicola Bay where contaminants threaten Florida's shellfish.

Slack, R.↗
NASA NTRS2022

Albuquerque Urban Development: Enhancing Urban Cooling Interventions by Modeling Urban Forestry through NASA Earth Observations in Albuquerque, New Mexico

The City of Albuquerque, New Mexico is experiencing increasing urban heat island(UHI) effects, which impact the health, safety, and comfort of the community. In partnership with the City of Albuquerque Department of Environmental Health, Department of Parks and Recreation, and Let’s Plant Albuquerque!, this project used satellite Earth observations from April 2016–August 2022 to model increases in tree canopy within the City of Albuquerque to help combat the urban heat island in the city’s warmer areas over the next decade. Using Landsat 8’s Thermal Infrared Sensor (TIRS) and the Ecosystem Spaceborne Thermal Radiometer Experiment on the International Space Station (ECOSTRESS), along with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Cooling and ENVI-Met models, the team modeled tree cover interventions and created land surface temperature maps. These outputs will help the city make data-driven decisions for their tree planting goal in a targeted approach.

Max Stewart↗
DOE OSTI2026

A Comprehensive Comparison of Building Height Products at a Global Level

The built environment continues to expand due to increasing human populations with some projections indicating additional growth of 2.5 billion persons by 2050. Buildings are a key component of the built environment and information on building height and urban morphology in general can help to better understand and address a host of development and urban planning issues including energy consumption, urbanization, population dynamics, urban heat islands, environmental degradation, and others. To date, several approaches have been developed to address the need for mapping building height at scale. These approaches can be grouped into two main categories: 1) directly measure height from remote sensing products or 2) indirectly estimate height from footprint morphology features at the building-by-building level. Due to a number of these global height datasets now being available for consumption and use across domains, there is a critical need to understand the differences displayed between these products and how the use of these may influence the outcome when applied. In this analysis, we compare and evaluate 5 global building height datasets: Global Human Settlement, World Settlement Footprint 3D, Global Building Morphology dataset, three-dimensional Global Building Footprints, and an approach developed at Oak Ridge National Laboratory (ORNL). Our analysis shows that, across 5 cities at a global scale with high-quality building height reference data, the approach developed by ORNL of inferring height at the building-by-building level is more accurate and consistent. This was evidenced by the ORNL product improving upon the baseline in all 5 cities, the only product to do so. These findings will aid researchers in choosing a global building height product that best meets their needs and provides global availability, consistency, and accuracy.

42 ENGINEERING↗
NASA NTRS1982

Topoclimatological survey of Switzerland

The application of Heat Capacity Mapping Mission data to subsynoptic climate analysis of Switzerland was examined. The data included the surface temperature distributions of urban heat islands and the Swiss Alps. Analog and digital data evaluation procedures are described as well as the ground truth acquisition and comparison program. The dependence of the temperature distributions on topography and surface coverage types is assessed. The results indicate that air temperature inversion zones are detectable.

Winiger, M.↗
NASA NTRS2020

Toa Baja Disasters: Utilizing Open-Source Earth Observations to Inform the Toa Baja Municipality’s Flood Mitigation Efforts and Educate the Public

Toa Baja, located just west of San Juan in Puerto Rico, is known as “the underwater city” due to its propensity to flood. The city contains the mouth of the island’s longest river, Río de la Plata, which drains into the Atlantic Ocean on the northern edge of the municipality. Proximity to these major water features and the flat, low terrain contribute to the flood-prone nature of the area. During tropical storm events, such as Hurricane Maria in 2017, Toa Baja experienced inundation of up to 20 feet. Changes in the global climate system are causing more intense and frequent tropical storms, making places like Toa Baja subject to irreparable damage. This NASA DEVELOP project collaborated with the Municipio Autónomo de Toa Baja, ResilientSEE, and the Massachusetts Institute of Technology Urban Risk Lab to supplement recent 2018 Federal Emergency Management Agency Hydraulic Engineering Centers-River Analysis System flood maps, which designated 63% of the area as a flood plain. The analysis provides a high-resolution interpretation of flood susceptibility using a variety of factors that collectively influence the likelihood of flooding. Sentinel-1 C-Band Synthetic Aperture Radar (C-SAR) data processed with Google Earth Engine scripting identified historical inundation and was used for validation purposes. Socioeconomic factors were combined with the inundation layer producing a final risk output. These outputs will improve public understanding of exposure to flood risk in Toa Baja and provide scientific evidence for flood mitigation advocacy.

Disasters↗
NASA NTRS2020

Toa Baja Disasters: Utilizing Open-Source Earth Observations to Inform the Toa Baja Municipality’s Flood Mitigation Efforts and Educate the Public

Toa Baja, located just west of San Juan in Puerto Rico, is known as “the underwater city” due to its propensity to flood. The city contains the mouth of the island’s longest river, Río de la Plata, which drains into the Atlantic Ocean on the northern edge of the municipality. Proximity to these major water features and the flat, low terrain contribute to the flood-prone nature of the area. During tropical storm events, such as Hurricane Maria in 2017, Toa Baja experienced inundation of up to 20 feet. Changes in the global climate system are causing more intense and frequent tropical storms, making places like Toa Baja subject to irreparable damage. This NASA DEVELOP project collaborated with the Municipio Autonómo de Toa Baja, ResilientSEE, and the Massachusetts Institute of Technology Urban Risk Lab to supplement recent 2018 Federal Emergency Management Agency Hydraulic Engineering Centers-River Analysis System flood maps, which designated 63% of the area as a flood plain. The analysis provides a high-resolution interpretation of flood susceptibility using a variety of factors that collectively influence the likelihood of flooding. Sentinel-1 C-Band Synthetic Aperture Radar (C-SAR) data processed with Google Earth Engine scripting identified historical inundation and was used for validation purposes. Socioeconomic factors were combined with the inundation layer producing a final risk output. These outputs will improve public understanding of exposure to flood risk in Toa Baja and provide scientific evidence for flood mitigation advocacy.

Disasters↗