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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 NTRS2016

Space Architecture for MoonVillage

The concept of a multinational MoonVillage, as proposed by Jan Wörner of ESA, is analyzed with respect to diverse factors affecting its implementation feasibility: potential activities and scale as a function of location, technology, and purpose; potential participants and their roles; business models for growth and sustainability as compared to the ISS; and implications for the field of space architecture. Environmental and operations constraints that govern all types of MoonVillage are detailed. Findings include: 1) while technically feasible, a MoonVillage would be more distributed and complex a project than the ISS; 2) significant and distinctive opportunities exist for willing participants, at all evolutionary scales and degrees of commercialization; 3) the mixed-use space business park model is essential for growth and permanence; 4) growth depends on exporting lunar material products, and the rate and extent of growth depends on export customers including terrestrial industries; 5) industrial-scale operations are a precondition for lunar urbanism, which goal in turn dramatically drives technology requirements; but 6) industrial viability cannot be discerned until significant in situ operations occur; and therefore 7) government investment in lunar surface operations is a strictly enabling step. Because of the resources it could apply, the U.S. government holds the greatest leverage on growth, no matter who founds a MoonVillage. The interplanetary business to be built may be cause for engagement.

Sherwood, Bent↗
NASA NTRS1975

Natural Resources Inventory and Land Evaluation in Switzerland

The author has identified the following significant results. A system was developed to operationally map and measure the areal extent of various land use categories for updating existing and producing new and actual thematic maps showing the latest state of rural and urban landscapes and its changes. The processing system includes: (1) preprocessing steps for radiometric and geometric corrections; (2) classification of the data by a multivariate procedure, using a stepwise linear discriminant analysis based on carefully selected training cells; and (3) output in form of color maps by printing black and white theme overlays of a selected scale with photomation system and its coloring and combination into a color composite.

Haefner, H.↗
NASA NTRS1985

The Timing of Noise-Sensitive Activities in Residential Areas

Data from a nationally representative survey of time use was analyzed to provide estimates of the percentage of the population which is engaged in noise sensitive activities during each hour of the day on weekdays, Fridays, Saturdays and Sundays. Estimates are provided of the percentage engaged in aural communication activities at home, sleeping at home, or simply at home. The day can be roughly divided into four noise sensitivity periods consisting of two relatively steady state periods, night and day and the early morning and evening transition periods. Weekends differ from weekdays in that the morning transition period is one hour later and the numbers of people engaged in aural communication during the day at home are approximately one-half to three-quarters greater. The extent and timing of noise sensitive activities was found to be similiar for all parts of the United States, for different sizes of urban areas, and for the three seasons surveyed (September through May). The timing of activity periods does not differ greatly by sex or age even though women and people over 65 are much more likely to be at home during the daytime.

Fields, J. M.↗
NASA NTRS2008

Land-use and Land-cover Change from 1974 to 2008 around Mobile Bay

This project is a Gulf of Mexico Application Pilot in which NASA Stennis Space Center (SSC) is working within a regional collaboration network of the Gulf of Mexico Alliance. NASA researchers, with support from the NASA SSC Applied Science Program Steering Committee, employed multi-temporal Landsat data to assess land-use and land-cover (LULC) changes in the coastal counties of Mobile and Baldwin, AL, between 1974 and 2008. A multi-decadal time-series, coastal LULC product unique to NASA SSC was produced. The geographic extent and nature of change was quantified for the open water, barren, upland herbaceous, non-woody wetland, upland forest, woody wetland, and urban landscapes. The National Oceanic and Atmospheric Administration (NOAA) National Coastal Development Data Center (NCDDC) will assist with the transition of the final product to the operational end user, which primarily is the Mobile Bay National Estuary Program (MBNEP). We found substantial LULC change over the 34-year study period, much more than is evident when the change occurring in the last years. Between 1974 and 2008, the upland forest landscape lost almost 6% of the total acreage, while urban land cover increased by slightly more than 3%. With exception to open water, upland forest is the dominant landscape, accounting for about 25-30% of the total area.

Ellis, Jean↗
DOE OSTI2021

Wind plants can impact long-term local atmospheric conditions

Abstract Long-term weather and climate observatories can be affected by the changing environments in their vicinity, such as the growth of urban areas or changing vegetation. Wind plants can also impact local atmospheric conditions through their wakes, characterized by reduced wind speed and increased turbulence. We explore the extent to which the wind plants near an atmospheric measurement site in the central United States have affected their long-term measurements. Both direct observations and mesoscale numerical weather prediction simulations demonstrate how the wind plants induce a wind deficit aloft, especially in stable conditions, and a wind speed acceleration near the surface, which extend $$\sim 30$$ ∼ 30 km downwind of the wind plant. Turbulence kinetic energy is significantly enhanced within the wind plant wake in stable conditions, with near-surface observations seeing an increase of more than 30% a few kilometers downwind of the plants.

17 WIND ENERGY↗
NASA NTRS2017

Global navigation satellite system reflectometry (GNSS-R) algorithms for wetland observations

It is important to closely monitor the state of the world’s wetlands, as climate change and human encroachment in a rapid global urbanization trend threaten to cause large-scale wetland collapse. Because wetlands are often difficult to observe in situ, remote sensing is the only viable way to map wetland extent globally. However, current remote sensing methods suffer limitations in capturing wetland extent, and more importantly, wetland dynamics at appropriate spatial and temporal scales. GNSSReflectometry could help fill the current observation gap, as experimental data show that ground-reflected GNSS signals are very sensitive to changes in inundated areas. Furthermore, because this technique only requires a custom developed receiver and antenna system, a constellation of such instruments can potentially be launched at relatively low cost, providing global observations at sub-daily intervals. One challenge remains, however, which is quantitatively formulating the geophysical product of reflections over the land surface in various states of inundation. Here, we use a novel reflection dataset, derived from the SMAP radar receiver, to elucidate the sensitivity of reflections to small land surface features and their seasonal variations. Additionally, we quantify the dynamic range of reflections over both open and closed wetlands, and suggest an algorithm for wetland type classification.

UNKNOWN↗
NASA NTRS2008

Quantifying VOC-Reaction Tracers, Ozone Production, and Continuing Aerosol Production Rates in Urban and Far-Downwind Atmospheres

We have found a surprisingly informative decomposition of the complex question of smoggy ozone production (basically, [HO2] in a more locally determined field of [NO]) in the process of linked investigations of modestly smoggy Eastern North America (by NASA aircraft, July 2004) and rather polluted Flushing, NYC (Queens College, July, 2001). In both rural and very polluted situations, we find that a simple contour graph parameterization of the local principal ozone production rate can be estimated using only the variables [NO] and j(sub rads) [HCHO]: Po(O3) = c (j(sub rads) [HCHO])(sup a) [HCHO](sup b). Here j(sub rads) is the photolysis of HCHO to radicals, presumably capturing many harder-UV photolytic processes and the principle ozone production is that due to HO2; mechanisms suggest that ozone production due to RO2 is closely correlated, often suggesting a limited range of different proportionality factors. The method immediately suggests a local interpretation for concepts of VOC limitation and NOx limitation. We believe that the product j(sub rads) [HCHO] guages the oxidation rate of observed VOC mixtures in a way that also provides [HO2] useful for the principle ozone production rate k [HO2] [NO], and indeed, all ozone chemical production. The success of the method suggests that dominant urban primary-HCHO sources may transition to secondary plume-HCHO sources in a convenient way. Are there other, simple, near-terminal oxidized VOC's which help guage ozone production and aerosol particle formation? Regarding particles, we report on, to the extent NASA Research resources allow, on appealing relationships between far-downwind (Atlantic PBL) HCHO and very fine aerosol (including sulfate. Since j(sub rads) [HCHO] provides a time-scale, we may understand distant-plume particle production in a more quantitative manner. Additionally we report on a statistical search in the nearer field for relationships between glyoxals (important near-terminal aromatic and isoprene reaction products) and aerosol production, looking for VOC's that might be most implicated. All three variables j(sub rads), [HCHO], and [NO] are relatively easily measured in widespread air pollution monitoring networks, and all are deducible form space-borne observations, though estimation of [NO] from [NO2] (the species observable from space) may require care. We report also on airborne and surface observations of HCHO, suggesting that concentrated (urban) and more diffuse (forest) sources may be distinguishable from space. The use of the 3.58 micron microwindow for HCHO remote sensing should allow much sharper resolution of HCHO than the UV. UV sensing requires large and expensive instruments, but even these seem justified since formaldehyde is so informative.

Chatfield, Robert↗
NASA NTRS2019

Analysis of Interactions Between Urban Air Mobility (UAM) Operations and Conventional Traffic in Urban Areas: Traffic Alert and Collision Avoidance (TCAS) Study for UAM Operations

This paper presents a preliminary modeling and analysis of interactions between proposed UAM operations and present-day conventional traffic if UAM operations occurred along FAA- approved helicopter routes and altitude ceilings. It assesses the extent to which the UAM operations will trigger TCAS resolution advisories (RA) aboard the conventional aircraft in the Dallas/Fort Worth (DFW) terminal airspace. It is observed that under deterministic UAM operational conditions, no RAs will be triggered. Furthermore, the impact of UAM altitude uncertainty is also evaluated. It is observed that restricting the UAM cruise altitudes to 990 feet above Mean Sea Level (MSL) or below reduced the chance of triggering an RA to under five percent throughout the day, even in the presence of maximum altitude error of 30 feet.

Bulusu, Vishwanath↗
NASA NTRS1993

Multifrequency observations

Analyses of synthetic aperture radar (SAR) image data were performed at DLR to classify various kinds of vegetation and different terrain types. The data were collected both with the DLR experimental synthetic aperture radar (E-SAR) in X-band, C-band, and L-band and with the NASA/JPL DC-8 SAR in C-band, L-band, and P-band. E-SAR is a single frequency and single polarization system (both parameters can be selected) but several flights were used to collect multifrequency/multipolarization data which were geometrically matched after processing. Classification of different crop types was based on comparison of the backscatter coefficients of calibrated SAR data in different frequency bands and polarizations. The DC-8 STAR collects polarimetric data in different bands simultaneously. Data acquired with the NASA/JPL DC-8 STAR are qualified for scientific investigations by reducing the cross-talk and channel imbalance to a tolerable extent. The data are absolutely calibrated by using reference targets with known backscattering cross-sections. The signatures and polarimetric features of terrain types, such as grassland, concrete, sea, forest (coniferous; deciduous) and urban areas, are extracted and discussed with respect to frequency and incidence angle dependence. A multifrequency polarimetric feature vector was applied for classification. The results of this new approach for separating and classifying different object classes are presented.

Glitz, R.↗
NASA NTRSDate not supplied

Lower Illinois River Valley Ecological Forecasting: Inundation Mapping of the Lower Illinois River Valley Using Synthetic Aperture Radar and Optical Satellite Imagery for Wetland Conservation and Restoration Prioritization Efforts

The Lower Illinois River Valley (LIRV) is home to some of the richest agricultural lands in the United States and its wetlands provide key ecosystem services like clean water and flood reduction. It has also experienced extensive degradation due to development and urban pollution. The Great Rivers Land Trust (GRLT), the National Great Rivers Research & Education Center, Principia College, and the American Geophysical Union’s (AGU) Thriving Earth Exchange sought to incorporate inundation and surface water extent layers into their geodatabases to more accurately identify priority areas for wetland restoration. This project aimed to determine the feasibility of detecting inundation extent and duration along the valley using remotely sensed data. The team used Sentinel-1 C-band Synthetic Aperture Radar (SAR) data to classify open water and inundated vegetation within the study site. The open water classification was compared to Dynamic Surface Water Extent (DSWE) derived from Landsat 8 Operational Land Imager. The team successfully created layers of inundation minimum and maximum extent, as well as inundation duration across the study area for 2019 and 2020. The open water classification resulted in an overall accuracy of 86% when validated against DSWE classifications. These analyses will help end users to identify high priority areas along the LIRV best suited for land conversion projects in the future.

Vanessa Machuca↗
NASA NTRS2022

Lower Illinois River Valley Ecological Forecasting: Inundation Mapping of the Lower Illinois River Valley Using Synthetic Aperture Radar and Optical Satellite Imagery for Wetland Conservation and Restoration Prioritization Efforts

The Lower Illinois River Valley (LIRV) is home to some of the richest agricultural lands in the United States and its wetlands provide key ecosystem services like clean water and flood reduction. It has also experienced extensive degradation due to development and urban pollution. The Great Rivers Land Trust (GRLT), the National Great Rivers Research & Education Center, Principia College, and the American Geophysical Union’s (AGU) Thriving Earth Exchange sought to incorporate inundation and surface water extent layers into their geodatabases to more accurately identify priority areas for wetland restoration. This project aimed to determine the feasibility of detecting inundation extent and duration along the valley using remotely sensed data. We used Sentinel-1 C-band Synthetic Aperture Radar (SAR) data to classify open water and inundated vegetation within the study site. The open water classification was compared to Dynamic Surface Water Extent(DSWE) derived from Landsat 8 Operational Land Imager. We successfully created layers of inundation minimum and maximum extent, as well as inundation duration across the study area for 2019 and 2020. The open water classification resulted in an overall accuracy of 86% when validated against DSWE classifications. These analyses will help end users to identify high priority areas along the LIRV best suited for land conversion projects in the future.

Vanessa Machuca↗
NASA NTRSDate not supplied

Cloud-Based Solutions for Monitoring Coastal Ecosystems and Prioritization of Restoration Efforts Across Belize

In recent years the availability of automated change detection algorithms in Google Earth Engine has permitted cloud-based processing of large time series of satellite imagery. Models such as the Continuous Change Detection and Classification (CCDC), CCDC-Spectral Mixture Analysis (CCDC-SMA), and Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) allow users to exploit decades of Earth Observations (EO) , leveraging the Landsat archive and data from other sensors to detect disturbance in forest ecosystems. Despite the wide adoption of these methods, robust documentation and growing community of users, little research has explored their use in mangrove environments. Mangroves are dynamic environments subject to changes due to not only the natural migration of mudflats but also coastal erosion, urban expansion, aquaculture practices, etc. This work aims to identify best practices for the application of these models to identify and monitor changes in the Belizean mangroves, which experienced an estimated 5.4% decrease in extent between 1980 and 2017 (Cherrington et al. 2020). Partnering directly with the Belizean Forest Department, our team will develop a replicable, efficient methodology to annually update the country’s mangrove extent employing EO-based change detection. This collaboration supports Belize’s marine conservation and climate resilience efforts, such as its Blue Bond agreement with the Nature Conservancy, building on national processes to monitor these critical ecosystems.

Mangroves↗
NASA NTRSDate not supplied

FloodPlanet: High-Resolution Commercial Imagery for Training and Validation of Deep Learning-Based Models of Inundation Extent

Flooding events are becoming increasingly frequent worldwide and are known to cause extensive damage. Public optical and radar satellite imagery can be used to detect large areas of inundation in rural areas, however, long revisit times and coarse spatial resolution limit applications for short-lived events and urban areas. Commercial constellations such as those operated by Planet offer increased spatial and temporal resolution and can supplement mapping efforts to provide more information to disaster response, relief, and mitigation efforts. Deep learning requires high quality labeled data for training across coincident sensors. The FloodPlanet dataset presented here contains labeled surface water for 18 events across the world based on Planetscope imagery with coincident Harmonized Landsat Sentinel-2 ( HLS) or Sentinel-1 and builds upon the previously existing Sen1Floods11, xBD, and NASA Sentinel-1 datasets. Sen1Floods11 includes 4,831 512x512 pixel overlapping tiles of coincident Sentinel-1 and Sentinel-2 data observing 11 flood events across the world from 2017-2019. The dataset contains a combination of automated and hand-labeled surface water for use in training and validation of inundation modeling efforts. The xBD dataset identifies flood-damaged buildings and indicates the scale of damage to each (none, minor, moderate, and major) from four flood events which occurred in the United States, India, Nepal, and Bangladesh from the same time period. The NASA dataset contains hand-labeled water bodies observed in Sentinel-1 imagery during five flood events within the 2017-2019 period. The effort presented here utilizes observations from these previously investigated flood events to generate labels of surface water at the 3-5m spatial resolution provided by Planetscope and facilitate the comparison between public and commercial data. A data pipeline was built which uses clustering algorithms to pick the most suitable overlapping chips between the public data and PlanetScope data for manual labeling. Labels were created manually using NASA’s ImageLabeler tool and include areas of high- and low-confidence water. The high confidence designation is reserved for areas of open, unobstructed water while low confidence is used for areas of suspected water beneath vegetation, clouds, or cloud shadows. Expected to be released in late 2022, the FloodPlanet dataset will include tiled imagery with a unique ID for each 1024x1024 pixel tile, 7 bands of HLS data, and high- and low-confidence flood labels in both shapefile and tiff formats. The authors will follow Spatial Temporal Access Catalog (STAC) guidelines to release FloodPlanet on the Radiant Earth ML hub, which hosts public datasets for machine learning.

Alexander Melancon↗
NASA NTRS2021

Developing a Set of Indicators to Identify, Monitor, and Track Impacts and Change in Forests of the United States

United States forestland is an important ecosystem type, land cover, land use, and economic resource that is facing several drivers of change including climatic. Because of its significance, forestland was identified through the National Climate Assessment(NCA) as a key sector and system of concern to be included in a system of climate indicators as part of a sustained assessment effort. Here, we describe 11 informative core indicators of forests and climate change impacts with metrics available or nearly available for use in the NCA efforts. The recommended indicators are based on a comprehensive conceptual model which recognizes forests as a land use, an ecosystem, and an economic sector. The indicators cover major forest attributes such as extent, structural components such as biomass, functions such as growth and productivity, and ecosystem services such as biodiversity and outdoor recreation. Interactions between humans and forests are represented through indicators focused on the wildland-urban interface, cost to mitigate wildfire risk, and energy produced from forest-based biomass. Selected indicators also include drought and disturbance from both wildfires and biotic agents. The forest indicators presented are an initial set that will need further refinement in coordination with other NCA indicator teams. Our effort ideally will initiate the collection of critical measurements and observations and lead to additional research on forest-climate indicators.

U.S. forests↗
NASA NTRS1973

Analysis of ERTS-1 imagery of Wyoming and its application to evaluation of Wyoming's natural resources

The author has identified the following significant results. The Wyoming investigation has progressed according to schedule during the Jan. - Feb., 1973 report period. A map of the maximum extent of Pleistocene glaciation was compiled for northwest Wyoming from interpretations of glacial features seen on ERTS-1 imagery. Using isodensitometry as a tool for image enhancement, techniques were developed which allowed accurate delineation of small urban areas and provided distinction of broad classifications within these small urban centers.

Marrs, R. W.↗
NASA NTRS1980

Utilizing LANDSAT imagery to monitor land-use change - A case study in Ohio

A study, performed in Ohio, of the nature and extent of interpretation errors in the application of Landsat imagery to land-use planning and modeling is reported. Potential errors associated with the misalignment of pixels after geometric correction and with misclassification of land cover or land use due to spectral similarities were identified on interpreted computer-compatible tapes of a portion of Franklin County for two adjacent days of 1975 and one day of 1973, and the extents of these errors were quantified by comparison with a ground-checked set of aerial-photograph interpretations. The open-space and agricultural categories are found to be the most consistently classified, while the more urban areas were classified correctly only from about 43 to 8% of the time. It is thus recommended that the direct application of Landsat data to land-use planning must await improvements in classification techniques and accuracy.

Gordon, S. I.↗
NASA NTRS2021

Hawai‘i Island Disasters: Using NASA Earth Observations to Assess Coastal Flood Risk with Measures of Land Cover Change, Flood Extent, and Vulnerability for Adaptation and Mitigation Planning on Hawai‘i Island

As the County of Hawai‘i faces an increased risk of extreme flooding events, sea-level rise, and other hazards associated with climate change, the need for building geospatial capacity to make better-informed decisions is critical. The County of Hawai‘i and Arizona State University partnered with NASA DEVELOP to complete a macro-scale risk analysis for the island of Hawai‘i analyzing flooding, land cover, vulnerability, and exposure factors using Earth observations and socio-economic data. The team assessed the variation in urban coastal vulnerability around the entire island of Hawai‘i, using satellite imagery of coastal land cover typology from satellite products such as Landsat 8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Sentinel-1 Synthetic Aperture Radar (SAR). The team made a sharable geodatabase containing datasets modeling vulnerability to coastal flooding as well as the Hawai‘i Flood Risk Toolbox (HiFloRT) which contains multiple tools for the County to map land cover, extreme rainfall and flood extent across the Island. The end products will allow the County of Hawai‘i to establish a protocol and standard framework for the utilization of Earth observations in future planning.

Garren Kalter↗
NASA NTRS2024

Cali Urban Development: Using NASA Earth Observations to Assess Wetlands and Land Reclamation in Cali, Colombia

Recent research has documented the global decline of wetlands, largely attributed to increased urbanization and agriculture. This NASA DEVELOP study partnered with two local environmental entities in Cali, Colombia: The Fundación Dinamizadores Ambientales and the Departamento Administrativo de Gestión del Medio Ambiente. The team utilized Earth observations to evaluate trends in wetland extent, potential, and land cover in Cali between 2002 and 2023. A supervised classifier was generated within Google Earth Engine to create land use analyses of the region using Landsat 5 TM, Landsat 8 OLI, and Landsat 9 OLI-2 imagery. To identify locations of wetland potential within the study area, wetland probability was assessed by inputting PlanetScope, Sentinel-2 MSI, and partner-provided datasets into the Wetland Intrinsic Potential Tool in ArcGIS Pro and R. Data from Sentinel-1 C-SAR, Sentinel 2-MSI, and Suomi-NPP VIIRS were used to evaluate wetland extent using the Wetland Extent 3.0 Tool in3 Python. Overall, results indicated high wetland potential, particularly in the southeast region where agricultural areas were previously wetlands. Outputs also suggest a vast network of riparian wetlands in Cali. This study did not investigate socioeconomic data as it relates to wetlands, which is an avenue for future research. This project supplemented research into links between land use change, wetland extent, and wetland potential, and provided partner organizations with an objective foundation from which they can identify at-risk wetlands and develop community initiatives for management, conservation, and education.

Cali↗