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Urban employment multipliers and their application to aerospace industry
Landsat digital enhancements and classification maps are shown to be useful for updating the urban expansion of standard metropolitan statistical areas on a macro scale. Automated procedures for detecting nonurban to urban land coverage change using multitemporal Landsat data are investigated for five metropolitan areas, showing an overall delineation similar to that obtained from large scale aerial photography. The evaluated change detection procedures include image differencing, principal component transformation prior to differencing, and post classification comparison. Results show that image differencing techniques in MSS band 5 provide the most accurate land cover change detections.
The use of Landsat data to delineate urban boundaries in Standard Metropolitan Statistical Areas is considered. The project, which is being conducted by the Census-Urbanized Area Application System Verification program, seeks to develop processing techniques and output products that meet urban fringe delineation requirements. A processing approach which will use an interactive processing system to produce digital enhancement and classification results is described.
A wide range of challenges must be overcome to conduct Urban Air Mobility (UAM) operations at scale with electric vertical takeoff and landing aircraft as envisioned. This paper documents an analysis of one potential infrastructure challenge for UAM: available electrical grid power capacity. The success of UAM depends on the availability of electricity, which will be increasingly impacted by the proliferation of ground electric vehicles (EVs) over time. Under the worst-case assumption that each U.S. Metropolitan Statistical Area (MSA) can only access and utilize the electricity generated by power plants within its boundaries, it is estimated that the maximum number of UAM aircraft that can charge simultaneously in the 25 most populous MSAs will decrease by about 61% from about 129,000 in the year 2021 to about 51,000 in the year 2050, due to the projected increase in ground EVs. Also, it is estimated that the number of these MSAs that may lack any available electrical grid power capacity for UAM operations will increase from zero MSAs in 2021 to eleven MSAs in 2050—including three MSAs that prospective UAM operators have announced as target markets (Los Angeles, Miami, and Orlando)—unless electricity demand for other purposes is reduced.
A wide range of challenges must be overcome to conduct Urban Air Mobility (UAM) operations at scale with electric vertical takeoff and landing aircraft as envisioned. This paper documents an analysis of one potential infrastructure challenge for UAM: available electrical grid power capacity. The success of UAM depends on the availability of electricity, which will be increasingly impacted by the proliferation of ground electric vehicles (EVs) over time. Under the worst-case assumption that each U.S. Metropolitan Statistical Area (MSA) can only access and utilize the electricity generated by power plants within its boundaries, it is estimated that the maximum number of UAM aircraft that can charge simultaneously in the 25 most populous MSAs will decrease by about 61% from about 129,000 in the year 2021 to about 51,000 in the year 2050, due to the projected increase in ground EVs. Also, it is estimated that the number of these MSAs that may lack any available electrical grid power capacity for UAM operations will increase from zero MSAs in 2021 to eleven MSAs in 2050—including three MSAs that prospective UAM operators have announced as target markets (Los Angeles, Miami, and Orlando)—unless electricity demand for other purposes is reduced.
The author has identified the following significant results. The Norfolk-Portsmouth metropolitan statistical area in southeastern Virginia was the site of intensive testing of a number of land resources assessment methods. Land use and land cover data at three levels of detail were derived by manual image interpretation from both aircraft and satellite sources and used to characterize the 1,766 sq km (682 sq mi) area from the perspective of its various resource-related activities and problems. Measurements at level 1 from 1:100, 000 scale maps revealed 42 percent of the test area (excluding bays and estuaries) to be forest, 28 percent agriculture, 23 percent urban and built-up, 4 percent nonforested wetlands, and 2 percent water. At the same scale and level of detail, 10 percent of the area underwent change from one land use category to another in the period 1959-70, 62 percent of which involved the relatively irreversible change from forest or agriculture to urban uses.
The study employs a 108-year precipitation data record to identify statistically significant anomalies in rainfall downwind of the Phoenix urban region. The analysis reveals that during the monsoon season locations northeastern suburbs and exurbs of the Phoenix metropolitan area have experienced statistically significant increases in mean precipitation of 12 to 14 percent from a pre-urban (1895-1949) to post-urban (1950-2003) period. Mean and median post-urban precipitation totals in the anomaly region are significantly greater, in the statistical sense, than regions west of the city and in nearby mountainous regions of similar or greater topography. Further analysis of satellite-based rainfall totals for the summer of 2003 also reveal the existence of the anomaly region during a severe drought period. The anomaly can not simply be attributed to maximum topographic relief and is hypothesize to be related to urban-topographic interactions.
President George W. Bush and the Congress created the Commission on the Future of the United States Aerospace Industry to evaluate the current and future health of the industry as well as the challenges that lie ahead for the U.S. workforce and the economy. To accomplish our mission, we commissioned a study on the economic impact of the aerospace industry nationally and on a state-by-state basis, using the best available U.S. government data. This study sought to define the core of the aerospace industry. The resulting data represents that very core those jobs specifically tied to commercial and civilian aerospace. U.S. Aerospace and Aviation: A State-by-State Analysis examines the civilian and commercial aerospace and aviation industry by employment, wages, payroll, and establishments. The report found that the U.S. civilian and commercial aerospace and aviation industry employed over 2 million workers in 2001.
One important objective of a cooperative project between the U.S. Bureau of Census and NASA is to develop the ability to accurately delineate the types of land cover in the urban-rural transition zone of metropolitan areas. The application of principal components analysis to multidate Landsat imagery is being investigated as a method of reducing the overlap between residential and agricultural spectral signatures. The statistical concepts of principal components analysis are discussed, as well as the results of this analysis when applied to multidate Landsat imagery of the Washington, D.C. metropolitan area.
Methods to accurately delineate the types of land cover in the urban-rural transition zone of metropolitan areas were considered. The application of principal components analysis to multidate LANDSAT imagery was investigated as a means of reducing the overlap between residential and agricultural spectral signatures. The statistical concepts of principal components analysis were discussed, as well as the results of this analysis when applied to multidate LANDSAT imagery of the Washington, D.C. metropolitan area.
Millimeter-wave (mmWave) communications will play a key role in enhancing the throughput, reliability, and security of next generation wireless networks. These advancements are achieved through the large bandwidth available in this band and through the use of highly directional links that will be used to overcome the large pathloss at these frequencies. Although the terrestrial application of mmWave systems is advancing at a rapid pace, the use of mmWave communication systems in aviation systems or airports is still in its infancy. This can be attributed to the challenges related to radio technology and lack of development, and characterization of mmWave wireless channels for the aviation field and the airport environment. Consequently, one of our goals is to develop methodologies that support mmWave air to ground links, and various links at airports, by applying new localization schemes that allow for application of highly directional links that can be deployed over longer distances despite the high path loss at mmWave frequencies. However, a very thorough understanding of the mmWave channel models are needed to enable such new applications. To this end, in this paper, we present a survey of the current channel models in the mmWave band. The 3-dimensional statistical channel model is also reviewed and its parameters and typical characteristics for this model are identified and computed through simulation for the Boise metropolitan area.
The use of probabilistic forecasting has been growing in a variety of disciplines because of its potential to emphasize the degree of uncertainty inherent in a prediction. Interpretation of probabilistic forecasts, however, is oftentimes difficult, deterring users who may benefit from such forecasts. To encourage broader use of probabilistic forecasts in the field of air quality, a process for interpreting forecasts from a statistical probabilistic air quality surface ozone model [the Regression in Self Organizing Map (REGiS)] is demonstrated. Four procedures to convert probabilistic to deterministic forecasts are explored for the Philadelphia, Pennsylvania, metropolitan area. These procedures calibrate the predicted probability of daily maximum 8-h-average ozone exceeding a standard value by 1) estimating climatological relative frequency, 2) establishing a probability of an exceedance threshold as 50%, 3) maximizing the threat score, and 4) determining the unit bias ratio. REGiS is trained using 2000–11 ozone-season (1 May–30 September) data, calibrated using 2012–14 data, and evaluated using 2015–18 data. Assessment of the calibration data with the Pierce skill score suggests an exceedance threshold based on climatological relative frequency for the conversion from probabilistic to deterministic forecasts. Calibrated REGiS generally compares well to predictions from the U.S. national air quality model and operational “expert” forecasts over the evaluation period. For other probabilistic models and situations, different procedures of converting probabilistic to deterministic forecasts may be more beneficial. The methods presented in this paper represent an approach for operational air quality forecasters seeking to use probabilistic model output to support forecasts designed to protect public health.
The NIMBUS-G environmental monitoring satellite has an instrument (a gas correlation spectrometer) onboard for measuring the mass of a given pollutant within a gas volume. The present paper treats the problem: How can this type measurement be used to estimate the distribution of pollutant levels in a metropolitan area. Estimation methods are used to develop this distribution. The pollution concentration caused by a point source is modeled as a Gaussian plume. The uncertainty in the measurements is used to determine the accuracy of estimating the source strength, the wind velocity, diffusion coefficients and source location.
Data from 27 PAM (Portable Automated Mesonet) stations, operational as a phase of project NIMROD (Northern Illinois Meteorological Research on Downburst), are presented. It was found that PAM-measured winds are influenced by the mesoscale obstruction of the Chicago metropolitan area, as well as by the misoscale obstruction of identified trees and buildings. The mesoscale obstruction was estimated within the range of near zero to 50%, increasing toward the city limits, while the misoscale obstruction was estimated as being as large as 58% near obstructing trees which were empirically calculated to cause a wind speed deficit 50-80 times their height. Despite a statistical analysis based on one-million PAM winds, wind speed and stability transmission factors could not be accurately calculated; thus, in order to calculate the airflow free from obstacle, PAM-measured winds must be corrected.
This study summarizes the results from fitting a Bayesian hierarchical spatiotemporal model to coronavirus disease 2019 (COVID-19) cases and deaths at the county level in the United States for the year 2020. Two models were created, one for cases and one for deaths, utilizing a scaled Besag, York, Mollié model with Type I spatial-temporal interaction. Each model accounts for 16 social vulnerability and 7 environmental variables as fixed effects. The spatial pattern between COVID-19 cases and deaths is significantly different in many ways. The spatiotemporal trend of the pandemic in the United States illustrates a shift out of many of the major metropolitan areas into the United States Southeast and Southwest during the summer months and into the upper Midwest beginning in autumn. Analysis of the major social vulnerability predictors of COVID-19 infection and death found that counties with higher percentages of those not having a high school diploma, having non-White status and being Age 65 and over to be significant. Among the environmental variables, above ground level temperature had the strongest effect on relative risk to both cases and deaths. Hot and cold spots, areas of statistically significant high and low COVID-19 cases and deaths respectively, derived from the convolutional spatial effect show that areas with a high probability of above average relative risk have significantly higher Social Vulnerability Index composite scores. The same analysis utilizing the spatiotemporal interaction term exemplifies a more complex relationship between social vulnerability, environmental measurements, COVID-19 cases, and COVID-19 deaths.
The Charles River watershed intersects 35 municipalities within the Boston Metropolitan Area and has a population of 1.2 million, making it one of the most densely populated watersheds in New England. In recent years, the watershed has observed higher rates of flood inundation, mainly due to increased development, extreme precipitation events, and increased surface runoff. As the frequency of flood events increases and a changing climate poses an ongoing threat to local communities, governments, and organizations in Massachusetts need accurate flood risk assessments. This project partnered with the Charles River Watershed Association, the Town of Natick’s Office of Sustainability, and the Massachusetts Audubon Society to assess the potential for watershed degradation, flood vulnerability, and flood susceptibility in the watershed. The team used Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-Band Synthetic Aperture Radar (C-SAR), and Sentinel-2 MultiSpectral Instrument (MSI) to assess the feasibility of identifying flood events using remote sensing. After identifying images that overlapped with the reported flood events, the team concluded that it was not feasible to use Earth observation data to detect localized flooding. Instead, the Federal Emergency Management Agency (FEMA) 100-year floodplain was used as a proxy for areas where flooding may occur. The team used statistical analysis and supervised classification to develop a flood susceptibility map, incorporating factors like soil drainage, height above nearest drainage, and topographic wetness index. This was overlaid with demographic and socioeconomic data to create a flood vulnerability map. The flood susceptibility map captured over 2/3 of reported flood events in the watershed, an improvement over the 1/3 of events captured by the FEMA 100-year and 500-year floodplain maps.
The Charles River watershed intersects 35 municipalities within the Boston Metropolitan Area and has a total population of 1.2 million, making it one of the most densely populated watersheds in New England. In recent years, the watershed has observed higher rates of flood inundation, mainly due to increased development, extreme precipitation events, and increased surface runoff. As the frequency of flooding events increases and a changing climate poses an ongoing threat to local communities, governments and organizations in Massachusetts are in need of accurate flood risk assessments. NASA DEVELOP partnered with the Charles River Watershed Association, the Town of Natick’s Office of Sustainability, and the Massachusetts Audubon Society to assess flood vulnerability and susceptibility in the watershed. The team used Landsat 5 Thematic Mapper, Landsat 8 Operational Land Imager, Sentinel-1 C-Band Synthetic Aperture Radar, and Sentinel-2 MultiSpectral Instrument to assess the feasibility of identifying the extent of past flood events using remote sensing. After identifying images that overlapped with reported flood events, the team concluded that it was not feasible to use Earth observation data to detect localized flooding in the time available for this study. Instead, the Federal Emergency Management Agency (FEMA) 100-year floodplain was used as a proxy for areas where flooding may occur. The team used statistical regression analysis and validation and supervised classification to develop a flood susceptibility map, incorporating several flood conditioning factors. The susceptibility maps were calibrated to various thresholds, including two that highlight hypothetical flooding under more liberal and more conservative planning scenarios. These were overlaid with demographic and socioeconomic data to create flood vulnerability maps. The team’s flood susceptibility maps showed an improvement in capturing known flood events over the FEMA 100-year and 500-year floodplain maps. These results will be improved with the addition of stormwater drainage mapping and precipitation data. Results can be used to fill in the gaps to help the stakeholders understand their communities’ vulnerability and susceptibility to flooding and improve their preparedness plans.
Fine particulate matter (PM2.5) substantially contributes to air pollution and negatively affects human health. While many studies have investigated the use of passive column-integrated aerosol optical depth to infer surface PM2.5, the use of lidar observations for air quality characterization is not nearly as extensive. Lidar measurements are critical, however, due to the vertical aerosol information they provide, including near the surface. In this presentation, we first provide an overview of various lidar-based approaches for estimating PM2.5 concentrations and then discuss how lidar measurements can assist other air quality applications. For example, estimates of PM2.5 have been obtained in a physics-based approach through CALIOP near-surface aerosol extinction retrievals, assumptions on the mass extinction efficiency, and incorporating other parameters (an aerosol hygroscopic growth factor and PM2.5/PM10 ratio). Application of this algorithm over the contiguous United States (CONUS) from 2006 to 2018 yielded larger PM2.5 values over the eastern and western CONUS (~10-15 μg/m³) and lower PM2.5 levels in the central CONUS (~5 μg/m³). These spatial patterns were similar to those from gridded PM2.5 concentrations obtained through in situ measurements at ground stations operated by the US Environmental Protection Agency. In another approach, the Cloud Aerosol Transport System (CATS) lidar was used with the Goddard Earth Observing System (GEOS) model in a 1D ensemble-based variational technique to obtain PM2.5 over the US and Europe, and the spatial patterns of the CATS/GEOS based PM2.5 concentrations generally captured those from surface stations (with corresponding hourly EPA PM2.5 vs CATS PM2.5 statistics of R=0.4 and bias=1.5 μg/m³). In our recent work, as part of the Models, In situ, and Remote sensing of Aerosols (MIRA) Working Group, we have applied both the CALIOP and CATS/GEOS based approaches over the highly polluted country of India during the post-monsoon season (September-October 2016). We derived elevated levels of two-month mean PM2.5 (~100 μg/m³) in northern India, especially near New Delhi. These high PM2.5 concentrations in the Indo-Gangetic plain are driven in large part from the seasonal burning of crop residue and meteorological conditions typical at this time of the year, such as low wind speeds and a shallow boundary layer. While the satellite-derived PM2.5 moderately replicates (R = ~0.7-0.9) the spatial variability in the two-month mean of surface in situ PM2.5 from monitoring sites operated by the Central and State Pollution Control Boards, we show results from specific scenes for which there are large deviations between the satellite-derived PM2.5 and in situ measurements. Other current work on this topic focuses on developing PM2.5 estimates using airborne high spectral resolution lidar measurements through machine learning regression algorithms and involves several parameters (e.g., aerosol extinction, color ratio, lidar ratio). Application of this method over major metropolitan areas in the US and Asia have resulted in high correlations (R = 0.93) with surface measurements. This airborne lidar approach can be adapted to spaceborne lidar measurements, and all three of these approaches can be applied to ESA’s EarthCARE Atmospheric Lidar instrument, setting the stage for the future Cloud Aerosol Lidar for Global Scale Observations of the Ocean-Land Atmosphere System (CALIGOLA) mission. Ultimately, beyond estimates of PM2.5, the aerosol vertical distribution from lidars can benefit studies involving passive sensor approaches for PM2.5 proxies (including from geostationary satellites), wildfire smoke plume injection heights, volcanic emissions (e.g., ash height retrievals), and aerosol/air quality model assimilation, evaluation, and forecasts.