Radiofrequency noise measurements in urban areas at 480 and 950 megahertz
Radiofrequency noise measurements in urban areas at 480 and 950 megahertz
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Radiofrequency noise measurements in urban areas at 480 and 950 megahertz
High resolution in-situ CO2 measurements were recorded onboard the NASA P-3B during the DISCOVER-AQ (Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality) Field Campaign, to investigate the ability of space-based observations to accurately assess near surface conditions related to air quality. This campaign includes, Washington DC/Baltimore, MD (July 2011), San Joaquin Valley, CA (January - February 2013), Houston, TX (September 2013), and Denver, CO (July-August 2014). Each of these campaigns consisted of missed approaches and approximately two hundred vertical soundings of CO2 within the lower troposphere (surface to about 5 km). In this study, surface (0 - 1 km) and column-averaged (0 - 3.5 km) CO2 mixing ratio values from the vertical soundings in the four geographically different urban areas are used to investigate the temporal and spatial variability of CO2 within the different urban atmospheric emission environments. Tracers such as CO, CH2O, NOx, and NMHCs are used to identify the source of CO2 variations in the urban sites. Additionally, we apply nominal CO2 column weighting functions for potential future active remote CO2 sensors operating in the 1.57-microns and 2.05-microns measurement regions to convert the in situ CO2 vertical mixing ratio profiles to variations in CO2 column optical depths, which is what the active remote sensors actually measure. Using statistics calculated from the optical depths at each urban site measured during the DISCOVER-AQ field campaign and for each nominal weighting function, we investigate the natural variability of CO2 columns in the lower troposphere; relate the CO2 column variability to the urban surface emissions; and show the measurement requirements for the future ASCENDS (Active Sensing of CO2 Emissions over Nights, Days, and Seasons) in the continental U.S. urban areas.
The urban heat island (UHI) has become a widely acknowledged, observed, and researched phenomena because of its broad implications. It is estimated that by the year 2025, 80% of the world's population will live in cities (UNFP, 1999). The UHI has been documented in the literature to affect local and regional temperature distributions, wind patterns and air quality. The UHI can also impact the development of clouds and precipitation in and around cities. This paper will focus primarily on the UHI's impact on precipitation. In the past 30 years, several observational and climatological studies have theorized that the UHI can have a significant influence on mesoscale circulations and resulting precipitation (see Shepherd et al. 2002 for a thorough review). More recent studies have continued to validate and extend the findings from pre and post-METROMEX investigations. Shepherd et al. (2002) was one of the first (and possibly the first) attempts to identify rainfall modification by urban areas using satellite-based rainfall measurements. Using a 15-month (spanning three years) analysis of mean rainfall rates, the cities of Atlanta, Montgomery, Dallas, Waco, and San Antonio were examined. Shepherd et al. (2002) found that the average percentage increase in mean rainfall rate in a hypothesized "downwind maximum impact area" over an "upwind control area" was 28.4% with a range of 14.6 to 51%. The typical distance of the downwind rainfall rate anomaly from the urban center was 30-60 km, consistent with earlier studies. This fact provides confidence that UHI-rainfall effects are real and detectable by TRMM satellite estimates.
Results and analysis of combined aerial and ground ultrahigh frequency noise survey in urban area
An urban area in central Pennsylvania and the surrounding locality were investigated separately at first by photointerpretation of ERTS-1 imagery and by computer processing of MSS tapes. Next the photointerpretation and processing were coordinated. The results of the cooperative effort of photointerpreters and computer processing analysts were much improved over independent efforts. It was found that single frames of U-2 photography could be projected onto printer output maps with little recognizable distortion in areas 10 to 25 cm square. In this way targets could be identified for use as training areas for computer processed signature identification. In addition, at any stage of category mapping, the level of success in correct classification could be assessed by this method. The results of the classification of the study area are discussed.
An automated procedure has been developed to derive relevant factors, which can increase the ability to produce objective, repeatable methods for determining aerodynamic surface roughness. Aerodynamic surface roughness is used for many applications, like atmospheric dispersive models and wind-damage models. For this technique, existing lidar data was used that was originally collected for terrain analysis, and demonstrated that surface roughness values can be automatically derived, and then subsequently utilized in disaster-management and homeland security models. The developed lidar-processing algorithm effectively distinguishes buildings from trees and characterizes their size, density, orientation, and spacing (see figure); all of these variables are parameters that are required to calculate the estimated surface roughness for a specified area. By using this algorithm, aerodynamic surface roughness values in urban areas can then be extracted automatically. The user can also adjust the algorithm for local conditions and lidar characteristics, like summer/winter vegetation and dense/sparse lidar point spacing. Additionally, the user can also survey variations in surface roughness that occurs due to wind direction; for example, during a hurricane, when wind direction can change dramatically, this variable can be extremely significant. In its current state, the algorithm calculates an estimated surface roughness for a square kilometer area; techniques using the lidar data to calculate the surface roughness for a point, whereby only roughness elements that are upstream from the point of interest are used and the wind direction is a vital concern, are being investigated. This technological advancement will improve the reliability and accuracy of models that use and incorporate surface roughness.
Analysis of urban area and surrounding locale in central Pennsylvania using ERTS-1 data supported by U-2 photography
A novel approach is introduced to correlating urbanization and rainfall modification. This study represents one of the first published attempts (possibly the first) to identify and quantify rainfall modification by urban areas using satellite-based rainfall measurements. Previous investigations successfully used rain gauge networks and around-based radar to investigate this phenomenon but still encountered difficulties due to limited, specialized measurements and separation of topographic and other influences. Three years of mean monthly rainfall rates derived from the first space-based rainfall radar, Tropical Rainfall Measuring Mission's (TRMM) Precipitation Radar, are employed. Analysis of data at half-degree latitude resolution enables identification of rainfall patterns around major metropolitan areas of Atlanta, Montgomery, Nashville, San Antonio, Waco, and Dallas during the warm season. Preliminary results reveal an average increase of 5.6% in monthly rainfall rates (relative to a mean upwind CONTROL area) over the metropolis but an average increase of approx. 28%, in monthly rainfall rates within 30-60 kilometers downwind of the metropolis. Some portions of the downwind area exhibit increases as high as 51%. It was also found that maximum rainfall rates found in the downwind impact area exceeded the mean value in the upwind CONTROL area by 48%-116% and were generally found at an average distance of 39 km from the edge of the urban center or 64 km from the center of the city. These results are quite consistent studies of St. Louis (e.g' METROMEX) and Chicago almost two decades ago and more recent studies in the Atlanta and Mexico City areas.
Unmanned aerial systems (UASs) in urban areas can pose significant safety risks to dynamic ground objects (DGOs) such as people, pets, and bikes; especially for off-nominal emergency traverses and landings. This paper will examine a framework for evaluating the UAS safety benefits which can be achieved by classifying DGO hazards, modeling their behavior, and assigning collision costs. DGOs are assumed to be any ground objects which are either moving or capable of moving. Safety benefits will be assessed by analyzing metrics computed from UAS and DGO trajectories which take into account intent and uncertainties. This paper will establish the theoretical relationships mapping these trajectories and DGO classifications to safety levels. Sensor capabilities will be mapped to DGO trajectory uncertainties, so that safety can be directly estimated from the sensor specifications for a given UAS trajectory.
Digitally processed SEASAT synthetic aperture raar (SAR) imagery of the Denver, Colorado urban area was examined to explore the potential of SAR data for mapping urban land cover and the compatability of SAR derived land cover classes with the United States Geological Survey classification system. The imagery is examined at three different scales to determine the effect of image enlargement on accuracy and level of detail extractable. At each scale the value of employing a simplistic preprocessing smoothing algorithm to improve image interpretation is addressed. A visual interpretation approach and an automated machine/visual approach are employed to evaluate the feasibility of producing a semiautomated land cover classification from SAR data. Confusion matrices of omission and commission errors are employed to define classification accuracies for each interpretation approach and image scale.
This study investigated the hourly inorganic aerosol chemistry and its impact on atmospheric visibility over an urban area in Central Taiwan, by relying on measurements of aerosol light extinction, inorganic gases, and PM 2.5 water-soluble ions (WSIs), and simulations from a thermodynamic equilibrium model. On average, the sulfate (SO 4 2− ), nitrate (NO 3 − ), and ammonium (NH 4 + ) components (SNA) contributed ∼90% of WSI concentrations, which in turn made up about 50% of the PM 2.5 mass. During the entire observation period, PM 2.5 and SNA concentrations, aerosol pH, aerosol liquid water content (ALWC), and sulfur and nitrogen conversion ratios all increased with decreasing visibility. In particular, the NO 3 − contribution to PM 2.5 increased, whereas the SO 4 2− contribution decreased, with decreasing visibility. The diurnal variations of the above parameters indicate that the interaction and likely mutual promotion between NO 3 − and ALWC enhanced the hygroscopicity and aqueous-phase reactions conducive for NO 3 − formation, thus led to severely impaired visibility. The high relative humidity (RH) at the study area (average 70.7%) was a necessary but not sole factor leading to enhanced NO 3 − formation, which was more directly associated with elevated ALWC and aerosol pH. Simulations from the thermodynamic model depict that the inorganic aerosol system in the study area was characterized by fully neutralized SO 4 2− (i.e. a saturated factor in visibility reduction) and excess NH 4 + amidst a NH 3 -rich environment. As a result, PM 2.5 composition was most sensitive to gas-phase HNO 3 , and hence NOx, and relatively insensitive to NH 3 . Consequently, a reduction of NOx would result in instantaneous cuts of NO 3 − , PM 2.5 , and ALWC, and hence improved visibility. On the other hand, a substantial amount of NH 3 reduction (>70%) would be required to lower the aerosol pH, driving more than 50% of the particulate phase NO 3 − to the gas phase, thereby making NH 3 a limiting factor in shifting PM 2.5 composition.
The usefulness of LANDSAT in classifying land cover and in identifying and classifying land use change was investigated using an urbanizing area as the study area. The question of what was the best technique for classification was the primary focus of the study. The many computer-assisted techniques available to analyze LANDSAT data were evaluated. Techniques of statistical training (polygons from CRT, unsupervised clustering, polygons from digitizer and binary masks) were tested with minimum distance to the mean, maximum likelihood and canonical analysis with minimum distance to the mean classifiers. The twelve output images were compared to photointerpreted samples, ground verified samples and a current land use data base. Results indicate that for a reconnaissance inventory, the unsupervised training with canonical analysis-minimum distance classifier is the most efficient. If more detailed ground truth and ground verification is available, the polygons from the digitizer training with the canonical analysis minimum distance is more accurate.
Data from the Tropical Rainfall Measuring Mission's (TRMM) Precipitation Radar (PR) were employed to identify warm season rainfall (1998-2000) patterns around Atlanta, Montgomery, Nashville, San Antonio, Waco, and Dallas. Results reveal an average increase of -28% in monthly rainfall rates within 30-60 kilometers downwind of the metropolis with a modest increase of 5.6% over the metropolis. Portions of the downwind area exhibit increases as high as 51%. The percentage changes are relative to an upwind control area. It was also found that maximum rainfall rates in the downwind impact area exceeded the mean value in the upwind control area by 48% - 116%. The maximum value was generally found at an average distance of 39 km from the edge of the urban center or 64 km from the center of the city. Results are consistent with METROMEX studies of St. Louis almost two decades ago and with more recent studies near Atlanta. Future work is extending the investigation to Phoenix, Arizona, an arid U.S. city, and several international cities like Mexico City, Johannesburg, and Brasilia. The study establishes the possibility of utilizing satellite-based rainfall estimates for examining rainfall modification by urban areas on global scales and over longer time periods. Such research has implications for weather forecasting, urban planning, water resource management, and understanding human impact on the environment and climate.
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.
There has been an increasing interest by the international research community to use the nighttime acquired "city-lights" data sets collected by the US Defense Meteorological Satellite Program's Operational Linescan system to study issues relative to urbanization. Many researchers are interested in using these data to estimate human demographic parameters over large areas and then characterize the interactions between urban development , natural ecosystems, and other aspects of the human enterprise. Many of these attempts rely on an ability to accurately identify urbanized area. However, beyond the simple determination of the loci of human activity, using these data to generate accurate estimates of urbanized area can be problematic. Sensor blooming and registration error can cause large overestimates of urban land based on a simple measure of lit area from the raw data. We discuss these issues, show results of an attempt to do a historical urban growth model in Egypt, and then describe a few basic processing techniques that use geo-spatial analysis to threshold the DMSP data to accurately estimate urbanized areas. Algorithm results are shown for the United States and an application to use the data to estimate the impact of urban sprawl on sustainable agriculture in the US and China is described.
Satellite observations of the total column dry-air CO2(XCO2) are expected to support the quantification and monitoring of fossil fuel CO2 (ffCO2) emissions from urban areas. We evaluate the utility of the Orbiting Carbon Observatory 2 (OCO-2) XCO2 retrievals to optimize whole-city emissions, using a Bayesian inversion system and high-resolution transport modeling. The uncertainties of constrained emissions related to transport model, satellite measurements, and local biospheric fluxes are quantified. For the first two uncertainty sources, we examine cities of different landscapes: “plume city” located in relatively flat terrain, represented by Riyadh and Cairo; “basin city” located in basin terrain, represented by Los Angeles (LA). The retrieved scaling factors of emissions and their uncertainties show prominent variabilities from track to track, due to the varying meteorological conditions and relative locations of the tracks transecting plumes. To explore the performance of multiple tracks in retrieving emissions, pseudo data experiments are carried out. The estimated least number of tracks required to constrain the total emissions for Riyadh (<10% uncertainty), Cairo (<10%), and LA (<5%) are 5, 8, and 7, respectively. Additionally, to evaluate the impact of biospheric fluxes on derivation of the ffXCO2 enhancements, we conduct simulations for Pearl River Delta metropolitan area. Significant fractions of local XCO2 enhancements associated with local biospheric XCO2 variations are shown, which potentially lead to biased estimates of ffCO2 emissions. We demonstrate that satellite measurements can be used to improve urban ffCO2 emissions with a sufficient amount of measurements and appropriate representations of the uncertainty components.
This paper reviews the studies which were undertaken jointly by Telesat and Industry Canada to provide an estimate of the market opportunities for residential multi-media services in the rural and small urban areas of Canada. This study is part of the Advanced Satcom program, a Ka-band satellite system proposal which is currently in the implementation proposal phase by the government and the Canadian space industry of which Telesat is an active member. Advanced Satcom extends the reach of terrestrial information highways to the remote and sparsely populated parts of the country in a cost-effective manner and thus provides a ubiquitous coverage of the information highways to all Canadians. Therefore, the rural and small urban markets are believed to be good opportunities for the Advanced Satcom. Although the results are primarily intended for fixed residential applications, they can also be used as input to market opportunity studies for wideband mobile applications.
This presentation is a blockchain infrastructure use case on setting up a Secure Air Space Network for Autonomous Vehicles that are operating in an Urban Area and Secure the Data using Block Chain Technology.