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At least 1,801 records · Page 100
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Laser remote sensing of atmospheric ammonia using a CO2 lidar system

A CO2 differential-absorption lidar system has been used for the remote sensing of ammonia in the atmosphere. For CO2 lidar returns backscattered from topographic targets at ranges up to 2.7 km, the path-averaged sensitivity of the DIAL system was 5 ppb of NH3. Concentrations of atmospheric ammonia were found to vary during the day from undetectable levels (less than 5 ppb) to as high as 20 ppb, depending on temperature and humidity conditions.

Force, A. P.↗
NASA NTRS1984

The linear array multi-band selection device role in remote-sensing instrument design

A figure-of-merit parameter for the scientific utility of multispectral linear array (MLA) instruments for civil remote sensing is presented. The number of spectral band-pair choices available to the user of a given MLA instrument is proposed as a useful measure of the flexibility and overall utility of that particular instrument design. The multiband selection device capability is analyzed mathematically, and a basis for a cost-benefit comparison of alternative multispectral instrument designs is provided.

Speaker, E. E.↗
NASA NTRS1985

Tunable solid state lasers for remote sensing; Proceedings of the Conference, Stanford University, CA, October 1-3, 1984

Papers are presented on solid-state lasers for remote sensing, diode-pumped Nd:YAG lasers, and tunable solid-state-laser systems. Topics discussed include titanium-sapphire tunable laser systems, the performance of slab geometry, and the development of slab lasers. Consideration is given to garnet host solid-state lasers, the growth of lasers and nonlinear materials, and nonlinear frequency conversion and tunable sources.

Byer, R. L.↗
NASA NTRS1986

International Symposium on Remote Sensing of Environment, 19th, Ann Arbor, MI, October 21-25, 1985, Proceedings. Volumes 1 & 2

The technology and applications of terrestrial remote sensing (RS) are discussed in reviews and reports. Topics examined include the future of the NASA earth-sciences program, NOAA plans for earth observations in the 1990s, space RS in France, international coordination of RS satellite programs, and applications of geocoded imagery. Consideration is given to spatial and tabular databases for order-three soil surveys, an AVHRR and Landsat regional inventory of irrigated agriculture, classification of wetlands, microwave radiometry of ocean surface winds and sea ice, and floodplain land-cover mapping with Thematic-Mapper data.

Source record↗
NASA NTRS1987

Excimer laser system for atmospheric remote sensing of ozone

A high-power narrow-linewidth XeCl excimer laser system developed for use by NASA in the remote sensing of atmospheric ozone is described. The laser system is designed for incorporation in a DIAL lidar utilizing stimulated Raman generation for the reference wavelength and sophisticated data averaging techniques. The laser output has a linewidth of 0.002 nm and a beam divergence of 0.15 mrad (FWHM). The laser was operated over a six-hour period with a constant average power of 18 W and a wavelength stable to within + or - 0.0006 nm.

Tan, K. O.↗
NASA NTRS1987

Monitoring the environment by remote sensing

Structural features of ecosystems, such as leaf area index, phytomass and canopy chemical contents, are beginning to be estimated from remotely sensed data. This development, in combination with ecological modeling, is permitting the estimation of functional features of ecosystems including primary productivity and nutrient cycling. Such techniques are also being applied to the problem of monitoring the effects of air or water pollutants on biota. Sensors that obtain data at a coarse spatial scale (1 km2 or more) are also permitting the observation of biospheric patterns at a large regional or global scale for the first time. When coupled with atmospheric measurements, field data and simulation models, such data may serve to address ecological processes, including pollution effects, at large regional or global scales.

Westman, Walter E.↗
NASA NTRS1987

The use of remotely sensed data for the monitoring of forest change in tropical areas

This paper provides an overview of the use of several remote sensing systems for monitoring changes in tropical forests. Particular attention is given to the use of data from the AVHRR sensors on the NOAA series of satellites, the Landsat MSS and TM, and aircraft and spacecraft acquired Synthetic Aperture Radar. Multisensor and multistage approaches to monitoring change in tropical forests is examined. Forest change monitoring through a geographic information system (GIS) approach is addressed. Finally some suggestions are made for research addressing forest change monitoring in tropical areas.

Joyce, Armond T.↗
NASA NTRS2003

Software for Simulating Remote Sensing Systems

The Application Research Toolbox (ART) is a collection of computer programs that implement algorithms and mathematical models for simulating remote sensing systems. The ART is intended to be especially useful for performing design-tradeoff studies and statistical analyses to support the rational development of design requirements for multispectral imaging systems. Among other things, the ART affords a capability to synthesize coarser-spatial-resolution image-data sets from finer-spatial-resolution data sets and multispectral-image-data products from hyperspectral-image-data products. The ART also provides for synthesis of image-degradation effects, including point-spread functions, misregistration of spectral images, and noise. The ART can utilize real or synthetic data sets, along with sensor specifications, to create simulated data sets. In one example of a typical application, simulated data pertaining to an existing multispectral sensor system are used to verify the data collected by the system in operation. In the case of a proposed sensor system, the simulated data can be used to conduct trade studies and statistical analyses to ensure that the sensor system will satisfy the requirements of potential scientific, academic, and commercial user communities.

Zanoni, Vicki↗
NASA NTRS2003

Software for Simulating Remote Sensing Systems

The Application Research Toolbox (ART) is a collection of computer programs that implement algorithms and mathematical models for simulating remote sensing systems. The ART is intended to be especially useful for performing design-tradeoff studies and statistical analyses to support the rational development of design requirements for multispectral imaging systems. Among other things, the ART affords a capability to synthesize coarser-spatial-resolution image-data sets from finer-spatial-resolution data sets and multispectral-image-data products from hyperspectral-image-data products. The ART also provides for synthesis of image-degradation effects, including point-spread functions, misregistration of spectral images, and noise. The ART can utilize real or synthetic data sets, along with sensor specifications, to create simulated data sets. In one example of a typical application, simulated data pertaining to an existing multispectral sensor system are used to verify the data collected by the system in operation. In the case of a proposed sensor system, the simulated data can be used to conduct trade studies and statistical analyses to ensure that the sensor system will satisfy the requirements of potential scientific, academic, and commercial user communities.

Vicki Zanoni↗
NASA NTRS2004

Simulating Remote Sensing Systems

The Application Research Toolbox (ART) is a collection of computer programs that implement algorithms and mathematical models for simulating remote sensing systems. The ART is intended to be especially useful for performing design-tradeoff studies and statistical analyses to support the rational development of design requirements for multispectral imaging systems. Among other things, the ART affords a capability to synthesize coarser-spatial-resolution image-data products. The ART also provides for simulations of image-degradation effects, including point-spread functions, misregistration of spectral images, and noise. The ART can utilize real or synthetic data sets, along with sensor specifications, to create simulated data sets. In one example of a particular application, simulated imagery of a coarse resolution system was created using high-resolution imagery from another system in order to perform a radiometric cross-comparison. In the case of a proposed sensor system, the simulated data can be used to conduct trade studies and statistical analyses to ensure that the sensor system will satisfy the requirements of potential scientific, academic, and commercial user communities.

Zanoni, Vicki↗
NASA NTRS2011

Estuarine Sediment Deposition during Wetland Restoration: A GIS and Remote Sensing Modeling Approach

Restoration of the industrial salt flats in the San Francisco Bay, California is an ongoing wetland rehabilitation project. Remote sensing maps of suspended sediment concentration, and other GIS predictor variables were used to model sediment deposition within these recently restored ponds. Suspended sediment concentrations were calibrated to reflectance values from Landsat TM 5 and ASTER using three statistical techniques -- linear regression, multivariate regression, and an Artificial Neural Network (ANN), to map suspended sediment concentrations. Multivariate and ANN regressions using ASTER proved to be the most accurate methods, yielding r2 values of 0.88 and 0.87, respectively. Predictor variables such as sediment grain size and tidal frequency were used in the Marsh Sedimentation (MARSED) model for predicting deposition rates for three years. MARSED results for a fully restored pond show a root mean square deviation (RMSD) of 66.8 mm (<1) between modeled and field observations. This model was further applied to a pond breached in November 2010 and indicated that the recently breached pond will reach equilibrium levels after 60 months of tidal inundation.

Newcomer, Michelle↗
DOE OSTI2024

Bulk cloud microphysical properties as seen from numerical simulation and remote sensing products: case study of a hailstorm event over the La Plata Basin

Hailstorms develop over the La Plata Basin, in south-eastern South America, more often during later winter and early austral spring, between September and October. These systems have significant socioeconomic impacts over the region. Thus, a better understanding of how atmospheric drivers modulate the formation of hailstorms is important to improve the forecast of such phenomena. In this study, we selected a hailstorm event observed over the eastern La Plata Basin during 14–15 July 2016 to evaluate the performance of the Brazilian developments on the Regional Atmospheric Modelling System (BRAMS) model. The ability of the model in simulating cloud microphysical properties was evaluated by comparing simulations driven by different global forcings against in situ and remote sensing observations. The model results showed good skill in capturing the basic characteristics of the thunderstorm, particularly in terms of the spatial distribution of hydrometeors. The simulated spatial distribution of hail covers locations where hail fall was reported. The BRAMS simulations suggest that, despite relatively low values of the convective available potential energy (CAPE) (700–1000 J kg -1 ), environments with strong 0–8-km bulk shear (60–70 kt, ~30.9–36.0 m s –1 ) can promote the formation of ice clouds and hail fall over the eastern La Plata Basin. To be more conclusive, however, further research is needed to understand how different combinations of CAPE and shear affect hail formation over the region.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

How Well Satellite Remote Sensing Can Inform Surface Level Ozone Production Sensitivity to Precursor Trace Gas Emissions?

Surface-level ozone (O3) pollution mitigation strategies rely on a clear understanding of the atmospheric chemical processes involving O3, NOx (nitric oxide (NO) + nitrogen dioxide (NO2)) and volatile organic compounds (VOCs). A robust spatiotemporal classification of O3 photochemical regimes with relative chemical sensitivity of local O3 formation to emission reductions of NOx (NOx-limited regime) versus VOCs (radical-limited regime) is required to design anthropogenic emission reduction policies in order to improve surface air quality. Numerous studies found that the ratio of satellite-retrieved Vertical Column Densities (VCDs) of formaldehyde (HCHO) to NO2 can diagnose O3 production sensitivity. However, uncertainty still remains about the accuracy of such estimates using satellite-based retrievals. This study conducts an investigation to identify how well satellite data can inform surface-level O3 sensitivity by validating satellite-based estimates against aircraft-based retrievals taken during the Long Island Sound Tropospheric Ozone Study (LISTOS-2018) and Ozone Water-Land Environmental Transition Study (OWLETS-2) field campaigns. For this study, we use HCHO and NO2 retrievals from the Ozone Monitoring Instrument (OMI) onboard the Aura satellite, and high spatiotemporal resolution TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (S5P) satellite. The Community Multiscale Air Quality (CMAQ) modelling system is used to provide trace gas vertical profiles to recalculate air mass factor for deriving consistent satellite-based VCDs. To demonstrate the ability of satellites to diagnose O3 production regimes, we validate satellite-derived O3 sensitivity regimes, along with the OMI and TROPOMI products applied, using HCHO and NO2 retrievals from the aircraft-based instruments: Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) spectrometer and Geostationary Coastal and Air Pollution Events Airborne Simulator (GCAS) instruments deployed during the LISTOS-2018 and OWLETS-2 campaigns. The results of this work will advance our fundamental knowledge of the spatiotemporal evolution of the complex O3-NOx-VOC chemistry, and will advance our understanding about the quality of remote-sensing, suborbital, and satellite data for accurately observing surface-level O3 production sensitivity.

Satellite↗
NASA NTRS2015

Municipality Level Simulations of Dengue Fever Incidence in Puerto Rico Using Ground Based and Remotely Sensed Climate Data

Dengue fever (DF) is caused by a virus transmitted between humans and Aedes genus mosquitoes through blood feeding. In recent decades incidence of the disease has drastically increased in the tropical Americas, culminating with the Pan American outbreak in 2010 which resulted in 1.7 million reported cases. In Puerto Rico dengue is endemic, however, there is significant inter‐annual, intraannual, and spatial variability in case loads. Variability in climate and the environment, herd immunity and virus genetics, and demographic characteristics may all contribute to differing patterns of transmission both spatially and temporally. Knowledge of climate influences on dengue incidence could facilitate development of early warning systems allowing public health workers to implement appropriate transmission intervention strategies. In this study, we simulate dengue incidence in several municipalities in Puerto Rico using population and meteorological data derived from ground based stations and remote sensing instruments. This data was used to drive a process based model of vector population development and virus transmission. Model parameter values for container composition, vector characteristics, and incubation period were chosen by employing a Monte Carlo approach. Multiple simulations were performed for each municipality and the results were compared with reported dengue cases. The best performing simulations were retained and their parameter values and meteorological input were compared between years and municipalities. Parameter values varied by municipality and year illustrating the complexity and sensitivity of the disease system. Local characteristics including the natural and built environment impact transmission dynamics and produce varying responses to meteorological conditions.

Remote Sensing↗
NASA NTRS2018

Remote Sensing of the Ocean Surface Refractive Index via Short-Wave Infrared Polarimetry

The linear polarization of sunlight reflected by ocean surfaces in the shortwave infrared (SWIR), at geometries where specular reflection dominates the signal, is a direct function of the refractive index of the surface microlayer(SML). This simple physical concept is at the base of a novel technique presented in this study. We invert observations obtained by the airborne Research Scanning Polarimeter (RSP) in the sun glint region, where each pixel's radiance is dominated by the signal originating from the wave slopes oriented precisely to cause specular reflection. The SWIR wavelength ensures minimization of aerosol interference when radiance travels through the atmosphere; strong absorption by the water body then limits the penetration depth to the first micrometer or so, effectively probing the SML. The resulting Degree of Linear Polarization (DoLP) is then governed by the refractive index via the Fresnel law for the specfic pixel geometry, independently of the wind speed. The selected dataset concerns several field deployments from both low- and high-altitude aircraft, including total reflectance measurements with the sole purpose of accounting for the residual aerosol effect.Stable retrievals from transects above pure seawater yielded values of refractive index that match the values published in the literature within an accuracy of 5 x 10 [superscript 4]. Flying over the oil spill caused by the explosion of the Deepwater Horizon platform, detected variations were found compatible with the presence of an oil slick. The robustness of the results, guaranteedby the high RSP polarimetric accuracy (less than or equal to 0:2%), opens the possibility for remote-sensing detection of other entities that similarly affect the refractive index including whitecaps, microplastics, biological gels, seaweed and grass mats.

Surface Microlayer↗

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