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At least 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Huntsville Urban Development II: Utilizing NASA Earth Observations to Map the Urban Heat Island and Evaluate Vulnerability in Huntsville, Alabama

Huntsville, Alabama has seen a boom in growth over recent years. One consequence of this urban expansion is the exacerbation of the Urban Heat Island (UHI) effect across the city. This project identified the areas within Huntsville the greatest potential for heat reduction and community health benefits from tree-planting efforts. The team created maps of land surface temperature (LST), the normalized difference vegetation index (NDVI), and the normalized difference built-up index (NDBI) over June through August from 2019 to 2022 using data from ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station, Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Landsat 9 OLI-2 and TIRS-2. The team identified areas with high LST, low NDVI, and high NDBI as areas with the greatest potential for heat reduction via tree-planting. Social factors relating to age, race, income, and self-reported health were adapted from Tree Equity Score to map community need for tree cover. When combining social with environmental factors, the team determined areas with the greatest potential for UHI mitigation: west-central and north downtown Huntsville. The team’s partner organization, the City of Huntsville, can use this priority map to guide their future tree-planting, and weigh the factors assessed according to their preference.

James Karroum↗
NASA NTRSDate not supplied

Utilizing Remote Sensing to Detect and Assess the Impacts of Estuarine Breach Events for Improved Coastal Wetland Monitoring and Management

Estuaries are extremely dynamic environments that provide a host of vital ecosystem services. California’s Marine Life Protection Act protects such ecosystems by creating Marine Protected Areas (MPAs). California has approximately 440,000 acres of estuarine habitat as well as 23 Estuarine Marine Protected Areas (EMPAs). Thus, in situ data collection is often difficult due to time and resource constraints. This project used remote sensing to gather data that examined the health and dynamics of California EMPAs to supplement ground-based field measurements. Through the use of Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Sentinel-2 MultiSpectral Instrument (MSI), and Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), this project assessed mouth state, inundation extent, turbidity, temperature, and tidal measurements for observable estuaries. The Normalized Difference Water Index (NDWI) from Sentinel-2 MSI captured estuary mouth state and inundation extent. Landsat 8 OLI and Sentinel-2 MSI detected differences in water quality metrics that correlated to changes in estuary mouth state (i.e., open or closed) through algorithms in Google Earth Engine (GEE). The GEE California Estuary Assessment (CEA) tool was created with partner input throughout development and culminated in a graphical user interface tailored to management needs. The CEA tool will allow the partners, the Ocean Protection Council, Moss Landing Marine Laboratories’ Central Coast Wetlands Group, the Southern California Coastal Water Research Project, and University of California Los Angeles (UCLA) and Davis (UCD), to better understand estuary dynamics and facilitate informed management decisions through satellite-based Earth observations.

Alex Gunnerson↗
NASA NTRS2024

San Joaquin Valley Health & Air Quality II: Assessing Urban Heat Island Distribution and its Intersections with Air Quality to Understand Converging Vulnerabilities

The city of Stockton, California, located within the San Joaquin Valley (SJV), is a major hub for agricultural production and has endured the continuous threat to community health from nitrogen dioxide (NO 2 ) and increasing temperatures. The convergence of these issues occurs within historically segregated communities that are disproportionately facing health risks related to heat and air quality. Little Manila Rising (LMR), a social and environmental justice (EJ) advocacy non-profit, partnered with NASA DEVELOP for a second term project to evaluate county wide urban heat islands, sociodemographic vulnerability, landcover classification, and the convergence of these variables. We utilized Landsat 8 Thermal Infrared Sensor (TIRS) and Operational Land Imager (OLI) data to produce a land surface temperature (LST) and Normalized Difference Vegetation Index map. They added Centers for Disease Control (CDC) socioeconomic data from 2020 to identify which communities in Stockton were more susceptible to these environmental factors. Additionally, we used NAIP imagery to create a landcover map differentiating developed infrastructure from tree canopy cover. We discovered that south Stockton, where LMR resides, had the worst convergence of heat, air pollution, low canopy coverage and sociodemographic vulnerability compared to northern and rural parts of the city. This was further substantiated by statistical analysis showing a strong positive relationship between areas of high LST and low vegetation. The results provided LMR with compelling evidence to use in their EJ advocacy, and in their efforts to inform state officials of the discriminatory issues they face.

Urban Heat Islands↗
NASA NTRSDate not supplied

The Plankton, Aerosol, Cloud, Ocean Ecosystem (Pace) Mission Overview: From Concept to Launch

Abstract for Technical Review: The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission is a strategic climate continuity mission that was defined in the 2010 document Responding to the Challenge of Climate and Environmental Change: NASA’s Plan for Climate-Centric Architecture for Earth Observations and Applications from Space (referred to as the “Climate Initiative”). Launched in February 2024, the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records. PACE will extend the high quality ocean ecological, ocean biogeochemical, cloud, and aerosol particle data records begun by NASA in the 1990s, building on the exceptional heritages of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging SpectroRadiometer (MISR), and the Visible Infrared Imaging Radiometer Suite (VIIRS). NASA Headquarters directed the mission development and implementation to the NASA Goddard Space Flight Center (GSFC) with guidance to utilize a Design-to-Cost (DTC) process. Mission studies were conducted to define appropriate approaches within and across elements while maximizing science capabilities at a high cost confidence. Mission baseline requirements development is also embedded within the DTC process, as these requirements were not established at the onset of the mission concept development. The PACE project office at NASA’s GSFC was responsible for the satellite development, launch and commissioning. The NASA Headquarters PACE Program Science office is responsible for supporting the science data processing system and assembling competed community science teams, which includes field-based vicarious calibration and data product validation efforts to support the PACE Project Science team. The PACE observatory is comprised of three instruments, the Ocean Color Instrument (OCI) and two polarimeters, the Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the Spectro-Polarimeter for Exploration (SPEXone) and the PACE spacecraft. The observatory has been commissioned and is performing on-orbit science data collection operations. Summary for Program: The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission is a strategic climate continuity mission that was defined in the 2010 document Responding to the Challenge of Climate and Environmental Change: NASA’s Plan for Climate-Centric Architecture for Earth Observations and Applications from Space (referred to as the “Climate Initiative”). Launched in February 2024 under the leadership of the NASA Goddard Space Flight Center (GSFC), the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records. PACE will extend the high quality ocean ecological, ocean biogeochemical, cloud, and aerosol particle data records begun by NASA in the 1990s. The PACE observatory is comprised of the Ocean Color Instrument (OCI), the Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the Spectro-Polarimeter for Exploration (SPEXone), along with the PACE spacecraft.

Robert H Estep, Jr↗
NASA NTRS2024

Cali Urban Development II: Investigating the Impacts of Land Use Change on Urban Heat and Social Vulnerability in Cali, Colombia

The surface urban heat island (SUHI) effect is an environmental phenomenon resulting in cities with higher temperatures than rural areas due to increased pavement and decreased cooling from vegetation. The city of Santiago de Cali in Colombia faces SUHI challenges exacerbated by land use change. The Cali municipal government agency, Departamento Administrativo de Gestión del Medio Ambiente, and the community organization Fundacion Dinamizadores Ambientales partnered with NASA DEVELOP to evaluate communities in Cali most vulnerable to urban heat. This project illustrated the utility of using NASA Earth observations to evaluate the relationship between land use, temperature, and social factors in Cali, Colombia between 2013 and 2023. The team used Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), and Landsat 9 OLI-2/TIRS-2 to generate land surface temperature (LST) maps in Google Earth Engine through NASA DEVELOP’s Urban Heat Exposure Assessment Tempe 2.0 tool. Cloud cover limited the project feasibility, but it improved with Landsat 9 data. In ArcGIS Pro, the team found that LST was significantly higher in urban areas than in wetlands or forests. Using R Studio, the team ran a principal component analysis and found that health care and green space access were negatively correlated, and Afro-Colombian ethnicity was positively correlated with LST. With knowledge of the most impacted and vulnerable regions, the partner organizations can prioritize establishing healthcare facilities and green spaces in those areas to reduce the impacts of urban heat.

vegetation loss↗
NASA NTRSDate not supplied

Mission Overview and Status from Concept to Launch

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission is a strategic climate continuity mission that was defined in the 2010 document Responding to the Challenge of Climate and Environmental Change: NASA’s Plan for Climate-Centric Architecture for Earth Observations and Applications from Space (referred to as the “Climate Initiative”). Launched in February 2024, the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records. PACE will extend the high quality ocean ecological, ocean biogeochemical, cloud, and aerosol particle data records begun by NASA in the 1990s, building on the exceptional heritages of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging SpectroRadiometer (MISR), and the Visible Infrared Imaging Radiometer Suite (VIIRS). NASA Headquarters directed the mission development and implementation to the NASA Goddard Space Flight Center (GSFC) with guidance to utilize a Design-to-Cost (DTC) process. Mission studies were conducted to define appropriate approaches within and across elements while maximizing science capabilities at a high cost confidence. Mission baseline requirements development is also embedded within the DTC process, as these requirements were not established at the onset of the mission concept development. The PACE project office at NASA’s GSFC was responsible for the satellite development, launch and commissioning. The NASA Headquarters PACE Program Science office is responsible for supporting the science data processing system and assembling competed community science teams, which includes field-based vicarious calibration and data product validation efforts to support the PACE Project Science team. The PACE observatory is comprised of three instruments, the Ocean Color Instrument (OCI) and two polarimeters, the Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the Spectro-Polarimeter for Exploration (SPEXone) and the PACE spacecraft. The observatory has been commissioned and is performing on-orbit science data collection operations. Summary for Program: The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission is a strategic climate continuity mission that was defined in the 2010 document Responding to the Challenge of Climate and Environmental Change: NASA’s Plan for Climate-Centric Architecture for Earth Observations and Applications from Space (referred to as the “Climate Initiative”). Launched in February 2024 under the leadership of the NASA Goddard Space Flight Center (GSFC), the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records. PACE will extend the high quality ocean ecological, ocean biogeochemical, cloud, and aerosol particle data records begun by NASA in the 1990s. The PACE observatory is comprised of the Ocean Color Instrument (OCI), the Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the Spectro-Polarimeter for Exploration (SPEXone), along with the PACE spacecraft.

Robby Estep↗
DOE OSTI2022

Compact imaging system using a co-linear, high-intensity LED illumination unit to minimize window reflections for background-oriented schlieren, shadowgraph, photogrammetry and machine vision measurements

One aspect of the present disclosure is an imaging system including an optical sensor defining an optical axis. The system further includes a light source. The system may include an optical beam splitter, and may also include an optional diffusing lens that may be configured to diffuse and/or collimate light from the light source and direct light exiting the diffusing lens to the optical beam splitter. The optical beam splitter is configured to direct light from the light source along the optical axis of the optical sensor.

Bathel, Brett F.↗
DOE OSTI2024

Systems and methods for optical sensor protection

The present disclosure relates to an optical sensor protection system. The system may have a sensor for receiving an incoming optical signal, a passive sensing and modulation component, and an active sensing and modulation subsystem. The passive sensing and modulation component is configured to sense when a first characteristic is associated with the incoming optical signal is present that adversely affects operation of the sensor, and redirects at least a portion of the incoming optical signal thereof away from the sensor to thus reduce an intensity of the incoming optical signal reaching the sensor. The sensor is located on an image plane downstream of the ISM subsystem, relative to a path of travel of the incoming optical signal. The active sensing and modulation subsystem has an active modulation component and is located upstream of the passive sensing and modulation component, relative to the path of travel of the incoming optical signal, and is also located on a conjugate image plane, and is configured to use the redirected portion of the incoming optical signal as feedback in controlling a modification of the incoming optical signal to reduce a risk of damage to the passive sensing and modulation component.

Panas, Robert Matthew↗
NASA NTRS1993

Imaging spectrometry of the Earth and other solar system bodies

Imaging spectrometry is a relatively new tool for remote sensing of the Earth and other bodies of the solar system. The technique dates back to the late 1970's and early 1980's. It is a natural extension of the earlier multi-spectral imagers developed for remote sensing that acquire images in a few, usually broad spectral bands. Imaging spectrometers combine aspects of classical spectrometers and imaging systems, making it possible to acquire literally hundreds of images of an object, each image in a separate, narrow spectral band. It is thus possible to perform spectroscopy on a pixel-by-pixel basis with the data acquired with an imaging spectrometer. Two imaging spectrometers have flown in space and several others are planned for future Earth and planetary missions. The French-built Phobos Infrared Spectrometer (ISM) was part of the payload of the Soviet Mars mission in 1988, and the JPL-built Near Infrared Mapping Spectrometer (NIMS) is currently en route to Jupiter aboard the Galileo spacecraft. Several airborne imaging spectrometers have been built in the past decade including the JPL-built Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) which is the only such sensor that covers the full solar reflected portion of the spectrum in narrow, contiguous spectral bands. NASA plans two imaging spectrometers for its Earth Observing System, the Moderate and the High Resolution Imaging Spectrometers (MODIS and HIRIS). A brief overview of the applications of imaging spectrometry to Earth science will be presented to illustrate the value of the tool to remote sensing and indicate the types of measurements that are required. The system design for AVIRS and a planetary imaging spectrometer will be presented to illustrate the engineering considerations and challenges that must be met in building such instruments. Several key sensor technology areas will be discussed in which miniaturization and/or enhanced performance through micromachining and nanofabrication may allow smaller, more robust, and more capable imaging spectrometers to be built in the future.

Vane, Gregg↗
DOE OSTI2021

Global field reconstruction from sparse sensors with Veronoi tessellation-assisted deep learning

Achieving accurate and robust global situational awareness of a complex time-evolving field from a limited number of sensors has been a longstanding challenge. This reconstruction problem is especially difficult when sensors are sparsely positioned in a seemingly random or unorganized manner, which is often encountered in a range of scientific and engineering problems. Moreover, these sensors can be in motion and can become online or offline over time. The key leverage in addressing this scientific issue is the wealth of data accumulated from the sensors. As a solution to this problem, we propose a data-driven spatial field recovery technique founded on a structured grid-based deep-learning approach for arbitrary positioned sensors of any numbers. It should be noted that the naïve use of machine learning becomes prohibitively expensive for global field reconstruction and is furthermore not adaptable to an arbitrary number of sensors. In the present work, we consider the use of Voronoi tessellation to obtain a structured-grid representation from sensor locations enabling the computationally tractable use of convolutional neural networks. One of the central features of the present method is its compatibility with deep-learning based super-resolution reconstruction techniques for structured sensor data that are established for image processing. The proposed reconstruction technique is demonstrated for unsteady wake flow, geophysical data, and three-dimensional turbulence. The current framework is able to handle an arbitrary number of moving sensors, and thereby overcomes a major limitation with existing reconstruction methods. The presented technique opens a new pathway towards the practical use of neural networks for real-time global field estimation.

Fukami, Kai↗
NASA NTRS2002

Earth Observing-1 Advanced Land Imager: Imaging Performance On-Orbit

This report analyzes the on-orbit imaging performance of the Advanced Land Imager (ALI) on the Earth Observing-1 satellite. The pre-flight calibrations are first summarized. The methods used to reconstruct and geometrically correct the image data from this push-broom sensor are described. The method used here does not refer to the position and attitude telemetry from the spacecraft. Rather, it is assumed that the image of the scene moves across the focal plane with a constant velocity, which can be ascertained from the image data itself. Next, an assortment of the images so reconstructed is presented. Color images sharpened with the 10-m panchromatic band data are shown, and the algorithm for producing them from the 30-m multispectral data is described. The approach taken for assessing spatial resolution is to compare the sharpness of features in the on-orbit image data with profiles predicted on the basis of the pre-flight calibrations. A large assortment of bridge profiles is analyzed, and very good fits to the predicted shapes are obtained. Lunar calibration scans are analyzed to examine the sharpness of the edge-spread function at the limb of the moon. The darkness of the space beyond the limb is better for this purpose than anything that could be simulated on the ground. From these scans, we find clear evidence of scattering in the optical system, as well as some weak ghost images. Scans of planets and stars are also analyzed. Stars are useful point sources of light at all wavelengths, and delineate the point-spread functions of the system. From a quarter-speed scan over the Pleiades, we find that the ALI can detect 6th magnitude stars. The quality of the reconstructed images verifies the capability of the ALI to produce Landsat-type multi spectral data. The signal-to-noise and panchromatic spatial resolution are considerably superior to those of the existing Landsat sensors. The spatial resolution is confirmed to be as good as it was designed to be.

ALI (ADVANCED LAND IMAGER)↗
NASA NTRS1976

Thoughts on standardization of parameters for image evaluation

Images received for image processing and analysis are obtained from a wide variety of sources and with a wide variety of sensors. Because it is desirable to have image processing algorithms be as universally applicable as possible, they should be designed, where possible, to be insensitive to the parametric variations of the source material. Where this is not possible, these variations must be taken into account. Therefore, it is necessary to consider what parameters may be defined in common across a suite of image types. Objective parameters or measurements of images which, in the proper combinations, may serve as surrogates for real images may be pixel-specific, location dependent, or combinations thereof. Parameters which have proven useful in defining the characteristics of images include the gray scale linearity, granularity of the quantization, spectral content, geometrical fidelity, resolution of the system expressed as either the point spread function or the modulation transfer function, and the spatial frequency content and characteristics of the data itself.

Billingsley, F. C.↗
NASA NTRS1998

Advanced x-ray imaging spectrometer

An x-ray spectrometer that also provides images of an x-ray source. Coded aperture imaging techniques are used to provide high resolution images. Imaging position-sensitive x-ray sensors with good energy resolution are utilized to provide excellent spectroscopic performance. The system produces high resolution spectral images of the x-ray source which can be viewed in any one of a number of specific energy bands.

Callas, John L.↗
DOE OSTI2020

Completion design improvement using a deep convolutional network

Maximizing stimulated natural and hydraulic fracture network is one of the primary hydraulic fracturing concerns for economic production from a horizontal shale gas well. Geomechanical facies and preexisting fractures in each stage are identified based on similarities in formation characteristics to optimize the locations of perforation clusters. This often requires analyzing large volumes of drilling, Logging While Drilling (LWD) and Measurement While Drilling (MWD) data. In this paper, we develop a methodology that calculates the mechanical specific energy (MSE) using real-time drill string acceleration signals directly from its definition. High resolution vibration signals have been collected using a tri-axial accerlometer, which was an auxiliary tool included in acoustic borehole imager. This technique provides a cost-efficient solution for engineered completion design. Furthermore, we adopt deep Convolutional Neural Network (CNN) with signal processing to build a data pipeline that effectively extracts patterns from dynamic acceleration signals for rock lateral MSE classification. First, we apply discrete wavelet transform and Short-Time Fourier Transform (STFT) for signal denoising and pattern recognition. Then we construct an image dataset using multi-scale image fusion at pixel level from 3 sensor channels, including axial, lateral acceleration spectrograms and zero-padded revolutions per minute (RPM). The resulted RGB image dataset includes 4,000 images of 5 MSE ranges with various rock strength conditions. Our results demonstrate that the proposed deep learning model can achieve more than 90% classification accuracy. The deep learning results, as a reference source, were applied in selected Marcellus Shale Energy and Environmental Lab (MSEEL) wells engineered completion located in the Marcellus shale gas site.

03 NATURAL GAS↗
DOE OSTI2025

Non-destructive evaluation and machine learning methods for inspection of spent nuclear fuel canisters: A state-of-the-art review

Nuclear energy is among the cleanest and most efficient energy sources currently available. The operation of nuclear power plants (NPPs) produces large amounts of high-level radioactive waste known as spent nuclear fuel (SNF). Currently, large amounts of SNF is stored in dry cask storage systems (DCSSs) for extended interim storage until a permanent disposal solution becomes available. During the extended interim storage, the DCSS, particularly the SNF canisters, may degrade and abnormal conditions may occur. Therefore, non-destructive evaluation (NDE) and machine learning (ML) approaches are necessary for inspection of SNF canisters. This paper presents a state-of-the-art review of literature by summarizing recent progress made on the applications of NDE and ML for inspection of SNF canisters. Sixteen NDE methods are examined and compared: visual inspection, ultrasonic guided waves (UGWs), laser-based approaches, acoustic emission (AE), eddy current testing (ECT), non-invasive acoustic sensing, dynamic modal testing, cosmic ray muons tomography, neutron imaging, gamma rays detection, fiber optical sensors, through-wall communications, X-ray computed tomography (CT), vibrothermography, monoenergetic photon sources, and surface acoustic wave (SAW) sensors. The technology readiness level (TRL) for each method is assessed and compared. Recent publications on ML-enhanced visual inspection, AE, non-invasive acoustic sensing, dynamic modal testing, and neutron imaging for SNF canisters are summarized and future research needs are identified. In conclusion, this review article provides a convenient reference on the state-of-the-art applications of NDE and ML methods for inspection of SNF canisters.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2008

Applications of a Forward-Looking Interferometer for the On-board Detection of Aviation Weather Hazards

The Forward-Looking Interferometer (FLI) is a new instrument concept for obtaining measurements of potential weather hazards to alert flight crews. The FLI concept is based on high-resolution Infrared (IR) Fourier Transform Spectrometry (FTS) technologies that have been developed for satellite remote sensing, and which have also been applied to the detection of aerosols and gases for other purposes. It is being evaluated for multiple hazards including clear air turbulence (CAT), volcanic ash, wake vortices, low slant range visibility, dry wind shear, and icing, during all phases of flight. Previous sensitivity and characterization studies addressed the phenomenology that supports detection and mitigation by the FLI. Techniques for determining the range, and hence warning time, were demonstrated for several of the hazards, and a table of research instrument parameters was developed for investigating all of the hazards discussed above. This work supports the feasibility of detecting multiple hazards with an FLI multi-hazard airborne sensor, and for producing enhanced IR images in reduced visibility conditions; however, further research must be performed to develop a means to estimate the intensities of the hazards posed to an aircraft and to develop robust algorithms to relate sensor measurables to hazard levels. In addition, validation tests need to be performed with a prototype system.

West, Leanne↗
NASA NTRS1984

Passive microwave remote sensing for sea ice research

Techniques for gathering data by remote sensors on satellites utilized for sea ice research are summarized. Measurement of brightness temperatures by a passive microwave imager converted to maps of total sea ice concentration and to the areal fractions covered by first year and multiyear ice are described. Several ancillary observations, especially by means of automatic data buoys and submarines equipped with upward looking sonars, are needed to improve the validation and interpretation of satellite data. The design and performance characteristics of the Navy's Special Sensor Microwave Imager, expected to be in orbit in late 1985, are described. It is recommended that data from that instrument be processed to a form suitable for research applications and archived in a readily accessible form. The sea ice data products required for research purposes are described and recommendations for their archival and distribution to the scientific community are presented.

Source record↗
NASA NTRS1999

Hyperspectral Thermal Fabry-Perot Modeling

Fabry-Perot interferometer are simple elegant, tunable filters that can be used to make compact hyperspectral thermal imaging system. To foster the development of these sensors, software tools for the design and simulation of tunable Fabry-Perot infrared imagers have been developed. The tools are provided at three levels: basic, design, and system. Basic tools describe a nearly ideal Fabry-Perot filter with perfectly flat and parallel mirrors in collimated space. Design tools that take into account non-ideal behavior such as mirror and collimation defects calculate free spectral range, finesse, and spectral width of the interferometer. System tools help analyze an integration of the Fabry-Perot filter into a camera system. They include spectral convolution, first-order optical layout, and an estimation of signal-to-noise ratio. The complete set of tools allows for simulations of system operation and performance with various illumination sources. Spectral images generated in such simulations were used to examine applicability of Fabry-Perot system in remote sensing of atmospheric gases including detection of environmental pollutants and hazardous gases. Different operating conditions and system configurations are presented.

Ryan, Robert↗