Engineering topics
Publications and source records attributed to Zanoni, Vicki.
The presentation gives a general description of the verification and validation capabilities available at the John C. Stennis Space Center in southern Mississippi.
The Coral Reef Early Warning System (CREWS) is operated by NOAA's Office of Oceanic and Atmospheric Research as part of its Coral Reef Watch program in response to the deteriorating global state of coral reef and related benthic ecosystems. In addition to sea surface temperatures (SSTs), the two most important parameters used by the CREWS network in generating coral reef bleaching alerts are 1) wind speed and direction and 2) photosynthetically available radiation (PAR). NASA remote sensing products that can enhance CREWS in these areas include SST and PAR products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and wind data from the Quick Scatterometer (QuikSCAT). CREWS researchers are also interested in chlorophyll, chromophoric dissolved organic matter (CDOM), and salinity. Chlorophyll and CDOM are directly available as NASA products, while rainfall (an available NASA product) can be used as a proxy for salinity. Other potential NASA inputs include surface reflectance products from MODIS, the Advanced Spaceborne Thermal Emission and Reflection Radiometer, and Landsat. This report also identifies NASA-supported ocean circulation models and products from future satellite missions that might enchance the CREWS DST.
NASA at Stennis Space Center (SSC) established a Space Act Agreement with Orbital Sciences Corporation (OSC) and ORBIMAGE Inc. to collaborate on the characterization of the OrbView-3 system and its imagery products and to develop characterization techniques further. In accordance with the agreement, NASA performed an independent radiometric, spatial, and geopositional accuracy assessment of OrbView-3 imagery acquired before completion of the system's initial on-orbit checkout. OSC acquired OrbView-3 imagery over SSC from July 2003 through January 2004, and NASA collected ground reference information coincident with many of these acquisitions. After evaluating all acquisitions, NASA deemed two multispectral images and five panchromatic images useful for characterization. NASA then performed radiometric, spatial, and geopositional characterizations.
The National Oceanic and Atmospheric Administration (NOAA) Harmful Algal Bloom (HAB) Mapping System and Bulletin provide a Web-based geographic information system (GIS) and an e-mail alert system that allow the detection, monitoring, and tracking of HABs in the Gulf of Mexico. NASA Earth Science data that potentially support HABMapS/Bulletin requirements include ocean color, sea surface temperature (SST), salinity, wind fields, precipitation, water surface elevation, and ocean currents. Modeling contributions include ocean circulation, wave/currents, along-shore current regimes, and chlorophyll modeling (coupled to imagery). The most immediately useful NASA contributions appear to be the 1-km Moderate Resolution Imaging Spectrometer (MODIS) chlorophyll and SST products and the (presently used) SeaWinds wind vector data. MODIS pigment concentration and SST data are sufficiently mature to replace imagery currently used in NOAA HAB applications. The large file size of MODIS data is an impediment to NOAA use and modified processing schemes would aid in NOAA adoption of these products for operational HAB forecasting.
The presentation describes efforts to develop a LIDAR in-flight product characterization range at Stennis Space Center as the next phase of the NASA Verification and Validation activities. It describes the status of surveying efforts on targets of interest to LIDAR vendors as well as the potential guidelines that will be used for product characterization.
Radiometric calibration of commercial imaging satellite products is required to ensure that science and application communities can place confidence in the imagery they use and can fully understand its properties. Inaccurate radiometric calibrations can lead to erroneous decisions and invalid conclusions and can limit intercomparisons with other systems. To address this calibration need, the NASA Stennis Space Center (SSC) Earth Science Applications (ESA) directorate,through the Joint Agency for Commercial Imagery Evaluation (JACIE) framework, established a commercial imaging satellite radiometric calibration team consisting of two groups: 1) NASA SSC ESA, supported by South Dakota State University, and 2) the University of Arizona Remote Sensing Group. The two groups determined the absolute radiometric calibration coefficients of the Digital Globe 4-band, 2.4-m QuickBird multispectral product covering the visible through near-infrared spectral region. For a 2-year period beginning in 2002, both groups employed some variant of a reflectance-based vicarious calibration approach, which required ground-based measurements coincident with QuickBird image acquisitions and radiative transfer calculations. The groups chose several study sites throughout the United States that covered nearly the entire dynamic range of the QuickBird sensor. QuickBird at-sensor radiance values were compared with those estimated by the two independent groups to determine the QuickBird sensor's radiometric accuracy. Approximately 20 at-sensor radiance estimates were vicariously determined each year. The estimates were combined to provide a high-precision radiometric gain calibration coefficient. The results of this evaluation provide the user community with an independent assessment of the QuickBird sensor's absolute calibration and stability over the 2-year period. While the techniques and method described reflect those developed at the NASA SSC, the results of both JACIE team groups are included in this paper.
This paper presents viewgraphs about a partnership between USGS and NASA who are jointly developing an airborne digital imagery characterization capability. The topics include: 1) USGS-NASA Product Characterization Approach; 2) Stennis Character Range; 3) Stennis Geodetic Targets; 4) Stennis Manhole Covers; 5) Stennis Edge Target; 6) Stennis Characterization Site; 7) Delivered Data; 8) Other Data Considerations; 9) Status; 10) Geopositional Assessment Approach; 11) Spatial Assessment Approach; 12) Edge Response; and 13) Results to Date.
NASA's Earth Science Applications Directorate evaluated the potential of NASA remote sensing data and modeling products to enhance the General NOAA Oil Modeling Environment (GNOME) decision support tool. NOAA's Office of Response and Restoration (OR&R) Hazardous Materials (HAZMAT) Response Division is interested in enhancing GNOME with near-realtime (NRT) NASA remote sensing products on oceanic winds and ocean circulation. The NASA SeaWinds sea surface wind and Jason-1 sea surface height NRT products have potential, as do sea surface temperature and reflectance products from the Moderate Resolution Imaging Spectroradiometer and sea surface reflectance products from Landsat and the Advanced Spaceborne Thermal Emission and Reflectance Radiometer. HAZMAT is also interested in the Advanced Circulation model and the Ocean General Circulation Model. Certain issues must be considered, including lack of data continuity, marginal data redundancy, and data formatting problems. Spatial resolution is an issue for near-shore GNOME applications. Additional work will be needed to incorporate NASA inputs into GNOME, including verification and validation of data products, algorithms, models, and NRT data.
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.
NASA purchased EarthSat GeoCover orthorectified Landsat imagery of global land areas covering three historical frames: (1) mid-1970's imagery from the Landsat Multispectral Scanner (MSS); (2) late 1980's-early 1990's imagery from the Landsat Thematic Mapper (TM); and (3) year 2000 imagery from the Landsat Enhanced Thematic Mapper Plus (ETM+). Because of the distinct time frames covered by these datasets, this imagery is valuable to land cover change research. Because geopositional accuracy plays a critical role in this area of scientific research. NASA performed an independent assessment of the geopositional accuracy of each EarthSat dataset using an independent set of government-provided ground control points (GCPs). These points were instrumental in the geopositional accuracy assessment of the TM imagery. Because of the orthorectification processes of the MSS imagery and the MSS pixel size, the aforementioned GCPs could not be used, and an alternate relative assessment procedure using the previously validated TM imagery as a "truth" dataset was used for the MSS data. Finally, the ETM data specification was defined in both an absolute sense with respect to ground coordinated and relative to the previously validated TM dataset. Therefore, two separate methods were used in validating the ETM-data. Results of the NASA independent assessments showed that the accuracies of the EarthSat GeoCover datasets met the defined specifications or were within the error and limitations of the verification methods employed.
Global agricultral intelligence is a key element of decision support eithin the U.S. Department of Agriculture (USDA). Estimeates of production and yield issued by the USDA for both foreign and domestic agriculture are primary sources of information for policy and management decision making. The USDA monitors the major global agricultural commodities through the Production Estimates and Crop Assessment Division (PECAD) of its Foreign Agricultural Service (FAS). Specifically, PECAD iintelligence focuses on global agricultural production and on conditions that affect food security. In conjunction with the USDA, NASA is evaluating the potential for products from NASA's Earth Science Enterprise (ESE) missions to add value to PECAD's decision support tools. NASA is usig a systems engineering approach to evaluate the potential enhancement of PECAD's decision support system (DSS)-first by understanding the components of the system and its input requirements, then by recommending NASA products that may be integrated as system inputs to improve the accuracy, quality, or efficiency of the DSS output. This report documents the evaluation phase of the systems engineering process and includes an examination of the system architecture, operations, and input requirements, as well as an initial assessment of specific ESE measurement systems and products that should be considered for their potential to enhance the PECAD DSS.
NASA purchased Earth Satellite Corporation (EarthSat) GeoCover orthorectified Landsat imagery of global land areas. The Landsat imagery covers three distinct time frames and is valuable to land cover change research. Since geopositional accuracy plays a critical role in this area of science research, NASA performed an independent assessment of the geopositional accuracy of each EarthSat dataset using an independent set of government provided ground control points. These points consisted of features identifiable within the 28.5 meter resolution TM imagery whose locations were accurately known.
The Earth science community needs to generate consistent and standard definitions for spatial, spectral, radiometric, and geometric properties describing passive electro-optical Earth observing sensors and their products. The parameters used to describe sensors and to describe their products are often confused. In some cases, parameters for a sensor and for its products are identical; in other cases, these parameters vary widely. Sensor parameters are bound by the fundamental performance of a system, while product parameters describe what is available to the end user. Products are often resampled, edge sharpened, pan-sharpened, or compressed, and can differ drastically from the intrinsic data acquired by the sensor. Because detailed sensor performance information may not be readily available to an international science community, standardization of product parameters is of primary performance. Spatial product parameters described include Modulation Transfer Function (MTF), point spread function, line spread function, edge response, stray light, edge sharpening, aliasing, ringing, and compression effects. Spectral product parameters discussed include full width half maximum, ripple, slope edge, and out-of-band rejection. Radiometric product properties discussed include relative and absolute radiometry, noise equivalent spectral radiance, noise equivalent temperature diffenence, and signal-to-noise ratio. Geometric product properties discussed include geopositional accuracy expressed as CE90, LE90, and root mean square error. Correlated properties discussed include such parameters as band-to-band registration, which is both a spectral and a spatial property. In addition, the proliferation of staring and pushbroom sensor architectures requires new parameters to describe artifacts that are different from traditional cross-track system artifacts. A better understanding of how various system parameters affect product performance is also needed to better ascertain the utility of existing datasets and products as well as to specify the performance of new sensors and products. Examples of simulations performed for the Landsat Data Continuity Mission illustrate how various parameters affect system and product performance. Specific examples include the effects of ground sample distance, MTF, and band-to-band registration on various products.
In an effort to more full explore the potential of commercial remotely sensed land data sources, the NASA Earth Science Enterprise (ESE) implemented an experimental Scientific Data Purchase (SDP) that solicited bids from the private sector to meet ESE-user data needs. The images from the Space Imaging IKONOS system provided a particularly good match to the current ESE missions such as Terra and Landsat 7 and therefore serve as a focal point in this analysis.
Radiometric calibration of commercial imaging satellite products is required to ensure that science and application communities can place confidence in the imagery they use and can fully understand its properties. Inaccurate radiometric calibrations can lead to erroneous decisions and invalid conclusions and can limit intercomparisons with other system. In addition, the user community has little or no insight into the design and operation of commercial sensors or into the methods involved in generating commercial products. To address this calibration need, the NASA Stennis Space Center (SSC) Earth Science Applications (ESA) Directorate established a commercial satellite imaging radiometric calibration team consisting of three independent groups: NASA, SSC,ESA, the University of Arizona Remote Sensing Group, and South Dacota State University. Each group determined the absolute radiometric calibration coefficients of the Space Imaging IKONOS 4-band, 4 m multispectral product covering the visible through near-infrared spectral region. For a three year period beginning in 2000, each team employed some variant of a reflectance-based vicarious calibration approach, requiring ground-based measurements coincident with IKONOS image acquisitions and radiative transfer calculations. Several study sites throughout the United States were employed that covered nearly the entire dynamic range of the IKONOS sensor. IKONOS at-sensor radiance values were compared to those estimated by each independent group to determine the IKONOS sensor's radiometric accuracy and stability. Over 10 individual vicariously determined at-sensor radiance estimates were used each year. When combined, these estimates provided a high-precision radiometric gain calibration coefficient. No significant calibration offset was observed. The results of this evaluation provide the scientific community with an independent assessment of the IKONOS sensor's absolute calibration and temporal stability over the 3-year period. While the techniques and method described in this paper reflect those developed at the NASA SSC, the results of the entire team are included.
The Joint Agency Commercial Imagery Evaluation (JACIE) team is a collaborative interagency group focused on the characterization of commercial remote sensing data products. The team members - the National Aeronautics and Space Administration (NASA), the National Imagery and Mapping Agency (NIMA), and the U.S. Geological Survey (USGS) - each have a vested interest in the purchase and use of commercial imagery to support government research and operational applications. For both research and applications, commercial products must be well characterized for precision, accuracy, and repeatability. Since commercial systems are built and operated with no government insight or oversight, the JACIE team provides an independent product characterization of delivered image and image-derived end products. End product characterization differs from the systems calibration approach that is typically used with government systems, where detailed system design information is available. The product characterization approach addresses three primary areas of product performance: geopositional accuracy, image quality, and radiometric accuracy. The JACIE team utilizes well-characterized test sites to support characterization activities. To characterize geopositional accuracy, the team utilizes sites containing several "photo-identifiable" targets and compares their precisely known locations with those defined by the commercial image product. In the area of image quality, spatial response is characterized using edge targets and pulse targets to measure edge response and to estimate image modulation transfer function. Additionally, imagery is also characterized using the National Imagery Interpretability Rating Scale, a means of quantifying the ability to identify certain targets (e.g., rail-cars, airplanes) within an image product. Radiometric accuracy is characterized using reflectance-based vicarious calibration methods at several uniform sites. Each JACIE agency performs an aspect of product characterization based on its area of expertise, thus minimizing duplication of effort. The JACIE team collaborated to perform comprehensive characterization of products from Space Imaging Inc.'s IKONOS satellite and from DigitalGlobe's QuickBird satellite and is currently characterizing products from OrbImage s OrbView-3. JACIE assessments have resulted in several improvements to commercial image product quality and have enhanced working relationships between government and industry. Assessment results are presented at an annual JACIE High Spatial Resolution Commercial Imagery Workshop.
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.
The Modulation Transfer Function (MTF) of an imaging system is one parameter that can be used to describe the spatial resolution or the image quality of an imaging system. MTF is the system's frequency response to an input. The MTF at a specific frequency normally ranges from 0-1, where 0 indicates no frequency response and 1 indicates perfect frequency response. The MTF value at Nyquist frequency is calculated because the Nyquist frequency is the maximum sampling frequency of the system. MTF analysis was performed on data acquired with an ADAR 5500 flown by Positive Systems on September 13-14, 2000 over Brookings, SD. Four edges from ground targets were used as inputs to the system. The system's MTF was calculated using a Matlab-based algorithm, which applied edge detection, numerical differentiation, and Fourier transformation methods to the target images.