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NASA NTRS1996

Modeling Global Biogenic Emission of Isoprene: Exploration of Model Drivers

Vegetation provides the major source of isoprene emission to the atmosphere. We present a modeling approach to estimate global biogenic isoprene emission. The isoprene flux model is linked to a process-based computer simulation model of biogenic trace-gas fluxes that operates on scales that link regional and global data sets and ecosystem nutrient transformations Isoprene emission estimates are determined from estimates of ecosystem specific biomass, emission factors, and algorithms based on light and temperature. Our approach differs from an existing modeling framework by including the process-based global model for terrestrial ecosystem production, satellite derived ecosystem classification, and isoprene emission measurements from a tropical deciduous forest. We explore the sensitivity of model estimates to input parameters. The resulting emission products from the global 1 degree x 1 degree coverage provided by the satellite datasets and the process model allow flux estimations across large spatial scales and enable direct linkage to atmospheric models of trace-gas transport and transformation.

Alexander, Susan E.↗

NASA NTRS1986

Spectral evolution of young stellar objects

An evolutionary sequence, from protostars to pre-main-sequence stars, is suggested for the classification of young stellar objects. This sequence is derived by comparing the predictions of the theoretical models of Adams and Shu with the morphological classification scheme of Lada and Wilking. We first define the spectral index in the near- and mid-infrared, n is identical to d log(nu F sub nu)/d log nu, and then interpret the class of sources with negative spectral indices as protostars. The inferred mass infall rates for these objects are generally consistent with the measured gas temperatures of approx. 35 K in Ophiuchus, and of approx. 10 K in Taurus. Fitting the data requires us to adopt cloud rotation rates in Ophiuchus which are typically an order of magnitude greater than in Taurus, and we speculate on the mechanistic origin for this difference. Next, we consider a subclass of T Tauri stars with near- and mid-infrared excesses and positive or zero spectral indices. We find that the objects with the steeper indices can be understood as the post-infall products from the collapse of rotating cloud cores, where the infrared excesses arise from the simple reprocessing of visible stellar photons in optically thick but spatially thin disks. The sources with flatter spectra may require massive accretion disks. Given the existence of protostars and naked star/disk systems, there is a natural interpretation of another subclass of T Tauri stars, those with two peaks in their emergent spectral energy distributions. These are readily explained as intermediate cases in which dust envelopes still surround the stars and disks. Finally, we find that the theory can be extended to explain the spectral energy distribution of FU Orionis, a famous outburst source. Our model suggests that FU Orionis has a disk, but it offers no discrimination between the competing ideas that the outburst took place on the star or in the disk.

Adams, F. C.↗

NASA NTRSDate not supplied

Satellite-Derived Imagery and Transfer Learning: A Novel Technique for Land Cover Classification

Land cover classification is a continuing research topic due to its relevance to land use and land cover changes from impacts such as climate change, agriculture, urbanization, and hazardous weather. Simple access to frequently changing land cover classifications could provide knowledge and decision support to various researchers and agencies across the globe for each of above-mentioned and related influences. This research aims to provide a novel technique for land cover classification of remote sensing imagery; harnessing Artificial Neural Networks and transfer learning (TL). Knowledge sharing techniques within machine learning are typically utilized when training datasets are sparse or transitions between data modalities is required. In this case, two data modalities, multi and hyperspectral data, are considered for knowledge transfer. The large number of continuous spectral bands available from hyperspectral sensors typically provide increased sophisticated land classification capability compared to more traditional multispectral imagery with limited discrete spectral bands. However, large-scale, frequent access to hyperspectral imagery is relatively limited. The proposed classification technique would therefore prove useful in regions in which hyperspectral data are not readily available for classification but multispectral data are. Image segmentation models are trained on each multi and hyperspectral datasets. Knowledge sharing techniques are then applied to each model to understand what knowledge, if any, is gained when moving between the data modalities. The datasets utilized for training and testing a U-Net model include the European Space Agency’s multispectral imager Sentinel-2 and the hyperspectral German Aerospace Center’s Earth Sensing Imaging Spectrometer (DESIS). Initially, only two classes, land and water, are classified for simplicity. However, more complex classes can be added if knowledge sharing is successful. The workflow for image classification through supervised image segmentation with a U-Net model will be discussed along with metrics calculated before and after TL for both Sentinel-2 and DESIS data are applied. Additionally, future steps to advance the sophistication of this technique as well as other applicable methodologies will be explored.

Emily Foshee↗

NASA NTRS1980

Landsat analysis of tropical forest succession employing a terrain model

Landsat multispectral scanner (MSS) data have yielded a dual classification of rain forest and shadow in an analysis of a semi-deciduous forest on Mindonoro Island, Philippines. Both a spatial terrain model, using a fifth side polynomial trend surface analysis for quantitatively estimating the general spatial variation in the data set, and a spectral terrain model, based on the MSS data, have been set up. A discriminant analysis, using both sets of data, has suggested that shadowing effects may be due primarily to local variations in the spectral regions and can therefore be compensated for through the decomposition of the spatial variation in both elevation and MSS data.

Barringer, T. H.↗

NASA NTRS2009

Evaluating the Impact of Land Use Change on Submerged Aquatic Vegetation Stressors in Mobile Bay

Alabama coastal systems have been subjected to increasing pressure from a variety of activities including urban and rural development, shoreline modifications, industrial activities, and dredging of shipping and navigation channels. The impacts on coastal ecosystems are often observed through the use of indicator species. One such indicator species for aquatic ecosystem health is submerged aquatic vegetation (SAV). Watershed and hydrodynamic modeling has been performed to evaluate the impact of land use change in Mobile and Baldwin counties on SAV stressors and controlling factors (temperature, salinity, and sediment) in Mobile Bay. Watershed modeling using the Loading Simulation Package in C++ (LSPC) was performed for all watersheds contiguous to Mobile Bay for land use scenarios in 1948, 1992, 2001, and 2030. Landsat-derived National Land Cover Data (NLCD) were used in the 1992 and 2001 simulations after having been reclassified to a common classification scheme. The Prescott Spatial Growth Model was used to project the 2030 land use scenario based on current trends. The LSPC model simulations provided output on changes in flow, temperature, and sediment for 22 discharge points into the Bay. Theses results were inputted in the Environmental Fluid Dynamics Computer Code (EFDC) hydrodynamic model to generate data on changes in temperature, salinity, and sediment on a grid with four vertical profiles throughout Mobile Bay. The changes in the aquatic ecosystem were used to perform an ecological analysis to evaluate the impact on SAV habitat suitability. This is the key product benefiting the Mobile Bay coastal environmental managers that integrates the influences of temperature, salinity, and sediment due to land use driven flow changes with the restoration potential of SAVs.

Al-Hamdan, Mohammad↗

NASA NTRS1987

Spectral evolution of young stellar objects

An evolutionary sequence, from protostars to pre-main sequence stars, for the classification of young stellar objects is derived by comparing the predictions of the theoretical protostar models of Adams and Shu (AS, 1986) with the morphological classification scheme of Lada and Wilking (1984). It is shown that the AS models adequately explain the emergent spectral energy distributions of unidentified objects with negative spectral indices in the mid-IR and near-IR in both Taurus and Ophiuchus. If the infalling dust envelope is then completely removed, the spectra of the underlying stars and nebular disks used by AS provide a natural explanation for the near-IR and mid-IR excesses and the positive spectral indices of embedded T Tauri stars. It is found that the addition of a simple physical model for residual dust envelopes can reproduce the far-IR excesses found in some of these T Tauri stars.

Adams, Fred C.↗

NASA NTRS2012

Using Remotely Sensed Data and Watershed and Hydrodynamic Models to Evaluate the Effects of Land Cover Land Use Change on Aquatic Ecosystems in Mobile Bay, AL

Alabama coastal systems have been subjected to increasing pressure from a variety of activities including urban and rural development, shoreline modifications, industrial activities, and dredging of shipping and navigation channels. The impacts on coastal ecosystems are often observed through the use of indicator species. One such indicator species for aquatic ecosystem health is submerged aquatic vegetation (SAV). Watershed and hydrodynamic modeling has been performed to evaluate the impact of land cover land use (LCLU) change in the two counties surrounding Mobile Bay (Mobile and Baldwin) on SAV stressors and controlling factors (temperature, salinity, and sediment) in the Mobile Bay estuary. Watershed modeling using the Loading Simulation Package in C++ (LSPC) was performed for all watersheds contiguous to Mobile Bay for LCLU scenarios in 1948, 1992, 2001, and 2030. Remotely sensed Landsat-derived National Land Cover Data (NLCD) were used in the 1992 and 2001 simulations after having been reclassified to a common classification scheme. The Prescott Spatial Growth Model was used to project the 2030 LCLU scenario based on current trends. The LSPC model simulations provided output on changes in flow, temperature, and sediment for 22 discharge points into the estuary. These results were inputted in the Environmental Fluid Dynamics Computer Code (EFDC) hydrodynamic model to generate data on changes in temperature, salinity, and sediment on a grid throughout Mobile Bay and adjacent estuaries. The changes in the aquatic ecosystem were used to perform an ecological analysis to evaluate the impact on SAV habitat suitability. This is the key product benefiting the Mobile Bay coastal environmental managers that integrates the influences of temperature, salinity, and sediment due to LCLU driven flow changes with the restoration potential of SAVs. Data products and results are being integrated into NOAA s EcoWatch and Gulf of Mexico Data Atlas online systems for dissemination to coastal resource managers and stakeholders.

Al-Hamdan, Mohammad Z.↗

NASA NTRS2012

Using Remotely Sensed Data and Watershed and Hydrodynamic Models to Evaluate the Effects of Land Cover Land Use Change on Aquatic Ecosystems in Mobile Bay, AL

Alabama coastal systems have been subjected to increasing pressure from a variety of activities including urban and rural development, shoreline modifications, industrial activities, and dredging of shipping and navigation channels. The impacts on coastal ecosystems are often observed through the use of indicator species. One such indicator species for aquatic ecosystem health is submerged aquatic vegetation (SAV). Watershed and hydrodynamic modeling has been performed to evaluate the impact of land cover land use (LCLU) change in the two counties surrounding Mobile Bay (Mobile and Baldwin) on SAV stressors and controlling factors (temperature, salinity, and sediment) in the Mobile Bay estuary. Watershed modeling using the Loading Simulation Package in C++ (LSPC) was performed for all watersheds contiguous to Mobile Bay for LCLU scenarios in 1948, 1992, 2001, and 2030. Remotely sensed Landsat-derived National Land Cover Data (NLCD) were used in the 1992 and 2001 simulations after having been reclassified to a common classification scheme. The Prescott Spatial Growth Model was used to project the 2030 LCLU scenario based on current trends. The LSPC model simulations provided output on changes in flow, temperature, and sediment for 22 discharge points into the estuary. These results were inputted in the Environmental Fluid Dynamics Computer Code (EFDC) hydrodynamic model to generate data on changes in temperature, salinity, and sediment on a grid throughout Mobile Bay and adjacent estuaries. The changes in the aquatic ecosystem were used to perform an ecological analysis to evaluate the impact on SAV habitat suitability. This is the key product benefiting the Mobile Bay coastal environmental managers that integrates the influences of temperature, salinity, and sediment due to LCLU driven flow changes with the restoration potential of SAVs. Data products and results are being integrated into NOAA s EcoWatch and Gulf of Mexico Data Atlas online systems for dissemination to coastal resource managers and stakeholders. Objective 1: Develop and utilize Land Use scenarios for Mobile and Baldwin Counties, AL as input to models to predict the affects on water properties (temperature,salinity,)for Mobile Bay through 2030. Objective 2: Evaluate the impact of land use change on seagrasses and SAV in Mobile Bay. Hypothesis: Urbanization will significantly increase surface flows and impact salinity and temperature variables that effect seagrasses and SAVs.

Al-Hamdan, Mohammad↗

NASA NTRS1981

Application of scanning microdensitometer data in selected plant science case studies

This paper treats a representative sample of projects aimed at vegetation mapping and assessment via analysis of both digital photographic and Landsat data projects and illustrates the potential for using scanning microdensitometric data extracted from color infrared photographs in the following applications: freshwater wetlands mapping, tree type mapping, and yield vs. reflectance modeling in corn fertilization experiments. These case studies are presented to illustrate the general applicability of scanning microdensitometer data in the contexts of image classification and enhancement as well as quantitative modeling of ground parameters.

Lillesand, T. M.↗

NASA NTRS1991

Parameter trade-offs for imaging spectroscopy systems

With the advent of the EOS era and of configurable sensors, users of these instruments are faced with the twin problems of specifying data acquisition parameters and extracting desired information from the voluminous data. An application of a system model is made to explore system parameter trade-offs for a model sensor based on the High Resolution Imaging Spectrometer. Radiometric performance was studied, along with the effect on classification accuracy of several system parameters. Using a model scene based on typical agricultural reflectance and atmospheric conditions, the atmosphere and sensor are seen to have significant effects on the mean received signal and noise performance. The effect of random uncorrelated errors in the radiometric calibration of the detector array is seen to degrade system performance, especially in the spectral bands below 1 micron. Accurate pixel-to-pixel relative radiometric calibration and the use of the Image Motion Compensation option are seen to improve classification accuracy, especially at high solar zenith angles. Feature sets chosen from characteristics of the scene performed best overall, but ones chosen based on signal-to-noise ratios were seen to be more robust.

Kerekes, John P.↗

NASA NTRS2002

Classification image analysis: estimation and statistical inference for two-alternative forced-choice experiments

We consider estimation and statistical hypothesis testing on classification images obtained from the two-alternative forced-choice experimental paradigm. We begin with a probabilistic model of task performance for simple forced-choice detection and discrimination tasks. Particular attention is paid to general linear filter models because these models lead to a direct interpretation of the classification image as an estimate of the filter weights. We then describe an estimation procedure for obtaining classification images from observer data. A number of statistical tests are presented for testing various hypotheses from classification images based on some more compact set of features derived from them. As an example of how the methods we describe can be used, we present a case study investigating detection of a Gaussian bump profile.

Non-NASA Center↗

NASA NTRS1993

Developing interpretable models with optimized set reduction for identifying high risk software components

Applying equal testing and verification effort to all parts of a software system is not very efficient, especially when resources are limited and scheduling is tight. Therefore, one needs to be able to differentiate low/high fault frequency components so that testing/verification effort can be concentrated where needed. Such a strategy is expected to detect more faults and thus improve the resulting reliability of the overall system. This paper presents the Optimized Set Reduction approach for constructing such models, intended to fulfill specific software engineering needs. Our approach to classification is to measure the software system and build multivariate stochastic models for predicting high risk system components. We present experimental results obtained by classifying Ada components into two classes: is or is not likely to generate faults during system and acceptance test. Also, we evaluate the accuracy of the model and the insights it provides into the error making process.

Briand, Lionel C.↗

NASA NTRS1997

A Categorization of Dynamic Analyzers

Program analysis techniques and tools are essential to the development process because of the support they provide in detecting errors and deficiencies at different phases of development. The types of information rendered through analysis includes the following: statistical measurements of code, type checks, dataflow analysis, consistency checks, test data,verification of code, and debugging information. Analyzers can be broken into two major categories: dynamic and static. Static analyzers examine programs with respect to syntax errors and structural properties., This includes gathering statistical information on program content, such as the number of lines of executable code, source lines. and cyclomatic complexity. In addition, static analyzers provide the ability to check for the consistency of programs with respect to variables. Dynamic analyzers in contrast are dependent on input and the execution of a program providing the ability to find errors that cannot be detected through the use of static analysis alone. Dynamic analysis provides information on the behavior of a program rather than on the syntax. Both types of analysis detect errors in a program, but dynamic analyzers accomplish this through run-time behavior. This paper focuses on the following broad classification of dynamic analyzers: 1) Metrics; 2) Models; and 3) Monitors. Metrics are those analyzers that provide measurement. The next category, models, captures those analyzers that present the state of the program to the user at specified points in time. The last category, monitors, checks specified code based on some criteria. The paper discusses each classification and the techniques that are included under them. In addition, the role of each technique in the software life cycle is discussed. Familiarization with the tools that measure, model and monitor programs provides a framework for understanding the program's dynamic behavior from different, perspectives through analysis of the input/output data.

Lujan, Michelle R.↗

NASA NTRS1983

Vegetation monitoring and classification using NOAA/AVHRR satellite data

A vegetation gradient model, based on a new surface hydrologic index and NOAA/AVHRR meteorological satellite data, has been analyzed along a 1300 km east-west transect across the state of Texas. The model was developed to test the potential usefulness of such low-resolution data for vegetation stratification and monitoring. Normalized Difference values (ratio of AVHRR bands 1 and 2, considered to be an index of greenness) were determined and evaluated against climatological and vegetation characteristics at 50 sample locations (regular intervals of 0.25 deg longitude) along the transect on five days in 1980. Statistical treatment of the data indicate that a multivariate model incorporating satellite-measured spectral greenness values and a surface hydrologic factor offer promise as a new technique for regional-scale vegetation stratification and monitoring.

Greegor, D. H., Jr.↗

NASA NTRS1986

The effect of Thematic Mapper spectral properties on land cover mapping for hydrologic modeling

The accuracy of unsupervised land-cover classification from all seven Landsat TM bands and from six combinations of three or four bands is evaluated using images of the Clinton River Basin, a suburban watershed near Detroit. Data from aerial TMS photography, USGS topographic maps, and ground surveys are employed to determine the classification accuracy. The mapping accuracy of all seven bands is found to be significantly better (6 percent overall, 12 percent for residential areas, and 13 percent for commercial districts) than that with bands 2, 3, and 4; but almost the same accuracy is obtained by including at least one band from each major spectral region (visible, NIR, and mid-IR).

Gervin, J. C.↗

NASA NTRS1993

Modifications to the three-component classification algorithm for SAR data

Previously, a scattering model was used to classify SAR data as due to three mechanisms: surface or odd-bounce scattering; double-bounce scattering; and volume scattering. A Bragg rough-surface model was used for odd-bounce scattering, and two reflecting surfaces, as in a dihedral corner reflector, represented the double-bounce scattering mechanism. A random collection of infinitely thin cylinders was used to model the volume scattering contribution. It was assumed that the cylinders are uniformly oriented in all directions. In this work, modifications are made to the above model, which include specifying a preferred orientation for the cylinders representing branches and using thin cylinders of finite thickness as opposed to infinitely thin ones. In the first case, the preferred orientation is represented by a probability density function in the form of a dirac delta function. For the second case, small argument asymptotic expansions of Bessel functions are used to express the volume scattering contributions in terms of quantities containing the average radius of the cylinders. Since in each case the number of unknowns exceeds that of equations, simplifying assumptions are made. The solutions are applied to multifrequency polarimetric SAR data to obtain the new unknowns in addition to the relative contributions of each scattering mechanism. Several results are presented. It is found that the calculated percentage of each mechanism does not change significantly compared to the original model; however, additional information is gained about the scatterers.

Moghaddam, M.↗

NASA NTRS2004

Human Health Risk Assessment Simulations in a Distributed Environment for Shuttle Launch

During the launch of a rocket under prevailing weather conditions, commanders at Cape Canaveral Air Force station evaluate the possibility of whether wind blown toxic emissions might reach civilian and military personnel in the near by area. In our model, we focused mainly on Hydrogen chloride (HCL), Nitrogen oxides (NOx) and Nitric acid (HNO3), which are non-carcinogenic chemicals as per United States Environmental Protection Agency (USEPA) classification. We have used the hazard quotient model to estimate the number of people at risk. It is based on the number of people with exposure above a reference exposure level that is unlikely to cause adverse health effects. The risk to the exposed population is calculated by multiplying the individual risk and the number in exposed population. The risk values are compared against the acceptable risk values and GO or NO-go situation is decided based on risk values for the Shuttle launch. The entire model is simulated over the web and different scenaria can be generated which allows management to choose an optimum decision.

Thirumalainambi, Rajkumar↗

NASA NTRS1993

Communications and control for electric power systems: Power flow classification for static security assessment

This report investigates the classification of power system states using an artificial neural network model, Kohonen's self-organizing feature map. The ultimate goal of this classification is to assess power system static security in real-time. Kohonen's self-organizing feature map is an unsupervised neural network which maps N-dimensional input vectors to an array of M neurons. After learning, the synaptic weight vectors exhibit a topological organization which represents the relationship between the vectors of the training set. This learning is unsupervised, which means that the number and size of the classes are not specified beforehand. In the application developed in this report, the input vectors used as the training set are generated by off-line load-flow simulations. The learning algorithm and the results of the organization are discussed.

Niebur, D.↗