Engineering topics

Dungan, Jennifer L.

Publications and source records attributed to Dungan, Jennifer L..

34 records · Page 2

NASA NTRS1997

Meta-Modeling: A Knowledge-Based Approach to Facilitating Model Construction and Reuse

In this paper, we introduce a new modeling approach called meta-modeling and illustrate its practical applicability to the construction of physically-based ecosystem process models. As a critical adjunct to modeling codes meta-modeling requires explicit specification of certain background information related to the construction and conceptual underpinnings of a model. This information formalizes the heretofore tacit relationship between the mathematical modeling code and the underlying real-world phenomena being investigated, and gives insight into the process by which the model was constructed. We show how the explicit availability of such information can make models more understandable and reusable and less subject to misinterpretation. In particular, background information enables potential users to better interpret an implemented ecosystem model without direct assistance from the model author. Additionally, we show how the discipline involved in specifying background information leads to improved management of model complexity and fewer implementation errors. We illustrate the meta-modeling approach in the context of the Scientists' Intelligent Graphical Modeling Assistant (SIGMA) a new model construction environment. As the user constructs a model using SIGMA the system adds appropriate background information that ties the executable model to the underlying physical phenomena under investigation. Not only does this information improve the understandability of the final model it also serves to reduce the overall time and programming expertise necessary to initially build and subsequently modify models. Furthermore, SIGMA's use of background knowledge helps eliminate coding errors resulting from scientific and dimensional inconsistencies that are otherwise difficult to avoid when building complex models. As a. demonstration of SIGMA's utility, the system was used to reimplement and extend a well-known forest ecosystem dynamics model: Forest-BGC.

Keller, Richard M.↗

NASA NTRS1995

Geostatistical Approaches for Spatial Estimation of Vegetation Quantities Using Ground and Image Data

A major challenge in the study of the earth system is the mapping of vegetation quantities over large regions. Geostatistical methods, such as cokriging and stochastic simulation, have the potential to exploit more fully both remotely sensed data and ground information and improve the spatial estimation of vegetation variables over traditional regression methods. A synthetic example constructed from imaging spectrometer data allows a useful comparison among regression, cokriging and a simple probability-field method. A range of linear relationships between direct (sampled) and ancillary images is used. The lowest root-mean-square-error is achieved with cokriging until the correlation between direct and ancillary data exceeds .89, at which point regression is the superior estimator. Probability-field simulation gives a range of possible realizations, most more precise than those from regression. The relationship between ground measurements and image data is thus demonstrated to be one of the critical factors in the choice of a spatial estimation method.

Dungan, Jennifer L.↗

NASA NTRS1994

Kriging in the Shadows: Geostatistical Interpolation for Remote Sensing

It is often useful to estimate obscured or missing remotely sensed data. Traditional interpolation methods, such as nearest-neighbor or bilinear resampling, do not take full advantage of the spatial information in the image. An alternative method, a geostatistical technique known as indicator kriging, is described and demonstrated using a Landsat Thematic Mapper image in southern Chiapas, Mexico. The image was first classified into pasture and nonpasture land cover. For each pixel that was obscured by cloud or cloud shadow, the probability that it was pasture was assigned by the algorithm. An exponential omnidirectional variogram model was used to characterize the spatial continuity of the image for use in the kriging algorithm. Assuming a cutoff probability level of 50%, the error was shown to be 17% with no obvious spatial bias but with some tendency to categorize nonpasture as pasture (overestimation). While this is a promising result, the method's practical application in other missing data problems for remotely sensed images will depend on the amount and spatial pattern of the unobscured pixels and missing pixels and the success of the spatial continuity model used.

Rossi, Richard E.↗

NASA NTRS1994

SIGMA: A Knowledge-Based Simulation Tool Applied to Ecosystem Modeling

The need for better technology to facilitate building, sharing and reusing models is generally recognized within the ecosystem modeling community. The Scientists' Intelligent Graphical Modelling Assistant (SIGMA) creates an environment for model building, sharing and reuse which provides an alternative to more conventional approaches which too often yield poorly documented, awkwardly structured model code. The SIGMA interface presents the user a list of model quantities which can be selected for computation. Equations to calculate the model quantities may be chosen from an existing library of ecosystem modeling equations, or built using a specialized equation editor. Inputs for dim equations may be supplied by data or by calculation from other equations. Each variable and equation is expressed using ecological terminology and scientific units, and is documented with explanatory descriptions and optional literature citations. Automatic scientific unit conversion is supported and only physically-consistent equations are accepted by the system. The system uses knowledge-based semantic conditions to decide which equations in its library make sense to apply in a given situation, and supplies these to the user for selection. "Me equations and variables are graphically represented as a flow diagram which provides a complete summary of the model. Forest-BGC, a stand-level model that simulates photosynthesis and evapo-transpiration for conifer canopies, was originally implemented in Fortran and subsequenty re-implemented using SIGMA. The SIGMA version reproduces daily results and also provides a knowledge base which greatly facilitates inspection, modification and extension of Forest-BGC.

Dungan, Jennifer L.↗

NASA NTRS1994

A Knowledge-Based Representation Scheme for Environmental Science Models

One of the primary methods available for studying environmental phenomena is the construction and analysis of computational models. We have been studying how artificial intelligence techniques can be applied to assist in the development and use of environmental science models within the context of NASA-sponsored activities. We have identified several high-utility areas as potential targets for research and development: model development; data visualization, analysis, and interpretation; model publishing and reuse, training and education; and framing, posing, and answering questions. Central to progress on any of the above areas is a representation for environmental models that contains a great deal more information than is present in a traditional software implementation. In particular, a traditional software implementation is devoid of any semantic information that connects the code with the environmental context that forms the background for the modeling activity. Before we can build AI systems to assist in model development and usage, we must develop a representation for environmental models that adequately describes a model's semantics and explicitly represents the relationship between the code and the modeling task at hand. We have developed one such representation in conjunction with our work on the SIGMA (Scientists' Intelligent Graphical Modeling Assistant) environment. The key feature of the representation is that it provides a semantic grounding for the symbols in a set of modeling equations by linking those symbols to an explicit representation of the underlying environmental scenario.

Keller, Richard M.↗

NASA NTRS1993

Leaf Areas And Spectral Properties Of Slash Pine

NASA technical memorandum describes experiments to test feasibility of estimating seasonal leaf areas of forest canopies from spectral radiances measured by remote instruments. Accurate estimates of seasonal leaf areas of forests necessary for studies of seasonal exchanges of energy between forest canopies and atmosphere. Potential use of remote sensing in estimating seasonal changes in leaf area index (LAI).

Curran, Paul J.↗

NASA NTRS1992

Reflectance spectroscopy of fresh whole leaves for the estimation of chemical concentration

Remotely sensed plant-canopy data in the visible and near-IR ranges are used to establish relations between the canopy reflectance and the chemical content of the leaves. The mathematical relation is generated by means of stepwise regression based on the derivative reflectance at certain wavelengths. Fourier filtering and sample control are used to minimize instrument noise and spectral overlap respectively, and absorption features are noted that correspond to sugar and protein. The coefficients of determination between estimated and measured concentrations are at least 0.82 for such substances as starch and chlorophyll. It is recommended in the analysis of remotly sensed canopy data that the chemicals with strong spectral overlaps with the chemical of interest be accounted for in order to estimate foliar chemical concentrations accurately.

Curran, Paul J.↗

NASA NTRS1992

Seasonal LAI in slash pine estimated with Landsat TM

The leaf area index (LAI, total area of leaves per unit area of ground) of most forest canopies varies throughout the year, yet for logistical reasons it is difficult to estimate anything more detailed than a seasonal maximum LAI. To determine if remotely sensed data can be used to estimate LAI seasonally, field measurements of LAI were compared to normalized difference vegetation index (NDVI) values derived using Lnandsat Thematic Mapper (TM) data, for 16 fertilized and control slash pine plots on 3 dates. Linear relationships existed between NDVI and LAI with R (sup 2) values of 0.35, 0.75, and 0.86 for February 1988, September 1988, and March, 1989, respectively. This is the first reported study in which NDVI is related to forest LAI recorded during the month of sensor overpass. Predictive relationships based on data from eight of the plots were used to estimate the LAI of the other eight plots with a root-mean-square error of 0.74 LAI, which is 15.6 percent of the mean LAI. This demonstrates the potential use of Landsat TM data for studying seasonal dynamics in forest canopies.

Curran, Paul J.↗

NASA NTRS1991

Estimating The SNR Of AVIRIS Data

Combination of established statistical and filtering techniques yields improved estimates of signal-to-noise ratio (SNR) of multispectral imagery produced by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Method yields more accurate estimate of random variability in AVIRIS imagery. Combination of techniques called "geostatistical" method. In first step of method, periodic noise removed by notch filtering in frequency domain. In subsequent steps, sensor noise and variability within picture elements isolated by use of semi-variance of picture elements.

Curran, Paul J.↗

NASA NTRS1991

The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration

The effect of a leaf pigment - red amaranthin - on red edge and chlorophyll concentration is investigated in amaranth leaves by means of treatments with nitrate and salts. A near-linear relationship between red edge and chlorophyll concentration is observed for leaves with low amaranthin concentration, and no relationship is noted at high concentrations. The study demonstrates the limitation inherent in estimating chlorophyll concentration by using remotely sensed red edge.

Curran, Paul J.↗

NASA NTRS1990

An image recorded by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)

The airborne visible/infrared imaging spectrometer (AVIRIS) is described, and an example of a false-color image recorded by this device is provided. The AVIRIS is capable of sensing in 209 visible and near-infrared wavebands with an 11km swath and a 20m spatial resolution. Evaluation flights for AVIRIS were made at an altitude of approximately 20km x 10.2km of low-lying and relatively flat irrigated land near Yuba City and Sacramento, California. Raw data were converted from digital numbers to radiance and radiometrically corrected at the NASA Jet Propulsion Laboratory. Notch filtering in the frequency domain of the image was used to remove periodic noise. The illustration of both spatial and spectral properties on the false-color image are explained. AVIRIS is designed to be flown in an ER-2 aircraft and will serve as a test-bed sensor for the High-Resolution Imaging Spectrometer (HIRIS) planned for the Earth Observing System.

Curran, Paul J.↗

NASA NTRS1990

Seasonal LAI in slash pine estimated with LANDSAT TM

The leaf area index (LAI, total area of leaves per unit area of ground) of most forest canopies varies throughout the year, yet for logistical reasons it is difficult to estimate anything more detailed than a seasonal maximum LAI. To determine if remotely sensed data can be used to estimate LAI seasonally, field measurements of LAI were compared to normalized difference vegetation index (NDVI) values derived using LANDSAT Thematic Mapper (TM) data, for 16 fertilized and control slash pine plots on 3 dates. Linear relationships existed between NDVI and LAI with R(sup 2) values of 0.35, 0.75, and 0.86 for February 1988, September 1988, and March, 1989, respectively. This is the first reported study in which NDVI is related to forest LAI recorded during the month of sensor overpass. Predictive relationships based on data from eight of the plots were used to estimate the LAI of the other eight plots with a root-mean-square error of 0.74 LAI, which is 15.6 percent of the mean LAI. This demonstrates the potential use of LANDSAT TM data for studying seasonal dynamics in forest canopies.

Curran, Paul J.↗

NASA NTRS1990

Exploring the Relationship Between Reflectance Red Edge and Chlorophyll Content in Slash Pine

Chlorophyll is a key indicator of the physiological status of a forest canopy. However, its distribution may vary greatly in time and space, so that the estimation of chlorophyll content of canopies or branches by extrapolation from leaf values obtained by destructive sampling is labor intensive and potentially inaccurate. Chlorophy11 content is related positively to the point of maximum slope in vegetation reflectance spectra which occurs at wavelengths between 690-740 nm and is known as the "red edge." The red edge of needles on individual slash pine (Piniis elliottii Engelm.) branches and in whole forest canopies was measured with a spectroradiometer. Branches were measured on the ground against a spectrally flat reflectance target and canopies were measured from observation towers against a spectrally variable understory and forest floor. There was a linear relationship between red edge and chlorophyll content of branches (R(exp 2) = 0.91). Measurements of the red edge and this relationship were used to estimate the chlorophyll content of other branches with an error that was lower than that associated with the colorimetric (laboratory) method. There was no relationship between the red edge and the chlorophyll content of whole canopies. This can be explained by the overriding influence of the understory and forest floor, an influence that was illustrated by spectral mixture modeling. The results suggest that the red edge could be used to estimate the chlorophyll content in branches but it is unlikely to be of value for the estimation of chlorophyll content in canopies unless the canopy cover is high.

Curran, Paul J.↗

NASA NTRS1989

Estimation of signal-to-noise - A new procedure applied to AVIRIS data

To make the best use of narrowband airborne visible/infrared imaging spectrometer (AVIRIS) data, an investigator needs to know the ratio of signal to random variability or noise (signal-to-noise ratio or SNR). The signal is land cover dependent and varies with both wavelength and atmospheric absorption; random noise comprises sensor noise and intrapixel variability (i.e., variability within a pixel). The three existing methods for estimating the SNR are inadequate, since typical laboratory methods inflate while dark current and image methods deflate the SNR. A new procedure is proposed called the geostatistical method. It is based on the removal of periodic noise by notch filtering in the frequency domain and the isolation of sensor noise and intrapixel variability using the semi-variogram. This procedure was applied easily and successfully to five sets of AVIRIS data from the 1987 flying season and could be applied to remotely sensed data from broadband sensors.

Curran, Paul J.↗

NASA NTRS1988

Estimating the signal-to-noise ratio of AVIRIS data

Curran, Paul J.↗

NASA NTRS1988

Zones of information in the AVIRIS spectra

To make the best use of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data an investigator needs to know the ratio of signal to random variability or noise (S/N ratio). The signal is land-cover dependent and decreases with both wavelength and atmospheric absorption and random noise comprises sensor noise and intra-pixel variability. The three existing methods for estimating the S/N ratio are inadequate as typical laboratory methods inflate, while dark current and image methods deflate the S/N ratio. We propose a new procedure called the geostatistical method. It is based on the removal of periodic noise by notch filtering in the frequency domain and the isolation of sensor noise and intra-pixel variability using the semi-variogram. This procedure was applied easily and successfully to five sets of AVIRIS data from the 1987 flying season.

Curran, Paul J.↗