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NASA NTRSDate not supplied

Remote Sensing of Lineage Functional Types for Modeling and Monitoring Biodiversity

Hyperspectral remote sensing has the potential to continuously scale plant function and plant diversity information from landscape to global extents. Numerous studies have indicated that VSWIR (400-2500 nm) reflectance properties of vegetation capture evolutionarily conserved biochemical, structural, and other functional attributes of plant species. Spectral properties conserved in plants provide the opportunity to both 1) aggregate species into lineages with improved classification accuracy and 2) link those lineages directly to plant traits. Full realization of this goal will enable parameterization of Land Surface Models (LSMs) with remotely sensed information, e.g., canopy nitrogen, and better representations of biodiversity and functional diversity in biogeographic studies. In this study, we use hyperspectral AVIRIS data from the 2013 HyspIRI campaign over the Southern Sierra Nevada, California flight box to investigate the potential for incorporating evolutionary thinking into landcover classification. We link the airborne hyperspectral data with vegetation plot data from roughly 1372 surveys and a phylogeny representing 1361 species. We aggregate species into lineages ranging from species level groups down to similar number of Plant Functional Types as often used in LSMs. We assessed the ability of Random Forest and Partial Least Squares Discriminant Analysis to discriminate across these different phylogenetic scales and determine the optimal number of lineages to classify. Although there are some temporal and spatial differences in our training data, our best approaches achieved moderate classification accuracy (Kappa > 0.65). Given an optimal number of lineages, we explored approaches to improve classifications including machine learning and unmixing approaches. This work suggests that lineage-based methods may be a promising way to leverage the huge amounts of data that will come from high resolution and high return interval hyperspectral data planned for the Surface Biology and Geology mission with sparsely sampled existing ground-based ecological data.

Hyperspectral↗

NASA NTRS1985

Applications of Fuzzy Clustering Techniques to Stratified by Tropopause MSU Temperature Retrievals

The fuzzy partitioned clustering method was applied to predict tropopause height only using microwave information with an eye towards using it on real data under cloudy conditions. In the second stage stratified by tropopause regression temperature retrievals included using only the three or four microwave channels for each 40 mb range. The first step in the experiment is the fuzzy partitioned clustering of the microwave brightness temperatures. This method is a combination of standard hard clustering and discriminant analysis. The fuzzy partitioned clustering uses all the generated probabilities of membership of each pattern vector in any of the given clusters. These probabilities are generated by discriminant analysis to locate the correct cluster. The ultimate goal of standard discriminant analysis is to provide the unique (correct) cluster to which the pattern vector belongs. It was only the maximum of all the generated probabilities. The method uses all the probabilities and weight the regressions generated within each cluster. These regression formulas predict the tropopause height from the microwave brightness temperatures. In the second step the microwave regression temperature retrievals are stratified by tropopause height every 40 mb. The control experiment is defined, the data are stratified by land/ocean, summer/winter, and latitude bands.

Munteanu, M. J.↗

NASA NTRS1974

Distinguishing between CAT and non-CAT areas by use of discriminant function analysis

The investigation considered is concerned with a method in which a statistical approach is employed to determine algebraic functions involving selected synoptic-scale parameters which would indicate areas and altitudes of CAT in the stratosphere over the western U.S. The statistical approach selected is based on discriminant function analysis. The functions are determined from combinations of synoptic-scale parameters and stratospheric turbulence data. It was found in the investigation that there is a relationship between selected combinations of synoptic-scale parameters of the upper troposphere and lower stratosphere and stratospheric clear-air turbulence.

Clark, T. L.↗

NASA NTRS1993

Multivariate statistical analysis: Principles and applications to coorbital streams of meteorite falls

Multivariate statistical analysis techniques (linear discriminant analysis and logistic regression) can provide powerful discrimination tools which are generally unfamiliar to the planetary science community. Fall parameters were used to identify a group of 17 H chondrites (Cluster 1) that were part of a coorbital stream which intersected Earth's orbit in May, from 1855 - 1895, and can be distinguished from all other H chondrite falls. Using multivariate statistical techniques, it was demonstrated that a totally different criterion, labile trace element contents - hence thermal histories - or 13 Cluster 1 meteorites are distinguishable from those of 45 non-Cluster 1 H chondrites. Here, we focus upon the principles of multivariate statistical techniques and illustrate their application using non-meteoritic and meteoritic examples.

Wolf, S. F.↗

DOE OSTI2024

Comparing gas composition from fast pyrolysis of live foliage measured in bench-scale and fire-scale experiments

Background: Fire models have used pyrolysis data from oxidising and non-oxidising environments for flaming combustion. In wildland fires pyrolysis, flaming and smouldering combustion typically occur in an oxidising environment (the atmosphere). Aims: Using compositional data analysis methods, determine if the composition of pyrolysis gases measured in non-oxidising and ambient (oxidising) atmospheric conditions were similar. Methods: Permanent gases and tars were measured in a fuel-rich (non-oxidising) environment in a flat flame burner (FFB). Permanent and light hydrocarbon gases were measured for the same fuels heated by a fire flame in ambient atmospheric conditions (oxidising environment). Log-ratio balances of the measured gases common to both environments (CO, CO 2 , CH 4 , H 2 , C 6 H 6 O (phenol), and other gases) were examined by principal components analysis (PCA), canonical discriminant analysis (CDA) and permutational multivariate analysis of variance (PERMANOVA). Key results: Mean composition changed between the non-oxidising and ambient atmosphere samples. PCA showed that flat flame burner (FFB) samples were tightly clustered and distinct from the ambient atmosphere samples. CDA found that the difference between environments was defined by the CO-CO 2 log-ratio balance. PERMANOVA and pairwise comparisons found FFB samples differed from the ambient atmosphere samples which did not differ from each other. Conclusion: Relative composition of these pyrolysis gases differed between the oxidising and non-oxidising environments. This comparison was one of the first comparisons made between bench-scale and field scale pyrolysis measurements using compositional data analysis. Implications: These results indicate the need for more fundamental research on the early time-dependent pyrolysis of vegetation in the presence of oxygen.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1992

Extracting galactic structure parameters from multivariated density estimation

Multivariate statistical analysis, including includes cluster analysis (unsupervised classification), discriminant analysis (supervised classification) and principle component analysis (dimensionlity reduction method), and nonparameter density estimation have been successfully used to search for meaningful associations in the 5-dimensional space of observables between observed points and the sets of simulated points generated from a synthetic approach of galaxy modelling. These methodologies can be applied as the new tools to obtain information about hidden structure otherwise unrecognizable, and place important constraints on the space distribution of various stellar populations in the Milky Way. In this paper, we concentrate on illustrating how to use nonparameter density estimation to substitute for the true densities in both of the simulating sample and real sample in the five-dimensional space. In order to fit model predicted densities to reality, we derive a set of equations which include n lines (where n is the total number of observed points) and m (where m: the numbers of predefined groups) unknown parameters. A least-square estimation will allow us to determine the density law of different groups and components in the Galaxy. The output from our software, which can be used in many research fields, will also give out the systematic error between the model and the observation by a Bayes rule.

Chen, B.↗

NASA NTRS1992

Space sickness predictors suggest fluid shift involvement and possible countermeasures

Preflight data from 64 first time Shuttle crew members were examined retrospectively to predict space sickness severity (NONE, MILD, MODERATE, or SEVERE) by discriminant analysis. From 9 input variables relating to fluid, electrolyte, and cardiovascular status, 8 variables were chosen by discriminant analysis that correctly predicted space sickness severity with 59 pct. success by one method of cross validation on the original sample and 67 pct. by another method. The 8 variables in order of their importance for predicting space sickness severity are sitting systolic blood pressure, serum uric acid, calculated blood volume, serum phosphate, urine osmolality, environmental temperature at the launch site, red cell count, and serum chloride. These results suggest the presence of predisposing physiologic factors to space sickness that implicate a fluid shift etiology. Addition of a 10th input variable, hours spent in the Weightless Environment Training Facility (WETF), improved the prediction of space sickness severity to 66 pct. success by the first method of cross validation on the original sample and to 71 pct. by the second method. The data suggest that WETF training may reduce space sickness severity.

Simanonok, K. E.↗

NASA NTRS1985

What are the best radar wavelengths, incidence angles and polarizations for geologic applications? A statistical approach

Linear discriminant analysis of multifrequency and multipolarization radar scatterometer data of lava flows and sedimentary rocks indicates that the lava flows can be separated by age and the sedimentary rocks can be discriminated from one another. The optimum wavelengths, polarizations and incidence angles among those available for these problems was determined by the discriminant analysis program. For separation of the lava flows, shorter wavelengths, smaller incidence angles and horizontal polarization are best. A SIR-C radar configuration could provide nearly complete discrimination of these lava flows. Conversely, the longer wavelengths, larger incidence angles and vertical polarization was preferred for sedimentary rocks, perhaps due to the slight vegetation cover. Satisfactory classification of sedimentary rocks requires more radar data than for the lavas. These results are potentially useful both for radar system configuration and for geological applications. The method developed here may provide a rationale for user specification of imaging system parameters.

Blom, R.↗

NASA NTRS1976

Geologic mapping using LANDSAT data

The feasibility of automated classification for lithologic mapping with LANDSAT digital data was evaluated using three classification algorithms. The two supervised algorithms analyzed, a linear discriminant analysis algorithm and a hybrid algorithm which incorporated the Parallelepiped algorithm and the Bayesian maximum likelihood function, were comparable in terms of accuracy; however, classification was only 50 per cent accurate. The linear discriminant analysis algorithm was three times as efficient as the hybrid approach. The unsupervised classification technique, which incorporated the CLUS algorithm, delineated the major lithologic boundaries and, in general, correctly classified the most prominent geologic units. The unsupervised algorithm was not as efficient nor as accurate as the supervised algorithms. Analysis of spectral data for the lithologic units in the 0.4 to 2.5 microns region indicated that a greater separability of the spectral signatures could be obtained using wavelength bands outside the region sensed by LANDSAT.

Siegal, B. S.↗

NASA NTRS1988

Multisensor classification of sedimentary rocks

A comparison is made between linear discriminant analysis and supervised classification results based on signatures from the Landsat TM, the Thermal Infrared Multispectral Scanner (TIMS), and airborne SAR, alone and combined into extended spectral signatures for seven sedimentary rock units exposed on the margin of the Wind River Basin, Wyoming. Results from a linear discriminant analysis showed that training-area classification accuracies based on the multisensor data were improved an average of 15 percent over TM alone, 24 percent over TIMS alone, and 46 percent over SAR alone, with similar improvement resulting when supervised multisensor classification maps were compared to supervised, individual sensor classification maps. When training area signatures were used to map spectrally similar materials in an adjacent area, the average classification accuracy improved 19 percent using the multisensor data over TM alone, 2 percent over TIMS alone, and 11 percent over SAR alone. It is concluded that certain sedimentary lithologies may be accurately mapped using a single sensor, but classification of a variety of rock types can be improved using multisensor data sets that are sensitive to different characteristics such as mineralogy and surface roughness.

Evans, Diane↗

NASA NTRS1984

Benchmark data on the separability among crops in the southern San Joaquin Valley of California

Landsat MSS data were input to a discriminant analysis of 21 crops on each of eight dates in 1979 using a total of 4,142 fields in southern Fresno County, California. The 21 crops, which together account for over 70 percent of the agricultural acreage in the southern San Joaquin Valley, were analyzed to quantify the spectral separability, defined as omission error, between all pairs of crops. On each date the fields were segregated into six groups based on the mean value of the MSS7/MSS5 ratio, which is correlated with green biomass. Discriminant analysis was run on each group on each date. The resulting contingency tables offer information that can be profitably used in conjunction with crop calendars to pick the best dates for a classification. The tables show expected percent correct classification and error rates for all the crops. The patterns in the contingency tables show that the percent correct classification for crops generally increases with the amount of greenness in the fields being classified. However, there are exceptions to this general rule, notably grain.

Morse, A.↗

NASA NTRS1994

Effect of radiance-to-reflectance transformation and atmosphere removal on maximum likelihood classification accuracy of high-dimensional remote sensing data

Many analysis algorithms for high-dimensional remote sensing data require that the remotely sensed radiance spectra be transformed to approximate reflectance to allow comparison with a library of laboratory reflectance spectra. In maximum likelihood classification, however, the remotely sensed spectra are compared to training samples, thus a transformation to reflectance may or may not be helpful. The effect of several radiance-to-reflectance transformations on maximum likelihood classification accuracy is investigated in this paper. We show that the empirical line approach, LOWTRAN7, flat-field correction, single spectrum method, and internal average reflectance are all non-singular affine transformations, and that non-singular affine transformations have no effect on discriminant analysis feature extraction and maximum likelihood classification accuracy. (An affine transformation is a linear transformation with an optional offset.) Since the Atmosphere Removal Program (ATREM) and the log residue method are not affine transformations, experiments with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were conducted to determine the effect of these transformations on maximum likelihood classification accuracy. The average classification accuracy of the data transformed by ATREM and the log residue method was slightly less than the accuracy of the original radiance data. Since the radiance-to-reflectance transformations allow direct comparison of remotely sensed spectra with laboratory reflectance spectra, they can be quite useful in labeling the training samples required by maximum likelihood classification, but these transformations have only a slight effect or no effect at all on discriminant analysis and maximum likelihood classification accuracy.

Hoffbeck, Joseph P.↗

DOE OSTI2024

Comparative Performance of Three Eye-Tracking Devices in Detection of Mild Traumatic Brain Injury in Acute Versus Chronic Subject Populations

ABSTRACT Introduction Presently, traumatic brain injury (TBI) triage in field settings relies on symptom-based screening tools such as the updated Military Acute Concussion Evaluation. Objective eye-tracking may provide an alternative means of neurotrauma screening due to sensitivity to neurotrauma brain-health changes. Previously, the US Army Medical Research and Development Command Non-Invasive NeuroAssessment Devices (NINAD) Integrated Product Team identified 3 commercially available eye-tracking devices (SyncThink EYE-SYNC, Oculogica EyeBOX, NeuroKinetics IPAS) as meeting criteria toward being operationally effective in the detection of TBI in service members. We compared these devices to assess their relative performance in the classification of mild traumatic brain injury (mTBI) subjects versus normal healthy controls. Materials and Methods Participants 18 to 45 years of age were assigned to Acute mTBI, Chronic mTBI, or Control group per study criteria. Each completed a TBI assessment protocol with all 3 devices counterbalanced across participants. Acute mTBI participants were tested within 72 hours following injury whereas time since last injury for the Chronic mTBI group ranged from months to years. Discriminant analysis was undertaken to determine device classification performance in separating TBI subjects from controls. Area Under the Curves (AUCs) were calculated and used to compare the accuracy of device performance. Device-related factors including data quality, the need to repeat tests, and technical issues experienced were aggregated for reporting. Results A total of 63 participants were recruited as Acute mTBI subjects, 34 as Chronic mTBI subjects, and 119 participants without history of TBI as controls. To maximize outcomes, poorer quality data were excluded from analysis using specific criteria where possible. Final analysis utilized 49 (43 male/6 female, mean [x̅] age = 24.3 years, SD [s] = 5.1) Acute mTBI subjects, and 34 (33 male/1 female, x̅ age = 38.8 years, s = 3.9) Chronic mTBI subjects were age- and gender-matched as closely as possible with Control subjects. AUCs obtained with 80% of total dataset ranged from 0.690 to 0.950 for the Acute Group and from 0.753 to 0.811 for the Chronic mTBI group. Validation with the remaining 20% of dataset produced AUCs ranging from 0.600 to 0.750 for Acute mTBI group and 0.490 to 0.571 for the Chronic mTBI group. Conclusions Potential eye-tracking detection of mTBI, per training model outcomes, ranged from acceptable to excellent for the Acute mTBI group; however, it was less consistent for the Chronic mTBI group. The self-imposed targeted performance (AUC of 0.850) appears achievable, but further device improvements and research are necessary. Discriminant analysis models differed for the Acute versus Chronic mTBI groups, suggesting performance differences in eye-tracking. Although eye-tracking demonstrated sensitivity in the Chronic group, a more rigorous and/or longitudinal study design is required to evaluate this observation. mTBI injuries were not controlled for this study, potentially reducing eye-tracking assessment sensitivity. Overall, these findings indicate that while eye-tracking remains a viable means of mTBI screening, device-specific variability in data quality, length of testing, and ease of use must be addressed to achieve NINAD objectives and DoD implementation.

General & Internal Medicine↗

NASA NTRS1982

Radar image processing for rock-type discrimination

Image processing and enhancement techniques for improving the geologic utility of digital satellite radar images are reviewed. Preprocessing techniques such as mean and variance correction on a range or azimuth line by line basis to provide uniformly illuminated swaths, median value filtering for four-look imagery to eliminate speckle, and geometric rectification using a priori elevation data. Examples are presented of application of preprocessing methods to Seasat and Landsat data, and Seasat SAR imagery was coregistered with Landsat imagery to form composite scenes. A polynomial was developed to distort the radar picture to fit the Landsat image of a 90 x 90 km sq grid, using Landsat color ratios with Seasat intensities. Subsequent linear discrimination analysis was employed to discriminate rock types from known areas. Seasat additions to the Landsat data improved rock identification by 7%.

Blom, R. G.↗

NASA NTRS2012

Refinement of a Method for Identifying Probable Archaeological Sites from Remotely Sensed Data

To facilitate locating archaeological sites before they are compromised or destroyed, we are developing approaches for generating maps of probable archaeological sites, through detecting subtle anomalies in vegetative cover, soil chemistry, and soil moisture by analyzing remotely sensed data from multiple sources. We previously reported some success in this effort with a statistical analysis of slope, radar, and Ikonos data (including tasseled cap and NDVI transforms) with Student's t-test. We report here on new developments in our work, performing an analysis of 8-band multispectral Worldview-2 data. The Worldview-2 analysis begins by computing medians and median absolute deviations for the pixels in various annuli around each site of interest on the 28 band difference ratios. We then use principle components analysis followed by linear discriminant analysis to train a classifier which assigns a posterior probability that a location is an archaeological site. We tested the procedure using leave-one-out cross validation with a second leave-one-out step to choose parameters on a 9,859x23,000 subset of the WorldView-2 data over the western portion of Ft. Irwin, CA, USA. We used 100 known non-sites and trained one classifier for lithic sites (n=33) and one classifier for habitation sites (n=16). We then analyzed convex combinations of scores from the Archaeological Predictive Model (APM) and our scores. We found that that the combined scores had a higher area under the ROC curve than either individual method, indicating that including WorldView-2 data in analysis improved the predictive power of the provided APM.

Tilton, James C.↗

NASA NTRS1997

Assessment of a remote sensing-based model for predicting malaria transmission risk in villages of Chiapas, Mexico

A blind test of two remote sensing-based models for predicting adult populations of Anopheles albimanus in villages, an indicator of malaria transmission risk, was conducted in southern Chiapas, Mexico. One model was developed using a discriminant analysis approach, while the other was based on regression analysis. The models were developed in 1992 for an area around Tapachula, Chiapas, using Landsat Thematic Mapper (TM) satellite data and geographic information system functions. Using two remotely sensed landscape elements, the discriminant model was able to successfully distinguish between villages with high and low An. albimanus abundance with an overall accuracy of 90%. To test the predictive capability of the models, multitemporal TM data were used to generate a landscape map of the Huixtla area, northwest of Tapachula, where the models were used to predict risk for 40 villages. The resulting predictions were not disclosed until the end of the test. Independently, An. albimanus abundance data were collected in the 40 randomly selected villages for which the predictions had been made. These data were subsequently used to assess the models' accuracies. The discriminant model accurately predicted 79% of the high-abundance villages and 50% of the low-abundance villages, for an overall accuracy of 70%. The regression model correctly identified seven of the 10 villages with the highest mosquito abundance. This test demonstrated that remote sensing-based models generated for one area can be used successfully in another, comparable area.

Insect Vectors/growth & development↗

DOE OSTI2024

Elliptically-Contoured Tensor-variate Distributions with Application to Image Learning

Statistical analysis of tensor-valued data has largely used the tensor-variate normal (TVN) distribution that may be inadequate for data arising from distributions with heavier or lighter tails. We study a general family of elliptically contoured (EC) TV distributions and derive its characterizations, moments, marginal, and conditional distributions. We describe procedures for maximum likelihood estimation from data that are (1) uncorrelated draws from an EC distribution, (2) from a scale mixture of the TVN distribution, and (3) from an underlying but unknown EC distribution, for which we extend Tyler’s robust estimator. A detailed simulation study highlights the benefits of choosing an EC distribution over the TVN for heavier-tailed data. We develop TV classification rules using discriminant analysis and EC errors and show that they better predict cats and dogs from images in the Animal Faces-HQ dataset than the TVN-based rules. A novel tensor-on-tensor regression and TV analysis of variance (TANOVA) framework under EC errors is also demonstrated to better characterize gender, age, and ethnic origin than the usual TVN-based TANOVA in the celebrated labeled faces of the wild dataset.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1973

Application of ERTS-1 imagery to the study of caribou movements and winter dispersal in relation to prevailing snowcover

The author has identified the following significant results. Step-wise discriminate analysis has demonstrated the feasibility of feature identification using linear discriminate functions of ERTS-1 MSS band densities and their ratios. The analysis indicated that features such as small streams can be detected even when they are in dark mountain shadow. The potential utility of this and similar analytic techniques appears considerable, and the limits it can be applied to analysis of ERTS-1 imagery are not yet fully known.

Lent, P. C.↗