A tool for manual endmember selection and spectral unmixing

Sampling a continuous radiance spectrum in many narrow contiguous spectral bands results in a high covariance between the bands. Hence, the true dimensionality of imaging spectrometer data is not determined by the number of spectral bands, but by the number of spectrally unique signatures whose mixtures reproduce the spectral variance observed in an image. Methods to unmix high dimensional multispectral data use principal components analysis to reduce the dimensionality. The variance of the spectral data is modeled as a linear combination of a finite set of endmembers in the space of the eigen-vectors that account for most of the variance. The number and characteristics of these endmembers are determined not only by the number and characteristics of the spectrally unique materials on the surface but also by processes (e.g., illumination, atmospheric scattering and absorption) affecting the signal received by the sensor. Selection of endmember spectra has typically been from a library. However, since most libraries are incomplete and do not account for the processes mentioned above, we have devised a computer display that allows researchers to explore interactively the eigenvector space of a representative and mean-corrected subset of the image data in search of extreme spectra to designate as endmembers. This display, which is based on parallel coordinates, is unique in the area of multidimensional visualization in that it includes not only a passive view of higher dimensional data but also the capability to interact and move geometrical objects in higher dimensional spaces

Detecting Fire and Grazing Patterns in Tallgrass Prairie Using Spectral Mixture Analysis

Global grasslands are typically under management practices (such as fire and grazing) that alter nutrient cycling, ecosystem composition, and distribution of organic matter from the unmanaged condition. We evaluated landscape-level response to fire and grazing treatments in the Konza Tallgrass Prairie Research Natural Area, Kansas, using spectral mixture analysis of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired 31 August 1990. Spectral mixture analysis derives the fractional abundances of spectrally unique components in the landscape. The reflectance spectra of these components are called endmembers. Endmember fractions values were compared against ground values of live biomass, current standing dead biomass, and litter for 12 watersheds. Analysis of variance (ANOVA) was performed on 37 watersheds with known burning and grazing histories for each of the remote sensing variables. Seven endmembers were selected from the AVIRIS data using a manual endmember selection method: nonphotosynthetic vegetation (NPV), soil, rock, shade, and three green vegetation endmembers (GV1, GV2, and GV3). Each vegetation endmember correlated differently to biomass measurements and revealed unique relationships to management treatments. From regressions, ANOVAs, and image analysis, these three endmembers were inferred to represent canopy vertical structure or leaf area index (LAI), greenness, and fractional cover of grass, respectively. There was a stronger relationship between the sum of GV1 and GV3 fractions and live grass biomass values than there was with the (unsummed) individual fractions. In an ANOVA, the sum separated both burn and grazing treatments as well as the treatment interaction. The NPV fraction was strongly correlated with ground measurements of litter and standing dead biomass, and significantly separated burn treatments. The soil fraction differentiated grazing treatments, and analysis of the soil fraction image revealed a spatial coherence of grazing patterns along drainages. Similar analyses were perfomed on the Normalized Difference Vegetation Index (NDVI), a commonly used two-band index computed from red and near-infrared reflectance. NDVI, shown in previous studies to estimate the fraction of photosynthetically active radiation absorbed by green vegetation (FPAR), was a poor indicator of canopy biomass, but it successfully separated fire treatments. Broad-scale assessment of the state and structure of managed grassland systems requires the identification of several indicator variables. Spectral mixture analysis, unlike NDVI, not only separated treatments but also allowed for the identification of five remotely sensible factors affected by the management treatments, namely, vertical structure, percentage cover or patchiness, greenness, and distribution of soil and litter

Optical and EUV Light Curves of Dwarf Nova Outbursts

We combine AAVSO and VSS/RASNZ optical and Extreme Ultraviolet Explorer EUV light curves of dwarf novae in outburst to place constraints on the nature of dwarf nova outbursts. From the observed optical-EUV time delays of 0.75-1.5 days, we show that the propagation velocity of the dwarf nova instability heating wave is ~ 3 km/s

FUSE observations of HD 5980: The wind structure of the eruptor

HD 5980 is a unique system containing one massive star (star A) that is apparently entering the luminous blue variable phase, and an eclipsing companion (star B) that may have already evolved beyond this phase to become a Wolf-Rayet star. In this paper we present the results from FUSE observations obtained in 1999, 2000, and 2002 and one far-UV observation obtained by ORFEUS/BEFS in 1993 shortly before the first eruption of HD 5980. The eight phase-resolved spectra obtained by FUSE in 2002 are analyzed in the context of a wind-eclipse model. This analysis shows that the wind of the eruptor obeyed a very fast velocity law in 2002, which is consistent with the line-driving mechanism. Large amplitude line-profile variations on the orbital period are shown to be due to the eclipse of star B by the wind of star A, although the eclipse due to gas flowing in the direction of star B is absent. This can only be explained if the wind of star A is not spherically symmetric, or if the eclipsed line radiation is "filled-in" by emission originating from somewhere else in the system, e.g., in the wind-wind collision region. Except for a slightly lower wind speed, the ORFEUS/BEFS spectrum is very similar to the spectrum obtained by FUSE at the same orbital phase: there is no indication of the impending eruption. However, the trend for decreasing wind velocity suggests the occurrence of the "bi-stability" mechanism, which in turn implies that the restructuring of the circumbinary environment caused by the transition from "fast, rarefied wind" to "slow, dense wind" was observed as the eruptive event. The underlying mechanism responsible for the long-term decrease in wind velocity that precipitated this change remains an open issue.Comment: 19 pages, 13 figure

The Prevalence of STIV c92-Like Proteins in Acidic Thermal Environments

A new type of viral-induced lysis system has recently been discovered for two unrelated archaeal viruses, STIV and SIRV2. Prior to the lysis of the infected host cell, unique pyramid-like lysis structures are formed on the cell surface by the protrusion of the underlying cell membrane through the overlying external S-layer. It is through these pyramid structures that assembled virions are released during lysis. The STIV viral protein c92 is responsible for the formation of these lysis structures. We searched for c92-like proteins in viral sequences present in multiple viral and cellular metagenomic libraries from Yellowstone National Park acidic hot spring environments. Phylogenetic analysis of these proteins demonstrates that, although c92-like proteins are detected in these environments, some are quite divergent and may represent new viral families. We hypothesize that this new viral lysis system is common within diverse archaeal viral populations found within acidic hot springs

Decoding of the light changes in eclipsing Wolf-Rayet binaries I. A non-classical approach to the solution of light curves

We present a technique to determine the orbital and physical parameters of eclipsing eccentric Wolf-Rayet + O-star binaries, where one eclipse is produced by the absorption of the O-star light by the stellar wind of the W-R star. Our method is based on the use of the empirical moments of the light curve that are integral transforms evaluated from the observed light curves. The optical depth along the line of sight and the limb darkening of the W-R star are modelled by simple mathematical functions, and we derive analytical expressions for the moments of the light curve as a function of the orbital parameters and the key parameters of the transparency and limb-darkening functions. These analytical expressions are then inverted in order to derive the values of the orbital inclination, the stellar radii, the fractional luminosities, and the parameters of the wind transparency and limb-darkening laws. The method is applied to the SMC W-R eclipsing binary HD 5980, a remarkable object that underwent an LBV-like event in August 1994. The analysis refers to the pre-outburst observational data. A synthetic light curve based on the elements derived for the system allows a quality assessment of the results obtained.Comment: Accepted for publication in Astronomy & Astrophysic

A comparison of spectral mixture analysis an NDVI for ascertaining ecological variables

In this study, we compare the performance of spectral mixture analysis to the Normalized Difference Vegetation Index (NDVI) in detecting change in a grassland across topographically-induced nutrient gradients and different management schemes. The Konza Prairie Research Natural Area, Kansas, is a relatively homogeneous tallgrass prairie in which change in vegetation productivity occurs with respect to topographic positions in each watershed. The area is the site of long-term studies of the influence of fire and grazing on tallgrass production and was the site of the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE) from 1987 to 1989. Vegetation indices such as NDVI are commonly used with imagery collected in few (less than 10) spectral bands. However, the use of only two bands (e.g. NDVI) does not adequately account for the complex of signals making up most surface reflectance. Influences from background spectral variation and spatial heterogeneity may confound the direct relationship with biological or biophysical variables. High dimensional multispectral data allows for the application position of techniques such as derivative analysis and spectral curve fitting, thereby increasing the probability of successfully modeling the reflectance from mixed surfaces. The higher number of bands permits unmixing of a greater number of surface components, separating the vegetation signal for further analyses relevant to biological variables

Quantifying Grassland-to-Woodland Transitions and the Implications for Carbon and Nitrogen Dynamics in the Southwest United States

Replacement of grasslands and savannas by shrublands and woodlands has been widely reported in tropical, temperate and high-latitude rangelands worldwide (Archer 1994). These changes in vegetation structure may reflect historical shifts in climate and land use; and are likely to influence biodiversity, productivity, above- and below ground carbon and nitrogen sequestration and biophysical aspects of land surface-atmosphere interactions. The goal of our proposed research is to investigate how changes in the relative abundance of herbaceous and woody vegetation affect carbon and nitrogen dynamics across heterogeneous savannas and shrub/woodlands. By linking actual land-cover composition (derived through spectral mixture analysis of AVIRIS, TM, and AVHRR imagery) with a process-based ecosystem model, we will generate explicit predictions of the C and N storage in plants and soils resulting from changes in vegetation structure. Our specific objectives will be to (1) continue development and test applications of spectral mixture analysis across grassland-to-woodland transitions; (2) quantify temporal changes in plant and soil C and N storage and turnover for remote sensing and process model parameterization and verification; and (3) couple landscape fraction maps to an ecosystem simulation model to observe biogeochemical dynamics under changing landscape structure and climatological forcings

Increasing repeat chlamydia testing in Family Planning clinics depends on perception of value and availability of low-burden flexible reminder systems.

Re‐infection after a chlamydia infection is common: 22% of young Australian women are re‐infected within 4‐5months (Walker, et al, 2012). Re‐infections increase the risk of pelvic inflammatory disease (PID) by 4‐6 fold (Bowring, et al, 2011). Retesting is an important strategy to detect re‐infection. Clinical guidelines note that repeat testing at least three months after a positive diagnosis be considered. AIM: To understand Australian Family Planning clinicians’ practices and perceptions of repeat chlamydia testing.CONCLUSION: Reminder systems to support repeat testing of positive chlamydia tests had been implemented in some FPCs, with low workload impact. It was too early for evaluation of clinical success. These FPCs could share locally developed systems and positive experiences with FPCs skeptical about their value. This may also enhance awareness of the clinical value of retesting and the consequences of re‐ infection. Audits may help determine if clients are indeed being caught through repeat visits and opportunistic testing