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

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

Concurrent invasions of European starlings in Australia and North America reveal population-specific differentiation in shared genomic regions.

A species' success during the invasion of new areas hinges on an interplay between the demographic processes common to invasions and the specific ecological context of the novel environment. Evolutionary genetic studies of invasive species can investigate how genetic bottlenecks and ecological conditions shape genetic variation in invasions, and our study pairs two invasive populations that are hypothesized to be from the same source population to compare how each population evolved during and after introduction. Invasive European starlings (Sturnus vulgaris) established populations in both Australia and North America in the 19th century. Here, we compare whole-genome sequences among native and independently introduced European starling populations to determine how demographic processes interact with rapid evolution to generate similar genetic patterns in these recent and replicated invasions. Demographic models indicate that both invasive populations experienced genetic bottlenecks as expected based on invasion history, and we find that specific genomic regions have differentiated even on this short evolutionary timescale. Despite genetic bottlenecks, we suggest that genetic drift alone cannot explain differentiation in at least two of these regions. The demographic boom intrinsic to many invasions as well as potential inversions may have led to high population-specific differentiation, although the patterns of genetic variation are also consistent with the hypothesis that this infamous and highly mobile invader adapted to novel selection (e.g., extrinsic factors). We use targeted sampling of replicated invasions to identify and evaluate support for multiple, interacting evolutionary mechanisms that lead to differentiation during the invasion process

Estimating vegetation structural effects on carbon uptake using satellite data fusion and inverse modeling

Regional analyses of biogeochemical processes can benefit significantly from observational information on land cover, vegetation structure (e.g., leaf area index), and biophysical properties such as fractional PAR absorption. Few remote sensing efforts have provided a suite of plant attributes needed to link vegetation structure to ecosystem function at high spatial resolution. In arid and semiarid ecosystems (e.g., savannas), high spatial heterogeneity of land cover results in significant functional interaction between dominant vegetation types, requiring new approaches to resolve their structural characteristics for regional-scale biogeochemical research. We developed and tested a satellite data fusion and radiative transfer inverse modeling approach to deliver estimates of vegetation structure in a savanna region of Texas. Spectral mixture analysis of Landsat data provided verifiable estimates of woody plant, herbaceous, bare soil, and shade fractions at 28.5 m resolution. Using these subpixel cover fractions, a geometric-optical model was inverted to estimate overstory stand density and crown dimensions with reasonable accuracy. The Landsat cover estimates were then used to spectrally unmix the contribution of woody plant and herbaceous canopies to AVHRR multiangle reflectance data. These angular reflectances were used with radiative transfer model inversions to estimate canopy leaf area index (LAI). The suite of estimated canopy and landscape variables indicated distinct patterns in land cover and structural attributes related to land use. These variables were used to calculate diurnal PAR absorption and carbon uptake by woody and herbaceous canopies in contrasting land cover and land use types. We found that both LAI and the spatial distribution of vegetation structural types exert strong control on carbon fluxes and that intercanopy shading is an important factor controlling functional processes in spatially heterogeneous environments

Art as experience: An inquiry into art and leadership using dolls and doll-making

This article reflects on an arts-based action inquiry process involving students on an MSc in Management Learning and Change. Following Dewey’s (1934/2005) contention that art is grounded in experience, we adopt a purposefully non-aggrandising perspective on ‘leadership as art’, arguing that this prompts greater critical attention to possibilities for inclusiveness in these realms of human endeavour. We propose the present inquiry, in which participants were invited to create leadership touchstones, or dolls, as a way of learning about leadership and themselves as leaders. Drawing from therapeutic and psychoanalytic perspectives, we explore dolls’ power to provoke, unsettle and evoke strong reactions on the part of their makers, and demonstrate how these dynamics played out in our inquiry. We highlight the conditions which enabled participants to engage with the tensions and ambiguities raised in ways which held open possibilities for reflexivity. We conclude that leadership, like art, can most constructively engage with the human condition when it is able to hold, not collapse, our experience of the uncanny, the abject, and the other—including the ‘other’ within the ‘self’—within the complexities of organisational life.This article reflects on an arts-based action inquiry process involving students on an MSc in Management Learning and Change. Following Dewey’s (1934/2005) contention that art is grounded in experience, we adopt a purposefully non-aggrandising perspective on ‘leadership as art’, arguing that this prompts greater critical attention to possibilities for inclusiveness in these realms of human endeavour. We propose the present inquiry, in which participants were invited to create leadership touchstones, or dolls, as a way of learning about leadership and themselves as leaders. Drawing from therapeutic and psychoanalytic perspectives, we explore dolls’ power to provoke, unsettle and evoke strong reactions on the part of their makers, and demonstrate how these dynamics played out in our inquiry. We highlight the conditions which enabled participants to engage with the tensions and ambiguities raised in ways which held open possibilities for reflexivity. We conclude that leadership, like art, can most constructively engage with the human condition when it is able to hold, not collapse, our experience of the uncanny, the abject, and the other—including the ‘other’ within the ‘self’—within the complexities of organisational life