Brian Curtiss

A method for manual endmember selection and spectral unmixing

Abstract The number of spectrally unique signatures needed to reproduce the statistically significant variance observed in multispectral and hyperspectral datasets can be estimated from the eigenvalues of a principal component analysis (PCA) of the data. In this article, we describe a multidimensional visualization method for interactively searching for a feasible set of spectral signatures in the space of the PCA eigenvectors that account for most of the variance. These spectral signatures, referred to as endmembers, are input to a linear mixture model which can be inverted to compute endmember abundances for each data spectrum. The visualization method discussed in this article and referred to as the manual endmember selection method (MESM) is based on Inselbergs (1985) parallel coordinate representation of multidimensional spaces. It is novel in the field of multidimensional visualization in that it includes not only a passive representation of higher-dimensional data but also the capability to interact with and move geometrical objects in more than 3 dimensions. The spectral shape of endmembers selected with the MESM may be influenced by processes such as multiple scatterings by surface materials and other factors such as illumination geometry that affect the signal received by the sensor. These processes and factors may produce significant errors in computed endmember abundances, if not accounted for in the endmember reflectance (Roberts, 1991). The MESM is one method for obtaining endmembers that account for all factors and processes significantly affecting the spectral data.

Opportunities for using the EOS Imaging Spectrometers and synthetic aperture radar in ecological models

Several promising approaches to assessing biochemical and architectural properties of landscapes are outlined. Strategies for using new EOS sensors in ecological models are examined. Ways in which ecological and remote sensing models can utilize information provided by the new sensors to characterize ecological properties at coarse scales and to estimate within-ecosystem properties are addressed.

Airborne Imaging Spectrometer-2: Radiometric Spectral Characteristics And Comparison Of Ways To Compensate For The Atmosphere

A field experiment and its results involving AIS-2 data for Rogers Lake, CA are described. The radiometry and spectral calibration of the instrument are critically examined in light of laboratory and field measurements. Three methods of compensating for the atmosphere in the search for ground reflectance are compared. We find, preliminarily, that the laboratory-determined responsivities are 30 to 50% less than expected for conditions of the flight for both short-and long-wavelength observations. The spectral sampling interval is 20 to 30 nm. The combined system-atmosphere-surface signal-to-noise ratio, as indexed by the mean response divided by the standard deviation for selected areas, lies between 40 and 110, depending upon how scene averages are taken, and is 30% less for flight conditions than for the laboratory. Atmospheric and surface variations may contribute to this difference. It is not possible to isolate instrument performance from the present data. As for methods of data reduction, the so-called scene average or log-residual method fails to recover any feature present in the surface reflectance, probably because of the extreme homogeneity of the scene. The empirical line method returns predicted surface reflectances that are systematically high but within a few percent of actual observed values using either calibrated or uncalibrated data. LOWTRAN-6, acting as an approximate theoretical model of the atmosphere for these exercises, predicts reflectance values 30 to 50% below the measured ones, based on the lower than expected radiances under solar illumination given by the instrument. This emphasizes the importance of accurate radiometric calibration in the study of surface or atmospheric properties.

Spectral characteristics of ozone-treated conifers

Abstract The reflectance spectra of three conifer species, Douglas fir, lodgepole pine and ponderosa pine, show clear changes in response to simulated ambient ozone (O3) exposures in open top chambers after one growing season. Ponderosa pine seedlings had slight symptoms of needle mottle-chlorosis associated with O3 injury and showed the greatest magnitude of spectral changes; lodgepole pine exhibited an intermediate spectral response following trends similar to those in ponderosa pine; Douglas fir showed no spectral or visible response to the O3 exposures. These results suggest that despite species-specific responses to O3 exposure, the pattern of spectral changes is similar. Reflectance patterns change across the visible and infrared spectrum, however, the greatest changes occurred in the region of chlorophyll absorbance (600–725 nm) and are indicative of chlorosis. Scheffe values are significant (P 0.90) for treatment differences in ponderosa pine at all wavebands in the 475–740 nm region. Reflectance spectroscopy provides a mechanism for rapid quantitative non-destructive estimation of environmental stress and may be linked to carbon balance or other ecosystem models. Simulation models of spectral mixtures typical of many environmental conditions indicate that early stages of O3-induced injury to the ecosystem could be identified with a high spectral and spectral resolution satellite scanner, such as the proposed Eos imaging spectrometer, HIRIS.

Large scale ecosystem modeling using parameters derived from imaging spectrometer data

The capability to predict the response of ecosystems to change relies on our ability to understand and model the effective functioning of biotic processes at large scales and the transport functions of the atmospheric/hydrospheric processes. To successfully evaluate changes in ecological processes at the required spatial and temporal scales remote sensing technology and ecosystem theory must be considered jointly. A review of developments in remote sensing analysis using high spectral resolution sensors has led to the selection of a potential set ofparameters to be used in ecosystem models. These parameters quantify the light interception properties that scale from leaf to landscape. Spectral mixture analysis forms a framework for the systematic separation of both vegetative and non-vegetative components at sub-pixel spatial resolution. The spectral concentrations of the vegetative components defined by the spectral mixture analysis are then used to drive canopy radiative transfer models from which the ecosystem parameters are inferred. 1.

Collection and quality control of spectral signatures in the field

Field spectral signatures are commonly collected in conjunction with remote sensing campaigns. Unfortunately, the lack of sufficient metadata associated with campaign-specific spectral signatures often makes it difficult for others to utilize them for their own applications. The first step in improving the utility of field collected spectral signatures is achieved by establishing fully documented procedures that minimize controllable error sources. A major source of error when collecting field spectral signatures is the variability of solar illumination. By periodically monitoring a static reference panel it is possible to both characterize the variance in solar illumination during collection as well as to correct collected spectra. In addition, recent advances in instrument sensitivity greatly reduce the time required to collect high quality spectra that in turn reduces the magnitude of potential errors associated with changes in solar illumination. Since libraries of field spectral signatures are commonly used to analyze remotely sensed imagery, it is important that field collection is performed at a relevant spatial scale and with illumination and view geometry that is similar to that for the image collection. This is particularly true of vegetation since the observed spectral signature is the result of the complex interaction of multiple illumination sources (i.e. direct sunlight, sky illumination and light scattered off other elements in the scene), canopy architecture and the reflectance properties of the individual elements within the canopy. Suggested field collection approaches that minimize these sources of error are presented.