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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Objective determination of image end-members in spectral mixture analysis

Spectral mixture analysis was shown to be a powerful, multifaceted tool for analysis of multi- and hyper-spectral data. The essence of the first phase of the approach is to determine a set of image end-members that best account for the spectral variance in an image cube within a constrained, linear least squares mixing model. The selection of the image end-member is usually achieved using a priori knowledge and successive trial and error solutions to refine the total number and physical location of the end-members. However, in many situations a more objective method of determining these essential components is desired. The problem of image end-member determination was approached objectively by using the inherent variance of the data. Unlike purely statistical methods such as factor analysis, this approach derives solutions that conform to a physically realistic model.

Tompkins, Stefanie↗
NASA NTRS1993

Objective determination of image end-members in spectral mixture analysis of AVIRIS data

Spectral mixture analysis has been shown to be a powerful, multifaceted tool for analysis of multi- and hyper-spectral data. Applications of AVIRIS data have ranged from mapping soils and bedrock to ecosystem studies. During the first phase of the approach, a set of end-members are selected from an image cube (image end-members) that best account for its spectral variance within a constrained, linear least squares mixing model. These image end-members are usually selected using a priori knowledge and successive trial and error solutions to refine the total number and physical location of the end-members. However, in many situations a more objective method of determining these essential components is desired. We approach the problem of image end-member determination objectively by using the inherent variance of the data. Unlike purely statistical methods such as factor analysis, this approach derives solutions that conform to a physically realistic model.

Tompkins, Stefanie↗
DOE OSTI2014

Prior-knowledge-based spectral mixture analysis for impervious surface mapping

In this study, we developed a prior-knowledge-based spectral mixture analysis (PKSMA) to map impervious surfaces by using endmembers derived separately for high- and low-density urban regions. First, an urban area was categorized into high- and low-density urban areas, using a multi-step classification method. Next, in high-density urban areas that were assumed to have only vegetation and impervious surfaces (ISs), the Vegetation-Impervious model (V-I) was used in a spectral mixture analysis (SMA) with three endmembers: vegetation, high albedo, and low albedo. In low-density urban areas, the Vegetation-Impervious-Soil model (V-I-S) was used in an SMA analysis with four endmembers: high albedo, low albedo, soil, and vegetation. The fraction of IS with high and low albedo in each pixel was combined to produce the final IS map. The root mean-square error (RMSE) of the IS map produced using PKSMA was about 11.0%, compared to 14.52% using four-endmember SMA. Particularly in high-density urban areas, PKSMA (RMSE = 6.47%) showed better performance than four-endmember (15.91%). The results indicate that PKSMA can improve IS mapping compared to traditional SMA by using appropriately selected endmembers and is particularly strong in high-density urban areas.

Impervious surface↗
NASA NTRS1992

Spectral mixture analysis of multispectral thermal infrared images

Remote spectral measurements of light reflected or emitted from terrestrial scenes is commonly integrated over areas sufficiently large that the surface comprises more than one component. Techniques have been developed to analyze multispectral or imaging spectrometer data in terms of a wide range of mixtures of a limited number of components. Spectral mixture analysis has been used primarily for visible and near-infrared images, but it may also be applied to thermal infrared data. Two approaches are reviewed: binary mixing and a more general treatment for isothermal mixtures of a greater number of components.

Gillespie, Alan R.↗
NASA NTRS1998

Incorporating Endmember Variability into Spectral Mixture Analysis Through Endmember Bundles

Variation in canopy structure and biochemistry induces a concomitant variation in the top-of-canopy spectral reflectance of a vegetation type. Hence, the use of a single endmember spectrum to track the fractional abundance of a given vegetation cover in a hyperspectral image may result in fractions with considerable error. One solution to the problem of endmember variability is to increase the number of endmembers used in a spectral mixture analysis of the image. For example, there could be several tree endmembers in the analysis because of differences in leaf area index (LAI) and multiple scatterings between leaves and stems. However, it is often difficult in terms of computer or human interaction time to select more than six or seven endmembers and any non-removable noise, as well as the number of uncorrelated bands in the image, limits the number of endmembers that can be discriminated. Moreover, as endmembers proliferate, their interpretation becomes increasingly difficult and often applications simply need the aerial fractions of a few land cover components which comprise most of the scene. In order to incorporate endmember variability into spectral mixture analysis, we propose representing a landscape component type not with one endmember spectrum but with a set or bundle of spectra, each of which is feasible as the spectrum of an instance of the component (e.g., in the case of a tree component, each spectrum could reasonably be the spectral reflectance of a tree canopy). These endmember bundles can be used with nonlinear optimization algorithms to find upper and lower bounds on endmember fractions. This approach to endmember variability naturally evolved from previous work in deriving endmembers from the data itself by fitting a triangle, tetrahedron or, more generally, a simplex to the data cloud reduced in dimension by a principal component analysis. Conceptually, endmember variability could make it difficult to find a simplex that both surrounds the data cloud and has vertices that are realistic endmember spectra with reflectances between 0 and 1. In this paper, we create endmember bundles and bounding fraction images for an AVIRIS subscene simulated with a plant canopy radiative transfer model. The simulated subscene is spatially patterned after a subscene from the AVIRIS image acquired August, 1993 over La Copita, Texas. In addition, for comparison, we performed a traditional unmixing with image endmembers.

Bateson, C. Ann↗
NASA NTRS1993

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.

Wessman, Carol A.↗
NASA NTRS2006

Spectral Variability among Rocks in Visible and Near Infrared Multispectral Pancam Data Collected at Gusev Crater: Examinations using Spectral Mixture Analysis and Related Techniques

Visible and Near Infrared (VNIR) multispectral observations of rocks made by the Mars Exploration Rover Spirit s Panoramic camera (Pancam) have been analysed using a spectral mixture analysis (SMA) methodology. Scenes have been examined from the Gusev crater plains into the Columbia Hills. Most scenes on the plains and in the Columbia Hills could be modeled as three endmember mixtures of a bright material, rock, and shade. Scenes of rocks disturbed by the rover s Rock Abrasion Tool (RAT) required additional endmembers. In the Columbia Hills there were a number of scenes in which additional rock endmembers were required. The SMA methodology identified relatively dust-free areas on undisturbed rock surfaces, as well as spectrally unique areas on RAT abraded rocks. Spectral parameters from these areas were examined and six spectral classes were identified. These classes are named after a type rock or area and are: Adirondack, Lower West Spur, Clovis, Wishstone, Peace, and Watchtower. These classes are discriminable based, primarily, on near-infrared (NIR) spectral parameters. Clovis and Watchtower class rocks appear more oxidized than Wishstone class rocks and Adirondack basalts based on their having higher 535 nm band depths. Comparison of the spectral parameters of these Gusev crater rocks to parameters of glass-dominated basaltic tuffs indicates correspondence between measurements of Clovis and Watchtower classes, but divergence for the Wishstone class rocks which appear to have a higher fraction of crystalline ferrous iron bearing phases. Despite a high sulfur content, the rock Peace has NIR properties resembling plains basalts.

Farrand, W. H.↗
NASA NTRS2000

Quantifying Vegetation Change in Semiarid Environments: Precision and Accuracy of Spectral Mixture Analysis and the Normalized Difference Vegetation Index

Because in situ techniques for determining vegetation abundance in semiarid regions are labor intensive, they usually are not feasible for regional analyses. Remotely sensed data provide the large spatial scale necessary, but their precision and accuracy in determining vegetation abundance and its change through time have not been quantitatively determined. In this paper, the precision and accuracy of two techniques, Spectral Mixture Analysis (SMA) and Normalized Difference Vegetation Index (NDVI) applied to Landsat TM data, are assessed quantitatively using high-precision in situ data. In Owens Valley, California we have 6 years of continuous field data (1991-1996) for 33 sites acquired concurrently with six cloudless Landsat TM images. The multitemporal remotely sensed data were coregistered to within 1 pixel, radiometrically intercalibrated using temporally invariante surface features and geolocated to within 30 m. These procedures facilitated the accurate location of field-monitoring sites within the remotely sensed data. Formal uncertainties in the registration, radiometric alignment, and modeling were determined. Results show that SMA absolute percent live cover (%LC) estimates are accurate to within ?4.0%LC and estimates of change in live cover have a precision of +/-3.8%LC. Furthermore, even when applied to areas of low vegetation cover, the SMA approach correctly determined the sense of clump, (i.e., positive or negative) in 87% of the samples. SMA results are superior to NDVI, which, although correlated with live cover, is not a quantitative measure and showed the correct sense of change in only 67%, of the samples.

Elmore, Andrew J.↗
NASA NTRS1991

Relationships of soil, grass, and bedrock over the Kaweah serpentine melange through spectral mixture analysis of AVIRIS data

A linear mixing model is used to model the spectral variability of an AVIRIS scene from the western foothills of the Sierra Nevada and calibrate these radiance data to reflectance. Five spectral endmembers from the AVIRIS data, plus an ideal 'shade' endmember were required to model the continuum reflectance of each pixel in the image. Three of the endmembers were interpreted to model the surface constituents green vegetation, dry grass, and illumination. These are the main transient surface constituents that are expected to change with shifts in land use or climatic influences and viewing conditions ('shade' only). The spectral distinction between the other three endmembers is very small, yet the spatial distributions are coherent and interpretable. These distributions cross anthropogenic and vegetation boundaries and are best interpreted as different soil types. Comparison of the fraction images to the bedrock geology maps indicates that substrate composition must be a factor contributing to the spectral properties of these endmembers. Detailed examination of the reflectance spectra of the three soil endmembers reveals that differences in the amount of ferric and ferrous iron and/or organic constituents in the soils is largely responsible for the differences in spectral properties of these endmembers.

Mustard, John F.↗
NASA NTRS1993

Relationships of soil, grass, and bedrock over the Kaweah Serpentinite Melange through spectral mixture analysis of AVIRIS data

A linear mixing model is used to model the spectral variability of an AVIRIS scene from the western foothills of the Sierra Nevada and calibrate these radiance data to reflectance. Five spectral endmembers from the AVIRIS data, plus an ideal 'shade' endmember were required to model the continuum reflectance of each pixel in the image. Three of the endmembers were interpreted to model the surface constituents green vegetation, dry grass, and illumination. Comparison of the fraction images to the bedrock geology maps indicates that substrate composition must be a factor contributing to the spectral properties of these endmembers. Detailed examination of the reflectance spectra of the three soil endmembers reveals that differences in the amount of ferric and ferrous iron and/or organic constituents in the soils is largely responsible for the differences in spectral properties of these endmembers.

Mustard, John F.↗
NASA NTRS2001

AVIRIS Land-Surface Mapping in Support of the Boreal Ecosystem-Atmosphere Study (BOREAS)

A key scientific objective of the original Boreal Ecosystem-Atmospheric Study (BOREAS) field campaign (1993-1996) was to obtain the baseline data required for modeling and predicting fluxes of energy, mass, and trace gases in the boreal forest biome. These data sets are necessary to determine the sensitivity of the boreal forest biome to potential climatic changes and potential biophysical feedbacks on climate. A considerable volume of remotely-sensed and supporting field data were acquired by numerous researchers to meet this objective. By design, remote sensing and modeling were considered critical components for scaling efforts, extending point measurements from flux towers and field sites over larger spatial and longer temporal scales. A major focus of the BOREAS follow-on program is concerned with integrating the diverse remotely sensed and ground-based data sets to address specific questions such as carbon dynamics at local to regional scales. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has the potential of contributing to BOREAS through: (1) accurate retrieved apparent surface reflectance; (2) improved landcover classification; and (3) direct assessment of biochemical/biophysical information such as canopy liquid water and chlorophyll concentration through pigment fits. In this paper, we present initial products for major flux tower sites including: (1) surface reflectance of dominant cover types; (2) a land-cover classification developed using spectral mixture analysis (SMA) and Multiple Endmember Spectral Mixture Analysis (MESMA); and (3) liquid water maps. Our goal is to compare these land-cover maps to existing maps and to incorporate AVIRIS image products into models of photosynthetic flux.

Roberts, Dar A.↗
NASA NTRSDate not supplied

Comprehensive Analysis of Thirty Years of Land Change in Georgia: Forest Degradation, Land-Use Patterns, and Drivers

In this talk, we present results from a study, funded by the NASA Land-Cover and Land-Use Change program, of thirty years of environmental change in the country of Georgia. The country, a former Soviet Union Republic, has experienced more political, economic and social change in the last 30 years than most countries. But the environmental implications of these extreme events remain largely unknown – the region is understudied, no national forest inventory has been completed in 20 years, and previous research in the area conducted by the authors were not successful. The latter statement is because of forest degradation. After the collapse of the Soviet Union, much of the forestry infrastructure broke down and people resorted to chopping down individual trees. The result was in many parts of the country a reduction in forest biomass but not to the point of a change in land cover. This type of forest degradation, which was either gradual over decades or abrupt, could not be readily identified using the methods and data at our disposal at the time. In the recent study, we had full access to the Landsat archive in combination with computing power. By using monitoring methods that are based on time series analysis, we were able to capture and identify the gradual processes driven by small scale activities that are so characteristic of the post-Soviet Georgian landscape. We developed an approach (“CCDC-SMA”) that combines time series analysis and spectral mixture analysis running on Google Earth Engine for monitoring abrupt and gradual forest degradation. By using this approach, we found that forest degradation was significantly larger than the area estimate of deforestation; 3,541 ± 556 km2 (11% of the forest area in 1987) compared to 158 ± 98 km2 from 1987 to 2020. The prevailing narrative is that legal and illegal cutting of trees for fuelwood is primarily responsible for this process. Yet, since independence from the Soviet Union in 1991, the country has undergone rapid socioeconomic and institutional changes which have not been explored as drivers of forest change. To further our understanding of the underlying causes, we combined forest disturbance estimates, Georgian statistical data, and historical institutional change data to examine socioeconomic drivers of forest degradation. We found that higher winter temperature and drought were associated with higher degradation at the regional scale, while major institutional changes and drought were associated with higher forest degradation at the national level. Access to natural gas, the major energy alternative to fuelwood, had no significant association with degradation. Our results challenge the narrative that poverty and a lack of alternative energy infrastructure drive forest degradation and suggest that government policies banning household fuelwood cutting, including the new Forest Code of 2020, may not reduce forest degradation. Given these results, improved data on wood harvesting and more research on the commercial drivers of degradation and their links to economic and political reforms is needed to better inform forest policy in the region, especially given ongoing risks from climate change.

Pontus Olofsson↗
NASA NTRS1992

A field measure of the shade fraction

'Shade' has a technical definition peculiar to linear spectral mixture analysis of imaging spectrometer data: it is the reduction in radiance from a surface due to lighting conditions and geometry, and includes topographic shading described by photometric functions as well as shadowing at all scales. 'Shade' is an important constituent of nearly all remotely sensed images, and is one endmember resolved in spectral mixture analysis, where it is represented as a fraction of the measured radiance and a characteristic spectrum. This spectrum is typically the null vector, provided the data have been corrected for atmospheric and instrument effects: i.e., 'shade' is the radiance from an ideal black surface. In topographic shading, irradiance is reduced - typically in proportion to cos(i), where i (incidence angle) is the angle between the sun and the local surface normal vectors. Therefore, the radiance is lowered by a multiplicative factor. Shadowing occurs when i is greater than 90 deg, or when sunlight is blocked by adjacent high terrain; the only irradiance is down-welling skylight and bounce light from adjacent terrain. In spectral mixture analysis, 'shade' is regarded as an additive term. In this regard, it is an accurate description of the proportion of a scene that consists of ideal shadows ('checkerboard mixing'); however, 'shade' represents the multiplicative cos(i) factor as well, as here it should be interpreted as the proportion of shadow that would darken the scene an equivalent amount. In either case, the 'shade' fraction is lessened by adjacency effects, because the scene has a non-zero reflectivity instead of the ideal black surface generally assumed.

Gillespie, Alan R.↗
NASA NTRS1998

Automated Subpixel Snow Parameter Mapping with AVIRIS Data

We describe an automated algorithm (MEMSCAG) for mapping subpixel snow covered area (SCA) and snow grain size with AVIRIS data. The algorithm is based on the multiple endmember approach to spectral mixture analysis in which the spectral endmembers and the number of endmembers can vary on a pixel-by-pixel basis. This approach accounts for surface cover heterogeneity within a scene. The mixture analysis runs on endmembers from a spectral library of snow, vegetation, rock, soil, and lake ice spectra. Snow endmembers of varying grain size were produced with a radiative transfer model. All non-snow endmembers were collected with a portable field spectrometer. Mapping is performed through sequential 2-endmember, 3-endmember, and 4- endmember mixture model runs, each subject to constraints on RMS, residuals, fractions and priority. Grain size is determined by the grain size of the snow endmember used in the optimal mixture model. We apply MEMSCAG to AVIRIS data collected over Mammoth Mountain, CA and the northern site of the BOREAS in Manitoba, Canada. MEMSCAG produces appropriate snow covered area estimates in all regions. A preliminary comparison of grain size estimates from MEMSCAG with field measurements demonstrates high accuracy.

Painter, Thomas H.↗
NASA NTRS1993

Mapping and monitoring changes in vegetation communities of Jasper Ridge, CA, using spectral fractions derived from AVIRIS images

An important application of remote sensing is to map and monitor changes over large areas of the land surface. This is particularly significant with the current interest in monitoring vegetation communities. Most of traditional methods for mapping different types of plant communities are based upon statistical classification techniques (i.e., parallel piped, nearest-neighbor, etc.) applied to uncalibrated multispectral data. Classes from these techniques are typically difficult to interpret (particularly to a field ecologist/botanist). Also, classes derived for one image can be very different from those derived from another image of the same area, making interpretation of observed temporal changes nearly impossible. More recently, neural networks have been applied to classification. Neural network classification, based upon spectral matching, is weak in dealing with spectral mixtures (a condition prevalent in images of natural surfaces). Another approach to mapping vegetation communities is based on spectral mixture analysis, which can provide a consistent framework for image interpretation. Roberts et al. (1990) mapped vegetation using the band residuals from a simple mixing model (the same spectral endmembers applied to all image pixels). Sabol et al. (1992b) and Roberts et al. (1992) used different methods to apply the most appropriate spectral endmembers to each image pixel, thereby allowing mapping of vegetation based upon the the different endmember spectra. In this paper, we describe a new approach to classification of vegetation communities based upon the spectra fractions derived from spectral mixture analysis. This approach was applied to three 1992 AVIRIS images of Jasper Ridge, California to observe seasonal changes in surface composition.

Sabol, Donald E., Jr.↗