Donald E. Sabol

Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon

Abstract Four time-sequential Landsat Thematic Mapper (TM) images of an area of Amazon forest, pasture, and second growth near Manaus, Brazil were classified according to dominant ground cover, using a new technique based on fractions of spectral endmembers. A simple four-endmember model consisting of reflectance spectra of green vegetation, nonphotosynthetic vegetation, soil, and shade was applied to all four images. Fractions of endmembers were used to define seven categories, each of which consisted of one or more classes of ground cover, where class names were based on field observations. Endmember fractions varied over time for many pixels, reflecting processes operating on the ground such as felling of forest, or regrowth of vegetation in previously cleared areas. Changes in classes over time were used to establish superclasses which grouped pixels having common histories. Sources of classification error were evaluated, including system noise, endmember variability, and low spectral contrast. Field work during each of the four years showed consistently high accuracy in per-image classification. Classification accuracy in any one year was improved by considering the multiyear context. Although the method was tested in the Amazon basin, the results suggest that endmember classification may be generally useful for comparing multispectral images in space and time.

Quantitative subpixel spectral detection of targets in multispectral images

Spectral mixture analysis was used to determine threshold detection limits of target materials in the presence of background materials within the field of view under various simulated but realistic compositional, instrumental, and topographical conditions. Detection thresholds were determined for the cases where the target is detected as (1) a component of a spectral mixture (continuum threshold analysis) and (2) residuals (residual threshold analysis). In continuum threshold analysis, the target was included as a component during unmixing thereby permitting evaluation of target detectability. In residual threshold analysis, the unmodeled target was detected as wavelength-dependent deviations of the spectral mixture (target included) from the predicted spectrum (mixtures of the modeled background spectra). High resolution laboratory spectra were used to test the “best case” for target detection in spectral mixtures. Data quality was then decreased to simulate the effects of various imaging instruments (spectral sampling and noise) and changes in lighting geometry. The results show that the contrast of the target spectrum, relative to mixtures of background spectra, determines which level of spectral mixture analysis (continuum or residual analysis) detects the target at the lower threshold. Continuum analysis provides lower thresholds when there is contrast between the target and mixtures of the background spectra throughout much of the spectrum, whereas residual analysis improves detectability when the uniqueness of the target is found in narrow absorption feature(s). Varying spectral sampling (by wavelength) changes the contrast (and detectability) of the target and background materials. Although simple examples of target and background materials are used in this paper, they illustrate a general approach for evaluating the spectral detectability of terrestrial and planetary targets at the subpixel scale. Using spectral mixing analysis as a framework makes it possible to specify the conditions that are necessary to detect a particular material and the most appropriate imaging system for a given application.

Quantification of land-atmosphere exchanges of water, energy and carbon dioxide in space and time over the heterogeneous Barrax site

To advance our understanding of land–atmosphere exchanges of water, energy and carbon dioxide (CO2) in space and time over heterogeneous land surfaces, two intensive field campaigns were carried out at the Barrax agricultural test site in Spain during 12–21 July 2004 (SPARC 2004) and 8–14 July 2005 (SEN2FLEX 2005) involving multiple field, satellite and airborne instruments for characterizing the state of the atmosphere, the vegetation and the soil from the visible to the microwave range of the spectrum. Part of the experimental area is a core site of area 25 km2, within which numerous crops are grown, on both irrigated and dry land, alongside fields of bare soil. The campaigns were carried out in the framework of the Earth Observation Envelope Programme of the European Space Agency (ESA) with the aim of supporting geophysical algorithm development, calibration/validation and the simulation of future spaceborne Earth Observation missions. Both campaigns were also contributions to the EU 6FP EAGLE Project. The emphasis of this contribution is on the in situ measurements of land–atmosphere exchanges of water, energy and CO2 as well as the thermal dynamic states of the atmosphere, the soil and the vegetation. Preliminary analysis and interpretation of the measurements are presented. These two data sets are open to the scientific community for collaborative investigations.

Sub-pixel artifact detection using remote sensing

Abstract The sub-pixel spectral detectability of obsidian and ceramic artifacts against typical soil backgrounds from two study areas in the western USA was analytically evaluated to determine the usefulness of remote sensing as a tool for artifact detection [in the visible (VIS), near-infrared (NIR), and/or thermal-infrared (TIR) portion of the spectrum]. In the VIS/NIR, surface concentrations of pottery needed to be 85% or greater to be detected against backgrounds of soil, rock, and vegetation. At the same wavelengths, obsidian is spectrally similar to shade and cannot be uniquely detected unless the effects of shade are independently removed. In cases where shade is not a major factor, obsidian at concentrations of 2–3% can be detected. In the TIR, pottery thresholds ranged from 12 to 48%, depending on the composition of the background. Obsidian detection thresholds ranged from 4 to 25% cover. These results indicate that surface mapping using remote sensing in the TIR has the potential to be an effective tool for prioritizing large areas for future ground surveys.

Temporal and spatial relationships between topography, atmospheric water vapor, liquid water and vegetation endmember fractions determined using AVIRIS

Temporal and spatial changes in water vapor, liquid water and endmember fractions were investigated using AVIRIS data collected in the vicinity of Jasper Ridge, CA, on three dates in 1992. Water vapor and liquid water were mapped using a Modtran-II based atmospheric model that accounts for spatially varying atmospheric properties. Spectral mixture analysis (SMA) was used to model vegetation as mixtures of green vegetation (GV), non-photosynthetic vegetation (NPV), soil, and shade. Atmospheric water vapor and elevation were negatively correlated for all three dates with anomalously high water vapor located over forested sites, equal in some places to over 100 meters in equivalent change in elevation. A local water vapor source was suggested, either as evapotranspired water by plants or entrained water vapor in canyons. Liquid water was negatively correlated to NPV and positively correlated to GV and shade. Anomalies occurred in which golf courses and forested wetland had a high GV fraction associated with low liquid water while redwoods had high liquid water yet low GV due to a high shade content. Qualitatively, liquid water absorption matched the expected pattern in changing leaf area, with low liquid water matching lower leaf areas and high liquid water matching high leaf areas, suggesting that SMA, when combined with the analysis of liquid water provides compatible, but distinct information about vegetation. SMA provides estimates of cover and architecture (through shade), while liquid water may provide an estimate of the ecological significant variable leaf area.<<ETX>>

Multiscale thermal-infrared measurements of the Mauna Loa caldera, Hawaii

Until recently, most thermal infrared measurements of natural scenes have been made at disparate scales, typically 10-3-10-2 (spectra) and 102-103m (satellite images), with occasional airborne images (101m) filling the gap. Temperature and emissivity fields are spatially heterogeneous over a similar range of scales, depending on scene composition. A common problem for the land surface, therefore, has been relating field spectral and temperature measurements to satellite data, yet in many cases this is necessary if satellite data are to be interpreted to yield meaningful information about the land surface. Recently, three new satellites with thermal imaging capability at the 101-102m scale have been launched: MTI, TERRA, and Landsat 7. MTI acquires multispectral images in the mid-infrared (3-5micrometers ) and longwave infrared (8-10micrometers ) with 20m resolution. ASTER and MODIS aboard TERRA acquire multispectral longwave images at 90m and 500-1000m, respectively, and MODIS also acquires multispectral mid-infrared images. Landsat 7 acquires broadband longwave images at 60m. As part of an experiment to validate the temperature and thermal emissivity values calculated from MTI and ASTER images, we have targeted the summit region of Mauna Loa for field characterization and near-simultaneous satellite imaging, both on daytime and nighttime overpasses, and compare the results to previously acquired 1--1m airborne images, ground-level multispectral FLIR images, and the field spectra. Mauna Loa was chosen in large part because the 4x6km summit caldera, flooded with fresh basalt in 1984, appears to be spectrally homogeneous at scales with 10-1 and 102m, facilitating the comparison of sensed temperature. The validation results suggest that, with careful atmospheric compensation, it is possible to match ground measurements with measurements from space, and to use the Mauna Loa validation site for cross-comparison of thermal infrared sensors and temperature/emissivity extraction algorithms.