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At least 55 records · Page 3Linked to original sources
USGS2010

Monitoring on-orbit calibration stability of the Terra MODIS and Landsat 7 ETM+ sensors using pseudo-invariant test sites

The ability to detect and quantify changes in the Earth's environment depends on sensors that can provide calibrated, consistent measurements of the Earth's surface features through time. A critical step in this process is to put image data from different sensors onto a common radiometric scale. This work focuses on monitoring the long-term on-orbit calibration stability of the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) sensors using the Committee on Earth Observation Satellites (CEOS) reference standard pseudo-invariant test sites (Libya 4, Mauritania 1/2, Algeria 3, Libya 1, and Algeria 5). These sites have been frequently used as radiometric targets because of their relatively stable surface conditions temporally. This study was performed using all cloud-free calibrated images from the Terra MODIS and the L7 ETM+ sensors, acquired from launch to December 2008. Homogeneous regions of interest (ROI) were selected in the calibrated images and the mean target statistics were derived from sensor measurements in terms of top-of-atmosphere (TOA) reflectance. For each band pair, a set of fitted coefficients (slope and offset) is provided to monitor the long-term stability over very stable pseudo-invariant test sites. The average percent differences in intercept from the long-term trends obtained from the ETM + TOA reflectance estimates relative to the MODIS for all the CEOS reference standard test sites range from 2.5% to 15%. This gives an estimate of the collective differences due to the Relative Spectral Response (RSR) characteristics of each sensor, bi-directional reflectance distribution function (BRDF), spectral signature of the ground target, and atmospheric composition. The lifetime TOA reflectance trends from both sensors over 10 years are extremely stable, changing by no more than 0.4% per year in its TOA reflectance over the CEOS reference standard test sites.

Remote Sensing of Environment↗
USGS2016

Assessing the role of climate and resource management on groundwater dependent ecosystem changes in arid environments with the Landsat archive

Groundwater dependent ecosystems (GDEs) rely on near-surface groundwater. These systems are receiving more attention with rising air temperature, prolonged drought, and where groundwater pumping captures natural groundwater discharge for anthropogenic use. Phreatophyte shrublands, meadows, and riparian areas are GDEs that provide critical habitat for many sensitive species, especially in arid and semi-arid environments. While GDEs are vital for ecosystem services and function, their long-term (i.e. ~ 30 years) spatial and temporal variability is poorly understood with respect to local and regional scale climate, groundwater, and rangeland management. In this work, we compute time series of NDVI derived from sensors of the Landsat TM, ETM +, and OLI lineage for assessing GDEs in a variety of land and water management contexts. Changes in vegetation vigor based on climate, groundwater availability, and land management in arid landscapes are detectable with Landsat. However, the effective quantification of these ecosystem changes can be undermined if changes in spectral bandwidths between different Landsat sensors introduce biases in derived vegetation indices, and if climate, and land and water management histories are not well understood. The objective of this work is to 1) use the Landsat 8 under-fly dataset to quantify differences in spectral reflectance and NDVI between Landsat 7 ETM + and Landsat 8 OLI for a range of vegetation communities in arid and semiarid regions of the southwestern United States, and 2) demonstrate the value of 30-year historical vegetation index and climate datasets for assessing GDEs. Specific study areas were chosen to represent a range of GDEs and environmental conditions important for three scenarios: baseline monitoring of vegetation and climate, riparian restoration, and groundwater level changes. Google's Earth Engine cloud computing and environmental monitoring platform is used to rapidly access and analyze the Landsat archive along with downscaled North American Land Data Assimilation System gridded meteorological data, which are used for both atmospheric correction and correlation analysis. Results from the cross-sensor comparison indicate a benefit from the application of a consistent atmospheric correction method, and that NDVI derived from Landsat 7 and 8 are very similar within the study area. Results from continuous Landsat time series analysis clearly illustrate that there are strong correlations between changes in vegetation vigor, precipitation, evaporative demand, depth to groundwater, and riparian restoration. Trends in summer NDVI associated with riparian restoration and groundwater level changes were found to be statistically significant, and interannual summer NDVI was found to be moderately correlated to interannual water-year precipitation for baseline study sites. Results clearly highlight the complementary relationship between water-year PPT, NDVI, and evaporative demand, and are consistent with regional vegetation index and complementary relationship studies. This work is supporting land and water managers for evaluation of GDEs with respect to climate, groundwater, and resource management.

Remote Sensing of Environment↗
USGS1971

Spectral reflectance and photometric properties of selected rocks

Studies of the spectral reflectance and photometric properties of selected rocks at the USGS Mill Creek, Oklahoma, remote sensing test site demonstrate that discrimination of rock types is possible through reflection measurements, but that the discrimination is complicated by surface conditions, such as weathering and lichen growth. Comparisons between fresh-broken, weathered, and lichen-covered granite show that whereas both degree of weathering and amount of lichen cover change the reflectance quality of the granite, lichen cover also considerably changes the photometric properties of the granite. Measurements of the spectral reflectance normal to the surface of both limestone and dolomite show limestone to be more reflective than dolomite in the wavelength range from 380 to 1550 nanometers. The reflectance difference decreases at view angles greater than 40° owing to the difference in the photometric properties of dolomite and limestone.

Remote Sensing of Environment↗
USGS1971

Aircraft measurements of microwave emission from Arctic Sea ice

Measurements of the microwave emission from Arctic Sea ice were made with aircraft at 8 wavelengths ranging from 0.510 to 2.81 cm. The expected contrast in emissivities between ice and water was observed at all wavelengths. Distributions of sea ice and open water were mapped from altitudes up to 11 km in the presence of dense cloud cover. Different forms of ice also exhibited strong contrasts in emissivity. Emissivity differences of up to 0.2 were observed between two types of ice at the 0.811-cm wavelength. The higher emissivity ice type is tentatively identified as having been formed more recently than the lower emissivity ice.

Alaska↗
USGS1975

Thermal-infrared spectra and chemical analyses of twenty-six igneous rock samples

Emittance spectra in the 7.5 μm to 14 μm wavelength region and chemical compositions of 26 igneous rocks are reported. Experimental measurements on the rocks were made under simulated daytime field conditions. Some surface silicate contaminants, such as clayey silt, significantly altered the spectral emittance of a fresh sample, whereas, for these samples, hydrous and anhydrous ferric oxide weathering products did not mask important silicate spectral information. In the 11.75 μm to 13.75 μm wavelength region, the mean emittance of all the silicate samples was0.956 ± 0.008, except for periodtite, which had an average emittance of 0.895. This region of uniform emittance should be useful in remote sensing experiments for the separation of the effects of temperature and chemical composition on the spectral emittance of silicate rocks.

Remote Sensing of Environment↗
USGS1978

Interpretation of a Landsat image of an unusual flood phenomenon in Australia

A Landsat image of part of the flooded area of Cooper Creek, Queensland, Australia, in February 1974, shows large dark areas within the flooded valley. The dark areas are believed to be wet, but unflooded, areas of dark alluvial soil. These striking features, which have not previously been identified on Landsat images, must be properly interpreted so as not to confuse them with clear water.

Queensland↗
USGS1980

Remote sensing data of SP Mountain and SP Lava flow in north-central Arizona

Multifrequency airborne radar image data of SP Mountain [Official name of feature (U.S. Geological Survey, 1970)] and SP flow (and vicinity) in north-central Arizona were obtained in diverse viewing directions and direct and cross-polarization, then compared with surface and aerial photography, LANDSAT multispectral scanner data, airborne thermal infrared imagery, surface geology, and surface roughness statistics. The extremely blocky, basaltic andesite of SP flow is significantly brighter on direct-polarization K-band (0.9-cm wavelength) images than on cross-polarized images taken simultaneously. Conversely, for the longer wavelength (25 cm) L-band radar images, the cross-polarization image returns from SP flow are brighter than the direct-polarized image. This effect is explained by multiple scattering and the strong wavelength dependence of polarization effects caused by the rectilinear basaltic andesite scatters. Two distinct types of surface relief on SP flow, one extremely blocky, the other subdued, are found to be clearly discriminated on the visible and thermal wavelength images but are separable only on the longer wavelength L-band radar image data. The inability of the K- and X- (3-cm wavelength) band radars to portray the differences in roughness between the two SP flow surface units is attributed to the radar frequency dependence of the surface-relief scale, which, described as the Rayleigh criterion, represents the transition between quasispecular and primarily diffuse backscatter.

Arizona↗
USGS1980

Nearshore current pattern off south Texas: an interpretation from aerial photographs.

Current patterns in a 4-km-wide zone along the south Texas coast were interpreted from patterns of water turbidity visible in aerial photographs taken during a winter day of moderate northerly winds. Features of the turbidity pattern remained recognizable on photographs taken 25 min apart. Currents measured from the movements of these features were southward and nearly parallel to shore, increasing from about 17 cm/sec in an offshore zone to about 40 cm/sec at the line of breaking waves. - from Authors

Remote Sensing of Environment↗
USGS1980

Possible fault detection in Cottonball Basin, California: an application of radar remote sensing.

An analysis of a 3-cm wavelength radar image of Cottonball Basin in Death Valley National Monument has revealed the existence of two suspect fault traces in evaporite deposits that are less than 2000-yr old. The traces are well defined on the image because the radar system was able to differentiate surface roughness variations at the centimeter scale. The features are not recognizable on data sets recorded in the visible and near-infrared spectral bands. - from Authors

Remote Sensing of Environment↗
USGS1981

Arid land monitoring using Landsat albedo difference images

The Landsat albedo, or percentage of incoming radiation reflected from the ground in the wavelength range of 0.5 [mu]m to 1.1 [mu]m, is calculated from an equation using the Landsat digital brightness values and solar irradiance values, and correcting for atmospheric scattering, multispectral scanner calibration, and sun angle. The albedo calculated for each pixel is used to create an albedo image, whose grey scale is proportional to the albedo. Differencing sequential registered images and mapping selected values of the difference is used to create quantitative maps of increased or decreased albedo values of the terrain. All maps and other output products are in black and white rather than color, thus making the method quite economical. Decreases of albedo in arid regions may indicate improvement of land quality; increases may indicate degradation. Tests of the albedo difference mapping method in the Desert Experimental Range in southwestern Utah (a cold desert with little long-term terrain change) for a four-year period show that mapped changes can be correlated with erosion from flash floods, increased or decreased soil moisture, and increases or decreases in the density of desert vegetation, both perennial shrubs and annual plants. All terrain changes identified in this test were related to variations in precipitation. Although further tests of this method in hot deserts showing severe "desertification" are needed, the method is nevertheless recommended for experimental use in monitoring terrain change in other arid and semiarid regions of the world.

Utah↗
USGS1981

The logic of multispectral classification and mapping of land

The use of multispectral reflectance data as surrogates for land attributes must be done within strict rules of logic and with a recognition of judgmental factors such as the use of a priori or a posteriori classification schemes. The naming and describing of spectral classes as surrogates of information classes is a critical element in the logic of mapping and must be complete and logically consistent. Maps of information classes derived from multispectral data should be portrayed without class boundaries so as to indicate the degree of homogeneity or heterogeneity of classes on maps in which these characteristics are of major importance.

Remote Sensing of Environment↗
USGS1982

An automatic optimum kernel-size selection technique for edge enhancement

Edge enhancement is a technique that can be considered, to a first order, a correction for the modulation transfer function of an imaging system. Digital imaging systems sample a continuous function at discrete intervals so that high-frequency information cannot be recorded at the same precision as lower frequency data. Because of this, fine detail or edge information in digital images is lost. Spatial filtering techniques can be used to enhance the fine detail information that does exist in the digital image, but the filter size is dependent on the type of area being processed. A technique has been developed by the authors that uses the horizontal first difference to automatically select the optimum kernel-size that should be used to enhance the edges that are contained in the image.

Remote Sensing of Environment↗
USGS1982

A relation between landsat digital numbers, surface reflectance, and the cosine of the solar zenith angle

A method for estimating the reflectance of ground sites from satellite radiance data is proposed and tested. The method uses the known ground reflectance from several sites and satellite data gathered over a wide range of solar zenith angles. The method was tested on each of 10 different Landsat images using 10 small sites in the Walker Lake, Nevada area. Plots of raw Landsat digital numbers (DNs) versus the cosine of the solar zenith angle (cos Z) for the the test areas are linear, and the average correlation coefficients of the data for Landsat bands 4, 5, 6, and 7 are 0.94, 0.93, 0.94, and 0.94, respectively. Ground reflectance values for the 10 sites are proportional to the slope of the DN versus cos Z relation at each site. The slope of the DN versus cos Z relation for seven additional sites in Nevada and California were used to estimate the ground reflectances of those sites. The estimates for nearby sites are in error by an average of 1.2% and more distant sites are in error by 5.1%. The method can successfully estimate the reflectance of sites outside the original scene, but extrapolation of the reflectance estimation equations to other areas may violate assumptions of atmospheric homogeneity.

Nevada↗
USGS1982

An assessment of Landsat data acquisition history on identification and area estimation of corn and soybeans

In the past decade, numerous studies have demonstrated the potential of satellite remote sensing for providing accurate timely crop area information. This study assessed the impact of Landsat data acquisition history on classification and area estimation accuracy of corn and soybeans in the U.S. Corn Belt. The results illustrate the importance of selecting Landsat acquisitions based on spectral differences in crops at certain development stages. Although early season information can provide estimates of total corn and soybean areas, acquisitions from about emergence and after tasseling of the corn seem to provide a minimal set for accurate identification of corn and soybeans in the U.S. Corn Belt. Additional acquisitions provide only marginally greater separability for corn and soybeans.

Corn Belt↗
USGS1987

Testing the consistency for mapping urban vegetation with high-altitude aerial photographs and landsat MSS data

Two methods of analysis were evaluated for mapping urban vegetation on high-altitude, color-infrared aerial photographs and Landsat MSS data of Syracuse, NY. The first method consisted of defining the spatial patterns (strata) of urban vegetation occurrence. The second method discriminated woody and herbaceous vegetation classes within defined strata. Emphasis was placed on evaluating the consistency of each method. Results indicate that consistent spatial patterns of urban vegetation strata were not achieved on either of the two data types tested due to the spatial complexity of the urban vegetation. However, for discriminating woody and herbaceous vegetation classes within defined strata, good consistency was noted among the interpreters of the high-altitude aerial photographs. The coarse spatial resolution of the Landsat MSS data resulted in low precision for identifying these two vegetation classes in this highly urbanized area. Where photointerpretation efforts are intended for mapping vegetation within numerous urban areas, the estimation of proportions of vegetation classes within defined strata should be a data analysis procedure more objective and consistently repeatable than is the delineation of vegetation patterns.

New York↗
USGS1988

Use of airborne imaging spectrometer data to map minerals associated with hydrothermally altered rocks in the northern Grapevine Mountains, Nevada, and California

Three flightlines of Airborne Imaging Spectrometer (AIS) data, acquired over the northern Grapevine Mountains, Nevada, and California, were used to map minerals associated with hydrothermally altered rocks. The data were processed to remove vertical striping, normalized using an equal area normalization, and reduced to reflectance relative to an average spectrum derived from the data. An algorithm was developed to automatically calculate the absorption band parameters band position, band depth, and band width for the strongest absorption feature in each pixel. These parameters were mapped into an intensity, hue, saturation (IHS) color system to produce a single color image that summarized the absorption band information, This image was used to map areas of potential alteration based upon the predicted relationships between the color image and mineral absorption band. Individual AIS spectra for these areas were then examined to identify specific minerals. Two types of alteration were mapped with the AIS data. Areas of quartz-sericite-pyrite alteration were identified based upon a strong absorption feature near 2.21 μm, a weak shoulder near 2.25 μm, and a weak absorption band near 2.35 μm caused by sericite (fine-grained muscovite). Areas of argillic alteration were defined based on the presence of montmorillonite, identified by a weak to moderate absorption feature near 2.21 μm and the absence of the 2.35 μm band. Montmorillonite could not be identified in mineral mixtures. Calcite and dolomite were identified based on sharp absorption features near 2.34 and 2.32 μm, respectively. Areas of alteration identified using the AIS data corresponded well with areas mapped using field mapping, field reflectance spectra, and laboratory spectral measurements.

California, Nevada↗
USGS1988

An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data

Digital analysis of remotely sensed data has become an important component of many earth-science studies. These data are often processed through a set of preprocessing or “clean-up” routines that includes a correction for atmospheric scattering, often called haze. Various methods to correct or remove the additive haze component have been developed, including the widely used dark-object subtraction technique. A problem with most of these methods is that the haze values for each spectral band are selected independently. This can create problems because atmospheric scattering is highly wavelength-dependent in the visible part of the electromagnetic spectrum and the scattering values are correlated with each other. Therefore, multispectral data such as from the Landsat Thematic Mapper and Multispectral Scanner must be corrected with haze values that are spectral band dependent. An improved dark-object subtraction technique is demonstrated that allows the user to select a relative atmospheric scattering model to predict the haze values for all the spectral bands from a selected starting band haze value. The improved method normalizes the predicted haze values for the different gain and offset parameters used by the imaging system. Examples of haze value differences between the old and improved methods for Thematic Mapper Bands 1, 2, 3, 4, 5, and 7 are 40.0, 13.0, 12.0, 8.0, 5.0, and 2.0 vs. 40.0, 13.2, 8.9, 4.9, 16.7, and 3.3, respectively, using a relative scattering model of a clear atmosphere. In one Landsat multispectral scanner image the haze value differences for Bands 4, 5, 6, and 7 were 30.0, 50.0, 50.0, and 40.0 for the old method vs. 30.0, 34.4, 43.6, and 6.4 for the new method using a relative scattering model of a hazy atmosphere.

Arizona↗