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At least 73 records · Page 4Linked to original sources
USGS1989

Airborne imaging spectrometer data of the Ruby Mountains, Montana: Mineral discrimination using relative absorption band-depth images

Airborne imaging spectrometer data collected in the near-infrared (1.2–2.4 μm) wavelength range were used to study the spectral expression of metamorphic minerals and rocks in the Ruby Mountains of southwestern Montana. The data were analyzed by using a new data enhancement procedure—the construction of relative absorption band-depth (RBD) images. RBD images, like bandratio images, are designed to detect diagnostic mineral absorption features, while minimizing reflectance variations related to topographic slope and albedo differences. To produce an RBD image, several data channels near an absorption band shoulder are summed and then divided by the sum of several channels located near the band minimum. RBD images are both highly specific and sensitive to the presence of particular mineral absorption features. Further, the technique does not distort or subdue spectral features as sometimes occurs when using other data normalization methods. By using RBD images, a number of rock and soil units were distinguished in the Ruby Mountains including weathered quartz - feldspar pegmatites, marbles of several compositions, and soils developed over poorly exposed mica schists. The RBD technique is especially well suited for detecting weak near-infrared spectral features produced by soils, which may permit improved mapping of subtle lithologic and structural details in semiarid terrains. The observation of soils rich in talc, an important industrial commodity in the study area, also indicates that RBD images may be useful for mineral exploration.

Montana↗
USGS1989

Mapping surface energy balance components by combining landsat thematic mapper and ground-based meteorological data

Surface energy balance components were evaluated by combining satellite-based spectral data with on-site measurements of solar irradiance, air temperature, wind speed, and vapor pressure. Maps of latent heat flux density (λE) and net radiant flux density (R n ) were produced using Landsat Thematic Mapper (TM) data for three dates: 23 July 1985, 5 April 1986, and 24 June 1986. On each date, a Bowen-ratio apparatus, located in a vegetated field, was used to measure λE and R n at a point within the field. Estimates of λE and R n were also obtained using radiometers aboard an aircraft flown at 150 m above ground level. The TM-based estimates differed from the Bowen-ratio and aircraft-based estimates by less than 12 % over mature fields of cotton, wheat, and alfalfa, where λE and R n ranged from 400 to 700 Wm −2 .

Remote Sensing of Environment↗
USGS1989

Arsenic- and selenium-induced changes in spectral reflectance and morphology of soybean plants

Soybean (Glycine max) plants were grown in hydroponic solutions treated with high concentrations of either arsenic or selenium. Spectral reflectance changes in arsenic-dosed plants included a shift to shorter wavelengths in the long-wavelength edge of the chlorophyll absorption band centered at 680 nm (the red edge) and higher reflectance in the 550-650 nm region. These results are consistent with vegetation reflectance anomalies observed in previous greenhouse experiments and in airborne radiometer studies. The selenium-dosed plants contrast, exhibited a shift to longer wavelengths of the red edge and lower reflectance between 550 nm and 650 wh when compared with control plants. Morphological effects of arsenic uptake included lower overall biomass, stunted and discolored roots, and smaller leaves oriented more vertically than leaves of control plants. Selenium-dosed plants also displayed morphological changes, but root and leaf biomass were less affected than were those of arsenic-dosed plants when compared to control plants.

Remote Sensing of Environment↗
USGS1990

Spectral estimates of net radiation and soil heat flux

Conventional methods of measuring surface energy balance are point measurements and represent only a small area. Remote sensing offers a potential means of measuring outgoing fluxes over large areas at the spatial resolution of the sensor. The objective of this study was to estimate net radiation (R n ) and soil heat flux (G) using remotely sensed multispectral data acquired from an aircraft over large agricultural fields. Ground-based instruments measured R n and G at nine locations along the flight lines. Incoming fluxes were also measured by ground-based instruments. Outgoing fluxes were estimated using remotely sensed data. Remote R n , estimated as the algebraic sum of incoming and outgoing fluxes, slightly underestimated R n measured by the ground-based net radiometers. The mean absolute errors for remote R n minus measured R n were less than 7%. Remote G, estimated as a function of a spectral vegetation index and remote R n , slightly overestimated measured G; however, the mean absolute error for remote G was 13%. Some of the differences between measured and remote values of R n and G are associated with differences in instrument designs and measurement techniques. The root mean square error for available energy (R n - G) was 12%. Thus, methods using both ground-based and remotely sensed data can provide reliable estimates of the available energy which can be partitioned into sensible and latent heat under nonadvective conditions.

Remote Sensing of Environment↗
USGS1991

Effect of phosphorus deficiency on spectral reflectance and morphology of soybean plants

Soybean plants were grown in hydroponic solutions having three concentration levels of phosphorus. Spectral reflectance changes included higher reflectance in the green and yellow portions of the electromagnetic spectrum in phosphorus-deficient plants and a difference in position of the long wavelength edge (the red edge) of the chlorophyll absorption band centered near 0.68 ??m. Plants having the least phosphorus in the growing medium did not show the normal shift of the red edge to longer wavelength which occurs as leaves mature. Shoot and root biomass were significantly lower in the phosphorus-deficient plants. These results are consistent with those obtained when soybean plants are dosed with elevated concentrations of metallic elements. We hypothesize that nutrient imbalances or anomalous metal concentrations in the soil set up physiological conditions at the soil/root interface that are responsible for the reflectance differences observed in laboratory and field studies of plants growing in substrates enriched in metallic elements.

Remote Sensing of Environment↗
USGS1991

Remote estimation of the diffuse attenuation coefficient in a moderately turbid estuary

Solutions of the radiative transfer equation are used to derive relationships of water reflectance to the diffuse attenuation coefficient (K) in moderately turbid water (K > 0.5 m −1 ). Data sets collected from the NOAA AVHRR and in situ observations from five different dates confirm the appropriateness of these relationships, in particular the logistic equation. Values of K calculated from the reflectance data agree to within 60% of the observed values, although the reflectance derived using a more comprehensive aerosol correction is sensitive to chlorophyll concentrations greater than 50 μg L −1 . Agreement between in situ and remote observations improves as the time interval between samples is narrowed.

Remote Sensing of Environment↗
USGS1992

Spectral ratio method for measuring emissivity

The spectral ratio method is based on the concept that although the spectral radiances are very sensitive to small changes in temperature the ratios are not. Only an approximate estimate of temperature is required thus, for example, we can determine the emissivity ratio to an accuracy of 1% with a temperature estimate that is only accurate to 12.5 K. Selecting the maximum value of the channel brightness temperatures is an unbiased estimate. Laboratory and field spectral data are easily converted into spectral ratio plots. The ratio method is limited by system signal:noise and spectral band-width. The images can appear quite noisy because ratios enhance high frequencies and may require spatial filtering. Atmospheric effects tend to rescale the ratios and require using an atmospheric model or a calibration site.

Colorado↗
USGS1992

Two-temperature method for measuring emissivity

Spectral emissivity can be uniquely determined from radiance measurements if the object can be observed at two different temperatures. The advantage of this approach is that the spectral emissivity is determined without a priori assumptions about spectral shape. Because the different temperatures are obtained by observing the scene at two times in the diurnal cycle (optimally after midday and midnight), the method assumes that emissivity is temporally invariant. This is valid for rocks and dry soils, not well established for vegetation, and not true when changes in soil moisture occur between the measurements. Accurate image registration and satisfactory signal:noise are critical factors that limit extensive use of this method.

Nevada↗
USGS1993

Mapping playa evaporite minerals with AVIRIS data: A first report from Death Valley, California

Efflorescent salt crusts in Death Valley, California, were mapped by using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data and a recently developed least-squares spectral band-fitting algorithm. Eight different saline minerals were remotely identified, including three borates, hydroboracite, pinnoite, and rivadavite, that have not been previously reported from the Death Valley efflorescent crusts. The three borates are locally important phases in the crusts, and at least one of the minerals, rivadavite, appears to be forming directly from brine. Borates and other evaporite minerals provide a basis for making remote chemical measurements of desert hydrologic systems. For example, in the Eagle Borax Spring area, the AVIRIS mineral maps pointed to elevated magnesium and boron levels in the ground waters, and to the action of chemical divides causing subsurface fractionation of calcium. Many other chemical aspects of playa brines should have an expression in the associated evaporite assemblages. Certain anhydrous evaporites, including anhydrite, glauberite, and thenardite, lack absorption bands in the visible and near-infrared wavelength range, and crusts composed of these minerals could not be characterized by using AVIRIS. In these situations, thermal-infrared remote sensing data may complement visible and near-infrared data for mapping evaporites. Another problem occurred in wet areas of Death Valley, where water absorption caused low signal levels in the 2.0-2.5 ??m wavelength region that obscured any spectral features of evaporite minerals. Despite these difficulties, the results of this study demonstrate the potential for using AVIRIS and other imaging spectrometer data to study playa chemistry. Such data can be useful for understanding chemical linkages between evaporites and ground waters, and will facilitate studies of how desert ground-water regimes change through time in response to climatic and other variables.

California↗
USGS1993

Evaluating landsat thematic mapper derived vegetation indices for estimating above-ground biomass on semiarid rangelands

Ground data from the Central Plains Experimental Range in northeast Colorado and Landsat satellite images of that area acquired in August 1989, June 1990, and September 1990 were used to evaluate the level of association that can be expected from a univariate model relating spectrally derived vegetation indices (difference, ratio, and normalized difference vegetation indices) and dried green vegetation biomass. The vegetation indices were related to the ground sample estimates using a sample point, spectral class, and greenness strata approach. No strong relationships were found between the vegetation indices and sample estimates of dried green biomass using the sample point approach. The spectral class approach produced significant results only for the June 1990 sample period ( r =0.96). Significant relationships were found for the August 1989, June 1990, and September 1990 samples periods ( r 2 =0.95, 0.71, and 0.95, respectively) when the data were aggregated by greenness strata. The high degree of association between green biomass and the NDVI, obtained when the data were combined into greenness strata, indicated that it is possible to predict green biomass levels on semiarid rangelands using univariate regression models.

Remote Sensing of Environment↗
USGS1993

Surficial geology of the Safsaf region, south-central Egypt, derived from remote-sensing and field data

We used a decorrelation-stretched image of Landsat Thematic Mapper (TM) Bands 1, 4, and 7 and field data to map and describe the main surficial units in the hyperarid Safsaf region in south-central Egypt. We show that the near-infrared bands on Landsat TM, which are sensitive to very subtle changes in mineralogy common to arid regions, significantly improve the geologist's capability to discriminate geologic units in desert regions. These data also provide the spatial and spectral information necessary to determine the migration patterns and provenance of eolian materials. The Safsaf area was the focus of our post flight field studies using Shuttle Imaging Radar (SIR) data following the discovery of buried paleochannels in North Africa. Most of the channels discernible on SIR images are not expressed in TM data, but traces of a few channels are present in both the SIR and the TM data within the Wadi Safsaf area. Here we present a detailed digital examination of the SIR and the TM-band reflectance and reflectance-ratio data at three locations of the more obvious surface expressions of the buried channels. Our results indicate that the TM expressions of the channels are not purely topographic but are more compositional in nature. Two possibilities may account for the TM expressions of the buried channels: 1) concentrations of windblown, iron-rich materials that accumulated along subtle curvilinear topograpohic traps, or 2) curvilinear exposures of an iron-rich underlying unit of the flat sand sheet.

Safsaf region↗
USGS1995

A remote sensing based vegetation classification logic for global land cover analysis

This article proposes a simple new logic for classifying global vegetation. The critical features of this classification are that 1) it is based on simple, observable, unambiguous characteristics of vegetation structure that are important to ecosystem biogeochemistry and can be measured in the field for validation, 2) the structural characteristics are remotely sensible so that repeatable and efficient global reclassifications of existing vegetation will be possible, and 3) the defined vegetation classes directly translate into the biophysical parameters of interest by global climate and biogeochemical models. A first test of this logic for the continental United States is presented based on an existing 1 km AVHRR normalized difference vegetation index database. Procedures for solving critical remote sensing problems needed to implement the classification are discussed. Also, some inferences from this classification to advanced vegetation biophysical variables such as specific leaf area and photosynthetic capacity useful to global biogeochemical modeling are suggested.

Remote Sensing of Environment↗
USGS1995

Recent data and information system initiatives for remotely sensed measurements of the land surface

As part of the International Satellite Land Satellite Climatology Program (ISLSCP) Workshop on Remote Sensing of the Land Surface for Studies of Global Change, five invited presentations were given on recent data initiatives relevant to the ISLSCP community. The presentations are summarized in this paper along with some observations by the authors on data systems for the land sciences community. The invited presentations are by no means all inclusive but were selected as examples of current data activities, representing a range of topics associated with data for land sciences including: the generation of global and local scale data sets, the reworking of historical data sets, new data initiatives and some programmatic aspects of land data base development. This paper serves to provide information on these data initiatives and to air some of the issues concerning land science data systems that were raised at the meeting.

Remote Sensing of Environment↗
USGS2003

IKONOS geometric characterization

The IKONOS spacecraft acquired images on July 3, 17, and 25, and August 13, 2001 of Brookings SD, a small city in east central South Dakota, and on May 22, June 30, and July 30, 2000, of the rural area around the EROS Data Center. South Dakota State University (SDSU) evaluated the Brookings scenes and the USGS EROS Data Center (EDC) evaluated the other scenes. The images evaluated by SDSU utilized various natural objects and man-made features as identifiable targets randomly distribution throughout the scenes, while the images evaluated by EDC utilized pre-marked artificial points (panel points) to provide the best possible targets distributed in a grid pattern. Space Imaging provided products at different processing levels to each institution. For each scene, the pixel (line, sample) locations of the various targets were compared to field observed, survey-grade Global Positioning System locations. Patterns of error distribution for each product were plotted, and a variety of statistical statements of accuracy are made. The IKONOS sensor also acquired 12 pairs of stereo images of globally distributed scenes between April 2000 and April 2001. For each scene, analysts at the National Imagery and Mapping Agency (NIMA) compared derived photogrammetric coordinates to their corresponding NIMA field-surveyed ground control point (GCPs). NIMA analysts determined horizontal and vertical accuracies by averaging the differences between the derived photogrammetric points and the field-surveyed GCPs for all 12 stereo pairs. Patterns of error distribution for each scene are presented.

Remote Sensing of Environment↗
USGS2003

Analysis of potential debris flow source areas on Mount Shasta, California, by using airborne and satellite remote sensing data

Remote sensing data from NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the first spaceborne imaging spectrometer, Hyperion, show hydrothermally altered rocks mainly composed of natroalunite, kaolinite, cristobalite, and gypsum on both the Mount Shasta and Shastina cones. Field observations indicate that much of the visible altered rock consists of talus material derived from fractured rock zones within and adjacent to dacitic domes and nearby lava flows. Digital elevation data were utilized to distinguish steeply sloping altered bedrock from more gently sloping talus materials. Volume modeling based on the imagery and digital elevation data indicate that Mount Shasta drainage systems contain moderate volumes of altered rock, a result that is consistent with Mount Shasta's Holocene record of mostly small to moderate debris flows. Similar modeling for selected areas at Mount Rainier and Mount Adams, Washington, indicates larger altered rock volumes consistent with the occurrence of much larger Holocene debris flows at those volcanoes. The availability of digital elevation and spectral data from spaceborne sensors, such as Hyperion and the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER), greatly expands opportunities for studying potential debris flow source characteristics at stratovolcanoes around the world. ?? 2003 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗
USGS2004

Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems

The objective of this paper is to review research conducted over the past decade on the application of multi-temporal remote sensing for monitoring changes of Arctic tundra lands. Emphasis is placed on results from the National Science Foundation Land–Air–Ice Interactions (LAII) program and on optical remote sensing techniques. Case studies demonstrate that ground-level sensors on stationary or moving track platforms and wide-swath imaging sensors on polar orbiting satellites are particularly useful for capturing optical remote sensing data at sufficient frequency to study tundra vegetation dynamics and changes for the cloud prone Arctic. Less frequent imaging with high spatial resolution instruments on aircraft and lower orbiting satellites enable more detailed analyses of land cover change and calibration/validation of coarser resolution observations. The strongest signals of ecosystem change detected thus far appear to correspond to expansion of tundra shrubs and changes in the amount and extent of thaw lakes and ponds. Changes in shrub cover and extent have been documented by modern repeat imaging that matches archived historical aerial photography. NOAA Advanced Very High Resolution Radiometer (AVHRR) time series provide a 20-year record for determining changes in greenness that relates to photosynthetic activity, net primary production, and growing season length. The strong contrast between land materials and surface waters enables changes in lake and pond extent to be readily measured and monitored.

Remote Sensing of Environment↗
USGS2004

Land cover mapping of North and Central America—Global Land Cover 2000

The Land Cover Map of North and Central America for the year 2000 (GLC 2000-NCA), prepared by NRCan/CCRS and USGS/EROS Data Centre (EDC) as a regional component of the Global Land Cover 2000 project, is the subject of this paper. A new mapping approach for transforming satellite observations acquired by the SPOT4/VGTETATION (VGT) sensor into land cover information is outlined. The procedure includes: (1) conversion of daily data into 10-day composite; (2) post-seasonal correction and refinement of apparent surface reflectance in 10-day composite images; and (3) extraction of land cover information from the composite images. The pre-processing and mosaicking techniques developed and used in this study proved to be very effective in removing cloud contamination, BRDF effects, and noise in Short Wave Infra-Red (SWIR). The GLC 2000-NCA land cover map is provided as a regional product with 28 land cover classes based on modified Federal Geographic Data Committee/Vegetation Classification Standard (FGDC NVCS) classification system, and as part of a global product with 22 land cover classes based on Land Cover Classification System (LCCS) of the Food and Agriculture Organisation. The map was compared on both areal and per-pixel bases over North and Central America to the International Geosphere–Biosphere Programme (IGBP) global land cover classification, the University of Maryland global land cover classification (UMd) and the Moderate Resolution Imaging Spectroradiometer (MODIS) Global land cover classification produced by Boston University (BU). There was good agreement (79%) on the spatial distribution and areal extent of forest between GLC 2000-NCA and the other maps, however, GLC 2000-NCA provides additional information on the spatial distribution of forest types. The GLC 2000-NCA map was produced at the continental level incorporating specific needs of the region.

Remote Sensing of Environment↗
USGS2004

Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest

Structural and functional analyses of ecosystems benefit when high accuracy vegetation coverages can be derived over large areas. In this study, we utilize IKONOS, Landsat 7 ETM+, and airborne scanning light detection and ranging (lidar) to quantify coniferous forest and understory grass coverages in a ponderosa pine ( Pinus ponderosa ) dominated ecosystem in the Black Hills of South Dakota. Linear spectral mixture analyses of IKONOS and ETM+ data were used to isolate spectral endmembers (bare soil, understory grass, and tree/shade) and calculate their subpixel fractional coverages. We then compared these endmember cover estimates to similar cover estimates derived from lidar data and field measures. The IKONOS-derived tree/shade fraction was significantly correlated with the field-measured canopy effective leaf area index (LAI e ) ( r 2 =0.55, p <0.001) and with the lidar-derived estimate of tree occurrence ( r 2 =0.79, p <0.001). The enhanced vegetation index (EVI) calculated from IKONOS imagery showed a negative correlation with the field measured tree canopy effective LAI and lidar tree cover response ( r 2 =0.30, r =−0.55 and r 2 =0.41, r =−0.64, respectively; p <0.001) and further analyses indicate a strong linear relationship between EVI and the IKONOS-derived grass fraction ( r 2 =0.99, p <0.001). We also found that using EVI resulted in better agreement with the subpixel vegetation fractions in this ecosystem than using normalized difference of vegetation index (NDVI). Coarsening the IKONOS data to 30 m resolution imagery revealed a stronger relationship with lidar tree measures ( r 2 =0.77, p <0.001) than at 4 m resolution ( r 2 =0.58, p <0.001). Unmixed tree/shade fractions derived from 30 m resolution ETM+ imagery also showed a significant correlation with the lidar data ( r 2 =0.66, p <0.001). These results demonstrate the power of using high resolution lidar data to validate spectral unmixing results of satellite imagery, and indicate that IKONOS data and Landsat 7 ETM+ data both can serve to make the important distinction between tree/shade coverage and exposed understory grass coverage during peak summertime greenness in a ponderosa pine forest ecosystem.

Remote Sensing of Environment↗