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Results for “Remote Sensing of the Environment”

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At least 37 records · Page 2Linked to original sources
USGS2005

Nitrogen controls on ecosystem carbon sequestration: a model implementation and application to Saskatchewan, Canada

A plant–soil nitrogen (N) cycling model was developed and incorporated into the Integrated BIosphere Simulator (IBIS) of Foley et al. [Foley, J.A., Prentice, I.C., Ramankutty, N., Levis, S., Pollard, D., Sitch, S., Haxeltine, A., 1996. An integrated biosphere model of land surface process, terrestrial carbon balance and vegetation dynamics. Global Biogeochem. Cycles 10, 603–628]. In the N-model, soil mineral N regulates ecosystem carbon (C) fluxes and ecosystem C:N ratios. Net primary productivity (NPP) is controlled by feedbacks from both leaf C:N and soil mineral N. Leaf C:N determines the foliar and canopy photosynthesis rates, while soil mineral N determines the N availability for plant growth and the efficiency of biomass construction. Nitrogen controls on the decomposition of soil organic matter (SOM) are implemented through N immobilization and mineralization separately. The model allows greater SOM mineralization at lower mineral N, and conversely, allows greater N immobilization at higher mineral N. The model's seasonal and inter-annual behaviours are demonstrated. A regional simulation for Saskatchewan, Canada, was performed for the period 1851–2000 at a 10 km × 10 km resolution. Simulated NPP was compared with high-resolution (1 km × 1 km) NPP estimated from remote sensing data using the boreal ecosystem productivity simulator (BEPS) [Liu, J., Chen, J.M., Cihlar, J., Park, W.M., 1997. A process-based boreal ecosystem productivity simulator using remote sensing inputs. Remote Sens. Environ. 44, 81–87]. The agreement between IBIS and BEPS, particularly in NPP spatial variation, was considerably improved when the N controls were introduced into IBIS.

Ecological Modelling↗
USGS1985

Landsat multispectral scanner and Landsat thematic mapper images and interpretations of Hannapah and the Royston Hills, Nevada

This Open-File Report consists of twenty-one 35-mm slides showing material that was presented at the Fourth Thematic Conference on Remote Sensing of the Environment held in San Francisco, California on April 1-4, 1985 (Purdy and others, 1985). The work was carried out in conjunction with the U.S. Geological Survey's Conterminous U.S. Mineral Appraisal Program for the Tonopah, Nevada 1° by 2° quadrangle (fig. 1). The slides show Landsat Multispectral Scanner (MSS) images, Landsat Thematic Mapper (TM) images, and geologic interpretations made from these data.

Nevada↗
USGS2010

EAARL coastal topography-western Florida, post-Hurricane Charley, 2004: seamless (bare earth and submerged.

Project Description These remotely sensed, geographically referenced elevation measurements of lidar-derived seamless (bare-earth and submerged) topography were produced as a collaborative effort between the U.S. Geological Survey (USGS), Coastal and Marine Geology Program (CMGP), St. Petersburg, FL, and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of a portion of the western Florida coastline beachface, acquired post-Hurricane Charley on August 17 and 18, 2004. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation. For more information about similar projects, please visit the Decision Support for Coastal Science and Management website. Selected References Brock, J.C., Wright, C.W., Sallenger, A.H., Krabill, W.B., and Swift, R.N., 2002, Basis and methods of NASA airborne topographic mapper Lidar surveys for coastal studies: Journal of Coastal Research, v. 18, no. 1, p. 1-13. Crane, Michael, Clayton, Tonya, Raabe, Ellen, Stoker, Jason, Handley, Larry, Bawden, Gerald, Morgan, Karen, and Queija, Vivian, 2004, Report of the U.S. Geological Survey Lidar workshop sponsored by the Land Remote Sensing Program and held in St. Petersburg, FL, November 2002: U.S. Geological Survey Open-File Report 2004-1456, 72 p. Nayegandhi, Amar, Brock, J.C., and Wright, C.W., 2009, Small-footprint, waveform-resolving Lidar estimation of submerged and sub-canopy topography in coastal environments: International Journal of Remote Sensing, v. 30, no. 4, p. 861-878. Sallenger, A.H., Wright, C.W., and Lillycrop, Jeff, 2005, Coastal impacts of the 2004 hurricanes measured with airborne Lidar; initial results: Shore and Beach, v. 73, nos. 2-3, p. 10-14. Resources Included Readme.txt File

Data Series↗
USGS2022

Assessing the accuracy and potential for improvement of the national land cover database’s tree canopy cover dataset in urban areas of the conterminous United States

The National Land Cover Database (NLCD) provides time-series data characterizing the land surface for the United States, including land cover and tree canopy cover (NLCD-TC). NLCD-TC was first published for 2001, followed by versions for 2011 (released in 2016) and 2011 and 2016 (released in 2019). As the only nationwide tree canopy layer, there is value in assessing NLCD-TC accuracy, given the need for cross-city comparisons of urban forest characteristics. Accuracy assessments have only been conducted for the 2001 data and suggest substantial inaccuracies for that dataset in cities. For the most recent NLCD-TC version, we used various datasets that characterize the built environment, weather, and climate to assess their accuracy in different contexts within 27 cities. Overall, NLCD underestimates tree canopy in urban areas by 9.9% when compared to estimates derived from those high-resolution datasets. Underestimation is greater in higher-density urban areas (13.9%) than in suburban areas (11.0%) and undeveloped areas (6.4%). To evaluate how NLCD-TC error in cities could be reduced, we developed a decision tree model that uses various remotely sensed and built-environment datasets such as building footprints, urban morphology types, NDVI (Normalized Difference Vegetation Index), and surface temperature as explanatory variables. This predictive model removes bias and improves the accuracy of NLCD-TC by about 3%. Finally, we show the potential applications of improved urban tree cover data through the examples of ecosystem accounting in Seattle, WA, and Denver, CO. The outputs of rainfall interception and urban heat mitigation models were highly sensitive to the choice of tree cover input data. Corrected data brought results closer to those from high-resolution model runs in all cases, with some variation by city, model, and ecosystem type. This suggests paths forward for improving the quality of urban environmental models that require tree canopy data as a key model input.

Remote Sensing↗
USGS2016

Landsat 8 and ICESat-2: Performance and potential synergies for quantifying dryland ecosystem vegetation cover and biomass

The Landsat 8 mission provides new opportunities for quantifying the distribution of above-ground carbon at moderate spatial resolution across the globe, and in particular drylands. Furthermore, coupled with structural information from space-based and airborne laser altimetry, Landsat 8 provides powerful capabilities for large-area, long-term studies that quantify temporal and spatial changes in above-ground biomass and cover. With the planned launch of ICESat-2 in 2017 and thus the potential to couple Landsat 8 and ICESat-2 data, we have unprecedented opportunities to address key challenges in drylands, including quantifying fuel loads, habitat quality, biodiversity, carbon cycling, and desertification. In this study, we explore the strengths of Landsat 8's Operational Land Imager (OLI) in estimating vegetation structure in a dryland ecosystem, and compare these results to Landsat 5's Thematic Mapper (TM). We also demonstrate the potential of OLI when coupled with light detection and ranging (lidar) in estimating vegetation cover and biomass in a dryland ecosystem. The OLI and TM predictions were similarly positive, indicating data from these sensors may be used in tandem for long-term time-series analysis. Results indicate shrub and herbaceous cover are well predicted with multi-temporal OLI data, and a combination of OLI and lidar derivatives improves most of these estimates and reduces uncertainty. For example, significant improvements were made for shrub cover (R 2 = 0.64 and 0.78 using OLI only and both OLI and lidar data, respectively). Importantly, a time series of OLI, with some improvement from lidar, provides strong estimates of herbaceous cover (68% of the variance is explained with OLI alone). In contrast, OLI data explain roughly 59% of the variance in total shrub biomass, however approximately 71% of the variance is explained when combined with lidar derivatives. To estimate the potential synergies of OLI and ICESat-2 we used simulated ICESat-2 photon data to predict vegetation structure. In a shrubland environment with a vegetation mean height of 1 m and mean vegetation cover of 33%, vegetation photons are able to explain nearly 50% of the variance in vegetation height. These results, and those from a comparison site, suggest that a lower detection threshold of ICESat-2 may be in the range of 30% canopy cover and roughly 1 m height in comparable dryland environments and these detection thresholds could be used to combine future ICESat-2 photon data with OLI spectral data for improved vegetation structure. Overall, the synergistic use of Landsat 8 and ICESat-2 may improve estimates of above-ground biomass and carbon storage in drylands that meet these minimum thresholds, increasing our ability to monitor drylands for fuel loading and the potential to sequester carbon.

Remote Sensing of Environment↗
USGS2001

Comments on "Failures in detecting volcanic ash from a satellite-based technique"

The recent paper by Simpson et al. [Remote Sens. Environ. 72 (2000) 191.] on failures to detect volcanic ash using the 'reverse' absorption technique provides a timely reminder of the danger that volcanic ash presents to aviation and the urgent need for some form of effective remote detection. The paper unfortunately suffers from a fundamental flaw in its methodology and numerous errors of fact and interpretation. For the moment, the 'reverse' absorption technique provides the best means for discriminating volcanic ash clouds from meteorological clouds. The purpose of our comment is not to defend any particular algorithm; rather, we point out some problems with Simpson et al.'s analysis and re-state the conditions under which the 'reverse' absorption algorithm is likely to succeed. ?? 2001 Elsevier Science Inc. All rights reserved.

Remote Sensing of Environment↗
USGS2020

Quantifying uncertainty for remote spectroscopy of surface composition

Remote surface measurements by imaging spectrometers play an important role in planetary and Earth science. To make these measurements, investigators calibrate instrument data to absolute units, invert physical models to estimate atmospheric effects, and then determine surface properties from the spectral reflectance. This study quantifies the uncertainty in this process. Global missions demand predictive uncertainty models that can estimate future errors for varied environments and observing conditions. Here we validate uncertainty predictions with remote surface composition retrievals and in situ measurements in a field analogue of Earth and planetary exploration. We consider rover transects at Cuprite, Nevada, and remote observations by NASA's Next- Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). We show that accounting for input uncertainties can benefit mineral detection methods such as constrained spectrum fitting. This suggests that operational uncertainty estimates could improve future NASA missions like the Earth Mineral dust source InvesTigation (EMIT) and the Lunar Trailblazer mission, as well as NASA's Decadal Surface Biology and Geology (SBG) Investigation.

Remote Sensing of Environment↗
USGS2015

Prospective HyspIRI global observations of tidal wetlands

Tidal wetlands are highly productive and act as critical habitat for a wide variety of plants, fish, shellfish, and other wildlife. These ecotones between aquatic and terrestrial environments also provide protection from storm damage, run-off filtering, and recharge of aquifers. Many wetlands along coasts have been exposed to stress-inducing alterations globally, including dredge and fill operations, hydrologic modifications, pollutants, impoundments, fragmentation by roads/ditches, and sea level rise. For wetland protection and sensible coastal development, there is a need to monitor these ecosystems at global and regional scales. Recent advances in satellite sensor design and data analysis are providing practical methods for monitoring natural and man-made changes in wetlands. However, available satellite remote sensors have been limited to mapping primarily wetland location and extent. This paper describes how the HyspIRI hyperspectral and thermal infrared sensors can be used to study and map key ecological properties, such as species composition, biomass, hydrology, and evapotranspiration of tidal salt and brackish marshes and mangroves, and perhaps other major wetland types, including freshwater marshes and wooded/shrub wetlands.

Remote Sensing of Environment↗
USGS2008

Space-based detection of wetlands' surface water level changes from L-band SAR interferometry

Interferometric processing of JERS-1 L-band Synthetic Aperture Radar (SAR) data acquired over south Florida during 1993-1996 reveals detectable surface changes in the Everglades wetlands. Although our study is limited to south Florida it has implication for other large-scale wetlands, because south Florida wetlands have diverse vegetation types and both managed and natural flow environments. Our analysis reveals that interferometric coherence level is sensitive to wetland vegetation type and to the interferogram time span. Interferograms with time spans less than six months maintain phase observations for all wetland types, allowing characterization of water level changes in different wetland environments. The most noticeable changes occur between the managed and the natural flow wetlands. In the managed wetlands, fringes are organized, follow patterns related to some of the managed water control structures and have high fringe-rate. In the natural flow areas, fringes are irregular and have a low fringe-rate. The high fringe rate in managed areas reflects dynamic water topography caused by high flow rate due to gate operation. Although this organized fringe pattern is not characteristic of most large-scale wetlands, the high level of water level change enables accurate estimation of the wetland InSAR technique, which lies in the range of 5-10??cm. The irregular and low rate fringe pattern in the natural flow area reflects uninterrupted flow that diffuses water efficiently and evenly. Most of the interferograms in the natural flow area show an elongated fringe located along the transitional zone between salt- and fresh-water wetlands, reflecting water level changes due to ocean tides. ?? 2007 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗
USGS2021

NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet , recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

Remote Sensing of Environment↗
USGS2020

Landsat 9: Empowering open science and applications through continuity

The history of Earth observation from space is well reflected through the Landsat program. With data collection beginning with Landsat-1 in 1972, the program has evolved technical capabilities while maintaining continuity of land observations. In so doing, Landsat has provided a critical reference for assessing long-term changes to Earth's land environment due to both natural and human forcing. Poised for launch in mid-2021, the joint NASA-USGS Landsat 9 mission will continue this important data record. In many respects Landsat 9 is a clone of Landsat-8. The Operational Land Imager-2 (OLI-2) is largely identical to Landsat 8 OLI, providing calibrated imagery covering the solar reflected wavelengths. The Thermal Infrared Sensor-2 (TIRS-2) improves upon Landsat 8 TIRS, addressing known issues including stray light incursion and a malfunction of the instrument scene select mirror. In addition, Landsat 9 adds redundancy to TIRS-2, thus upgrading the instrument to a 5-year design life commensurate with other elements of the mission. Initial performance testing of OLI-2 and TIRS-2 indicate that the instruments are of excellent quality and expected to match or improve on Landsat 8 data quality. Landsat-9 will maintain the current data acquisition rate of up to 740 scenes per day, with these scenes available from the Landsat archive at no cost to users. In this communication, we provide background and rationale for the Landsat 9 mission, describe the instrument payloads and ground system, and discuss data products available from the Landsat 9 mission through USGS.

Remote Sensing of Environment↗
USGS2014

Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)

Forest cover loss and bare ground gain from 2006 to 2010 for the conterminous United States (CONUS) were quantified at a 30 m spatial resolution using Web-Enabled Landsat Data available from the USGS Center for Earth Resources Observation and Science (EROS) (http://landsat.usgs.gov/WELD.php). The approach related multi-temporal WELD metrics and expert-derived training data for forest cover loss and bare ground gain through a decision tree classification algorithm. Forest cover loss was reported at state and ecoregional scales, and the identification of core forests' absent of change was made and verified using LiDAR data from the GLAS (Geoscience Laser Altimetry System) instrument. Bare ground gain correlated with population change for large metropolitan statistical areas (MSAs) outside of desert or semi-desert environments. GoogleEarth™ time-series images were used to validate the products. Mapped forest cover loss totaled 53,084 km2 and was found to be depicted conservatively, with a user's accuracy of 78% and a producer's accuracy of 68%. Excluding errors of adjacency, user's and producer's accuracies rose to 93% and 89%, respectively. Mapped bare ground gain equaled 5974 km2 and nearly matched the estimated area from the reference (GoogleEarth™) classification; however, user's (42%) and producer's (49%) accuracies were much less than those of the forest cover loss product. Excluding errors of adjacency, user's and producer's accuracies rose to 62% and 75%, respectively. Compared to recent 2001–2006 USGS National Land Cover Database validation data for forest loss (82% and 30% for respective user's and producer's accuracies) and urban gain (72% and 18% for respective user's and producer's accuracies), results using a single CONUS-scale model with WELD data are promising and point to the potential for national-scale operational mapping of key land cover transitions. However, validation results highlighted limitations, some of which can be addressed by improving training data, creating a more robust image feature space, adding contemporaneous Landsat 5 data to the inputs, and modifying definition sets to account for differences in temporal and spatial observational scales. The presented land cover extent and change data are available via the official WELD website (ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/).

Remote Sensing of Environment↗
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↗