bluuebunny/crossref_metadata_2025_split huggingface dataset:
Biological Oceanography by Remote Sensing
Authors: Srokosz M.A. 
Published: September 2006

AbstractBiological oceanography may be studied from space using sensors on satellites that determine the color of the ocean. The presence of phytoplankton (microscopic algae) in the upper layers of the ocean changes the color of the water as seen from above. In simplified terms, this is due to the selective absorption of blue light by the phytoplankton pigments (primarily chlorophyll) which changes the appearance of the water from blue to green. These changes in color can be observed using a satellite‐borne spectroradiometer that measures the water‐leaving radiance in a number of bands in the visible part of the electromagnetic spectrum. The limitations of the technique are first, that only information on the phytoplankton in the upper layers of the ocean can be obtained (light does not penetrate very far into the ocean). Second, most of the signal measured by the satellite sensor originates in the atmosphere (due to the molecular and aerosol scattering of photons there), so careful correction for atmospheric effects is necessary if good ocean data are to be obtained. Of course, in the presence of clouds the sensor will not “see” the ocean surface at all, and no data will be obtained. Third, only one component of the ocean ecosystem, namely the phytoplankton, can be studied by this means. Despite these limitations, satellite observations of ocean color have given new insights into biological oceanography on a global scale that could not have been obtained by any other means of observation. Observations of ocean color have contributed to a better understanding of the biophysical interactions that determine the phytoplankton productivity, the seasonal and interannual variations of the phytoplankton biomass on global scales, and the role of phytoplankton in the climate system. They have also contributed to the improved modeling of biogeochemical processes in the ocean.
Elevation Modeling from Satellite Data
Authors: Toutin Thierry 
Published: September 2006

AbstractMost geoscientific applications using georeferenced cartographic data need a good knowledge and visualization of the topography of the earth's surface. For example, mapping of geomorphological features is hardly feasible from a single image; three‐dimensional (3‐D) information has to be generated or to be added for a better interpretation of the two‐dimensional (2‐D) data.Since the early emergence of earth observation satellites, researchers have investigated different methods of extracting 3‐D information using satellite data. Apart from a few early stereoimages acquired with hand‐held cameras during the Gemini and Apollo missions, the first experiments to extract 3‐D data using stereoviewing from space began with the Earth Terrain Camera (ETC) flown on board SkyLab in 1973–74.Since these early experiments, various analog or digital sensors in the visible or in the microwave spectrum have been flown to provide researchers and geoscientists with spatial data for extracting and interpreting 3‐D information on the earth's surface. Although the shape‐from‐shading technique can be applied to optical sensor (OPS) images, stereoviewing using space camera or digital scanner images was, and still is, the most common method used by the mapping, photogrammetry and remote sensing communities.However, side‐looking synthetic aperture radar (SAR) data also give the opportunity to extract 3‐D information using image‐processing techniques appropriate to the nature of the data. With SAR data, three main methods have been developed: radargrammetry, clinometry and interferometry. Radargrammetry (similar to the stereoviewing of optical data) uses two images acquired from different viewpoints to generate a stereopair and stereoviewing. Clinometry takes advantage of the SAR shading and shadowing in the image, and interferometry uses mainly the SAR signal data instead of the image. Other methods (polarimetry and altimetry) are also used for 3‐D information extraction.This article reviews the different methods and sensors used to extract absolute or relative elevation and assesses their performance using the results from various research and commercial organizations. It also discusses the respective advantages, difficulties and constraints of the sensors, the methods and the technologies used to take into account the strength of each. It further assesses how they perform as complementary sources and systems for extracting elevation data in an operational context.
Hyperspectral Remote Sensing: Data Collection and Exploitation
Authors: Sandor‐Leahy Stephanie , Shepanski John 
Published: September 2006

AbstractHyperspectral imaging (HSI) spectrometers are remote sensing instruments that acquire images in a large number, typically hundreds, of contiguous spectral channels throughout the visible to long‐wave infrared (IR) portions of the spectrum from 0.4 to 14 µm.1 These systems are usually flown on aircraft platforms and use either platform motion or mirror mechanisms to scan a region of the earth's surface. The high‐resolution spectral features represented in the data cube allow for discrimination of materials in a scene. Because of the large number of spectral channels that are acquired by these instruments, they are termed hyperspectral, in contrast to multispectral instruments that obtain relatively few spectral bands. Like all remote sensing technologies, HSI must contend with data perturbations caused by atmospheric effects. In addition, data analysis must account for spectral mixing of multiple constituent materials within each pixel's field of view (FOV).
Polar Environments Assessment by Remote Sensing
Authors: Li Shusun 
Published: September 2006

AbstractPolar regions are those surrounding the North and South Poles and situated within the Arctic and Antarctic Circles of the Earth. The polar regions are characterized by environments harsh to human and other organisms: low mean annual temperature and long nights and extensive snow and ice cover in winter. Because of the harsh conditions and poor accessibility, assessment of different aspects of the polar environment, such as light, temperature, snow and ice cover, soil and vegetation, largely relies on information acquired by remote sensors, i.e. airborne‐ and satellite‐borne electromagnetic instruments. This article briefly describes how and to what extent polar environmental information is currently derived through remote sensing methods.
Processing and Classification of Satellite Images
Authors: Wilkinson Graeme G. 
Published: September 2006

Abstract
Earth‐observing satellites primarily gather information about the environment as digital images acquired by multispectral imaging sensors. These images consist of large arrays of picture elements (pixels). Images are usually recorded in 3–7 spectral bands, though new generation sensors providing 18–36 spectral bands are in development. The spectral bands are located in the visible, infrared, millimeter, and microwave parts of the electromagnetic spectrum. The spatial resolutions of images at the subsatellite point at ground level (dictated by the sensor's instantaneous field of view, IFOV) are principally in the range 10 m–1 km depending on the satellite. Very‐high‐resolution satellite‐borne sensors providing imagery with resolution as high as 1 m will soon become routinely available. Images are primarily used for mapping Earth resources and monitoring the state of the environment (terrestrial, atmospheric, and oceanic). Pixels consist of digitized radiances which are usually quantized on a scale of 0–255. Interpretation of surface features requires the images to be processed and classified by computer. The processing involves several stages, typically: (1) geometrical rectification and adjustment to a suitable cartographic coordinate system, (2) correction and calibration for atmospheric or system effects on detected radiances, (3) classification or product generation. Classification involves the transformation of the detected multispectral radiances in the image into meaningful descriptors of the surface – usually as thematic classes which can be displayed as a map. The mathematical conversion process is usually based on training an appropriate classifier algorithm by use of example spectral radiances from known surface objects (ground truth data). Classifier algorithms may either be nonparametric or parametric (e.g. based on a statistical model). Neural network algorithms have found useful application as classifiers for satellite image data. Products may also be generated by computing environmentally meaningful indices from spectral radiances such as the normalized difference vegetation index (NDVI).
Satellite and Sensor Systems for Environmental Monitoring
Authors: Bakker Wim 
Published: September 2006

Abstract
More satellites monitoring some aspect of the Earth are launched each day. This article gives an overview of the science and practice of remote sensing (RS). It goes into the technical details of the physics behind RS, lists some typical contemporary and future Earth observing (EO) satellites, and explains the factors that determine the quality of images from space. This relatively short article cannot cover the entire field of RS, but the references and further reading list can be used for further study.
Sea Ice Monitoring by Remote Sensing
Authors: Sandven Stein , Johannessen Ola M. , Kloster Kjell 
Published: September 2006

AbstractSea ice is defined as ice which is formed as a result of freezing of seawater. Sea ice occurs at the surface of the ocean in areas where the surface temperature is cooled to the freezing point, which is about −1.8°C for sea water with a salinity of about 35 parts per thousand. Ice formed in lakes and rivers and icebergs coming from glaciers and ice sheets are not defined as sea ice.Monitoring by remote sensing is defined as any measurement technique which can be used to observe sea ice repeatedly by instruments on board earth observation satellites. Monitoring also includes the process of making observational data available for users a short time after the observations have been obtained (i.e. a few hours). Remote sensing of ice can also be done with aircraft, submarines and other vehicles, but these techniques are not discussed in this article.
Temperate Forest Resource Assessment by Remote Sensing
Authors: Danson F. Mark 
Published: September 2006

Abstract
Remote sensing from airborne and spaceborne platforms provides a means of mapping forest resources at a range of spatial scales. Local‐scale mapping using aerial photography is a well established technique for determining stocking density, forest cover and forest health over relatively small areas. Satellite remote sensing can be used to map forest resources over larger areas, including regional to global scales. Techniques have been developed to map forest species and forest cover using spectral and spatial information in remotely sensed images. This work has been extended to map key biophysical and biochemical properties such as leaf area index (LAI) and leaf nitrogen content. These data have been coupled with other information such as meteorological data to drive process‐based ecosystem simulation models which may be used to monitor the production of forests and the rates of exchanges of energy and matter with the atmosphere and hydrosphere. Forests play a key role as a source of wood products but they are increasingly also being seen as playing a key role in the global climate system. Remote sensing is the only technique available to monitor forest resources at local, regional and global scales, and to develop an understanding of their role in the global ecosystem.
Tropical Forest Resource Assessment by Remote Sensing
Authors: Boyd Doreen S. 
Published: September 2006

Abstract

The earth's tropical forests are a global resource, requiring action for their wise utilization, management and protection at spatial scales from the local through to the global. The acquisition of data pertaining to tropical forest resources presents many challenges,
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many of which may be overcome through the use of remote sensing. Reasons for the adoption of remote sensing for the assessment of tropical forest resources are compelling. Remote sensing can be used to observe the dynamics of tropical forests at almost any level of detail. It allows the frequent measurement and monitoring of the earth's tropical forests on a continuous basis, from which accurate and informed judgments on their resources at any given time can be made. Accordingly, there has been progressive evolution of remote sensing approaches for the collection of tropical forest resource information. With the imminent launch of new spaceborne sensors which will be able to acquire more information on tropical forests, remote sensing will continue to be a crucial tool in the assessment of tropical forest resources.
Atomic Absorption and Emission Spectrometry, Solution‐Based in Iron and Steel Analysis
Authors: Del Monte Maria Grazia 
Published: September 2006

AbstractThe use of solution‐based atomic absorption spectrometry (AAS) and atomic emission spectrometry (AES) in iron and steel analysis is presented. The article is arranged in different sections dealing with the history relevant to the introduction and the acceptance of these techniques in iron and steel laboratories and their application to iron and steel analysis. A special emphasis is given to the application studies described in the literature for determining the almost forty elements which can be present in steels.Topics such as sampling and sample preparation, spectrometric measurement, interferences, calibration, and quality assurance criteria are discussed. Finally a brief overview of the standard methods of analysis is presented.
Iron Ore, Sample Preparation and Analysis of
Authors: Bhargava Om P. 
Published: September 2006

Abstract
The iron ore industry is an important segment of the world economy. The main mineral oxides are magnetite and hematite. The composition of iron ore playsan important role in both the iron (blast furnace) and steelmaking operations. Sampling is done both at the source and at the destination. Sampling protocols and preparation are described. The determination of iron, phosphorus, silica and manganese by methods such as gravimetric (Si) and volumetric (P and Mn) have been replaced by more rapid photometric or flame atomic absorption spectrometry (FAAS) methods. Instrumental methods such as combustion high‐frequency furnace infrared (IR) (for C and S) FAAS and graphite furnace electrothermal methods, X‐ray fluorescence (XRF) and inductively coupled plasma (ICP) spectrometric methods are used to determine analytes for routine process control. These methods are illustrated in the article. Rapid methods for chemical analysis and quality control, developed by the author, could also be cost effective.
Nickel Ore and Metals Analysis
Authors: Johnston J.R. 
Published: September 2006

AbstractThis article examines contemporary routine and umpire quality methods for the analysis of nickel ores, nickel metal and nickel alloys. Sample preparation and sampling are also discussed, but the emphasis is on sample preparation and analysis, since sampling is all too often done by people not part of the analytical laboratory.In order to analyze fully these materials, a laboratory must have at its disposal a wide range of instruments and methods, from the classical to the most modern, and frequently combinations of both. In keeping with the theme of this Encyclopedia, detailed methods are not given, and the reader should consult the references. The references themselves cover the period from roughly 1972 through 1997 but are not to be considered a definitive list. Unfortunately, most of the modern methods of analysis developed by chemists in nickel producers' laboratories have not been published, except as international or national standards. As an aid to the reader, a summary of International Organization for Standardization (ISO) and national standards is given immediately before the References. The author apologizes for any errors or omissions.The section on nickel ores is divided into two broad classifications: sulfide and lateritic ores. Greater emphasis is given to the sample preparation of laterites since they are more difficult to handle and many laboratories do not have as much experience working with them as with sulfides. Each broad classification is broken down into the analysis of “pay metals”, which are the reason the ore is mined, elements of environmental concern and other elements which are required for the metallurgical processing of the ore.Products from refineries fall into three broad categories: nickel metal in various forms, nickel oxides and ferronickel. For nickel metal, the emphasis is on trace‐element determinations; for nickel oxides and ferronickel, methods for the determination of nickel are also discussed.Nickel alloys cover a wide range of materials, from stainless steels to high‐nickel “superalloys”. For these samples the analytical requirements range from the major components of the alloy to minor alloying constituents to trace‐element determinations.As an aid to the analytical chemist in the laboratory, tables are presented summarizing recommended methods of analysis, and in most instances an alternative method, in case the laboratory does not have the equipment necessary to select the first choice. These methods have proven themselves in the laboratories of nickel producers and commercial laboratories familiar with the analysis of nickel‐bearing samples.
Thermal Evolution Methods for Carbon, Sulfur, Oxygen, Nitrogen and Hydrogen in Iron and Steel Analysis
Authors: Lawrenz Dennis A. , Mitchell Joel 
Published: September 2006

Abstract
A thermal evolution method is a method of destructive analysis in which the sample is subjected to heating, combustion, or fusion1 to quantify the chemical composition of iron and steel. Thermal evolution methods have been used for many years to determine carbon, sulfur, oxygen, nitrogen, and hydrogen contents of iron and steel. Carbon and sulfur are determined using combustion techniques. Oxygen and nitrogen are determined using inert gas fusion techniques. Hydrogen is determined using either inert gas fusion or hot extraction techniques. Analytical instrumentation for all of these techniques is commercially available.
X
‐ray Fluorescence Spectrometry in the Iron and Steel Industry
Authors: Bonvin Didier 
Published: September 2006

Abstract
In reviewing the current status of wavelength‐dispersive X‐ray fluorescence (WDXRF) spectrometry in the iron and steel industry, attention is given to aspects such as sampling and specimen preparation, calibration requirements and analytical demands for the various material types and the choice of instrument configuration.
Auger Electron Spectroscopy in Analysis of Surfaces
Authors: Lannon John M. , Stinespring Charter D. 
Published: September 2006

Abstract
Auger electron spectroscopy (AES) is an electron energy spectroscopy which probes the electronic energy levels of atoms, using electron or X‐ray beam stimulation. Irradiation of a sample with one of these stimuli causes electron emissions which can be energy analyzed to obtain an energy distribution. Since the energies of the Auger electrons are characteristic of the atomic core and/or valence level energies, they contain chemical information about their source atoms. As a result, the recorded energy spectra can be analyzed to determine the atomic composition (atomic percent) of the sample, and, in some cases, extract chemical bonding information. Further, since the escape depth of Auger electrons is limited to the first few monolayers of the sample, the technique has a high surface sensitivity. When used in conjunction with an ion source, AES can provide compositional information as a function of depth (depth profiling). Although AES was initially used primarily for fundamental surface science applications, the technique has developed into an important analytical tool for a broad range of surface and materials science applications, including surface reaction studies, surface segregation studies, and thin film growth studies.
Electron Energy Loss Spectroscopy in Analysis of Surfaces
Authors: Erskine J.L. 
Published: September 2006

AbstractElectron energy loss spectroscopy (EELS) is a versatile and sensitive probe of electronic and vibrational excitations at surfaces. The technique is extensively used as a research and analytical tool in a broad range of surface science applications including research on intrinsic physical and chemical properties of surfaces, chemisorption phenomena, and chemical processes at surfaces. The spectroscopy is based on measuring the energy loss and momentum transfer of electrons in a highly collimated, monochromatic (narrow energy spread) beam after the electrons scatter from a surface. Vibrational excitations of atoms and molecules adsorbed on surfaces, collective excitations of ordered chemisorbed structures, the intrinsic collective excitations of surface atoms (surface phonons), and collective electronic excitations (surface plasmons) can be studied using the technique. Under favorable conditions, vibrational losses from surface adsorbate concentrations below 10−3monolayer (1012atoms cm−2) can be detected. EELS is a surface science probe, typically requiring an ultrahigh vacuum (UHV) environment, and is generally employed in conjunction with complementary surface analytical probes such as Auger electron spectroscopy (AES), low‐energy electron diffraction (LEED) and thermal desorption mass spectroscopy (TDS). EELS is an ideal tool for determining surface chemical composition, and provides vibrational loss spectra similar to infrared reflection absorption spectroscopy (IRAS), Raman spectroscopy, and helium atom scattering spectroscopy (HAS).