Spatial statistics to estimate peat thickness using airborne radiometric data

Soil carbon stores are a major component of the annual returns required by EU governments to the Intergovernmental Panel on Climate Change. Peat has a high proportion of soil carbon due to the relatively high carbon density of peat and organic-rich soils. For this reason it has become increasingly important to measure and model soil carbon stores and changes in peat stocks to facilitate the management of carbon changes over time. The approach investigated in this research evaluates the use of airborne geophysical (radiometric) data to estimate peat thickness using the attenuation of bedrock geology radioactivity by superficial peat cover. Remotely sensed radiometric data are validated with ground peat depth measurements combined with non-invasive geophysical surveys. Two field-based case studies exemplify and validate the results. Variography and kriging are used to predict peat thickness from point measurements of peat depth and airborne radiometric data and provide an estimate of uncertainty in the predictions. Cokriging, by assessing the degree of spatial correlation between recent remote sensed geophysical monitoring and previous peat depth models, is used to examine changes in peat stocks over time. The significance of the coregionalisation is that the spatial cross correlation between the remote and ground based data can be used to update the model of peat depth. The result is that by integrating remotely sensed data with ground geophysics, the need is reduced for extensive ground-based monitoring and invasive peat depth measurements. The overall goal is to provide robust estimates of peat thickness to improve estimates of carbon stocks. The implications from the research have a broader significance that promotes a reduction in the need for damaging onsite peat thickness measurement and an increase in the use of remote sensed data for carbon stock estimations.

Best practices in prediction for decision-making : lessons from the atmospheric and earth sciences

Predictions that result from scientific research hold great appeal for decision-makers who are grappling with complex and controversial environmental issues, by promising to enhance their ability to determine a need for and outcomes of alternative decisions. A problem exists in that decision-makers and scientists in the public and private sectors solicit, produce, and use such predictions with little understanding of their accuracy or utility, and often without systematic evaluation or mechanisms of accountability. In order to contribute to a more effective role for ecological science in support of decision-making, this paper discusses three ``best practices'' for quantitative ecosystem modeling and prediction gleaned from research on modeling, prediction, and decision-making in the atmospheric and earth sciences. The lessons are distilled from a series of case studies and placed into the specific context of examples from ecological science.

A glance at the physical sciences; or, The wonders of nature, in earth, air, and sky;

Added t.p., engraved.

A glance at the physical sciences; or, The wonders of nature, in earth, air, and sky /

Mode of access: Internet.

The largest eruptions in Earth's history

Large igneous provinces (LIPs) host the most frequently recurring, largest volume basaltic & silicic eruptions on Earth. The largest volume (>1000 km^3 DRE) and magnitude (>M8) eruptions produce areally extensive (10^4-10^5 km^2) basaltic flow fields and sills, and silicic ignimbrites that are the main LIP building blocks. Basaltic and silicic eruptions have comparable magnitudes, but silicic ignimbrite volumes may be significantly underestimated due to unrecognized and correlated, but voluminous co-ignimbrite ash deposits. Magma composition is no barrier to individual eruption volume. Despite similar magnitudes, flood basaltic and silicic eruptions are very different in eruption mechanism, duration, intensity, vent configuration, and emplacement style. Flood basalts are dominantly effusive Hawaiian-Strombolian, with magma discharge rates of ~10^7-10^8 kg s^-1, and produce dominantly compound pahoehoe flow fields over eruption durations most likely >10 yrs. Most silicic eruptions are moderately to highly explosive, producing cocurrent pyroclastic fountains (rarely Plinian) and suggested to be of short-duration (hours to days) and high intensity (~10^11 kg s^-1). Eruption frequencies are elevated for largemagnitude eruptions of both magma types during LIP formation. In basalt-dominated provinces, large magnitude (>M8) eruptions have much shorter recurrence intervals (10^3-10^4 years) than similar magnitude silicic eruptions (~10^5 years). The huge volumes of magma erupted rapidly in LIPs raises several unresolved issues in terms of locus of magma generation and storage (if any) in the crust prior to eruption, the paths and rates of ascent from magma reservoirs to the surface, and relative aerosol contributions to the stratosphere from the flood basaltic and rhyolitic eruptions.

Distributions of temperature, velocity and viscosity at symmetrical axis of cylindrical plume in earth's mantle with variable viscosity

The parameters at the symmetrical axis of a cylindrical plume characterize the strength of this plume and provide a boundary condition which must be given to investigate the structure of a plume. For Newtonian fluid with a temperature-and pressure-dependence viscosity, an asymptotical solution of hydrodynamic equations at the symmetrical axis of the plume is found in the present paper. The temperature, upward velocity and viscosity at the symmetrical axis have been obtained as functions of depth, The calculated results have been given for two typical sets of Newtonian rheological parameters. The results obtained show that the temperature distribution along the symmetrical axis is nearly independent of the theological parameters. The upward velocity at the symmetrical axis, however, is strongly dependent on the rheological parameters.

Above-water reflectance for the evaluation of adjacency effects in Earth observation data: initial results and methods comparison for near-coastal waters in the Western Channel, UK

Un-supervised hyperspectral remote-sensing reflectance data (<15 km from the shore) were collected from a moving research vessel. Two different processing methods were compared. The results were similar to concurrent Aqua-MODIS and Suomi-NPP-VIIRS satellite data.

An integrated view of data quality in Earth observation

Data quality is a difficult notion to define precisely, and different communities have different views and understandings of the subject. This causes confusion, a lack of harmonization of data across communities and omission of vital quality information. For some existing data infrastructures, data quality standards cannot address the problem adequately and cannot fulfil all user needs or cover all concepts of data quality. In this study, we discuss some philosophical issues on data quality. We identify actual user needs on data quality, review existing standards and specifications on data quality, and propose an integrated model for data quality in the field of Earth observation (EO). We also propose a practical mechanism for applying the integrated quality information model to a large number of datasets through metadata inheritance. While our data quality management approach is in the domain of EO, we believe that the ideas and methodologies for data quality management can be applied to wider domains and disciplines to facilitate quality-enabled scientific research.

Rocks are human beings: researching the humanizing metaphor in earth science scientific texts

This paper describes a corpus-based analysis of the humanizing metaphor and supports that constitutive metaphor in science and technology may be highly metaphorical and active. The study, grounded in Lakoff’s Theory of Metaphor and in Langacker’s relational networks, consists of two phases: firstly, Earth Science metaphorical terms were extracted from databases and dictionaries and, then, contextualized by means of the “Wordsmith” tool in a digitalized corpus created to establish their productivity. Secondly, the terms were classified to disclose the main conceptual metaphors underlying them; then, the mappings and the relational networks of the metaphor were described. Results confirm the systematicity and productivity of the metaphor in this field, show evidence that metaphoricity of scientific terms is gradable, and support that Earth Science metaphors are not only created in terms of their concrete salient properties and attributes, but also on abstract human anthropocentric projections.

Computers in the Federal Government : a compilation of statistics, 1978 /

Edition of 1977 compiled by Information Technology Division, Institute for Computer Sciences and Technology, National Bureau of Standards.

Science and technology : 47 video, film, and sound/slide programs for earth sciences, computer sciences, civil engineering, and general science classes.

Shipping list no.: 88-100-P.

Introduction to Special issue on the Symposium on the Application of Neural Networks to the Earth Sciences

Papers in this issue of Natural Resources Research are from the “Symposium on the Application of Neural Networks to the Earth Sciences,” held 20–21 August 2002 at NASA Moffet Field, Mountain View, California. The Symposium represents the Seventh International Symposium on Mineral Exploration (ISME-02). It was sponsored by the Mining and Materials Processing Institute of Japan (MMIJ), the US Geological Survey, the Circum-Pacific Council, and NASA. The ISME symposia have been held every two years in order to bring together scientists actively working on diverse quantitative methods applied to the earth sciences. Although the title, International Symposium on Mineral Exploration, suggests exclusive focus on mineral exploration, interests and presentations always have been wide-ranging—talks presented at this symposium are no exception.

Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.

Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.