Maximum A Posteriori Resampling of Noisy, Spatially Correlated Data

In any geologic application, noisy data are sources of consternation for researchers, inhibiting interpretability and marring images with unsightly and unrealistic artifacts. Filtering is the typical solution to dealing with noisy data. However, filtering commonly suffers from ad hoc (i.e., uncalibrated, ungoverned) application. We present here an alternative to filtering: a newly developed method for correcting noise in data by finding the “best” value given available information. The motivating rationale is that data points that are close to each other in space cannot differ by “too much,” where “too much” is governed by the field covariance. Data with large uncertainties will frequently violate this condition and therefore ought to be corrected, or “resampled.” Our solution for resampling is determined by the maximum of the a posteriori density function defined by the intersection of (1) the data error probability density function (pdf) and (2) the conditional pdf, determined by the geostatistical kriging algorithm applied to proximal data values. A maximum a posteriori solution can be computed sequentially going through all the data, but the solution depends on the order in which the data are examined. We approximate the global a posteriori solution by randomizing this order and taking the average. A test with a synthetic data set sampled from a known field demonstrates quantitatively and qualitatively the improvement provided by the maximum a posteriori resampling algorithm. The method is also applied to three marine geology/geophysics data examples, demonstrating the viability of the method for diverse applications: (1) three generations of bathymetric data on the New Jersey shelf with disparate data uncertainties; (2) mean grain size data from the Adriatic Sea, which is a combination of both analytic (low uncertainty) and word-based (higher uncertainty) sources; and (3) side-scan backscatter data from the Martha\u27s Vineyard Coastal Observatory which are, as is typical for such data, affected by speckle noise. Compared to filtering, maximum a posteriori resampling provides an objective and optimal method for reducing noise, and better preservation of the statistical properties of the sampled field. The primary disadvantage is that maximum a posteriori resampling is a computationally expensive procedure

CAREER: Data Management for Ad-Hoc Geosensor Networks

This project explores data management methods for geosensor networks, i.e. large collections of very small, battery-driven sensor nodes deployed in the geographic environment that measure the temporal and spatial variations of physical quantities such as temperature or ozone levels. An important task of such geosensor networks is to collect, analyze and estimate information about continuous phenomena under observation such as a toxic cloud close to a chemical plant in real-time and in an energy-efficient way. The main thrust of this project is the integration of spatial data analysis techniques with in-network data query execution in sensor networks. The project investigates novel algorithms such as incremental, in-network kriging that redefines a traditional, highly computationally intensive spatial data estimation method for a distributed, collaborative and incremental processing between tiny, energy and bandwidth constrained sensor nodes. This work includes the modeling of location and sensing characteristics of sensor devices with regard to observed phenomena, the support of temporal-spatial estimation queries, and a focus on in-network data aggregation algorithms for complex spatial estimation queries. Combining high-level data query interfaces with advanced spatial analysis methods will allow domain scientists to use sensor networks effectively in environmental observation. The project has a broad impact on the community involving undergraduate and graduate students in spatial database research at the University of Maine as well as being a key component of a current IGERT program in the areas of sensor materials, sensor devices and sensor. More information about this project, publications, simulation software, and empirical studies are available on the project\u27s web site (http://www.spatial.maine.edu/~nittel/career/)

Unique metabolites protect earthworms against plant polyphenols

All higher plants produce polyphenols, for defence against above-ground herbivory. These polyphenols also influence the soil micro- and macro-fauna that break down plant leaf litter. Polyphenols therefore indirectly affect the fluxes of soil nutrients and, ultimately, carbon turnover and ecosystem functioning in soils. It is unknown how earthworms, the major component of animal biomass in many soils, cope with high-polyphenol diets. Here, we show that earthworms possess a class of unique surface-active metabolites in their gut, which we term ‘drilodefensins’. These compounds counteract the inhibitory effects of polyphenols on earthworm gut enzymes, and high-polyphenol diets increase drilodefensin concentrations in both laboratory and field populations. This shows that drilodefensins protect earthworms from the harmful effects of ingested polyphenols. We have identified the key mechanism for adaptation to a dietary challenge in an animal group that has a major role in organic matter recycling in soils worldwide

Unique metabolites protect earthworms against plant polyphenols

All higher plants produce polyphenols, for defence against above-ground herbivory. These polyphenols also influence the soil micro- and macro-fauna that break down plant leaf litter. Polyphenols therefore indirectly affect the fluxes of soil nutrients and, ultimately, carbon turnover and ecosystem functioning in soils. It is unknown how earthworms, the major component of animal biomass in many soils, cope with high-polyphenol diets. Here, we show that earthworms possess a class of unique surface-active metabolites in their gut, which we term ‘drilodefensins’. These compounds counteract the inhibitory effects of polyphenols on earthworm gut enzymes, and high-polyphenol diets increase drilodefensin concentrations in both laboratory and field populations. This shows that drilodefensins protect earthworms from the harmful effects of ingested polyphenols. We have identified the key mechanism for adaptation to a dietary challenge in an animal group that has a major role in organic matter recycling in soils worldwide

The development of correlation log

A measure of ship speed is needed for dead reckoning navigation, docking, and as an input to satellite navigation systems. Ship speed is also used as an input to fire control systems on Naval vessels. The need for an accurate speed measuring device, that measures ship speed relative to the sea bed is thus apparent. All non acoustic logs measure ship speed relative to the water, and absolute ship speed can only be estimated if a knowledge of water currents is available. An acoustic log that provides an absolute measure of ship speed at limited operating depths is the Doppler log. For deep water the Doppler log measures speed relative to the water and it is thus affected by currents. A new development in acoustic logs is the correlation log. The correlation log can measure absolute speed at much greater depths than can the Doppler log. This is because it utilises a wide beam pointing vertically at the sea bed. The.wide beam permits a low operating frequency to be used which implies low attenuation. The high backscattering strength at normal angles of incidence combined with the low attenuation, means that relative to the Doppler log, the correlation log can measure absolute speed at much greater depths. The correlation log consists of a transmitter, which utilises tone burst transmission, and two or more receivers in line with the direction of motion. The signals received by two transducers will be similar except for a time shift 'T', which is given by the equation T = d/2V, where V is the speed and d the transducer separation. A device based on these principles has been built and tested. Results have shown that the system concepts are viable and will lead to an absolute speed measuring device that can operate at great depth

Statistical modeling of spatially stratified heterogeneous data

Spatial statistics is an important methodology for geospatial data analysis. It has evolved to handle spatially autocorrelated data and spatially (locally) heterogeneous data, which aim to capture the first and second laws of geography, respectively. Examples of spatially stratified heterogeneity (SSH) include climatic zones and land-use types. Methods for such data are relatively underdeveloped compared to the first two properties. The presence of SSH is evidence that nature is lawful and structured rather than purely random. This induces another “layer” of causality underlying variations observed in geographical data. In this article, we go beyond traditional cluster-based approaches and propose a unified approach for SSH in which we provide an equation for SSH, display how SSH is a source of bias in spatial sampling and confounding in spatial modeling, detect nonlinear stochastic causality inherited in SSH distribution, quantify general interaction identified by overlaying two SSH distributions, perform spatial prediction based on SSH, develop a new measure for spatial goodness of fit, and enhance global modeling by integrating them with an SSH q statistic. The research advances statistical theory and methods for dealing with SSH data, thereby offering a new toolbox for spatial data analysis

VLIZ Young Scientists' Day, Brugge, Belgium 13 March 2002: book of abstracts

On Wednesday, 13 March 2002, the second 'Young Scientists' Day' was organized in Provinciehuis Boeverbos, Sint-Andries (Brugge), Belgium. The programme included a plenary lecture by a post-doc scientist; four oral presentations by young scientists; a poster competition for young scientists; the presentation by the laureates of 'VLIZ Annual Grants in Marine Sciences 2001' and the 'Annual VLIZ North Sea Award 2001'. This volume comprises the abstracts of the oral and poster presentations, and thus gives an overview of results in marine sciences in Belgium for the past year

A Comparison of the Spatial Linear Model to Nearest Neighbor (k-NN) Methods for Forestry Applications

Forest surveys provide critical information for many diverse interests. Data are often collected from samples, and from these samples, maps of resources and estimates of aerial totals or averages are required. In this paper, two approaches for mapping and estimating totals; the spatial linear model (SLM) and k-NN (k-Nearest Neighbor) are compared, theoretically, through simulations, and as applied to real forestry data. While both methods have desirable properties, a review shows that the SLM has prediction optimality properties, and can be quite robust. Simulations of artificial populations and resamplings of real forestry data show that the SLM has smaller empirical root-mean-squared prediction errors (RMSPE) for a wide variety of data types, with generally less bias and better interval coverage than k-NN. These patterns held for both point predictions and for population totals or averages, with the SLM reducing RMSPE from 9% to 67% over some popular k-NN methods, with SLM also more robust to spatially imbalanced sampling. Estimating prediction standard errors remains a problem for k-NN predictors, despite recent attempts using model-based methods. Our conclusions are that the SLM should generally be used rather than k-NN if the goal is accurate mapping or estimation of population totals or averages

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas