Implications of low-pass filtering on power spectra and autocorrelation functions of turbulent velocity signals

Filtering either through the electronics of an instrument or through digital procedure is performed routinely on geophysical data. When velocity fluctuations are measured in turbulent flows using electromagnetic current meters (ECMs), a builtin lowpass Butterworth filter of order n usually attenuates fluctuations at high frequencies. However, the effects of this filter may not be acknowledged in turbulence studies, thus impeding comparisons between data collected with different ECMs. This paper explores the implications of the filters on the characteristics of velocity signals, mainly on variance, power spectra, and correlation analyses. Variance losses resulting from filtering can be important but will vary with the order n of the Butterworth filter, decreasing as n increases. Knowing the filter response, it is possible to reconstruct the original signal spectrum to evaluate the effect of filtering on variance and to allow comparisons between data collected with different instruments. The autocorrelation function also is affected by filtering which increases the value of the coefficients in the first lags, resulting in an overestimation of the integral length scale of coherent structures. These important effects add to those related to size and shape differences in ECM sensors and must be taken into account in comparative studies.     

Mapping Proterozoic unconformity-related uranium deposits in the Rockhole area, Northern Territory, Australia using landsat ETM+

The Rockhole area, Northern Territory, Australia, hosts a number of Proterozoic unconformity-related uranium deposits. The geology of the area features within Paleoproterozoic rocks of the Pine Creek Orogen, near the unconformity with overlying platform cover sandstone of the Paleo- to Mesoproterozoic McArthur Basin. Landsat Enhanced Thematic Mapper plus (ETM+) data was used in the Rockhole area to assist in mapping geological structures and lithology, and to identify anomalous concentrations of ferrous minerals, the product of alteration, which can be indicators of buried uranium mineralization. Several image-processing procedures were applied to the ETM+ data to identify, isolate and enhance mineralogical information as simple and complex false color composites. ETM+ 754 shown as red green and blue respectively was the best simple image. Overall, complex images based on Principal Component Analysis proved to be the most useful products. Sandstone, shale and siltstone, the target lithologies, Koolpin Formation, the target stratigraphic unit, and bleaching pattern due to the removal of iron(II) compounds, the target alteration pattern, were confidently mapped to provide information required by the mineral emplacement model, which ultimately identified areas of likely uranium mineralization. Thus the contrasting behavior of the two principle oxidation states of uranium and iron can be utilized to map/delineate bleached alteration zones associated with economic concentrations of uranium using multispectral sensors like Landsat or better hyperspectral sensors.     

Response of the barrier island coastal region of southwestern Nigeria to climate and non-climate forcing

Despite threats emanating from the influence of climate and non-climate forcing on the barrier island coastal region of southwestern Nigeria, the extent of the coastal erosion is poorly understood. We report evidence of coastal erosion and sediment accumulation in the region over a 34-year period (1973–2017), using Landsat imagery at intervals of approximately six years. Landsat image corrections and various water-extraction algorithms were used to systematically delineate coastal erosion and accumulation in the area. The region was subdivided into western and eastern subregions separated by Lagos Harbour. In the west, erosion took place during the periods 1973–1979, 1979–1984, 1990–1999 and 2005–2011, whereas in the east, erosion occurred during 1973–1979, 1990–1999 and 1999–2005. Coastal sediment accumulation occurred in the east during 1979–1984, 1984–1990, 2005–2011 and 2011–2017, whereas gains in the west occurred during 1984–1990, 1999–2005 and 2011–2017. The study revealed substantial net erosion of 1 228.1 ha in the region as a whole, over the full period. Sediment accumulation accompanying the coastal erosion appears to be linked to longshore drift. Erosion between 1973 and 2011 was probably attributable to climate change (storms and tidal conditions), longshore drift, the inflow and outflow of water at Lagos Harbour, coastal morphology and, possibly, human impacts. However, the coastal changes between 2011 and 2017 were more obviously associated with human activities, such as development of the Eko Atlantic construction project. Coastal surveillance, together with the use of environmentally sensitive protective measures, could possibly help to reduce coastal erosion in the region. Careful coastal management practices, including artificial nourishing and the installation of resilient structures (e.g. seawalls), should be undertaken to protect human settlements that are already at risk from sea-level rise.     

Prediction of UT1–UTC,LOD and AAM χ<Subscript>3</Subscript> by combination of least-squares and multivariate stochastic methods

This article presents the application of a multivariate prediction technique for predicting universal time (UT1–UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ ₃). The multivariate predictions of LOD and UT1–UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ ₃ time-series as the explanatory variable for the computation of LOD or UT1–UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ ₃ data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ ₃ predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1–UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1–UTC based on LS extrapolation or on LS + AR. In particular, the UT1–UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ ₃ time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ ₃ reveal the best accuracy for LS + AR.     

Predicting grain protein content of winter wheat using remote sensing data based on nitrogen status and water stress

Advanced site-specific knowledge of grain protein content of winter wheat from remote sensing data would provide opportunities to manage grain harvest differently, and to maximize output by adjusting input in fields. In this study, remote sensing data were utilized to predict grain protein content. Firstly, the leaf nitrogen content at winter wheat anthesis stage was proved to be significantly correlated with grain protein content (R² = 0.36), and spectral indices significantly correlated to leaf nitrogen content at anthesis stage were potential indicators for grain protein content. The vegetation index, VI_green, derived from the canopy spectral reflectance at green and red bands, was significantly correlated to the leaf nitrogen content at anthesis stage, and also highly significantly correlated to the final grain protein content (R² = 0.46). Secondly, the external conditions, such as irrigation, fertilization and temperature, had important influence on grain quality. Water stress at grain filling stage can increase grain protein content, and leaf water content is closely related to irrigation levels, therefore, the spectral indices correlated to leaf water content can be potential indicators for grain protein content. The spectral reflectance of TM channel 5 derived from canopy spectra or image data at grain filling stage was all significantly correlated to grain protein content (R² = 0.31 and 0.37, respectively). Finally, not only this study proved the feasibility of using remote sensing data to predict grain protein content, but it also provided a tentative prediction of the grain protein content in Beijing area using the reflectance image of TM channel 5.