Accuracy assessment of lidar-derived digital elevation models

Despite the relatively high cost of airborne lidar-derived digital elevation models (DEMs), such products are usually presented without a satisfactory associated estimate of accuracy. For the most part, DEM accuracy estimates are typically provided by comparing lidar heights against a finite sample of check point coordinates from an independent source of higher accuracy, supposing a normal distribution of the derived height differences or errors. This paper proposes a new methodology to assess the vertical accuracy of lidar DEMs using confidence intervals constructed from a finite sample of errors computed at check points. A non-parametric approach has been tested where no particular error distribution is assumed, making the proposed methodology especially applicable to non-normal error distributions of the type usually found in DEMs derived from lidar. The performance of the proposed model was experimentally validated using Monte Carlo simulation on 18 vertical error data-sets. Fifteen of these data-sets were computed from original lidar data provided by the International Society for Photogrammetry and Remote Sensing Working Group III/3, using their respective filtered reference data as ground truth. The three remaining data-sets were provided by the Natural Environment Research Council's Airborne Research and Survey Facility lidar system, together with check points acquired using high precision kinematic GPS. The results proved promising, the proposed models reproducing the statistical behaviour of vertical errors of lidar using a favourable number of check points, even in the cases of data-sets with non-normally distributed residuals. This research can therefore be considered as a potentially important step towards improving the quality control of lidar-derived DEMs.     

Method of Data Reduction and Uncertainty Estimation for Platinum-Group Element Data Using Inductively Coupled Plasma-Mass Spectrometry

A data reduction method is described for determining platinum-group element (PGE) abundances by inductively coupled plasma-mass spectrometry (ICP-MS) using external calibration or the method of standard addition. Gravimetric measurement of volumes, the analysis of reference materials and the use of procedural blanks were all used to minimise systematic errors. Internal standards were used to correct for instrument drift. A linear least squares regression model was used to calculate concentrations from drift-corrected counts per second (cps). Furthermore, mathematical manipulations also contribute to the uncertainty estimates of a procedure. Typical uncertainty estimate calculations for ICP-MS data manipulations involve: (1) Carrying standard deviations from the raw cps through the data reduction or (2) calculating a standard deviation from multiple final concentration calculations. It is demonstrated that method 2 may underestimate the uncertainty estimate of the calculated data. Methods 1 and 2 do not typically include an uncertainty estimate component from a regression model. As such models contribute to the uncertainty estimates affecting the calculated data, an uncertainty estimate component from the regression must be included in any final error calculations. Confidence intervals are used to account for uncertainty estimates from the regression model. These confidence intervals are simpler to calculate than uncertainty estimates from method 1, for example. The data reduction and uncertainty estimation method described here addresses problems of reporting PGE data from an article in the literature and addresses both precision and accuracy. The method can be applied to any analytical technique where drift corrections or regression models are used.     

GPS卫星原子钟频率稳定度表征方法分析

原子钟频率稳定度的表征通常在时域和频域进行,而对于“尺度域”小波方差表征方法的分析研究较少。对该方法进行系统归纳总结的基础上,给出时域尺度域两类不同表征方法的实用计算公式及其置信度计算方法,通过对各种方差特性的比较分析,指出其适用范围。分析表明：时域系列方差计算简单、直接,各具特点,可以满足不同的实际需要;尺度域的小波方差是一种具有独特优越性的新方法,但可以达到时域表征方法的效果,适用于进行原子钟频率稳定度的估计。     

Automated threshold selection methods for extreme wave analysis

The study of the extreme values of a variable such as wave height is very important in flood risk assessment and coastal design. Often values above a sufficiently large threshold can be modelled using the Generalized Pareto Distribution, the parameters of which are estimated using maximum likelihood. There are several popular empirical techniques for choosing a suitable threshold, but these require the subjective interpretation of plots by the user.In this paper we present a pragmatic automated, simple and computationally inexpensive threshold selection method based on the distribution of the difference of parameter estimates when the threshold is changed, and apply it to a published rainfall and a new wave height data set. We assess the effect of the uncertainty associated with our threshold selection technique on return level estimation by using the bootstrap procedure. We illustrate the effectiveness of our methodology by a simulation study and compare it with the approach used in the JOINSEA software. In addition, we present an extension that allows the threshold selected to depend on the value of a covariate such as the cosine of wave direction.     

Very quiet intervals of geomagnetic activity and the solar wind structure

Extended periods of very low geomagnetic activity as described by very quiet intervals (VQI's) occur only at those times when the solar wind velocityV has a generally decreasing trend, i.e., they mainly occur either after the velocity peak of a high speed solar stream has passed the Earth, or at times when the Earth is immersed in a low speed solar plasma provided that the daily mean value ofdV/dt is negative. The VQI's most frequently start whendV/dt<0 anddB _Z/dt>0 (B _Z is the geocentric solar magnetrospheric-GSMZ-component of the IMF) and end most likely whendV/dt>0 anddB _Z/dt<0. The temporal trends of the solar wind (SW) velocity affect the variation of thea _p index only when the level of geomagnetic activity is generally low.It is suggested that a gradual expansion or contraction of the magnetosphere, associated with a slow variation of the SW pressure, plays a role in the modification of the reconnection-driven magnetohydrodynamic (MHD) fluctuations in the magnetosphere.     

Estimation of quantiles and confidence intervals for the log-Gumbel distribution

The log-Gumbel distribution is one of the extreme value distributions which has been widely used in flood frequency analysis. This distribution has been examined in this paper regarding quantile estimation and confidence intervals of quantiles. Specific estimation algorithms based on the methods of moments (MOM), probability weighted moments (PWM) and maximum likelihood (ML) are presented. The applicability of the estimation procedures and comparison among the methods have been illustrated based on an application example considering the flood data of the St. Mary's River.     

Lichens and the determination of river channel capacity

The identification of channel capacity associated with a particular frequency of peak discharges is necessary for discharge estimation for planning purposes at ungauged sites. Although lichen limits have been suggested to be useful for this purpose, previous studies have not elaborated their hydrological significance. Lichen limits are clearly defined on the sides of rock channels in New England. Australia and they are analyzed in relation to discharge at 6 gauging stations with up to 52 years of continuous record. It is demonstrated that the lowest lichen limit is maintained by peak discharges which occur on average at least once or twice each year. Recurrence intervals based on Annual Series and on Annual Exceedance Series are calculated and for the annual series are fairly consistent for the lowest lichen limit and range from 1·14 to 1·37 years. Lichenometry can be applied to the analysis of river channels in relation to the frequency of peak discharges. By reference to growth curves based upon lichens on Armidale tombstones it is shown that lichenometry may be employed to indicate dates for channel modification due to the removal of blocks and to dam construction and also to date the rock surface between lichen limits. Detailed analysis of the record from gauging stations indicates that where several lichen limits occur in a channel cross section each limit is related to periods when peak discharges exceeded the limits on at least 5 occasions. The lichen-free surfaces were then recolonized by Parmelia spp. and the size of the lichen thalli indicates the time elapsing since these frequent high discharges.     

Stability of motion in the Sitnikov 3-body problem

We study the stability of motion in the 3-body Sitnikov problem, with the two equal mass primaries (m ₁ = m ₂ = 0.5) rotating in the x, y plane and vary the mass of the third particle, 0 ≤ m ₃ < 10⁻³, placed initially on the z-axis. We begin by finding for the restricted problem (with m ₃ = 0) an apparently infinite sequence of stability intervals on the z-axis, whose width grows and tends to a fixed non-zero value, as we move away from z = 0. We then estimate the extent of “islands” of bounded motion in x, y, z space about these intervals and show that it also increases as |z| grows. Turning to the so-called extended Sitnikov problem, where the third particle moves only along the z-axis, we find that, as m ₃ increases, the domain of allowed motion grows significantly and chaotic regions in phase space appear through a series of saddle-node bifurcations. Finally, we concentrate on the general 3-body problem and demonstrate that, for very small masses, m ₃ ≈ 10⁻⁶, the “islands” of bounded motion about the z-axis stability intervals are larger than the ones for m ₃ = 0. Furthermore, as m ₃ increases, it is the regions of bounded motion closest to z = 0 that disappear first, while the ones further away “disperse” at larger m ₃ values, thus providing further evidence of an increasing stability of the motion away from the plane of the two primaries, as observed in the m ₃ = 0 case.     

Comparison of bootstrap confidence intervals for an ANN model of a karstic aquifer response

Following many applications artificial neural networks (ANNs) have found in hydrology, a question has been rising for quantification of the output uncertainty. A pre‐optimized ANN simulated the hydraulic head change at two observation wells, having as input hydrological and meteorological parameters. In order to calculate confidence intervals (CI) for the ANN output two bootstrap methods were examined namely bootstrap percentile and BCa (Bias‐Corrected and accelerated). The actual coverage of the CI was compared to the theoretical coverage for different certainty levels as a means of examining the method's reliability. The results of this work support the idea that the bootstrap methods provide a simple tool for confidence interval computation of ANNs. Comparing the two methods, the percentile requires fewer calculations and yields narrower intervals with similar actual coverage to that of BCa. Overall, the actual coverage was proved lower than desired when not modeled points were present in the data subset. Copyright © 2011 John Wiley & Sons, Ltd.