Data Density (data + density)

Distribution by Scientific Domains

Selected Abstracts

Comparison of LiDAR waveform processing methods for very shallow water bathymetry using Raman, near-infrared and green signals

Tristan Allouis
Abstract Airborne light detection and ranging (LiDAR) bathymetry appears to be a useful technology for bed topography mapping of non-navigable areas, offering high data density and a high acquisition rate. However, few studies have focused on continental waters, in particular, on very shallow waters (<2,m) where it is difficult to extract the surface and bottom positions that are typically mixed in the green LiDAR signal. This paper proposes two new processing methods for depth extraction based on the use of different LiDAR signals [green, near-infrared (NIR), Raman] of the SHOALS-1000T sensor. They have been tested on a very shallow coastal area (Golfe du Morbihan, France) as an analogy to very shallow rivers. The first method is based on a combination of mathematical and heuristic methods using the green and the NIR LiDAR signals to cross validate the information delivered by each signal. The second method extracts water depths from the Raman signal using statistical methods such as principal components analysis (PCA) and classification and regression tree (CART) analysis. The obtained results are then compared to the reference depths, and the performances of the different methods, as well as their advantages/disadvantages are evaluated. The green/NIR method supplies 42% more points compared to the operator process, with an equivalent mean error (,42,cm verusu ,45,cm) and a smaller standard deviation (253,cm verusu 335,cm). The Raman processing method provides very scattered results (standard deviation of 403,cm) with the lowest mean error (,31,cm) and 40% more points. The minimum detectable depth is also improved by the two presented methods, being around 1,m for the green/NIR approach and 05,m for the statistical approach, compared to 15,m for the data processed by the operator. Despite its ability to measure other parameters like water temperature, the Raman method needed a large amount of reference data to provide reliable depth measurements, as opposed to the green/NIR method. Copyright 2010 John Wiley & Sons, Ltd. [source]

A numerical comparison of 2D resistivity imaging with 10 electrode arrays

Torleif Dahlin
ABSTRACT Numerical simulations are used to compare the resolution and efficiency of 2D resistivity imaging surveys for 10 electrode arrays. The arrays analysed include pole-pole (PP), pole-dipole (PD), half-Wenner (HW), Wenner-, (WN), Schlumberger (SC), dipole-dipole (DD), Wenner-, (WB), ,-array (GM), multiple or moving gradient array (GD) and midpoint-potential-referred measurement (MPR) arrays. Five synthetic geological models, simulating a buried channel, a narrow conductive dike, a narrow resistive dike, dipping blocks and covered waste ponds, were used to examine the surveying efficiency (anomaly effects, signal-to-noise ratios) and the imaging capabilities of these arrays. The responses to variations in the data density and noise sensitivities of these electrode configurations were also investigated using robust (L1 -norm) inversion and smoothness-constrained least-squares (L2 -norm) inversion for the five synthetic models. The results show the following. (i) GM and WN are less contaminated by noise than the other electrode arrays. (ii) The relative anomaly effects for the different arrays vary with the geological models. However, the relatively high anomaly effects of PP, GM and WB surveys do not always give a high-resolution image. PD, DD and GD can yield better resolution images than GM, PP, WN and WB, although they are more susceptible to noise contamination. SC is also a strong candidate but is expected to give more edge effects. (iii) The imaging quality of these arrays is relatively robust with respect to reductions in the data density of a multi-electrode layout within the tested ranges. (iv) The robust inversion generally gives better imaging results than the L2 -norm inversion, especially with noisy data, except for the dipping block structure presented here. (v) GD and MPR are well suited to multichannel surveying and GD may produce images that are comparable to those obtained with DD and PD. Accordingly, the GD, PD, DD and SC arrays are strongly recommended for 2D resistivity imaging, where the final choice will be determined by the expected geology, the purpose of the survey and logistical considerations. [source]

Facial Soft Tissue Depths in Craniofacial Identification (Part II): An Analytical Review of the Published Sub-Adult Data,

Carl N. Stephan Ph.D.
Abstract:, Prior research indicates that while statistically significant differences exist between subcategories of the adult soft tissue depth data, magnitudes of difference are small and possess little practical meaning when measurement errors and variations between measurement methods are considered. These findings raise questions as to what variables may or may not hold meaning for the sub-adult data. Of primary interest is the effect of age, as these differences have the potential to surpass the magnitude of measurement error. Data from the five studies in the literature on sub-adults which describe values for single integer age groups were pooled and differences across the ages examined. From 1 to 18 years, most soft tissue depth measurements increased by less than 3 mm. These results suggest that dividing the data for children into more than two age groups is unlikely to hold many advantages. Data were therefore split into two groups with the division point corresponding to the mid-point of the observed trends and main data density (0,11 and 12,18 years; division point = 11.5 years). Published sub-adult data for seven further studies which reported broader age groups were pooled with the data above to produce the final tallied soft tissue depth tables. These tables hold the advantages of increased sample sizes (pogonion has greater than 1770 individuals for either age group) and increased levels of certainty (as random and opposing systematic errors specific to each independent study should average out when the data are combined). [source]

4D-Var assimilation of MERIS total column water-vapour retrievals over land

Peter Bauer
Abstract Experiments with the active assimilation of total column water-vapour retrievals from Envisat MERIS observations have been performed at the European Centre for Medium-Range Weather Forecasts (ECMWF), focusing on the summer 2006 African Monsoon Multidisciplinary Analysis (AMMA) field campaign period. A mechanism for data quality control, observation error definition and variational bias correction has been developed so that the data can be safely treated within 4D-Var, like other observations that are currently assimilated in the operational system. While data density is limited due to the restriction to daylight and cloud-free conditions, a systematic impact on mean moisture analysis was found, with distinct regional and seasonal features. The impact can last 1--2 days into the forecast but has little effect on forecast accuracy in terms of both moisture and dynamics. This is mainly explained by the weak dynamic activity in the areas of largest data impact. Analysis and short-range forecast evaluation with radiosonde observations revealed a strong dependence on radiosonde type. Compared with Vaisala RS92 observations, the addition of MERIS total column water-vapour observations produced neutral to positive impact, while contradictory results were obtained when all radiosonde types were used in generating the statistics. This highlights the issue of radiosonde moisture biases and the importance of sonde humidity bias correction in numerical weather prediction (NWP). Copyright 2009 Royal Meteorological Society [source]