Developing Basic Design and Mathematical Framework for a Mobile Mapping System—a Case Study Using Available Sensors

There is a growing demand for technologies that support capturing of comprehensive and good quality 3D spatial data at a faster rate with low investment and minimal effort, while also causing least disturbance to other activities in the area. Mobile mapping systems (MMS), which are being developed in a few western countries, solve this problem but their import is highly expensive. While the components of a MMS are easily available off-the-shelf at lower cost, the main reason for their high cost is the intellectual property involved in the sensor design, integration, calibration, and the related software. Developing the intellectual property locally can bring down the cost of MMS to a large extent. Keeping this in mind, a MMS has been developed in this research using the locally available sensors. This paper describes the methodology to integrate navigation and mapping sensors including the developed calibration procedures. It further describes the time synchronization technique developed for multi-sensor data fusion and algorithms implemented by software package for data processing. The sensors integrated in the MMS include a standard GPS, IMU and a standard laser scanner. A Kalman filter is implemented to integrate the GPS and IMU data, which provides position and orientation information for the sensors. A simulation software package is also developed to verify, understand and develop the equations used in MMS. Field tests have been performed using the developed MMS and the results are shown for a few cases. Results validate the designed algorithms and indicate the successful development of the MMS, which has potential to be further developed and used in field. Though a number of papers are available on MMS, the thrust of this paper is to present a complete methodology for developing a MMS using locally available sensors. Unlike available papers, this paper outlines all aspects of design, calibration and operation, where each of these aspects is handled in a novel way as demanded by the available sensors. This is particularly useful for individuals or organizations interested in procuring sensor components off-the-shelf and develop their own (low cost) Mobile Mapping system.     

Declining public concern about climate change: Can we blame the great recession?

Social surveys suggest that the American public's concern about climate change has declined dramatically since 2008. This has led to a search for explanations for this decline, and great deal of speculation that there has been a fundamental shift in public trust in climate science. We evaluate over thirty years of public opinion data about global warming and the environment, and suggest that the decline in belief about climate change is most likely driven by the economic insecurity caused by the Great Recession. Evidence from European nations further supports an economic explanation for changing public opinion. The pattern is consistent with more than forty years of public opinion about environmental policy. Popular alternative explanations for declining support – partisan politicization, biased media coverage, fluctuations in short-term weather conditions – are unable to explain the suddenness and timing of opinion trends. The implication of these findings is that the “crisis of confidence” in climate change will likely rebound after labor market conditions improve, but not until then.     

Statistical significance of trends in monthly heavy precipitation over the US

Trends in monthly heavy precipitation, defined by a return period of one year, are assessed for statistical significance in observations and Global Climate Model (GCM) simulations over the contiguous United States using Monte Carlo non-parametric and parametric bootstrapping techniques. The results from the two Monte Carlo approaches are found to be similar to each other, and also to the traditional non-parametric Kendall’s τ test, implying the robustness of the approach. Two different observational data-sets are employed to test for trends in monthly heavy precipitation and are found to exhibit consistent results. Both data-sets demonstrate upward trends, one of which is found to be statistically significant at the 95% confidence level. Upward trends similar to observations are observed in some climate model simulations of the twentieth century, but their statistical significance is marginal. For projections of the twenty-first century, a statistically significant upwards trend is observed in most of the climate models analyzed. The change in the simulated precipitation variance appears to be more important in the twenty-first century projections than changes in the mean precipitation. Stochastic fluctuations of the climate-system are found to be dominate monthly heavy precipitation as some GCM simulations show a downwards trend even in the twenty-first century projections when the greenhouse gas forcings are strong.     

Discriminating four tectonic settings: Five new geochemical diagrams for basic and ultrabasic volcanic rocks based on log — ratio transformation of major-element data

We present five new discriminant function diagrams based on an extensive database representative of basic and ultrabasic rocks from four tectonic settings of island arc, continental rift, ocean-island, and mid-ocean ridge. These diagrams were obtained after log_e-transformation of concentration ratios of major-elements — a technique recommended for a correct statistical treatment of compositional data. Higher % success rates (overall values from ∼ 83 to 97%) were obtained for proposing these new diagrams as compared to those (∼82 to 94%) obtained from the discriminant analysis of the raw major-element concentration data (i.e., without the log_e-transformation and without taking ratios of the compositional data, but using exactly the same database to provide an unbiased comparison), suggesting that such a data transformation constitutes a statistically correct and recommended technique. The new diagrams also resulted in less mis-classification of basic and ultrabasic rocks from known tectonic settings than the diagrams obtained from the raw data. The use of these highly successful new discriminant function diagrams is illustrated using Miocene to Recent basic and ultrabasic rocks from three areas of Mexico with complex or controversial tectonic settings (Mexican Volcanic Belt, Los Tuxtlas volcanic field, and Eastern Alkaline Province), as well as older rocks from three areas (Deccan, Malani, and Bastar) of India. Additionally, the major-element data from two ‘known’ continental arc settings are used to show that they are similar to those from the island arc setting. Continental rift setting is inferred for all Mexican cases and for one cratonic area of India (Malani) and an IAB setting for the Bastar craton. The Deccan flood basalt province of India is used to warn against an indiscriminate use of those discrimination diagrams that do not explicitly include the likely setting of the area under evaluation. An Excel template is also provided for an easy application of these new diagrams for discriminating the four settings considered in this work.