Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems

The present book contains ten articles illustrating the different possible uses of UAVs and satellite remotely sensed data integration in Geographical Information Systems to model and predict changes in both the natural and the human environment. It illustrates the powerful instruments given by modern geo-statistical methods, modeling, and visualization techniques. These methods are applied to Arctic, tropical and mid-latitude environments, agriculture, forest, wetlands, and aquatic environments, as well as further engineering-related problems. The present Special Issue gives a balanced view of the present state of the field of geoinformatics

A Smartphone-based Connected Vehicle Solution for Winter Road Surface Condition Monitoring

The monitoring of winter road surface conditions (RSCs) is essential to transportation agencies and the traveling public, since the former needs to be aware of the location and severity of existing RSCs in order to effectively maintain safe roadways with minimal environmental impact, while the latter uses RSC information to make informed travel decisions. However, current RSC monitoring practice still relies on methods that are time-consuming, labour-intensive and lacking in objectivity, therefore limiting their ability to provide sufficient spatial and temporal coverage across a road network. This research was motivated by the need for accurate, timely and reliable RSC monitoring for winter maintenance personnel and the travelling public. To achieve this objective, the field performance of a smartphone-based RSC monitoring system was evaluated on a section of Highway 6 in Ontario, Canada during the winter of 2014. A comparison between this system and current monitoring methods indicated that the former was capable of providing reliable results particularly at the maintenance route level; however, classification accuracy was found to vary according to RSC type. To improve the results produced by the smartphone-based system, this thesis proposes a connected- vehicle (CV) based RSC monitoring system that utilizes Road Weather Information System (RWIS) data in addition to the smartphone-based system’s data. Three techniques in artificial neural networks (ANNs), random trees (RTs), and random forests (RFs) were tested as the underlying models of the CV system, and the results indicated that all three models successfully increased the classification accuracy of the smartphone-based system. RFs were found to provide the most accurate RSC classifications for the standard (three-class) classification scheme while RTs were found to be most accurate when using a more detailed (five-class) classification scheme. Model transferability was also tested using data captured from a different test site during the winter of 2015; and it was found that although the proposed CV system significantly increased the reliability of RSC classifications, the underlying models were non-transferable and would therefore require local calibration before being used at different sites across a road network

Risk Assessment of Urban Gas Pipeline Based on Different Unknown Measure Functions

Several risk factors threaten the safety of urban gas pipeline. How to effectively identify various risk factors affecting urban gas pipeline and put forward scientific risk assessment method is the focus in the field of urban safety research. To explore the uncertain factors in the process of gas pipeline risk assessment, and propose a practical assessment method, a three-layer index system for the risk assessment of urban gas pipeline was established using unascertained measure theory, which included 5 first-class evaluation factors and 34 second-class evaluation indexes. Four unascertained measure models (linear, parabolic, exponential and sinusoidal) were constructed, and the unascertained measure values of each evaluation index under four unknown measure function models were calculated. The weight of evaluation factors was determined by Analytic Hierarchy Process (AHP), and the confidence criterion was used for discriminant evaluation. Results demonstrate that the risk assessment models constructed with different measurement functions can effectively reduce the uncertainty of urban gas pipeline risk assessment, but for the same object, the risk level of the linear measurement model in 4# pipeline is lower than other measurement functions, and the risk level of sinusoidal measurement model in 8# pipeline is higher than other measurement functions. Therefore, considering the evaluation results under different measure functions and focusing on monitoring objects with different results is necessary when using unascertained measure theory for risk assessment. The conclusions obtained from this study clarify the application conditions of unascertained measure theory in urban gas pipeline risk assessment, which helps to reduce the uncertainty in the assessment process and improve the accuracy of the assessment results

Soil water management: evaluation of infiltration in furrow irrigarion systems, assessing water and salt content spatially and temporally in the Parc Agrari del Baix Llobregat area.

Sustainability of irrigated agriculture is a growing concern in the Baix Llobregat area. Although irrigated land accounts for a substantial proportion of food supply to the local market, it has been, and still is increasingly degraded by poor agricultural management. This dissertation focuses on ways to evaluate furrow irrigation and to assess soil water content and soil salinity (temporally and spatially) under usual farmers's management practices. This dissertation meets these goals through an extensive study of relevant literature and the implementation of practical research. The latter was carried out with a case study on representative fields of the area. Empirical and stochastic models were applied to evaluate furrow irrigation as well as to monitor water flow and solute transport in the root zone. This research produced a number of key findings: first, evaluating furrow irrigation confirmed that 40-43 % of the applied water would have been saved in the study fields if irrigation was stopped as soon as soil water deficit was fully recharge taking the amount of water needed for salt leaching into account, and that the application efficiency (AE) would increase from 48% to 84% and from 41% to 68% (Field 1 and Field 2, respectively). Second, the predictions of soil water content using ARIMA models were logical, and the next irrigation time and its effect on soil water content at the depth of interest were correctly estimated. Third, considering the linear relationship eb-sb, by transforming the Hilhorst (2000) model, which is based on the deterministic linear relationship eb-sb, into a time- varying Dynamic Linear Model (DLM) enabled us to validate this relationship under field conditions. An offset esb=0 value was derived that would ensure the accurate prediction of sp from measurements of sb. It was shown that the offset esb=0 varied for each depth in the same soil profile. A reason for this might be changes in soil temperature along the soil profile. The sp was then calculated for each depth in the root zone. Fourth, by using a (multiple input--single output) transfer function model, the results showed that soil water content and soil temperature had a significant impact on soil salinity, and soil salinity, predicted as a function of soil water and soil temperature, was correctly estimated. Finally, applying the analysis of variance (ANOVA), the results showed that the irrigation frequency, according to the farmer's usual management practice, had statistically significant effects on soil salinity behaviour, depending on soil depth and position (furrow, ridge). Moreover, it was shown that at the end of the crop's cycle the farmers left the field with less soil salinity, for each depth, than at the beginning of the crop's agricultural cycle

Heavy metal/toxins detection using electronic tongues

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOThe growing concern for sustainability and environmental preservation has increased the demand for reliable, fast response, and low-cost devices to monitor the existence of heavy metals and toxins in water resources. An electronic tongue (e-tongue) is a multisensory array mostly based on electroanalytical methods and multivariate statistical techniques to facilitate information visualization in a qualitative and/or quantitative way. E-tongues are promising analytical devices having simple operation, fast response, low cost, easy integration with other systems (microfluidic, optical, etc) to enable miniaturization and provide a high sensitivity for measurements in complex liquid media, providing an interesting alternative to address many of the existing environmental monitoring challenges, specifically relevant emerging pollutants such as heavy metals and toxins.73119FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOSem informaçã

Application of Deep Neural Network to Predict the High-Cycle Fatigue Life of AISI 1045 Steel Coated by Industrial Coatings

In this study, deep learning approach was utilized for fatigue behavior prediction, analysis, and optimization of the coated AISI 1045 mild carbon steel with galvanization, hardened chromium, and nickel materials with different thicknesses of 13 and 19 mu m were used for coatings and afterward fatigue behavior of related specimens were achieved via rotating bending fatigue test. Experimental results revealed fatigue life improvement up to 60% after applying galvanization coat on untreated material. Obtained experimental data were used for developing a Deep Neural Network (DNN) modelling and accuracy of more than 99%.was achieved. Predicted results have a fine agreement with experiments. In addition, parametric analysis was carried out for optimization which indicated that coating thickness of 10-15 mu m had the highest effects on fatigue life improvement

Deciphering the dynamics of inorganic carbon export from intertidal salt marshes using high-frequency measurements

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Marine Chemistry 206 (2018): 7-18, doi:10.1016/j.marchem.2018.08.005.The lateral export of carbon from coastal marshes via tidal exchange is a key component of the marsh carbon budget and coastal carbon cycles. However, the magnitude of this export has been difficult to accurately quantify due to complex tidal dynamics and seasonal cycling of carbon. In this study, we use in situ, high-frequency measurements of dissolved inorganic carbon (DIC) and water fluxes to estimate lateral DIC fluxes from a U.S. northeastern salt marsh. DIC was measured by a CHANnelized Optical Sensor (CHANOS) that provided an in situ concentration measurement at 15-min intervals, during periods in summer (July – August) and late fall (December). Seasonal changes in the marsh had strong effects on DIC concentrations, while tidally-driven water fluxes were the fundamental vehicle of marsh carbon export. Episodic events, such as groundwater discharge and mean sea water level changes, can impact DIC flux through altered DIC concentrations and water flow. Variability between individual tides within each season was comparable to mean variability between the two seasons. Estimated mean DIC fluxes based on a multiple linear regression (MLR) model of DIC concentrations and high-frequency water fluxes agreed reasonably well with those derived from CHANOS DIC measurements for both study periods, indicating that high-frequency, modeled DIC concentrations, coupled with continuous water flux measurements and a hydrodynamic model, provide a robust estimate of DIC flux. Additionally, an analysis of sampling strategies revealed that DIC fluxes calculated using conventional sampling frequencies (hourly to two-hourly) of a single tidal cycle are unlikely to capture a representative mean DIC flux compared to longer-term measurements across multiple tidal cycles with sampling frequency on the order of tens of minutes. This results from a disproportionately large amount of the net DIC flux occurring over a small number of tidal cycles, while most tides have a near-zero DIC export. Thus, high-frequency measurements (on the order of tens of minutes or better) over the time period of interest are necessary to accurately quantify tidal exports of carbon species from salt marshes.This work was funded by NSF Graduate Research Fellowship Program, NSF Ocean Sciences Postdoctoral Fellowship (OCE-1323728), Link FoundationOcean Engineering and Instrumentation Fellowship, National Institute of Science and Technology (NIST no. 60NANB10D024), the USGS LandCarbon and Coastal & Marine Geology Programs, NSF Chemical Oceanography Program (OCE-1459521), NSF Ocean Technology and Interdisciplinary Coordination program (OCE-1233654) and NOAA Science Collaborative (NA09NOS4190153)