Development of a Mid-Infrared Sea and Lake Ice Index (MISI) Using the GOES Imager

An automated ice-mapping algorithm has been developed and evaluated using data from the GOES-13 imager. The approach includes cloud-free image compositing as well as image classification using spectral criteria. The algorithm uses an alternative snow index to the Normalized Difference Snow Index (NDSI). The GOES-13 imager does not have a 1.6 µm band, a requirement for NDSI; however, the newly proposed Mid-Infrared Sea and Lake Ice Index (MISI) incorporates the reflective component of the 3.9 µm or mid-infrared (MIR) band, which the GOES-13 imager does operate. Incorporating MISI into a sea or lake ice mapping algorithm allows for mapping of thin or broken ice with no snow cover (nilas, frazil ice) and thicker ice with snow cover to a degree of confidence that is comparable to other ice mapping products. The proposed index has been applied over the Great Lakes region and qualitatively compared to the Interactive Multi-sensor Snow and Ice Mapping System (IMS), the National Ice Center ice concentration maps and MODIS snow cover products. The application of MISI may open additional possibilities in climate research using historical GOES imagery. Furthermore, MISI may be used in addition to the current NDSI in ice identification to build more robust ice-mapping algorithms for the next generation GOES satellites

Fine Structure in Manhattan’s Daytime Urban Heat Island: A New Dataset

A street-level temperature and humidity dataset, with high resolution spatial and temporal components, has been created for the island of Manhattan, suitable for use by the urban health and modelling communities. It consists of a set of pedestrian measurements over the course of two summers converted into anomaly maps, and a set of ten light-post mounted installations measuring temperature, relative humidity, and illumination at three minute intervals over three months. The quality control and data reduction, used to produce the anomaly maps, is described, and the relationships between spatial and temporal variability are investigated. The data sets are available for download via the project Web site

Board # 29 : A PATTERN RECOGNITION APPROACH TO SIGNAL TO NOISE RATIO ESTIMATION OF SPEECH

A blind approach for estimating the signal to noise ratio (SNR) of a speech signal corrupted by additive noise is proposed. The method is based on a pattern recognition paradigm using various linear predictive based features, a vector quantizer classifier and estimation combination. Blind SNR estimation is very useful in biometric speaker identification systems in which a confidence metric is determined along with the speaker identity. The confidence metric is partially based on the mismatch between the training and testing conditions of the speaker identification system and SNR estimation is very important in evaluating the degree of this mismatch. The educational impact of this project is two-fold: 1. Undergraduate students are initiated into research/development by working on a team to achieve a software implementation of the SNR estimation system. The students will also evaluate the performance of the system by experimenting with different features and classifiers. Producing a paper in a refereed technical conference is the objective. 2. The students will also write a laboratory manual for a portion of this project to be run in a junior level signals and systems class and a senior level class on speech processing. Producing a paper in a refereed education conference is the objective. The learning outcomes for the students engaged in research and for the students doing the project in a class include: • Enhanced application of math skills • Enhanced software implementation skills • Enhanced interest in signal processing • Enhanced ability to analyze experimental results • Enhanced communication skills. The assessment instruments include: • Student surveys (target versus control group comparison that includes a statistical analysis) • Faculty tracking of student learning outcomes based on student work • Faculty evaluation of student work based on significant rubrics • A concept inventory tes

Dynamics of the urban lightscape

The manifest importance of cities and the advent of novel data about them are stimulating interest in both basic and applied “urban science” (Bettencourt et al., 2007 [4]; Bettencourt, 2013 [3]). A central task in this emerging field is to document and understand the “pulse of the city” in its diverse manifestations (e.g., in mobility, energy use, communications, economics) both to define the normal state against which anomalies can be judged and to understand how macroscopic city observables emerge from the aggregate behavior of many individuals (Louail, 2013 [9]; Ferreira et al., 2013 [6]). Here we quantify the dynamics of an urban lightscape through the novel modality of persistent synoptic observations from an urban vantage point. Established astronomical techniques are applied to visible light images captured at 0.1 Hz to extract and analyze the light curves of 4147 sources in an urban scene over a period of 3 weeks. We find that both residential and commercial sources in our scene exhibit recurring aggregate patterns, while the individual sources decorrelate by an average of one hour after only one night. These highly granular, stand-off observations of aggregate human behavior – which do not require surveys, in situ monitors, or other intrusive methodologies – have a direct relationship to average and dynamic energy usage, lighting technology, and the impacts of light pollution. They may also be used indirectly to address questions in urban operations as well as behavioral and health science. Our methodology can be extended to other remote sensing modalities and, when combined with correlative data, can yield new insights into cities and their inhabitants

Investigating Effects of Landfill Soil Gases on Landfill Elevated Subsurface Temperature

Subsurface temperature is a critical indicator for the identification of the risk associated with subsurface fire hazards in landfills. Most operational landfills in the United States (US) have experienced exothermic reactions in their subsurface. The subsurface landfill area is composed of various gases generated from chemical reactions inside the landfills. Federal laws in the US mandate the monitoring of gases in landfills to prevent hazardous events such as landfill fire breakouts. There are insufficient investigations conducted to identify the causes of landfill fire hazards. The objective of this research is to develop a methodological approach to this issue. In this study, the relationship was investigated between the subsurface elevated temperature (SET) and soil gases (i.e., methane, carbon dioxide, carbon monoxide, nitrogen, and oxygen) with the greatest influence in landfills. The significance level of the effect of soil gases on the SET was assessed using a decision tree approach. A naïve Bayes technique for conditional probability was implemented to investigate how different gas combinations can affect different temperature ranges with respect to the safe and unsafe states of these gases. The results indicate that methane and carbon dioxide gases are strongly associated with SETs. Among sixteen possible gas combinations, three were identified as the most probable predictors of SETs. A three-step risk assessment framework is proposed to identify the risk of landfill fire incidents. The key findings of this research could be beneficial to landfill authorities and better ensure the safety of the community health and environment

Flood Vulnerability Assessment and Data Visualization for Lifeline Transportation Network

69A3551847102Developing an effective real-time evacuation strategy during extreme storm events such as hurricanes has been a topic of critical significance to the emergency planning and response community. The spatial and temporal variabilities of inland flooding during hurricanes present significant challenges for robust evacuation planning. In this study, a framework for real-time evacuation planning was developed that combines the results obtained from hydrodynamic modeling and traffic microsimulation. First, a fine-scale hydrodynamic model was developed based on depth-averaged 2D shallow-water equations (SWE) to obtain information pertaining to flood depth and velocity for planning evacuation routes during a storm event. Next, a traffic microsimulation was conducted using time-dependent information from the hydrodynamic model regarding the traffic velocities along evacuation routes during an event. An optimization technique was also implemented to reduce the overall travel time by about 6% from that of the base model. The last component of the framework involves combining the results from both models to generate a time-lapse animation of emergency evacuation based on a geographic information system (GIS). The results obtained using this framework could be easily accessed by the general public and decision-makers to enable efficient evacuation planning during extreme storm events

Assessing the effects of increased impervious surface on the aquifer recharge through river flow network, case study of Jackson, Tennessee, USA

Understanding pathways connecting urbanization to the recharge process across the land surface and river environment is of great significance in achieving low-impact development. Accordingly, the contribution of an urbanized region with a low and high development rate, along with the expected overflow into the river network resulting from increased impervious surfaces, was assessed in the recharge rate at Jackson, Tennessee. To this end, first, the losses were calculated using the standard and modified SCS-CN methods for the maximum probable flood condition. Then, TUFLOW was applied to simulate the two-dimensional flood for a historic 24-h probable maximum precipitation event with a 100-year return period. The results of TUFLOW were later calibrated using the results of standard and modified SCS-CN methods. A calibrated MODFLOW was employed to assess the effects of urbanization and, consequently, the plausible extended river network on the recharge rate. Results revealed that the West Wood contribution in groundwater recharge was 19 % less than the Musa Street, while it supplies approximately 2.7 % more flow than Musa Street. The performance evaluation results of TUFLOW showed 0.4916 and 0.689 as Nash–Sutcliffe, respectively, for the standard and modified SCS-CN methods. Although the flow velocity and depth were respectively increased by 3.3 % and 8.3 % under modified SCS-CN compared to the standard one, the soil water storage capacity remained constant at equal to 0.16 mm. Results revealed that the maximum soil water storage capacity was fulfilled soon through the modified SCS-CN than the standard method leading to higher flood volume and discharge. To this end, the discharge resulting from modified SCS-CN was approximately 1.5 times higher than that in the standard method under the same precipitation condition. Our findings suggest that designing any construction, mainly dams downstream, based on the modified SCS-CN estimations will provide more safety, particularly in crowded regions. Also, overflowing the excess surface runoff into the river network resulted from the increased impervious surface amplifying the flow volume, depth, and velocity across the river networks, finally leaving the area without increasing the aquifer\u27s recharge rate. The results provide insights into possible sustainable development options and flood management in the built-up area

Numerical analysis of surface hydrogeological water budget to estimate unconfined aquifers recharge

Under changing climate, groundwater resources are the main drivers of socioeconomic development and ecosystem sustainability. This study assessed the contribution of two adjacent watersheds, Muse Street (MS) and West Wood (WW), with low and high urban development, to the Memphis aquifer recharge process in central Jackson, Tennessee, USA. The numerical MODFLOW model was created using data from 2017 to 2019 and calibrated using reported water budget components derived from in-situ data. The calibrated MODFLOW model was then used to investigate the impact of high and low urban developments on the recharge rate. The hydraulic parameters and recharge rates were optimized by adjusting the groundwater level based on the observed water level using PEST. The stochastic modeling was also carried out using the Latin Hypercube approach to reduce the uncertainty. The calibration results were satisfactory, with RMSE of 0.124 and 0.63 obtained in the WW and MS watersheds, respectively, indicating accurate estimation of the input parameters, precisely the hydrodynamic coefficients. The study results indicate that, per unit area, the MS watershed contributes 119% more to recharge and 186% more to riverbed leakage compared to the WW watershed. However, regarding total recharge and riverbed leakage, the WW watershed contributed more than the MS watershed. The results of this study have enhanced the knowledge of the impact of urbanization on hydrology and the recharge process in watersheds with diverse land uses