Space weather studies of IONOLAB group

IONOLAB is an interdisciplinary research group dedicated for handling the challenges of near earth environment on communication, positioning and remote sensing systems. IONOLAB group contributes to the space weather studies by developing state-of-the-art analysis and imaging techniques. On the website of IONOLAB group, www.ionolab.org, four unique space weather services, namely, IONOLAB-TEC, IRI-PLAS-2015, IRI-PLAS-MAP and IRI-PLAS-STEC, are provided in a user friendly graphical interface unit. Newly developed algorithm for ionospheric tomography, IONOLAB-CIT, provides not only 3-D electron density but also tracking of ionospheric state with high reliability and fidelity. The algorithm for ray tracing through ionosphere, IONOLAB-RAY, provides a simulation environment in all communication bands. The background ionosphere is generated in voxels where IRI-Plas electron density is used to obtain refractive index. One unique feature is the possible update of ionospheric state by insertion of Total Electron Content (TEC) values into IRI-Plas. Both ordinary and extraordinary paths can be traced with high ray and low ray scenarios for any desired date, time and transmitter location. 2-D regional interpolation and mapping algorithm, IONOLAB-MAP, is another tool of IONOLAB group where automatic TEC maps with Kriging algorithm are generated from GPS network with high spatio-temporal resolution. IONOLAB group continues its studies in all aspects of ionospheric and plasmaspheric signal propagation, imaging and mapping. © 2016 IEEE

Spatial statistics and analysis of earth's ionosphere

Thesis (Ph.D.)--Boston UniversityThe ionosphere, a layer of Earths upper atmosphere characterized by energetic charged particles, serves as a natural plasma laboratory and supplies proxy diagnostics of space weather drivers in the magnetosphere and the solar wind. The ionosphere is a highly dynamic medium, and the spatial structure of observed features (such as auroral light emissions, charge density, temperature, etc.) is rich with information when analyzed in the context of fluid, electromagnetic, and chemical models. Obtaining measurements with higher spatial and temporal resolution is clearly advantageous. For instance, measurements obtained with a new electronically-steerable incoherent scatter radar (ISR) present a unique space-time perspective compared to those of a dish-based ISR. However, there are unique ambiguities for this modality which must be carefully considered. The ISR target is stochastic, and the fidelity of fitted parameters (ionospheric densities and temperatures) requires integrated sampling, creating a tradeoff between measurement uncertainty and spatio-temporal resolution. Spatial statistics formalizes the relationship between spatially dispersed observations and the underlying process(es) they represent. A spatial process is regarded as a random field with its distribution structured (e.g., through a correlation function) such that data, sampled over a spatial domain, support inference or prediction of the process. Quantification of uncertainty, an important component of scientific data analysis, is a core value of spatial statistics. This research applies the formalism of spatial statistics to the analysis of Earth's ionosphere using remote sensing diagnostics. In the first part, we consider the problem of volumetric imaging using phased-array ISR based on optimal spatial prediction ("kriging"). In the second part, we develop a technique for reconstructing two-dimensional ion flow fields from line-of-sight projections using Tikhonov regularization. In the third part, we adapt our spatial statistical approach to global ionospheric imaging using total electron content (TEC) measurements derived from navigation satellite signals

Development of a Proximal Soil Sensing System for the Continuous Management of Acid Soil

The notion that agriculturally productive land may be treated as a relatively homogeneous resource at thewithin-field scale is not sound. This assumption and the subsequent uniform application of planting material,chemicals and/or tillage effort may result in zones within a field being under- or over-treated. Arising fromthese are problems associated with the inefficient use of input resources, economically significant yield losses,excessive energy costs, gaseous or percolatory release of chemicals into the environment, unacceptable long-term retention of chemicals and a less-than-optimal growing environment. The environmental impact of cropproduction systems is substantial. In this millennium, three important issues for scientists and agrariancommunities to address are the need to efficiently manage agricultural land for sustainable production, the maintenance of soil and water resources and the environmental quality of agricultural land.Precision agriculture (PA) aims to identify soil and crop attribute variability, and manage it in an accurate and timely manner for near-optimal crop production. Unlike conventional agricultural management where an averaged whole-field analytical result is employed for decision-making, management in PA is based on site-specific soil and crop information. That is, resource application and agronomic practices are matched with variation in soil attributes and crop requirements across a field or management unit. Conceptually PA makes economic and environmental sense, optimising gross margins and minimising the environmental impact of crop production systems. Although the economic justification for PA can be readily calculated, concepts such as environmental containment and the safety of agrochemicals in soil are more difficult to estimate. However,it may be argued that if PA lessens the overall agrochemical load in agricultural and non-agricultural environments, then its value as a management system for agriculture increases substantially.Management using PA requires detailed information of the spatial and temporal variation in crop yield components, weeds, soil-borne pests and attributes of physical, chemical and biological soil fertility. However,detailed descriptions of fine scale variation in soil properties have always been difficult and costly to perform.Sensing and scanning technologies need to be developed to more efficiently and economically obtain accurate information on the extent and variability of soil attributes that affect crop growth and yield. The primary aim of this work is to conduct research towards the development of an 'on-the-go' proximal soil pH and lime requirement sensing system for real-time continuous management of acid soil. It is divided into four sections.Section one consists of two chapters; the first describes global and historical events that converged into the development of precision agriculture, while chapter two provides reviews of statistical and geostatistical techniques that are used for the quantification of soil spatial variability and of topics that are integral to the concept of precision agriculture. The review then focuses on technologies that are used for the complete enumeration of soil, namely remote and proximal sensing.Section two comprises three chapters that deal with sampling and mapping methods. Chapter three provides a general description of the environment in the experimental field. It provides descriptions of the field site,topography, soil condition at the time of sampling, and the spatial variability of surface soil chemical properties. It also described the methods of sampling and laboratory analyses. Chapter four discusses some of the implications of soil sampling on analytical results and presents a review that quantifies the accuracy,precision and cost of current laboratory techniques. The chapter also presents analytical results that show theloss of information in kriged maps of lime requirement resulting from decreases in sample size. The messageof chapter four is that the evolution of precision agriculture calls for the development of 'on-the-go' proximal soil sensing systems to characterise soil spatial variability rapidly, economically, accurately and in a timely manner. Chapter five suggests that for sparsely sampled data the choice of spatial modelling and mapping techniques is important for reliable results and accurate representations of field soil variability. It assesses a number of geostatistical methodologies that may be used to model and map non-stationary soil data, in this instance soil pH and organic carbon. Intrinsic random functions of order k produced the most accurate and parsimonious predictions of all of the methods tested.Section three consists of two chapters whose theme pertains to sustainable and efficient management of acid agricultural soil. Chapter six discusses soil acidity, its causes, consequences and current management practices.It also reports the global extent of soil acidity and that which occurs in Australia. The chapter closes by proposing a real-time continuous management system for the management of acid soil. Chapter seven reports results from experiments conducted towards the development of an 'on-the-go' proximal soil pH and lime requirement sensing system that may be used for the real-time continuous management of acid soil. Assessment of four potentiometric sensors showed that the pH Ion Sensitive Field Effect Transistor (ISFET)was most suitable for inclusion in the proposed sensing system. It is accurate and precise, drift and hysteresis are low, and most importantly it's response time is small. A design for the analytical system was presented based on flow injection analysis (FIA) and sequential injection analysis (SIA) concepts. Two different modes of operation were described. Kinetic experiments were conducted to characterise soil:0.01M CaCl2 pH(pHCaCl2) and soil:lime requirement buffer (pH buffer) reactions. Modelling of the pH buffer reactions described their sequential, biphasic nature. A statistical methodology was devised to predict pH buffer measurements using only initial reaction measurements at 0.5s, 1s, 2s and 3s measurements. The accuracy of the technique was 0.1pH buffer units and the bias was low. Finally, the chapter describes a framework for the development of a prototype soil pH and lime requirement sensing system and the creative design of the system.The final section relates to the management of acid soil by liming. Chapter eight describes the development of empirical deterministic models for rapid predictions of lime requirement. The response surface models are based on soil:lime incubations, pH buffer measurements and the selection of target pH values. These models are more accurate and more practical than more conventional techniques, and may be more suitably incorporated into the spatial decision-support system of the proposed real-time continuous system for the management of acid soil. Chapter nine presents a glasshouse liming experiment that was used to authenticate the lime requirement model derived in the previous chapter. It also presents soil property interactions and soil-plant relationships in acid and ameliorated soil, to compare the effects of no lime applications, single-rate and variable-rate liming. Chapter X presents a methodology for modelling crop yields in the presence of uncertainty. The local uncertainty about soil properties and the uncertainty about model parameters were accounted for by using indicator kriging and Latin Hypercube Sampling for the propagation of uncertainties through two regression functions; a yield response function and one that equates resultant pH after the application of lime. Under the assumptions and constraints of the analysis, single-rate liming was found to be the best management option

Constructing water vapor maps by fusing InSAR, GNSS and WRF data

The work aims at constructing maps of the total water vapor in the atmosphere by fusing InSAR, GNSS, and WRF data

Reconciling continuous soil variation and crop yield : a study of some implications of within-field variability for site-specific crop management

Within-field variability in cropping system attributes is often obvious but difficult to accurately and efficiently quantity. The magnitude of the variation also changes with attribute, location and time. Importantly, variability at this scale of the soil/crop system may give rise to economic, environmental and societal problems on cropping enterprises under traditional 'uniform' management. In general, the problems arise from a decision to use 'mean-of-field' information to guide the amelioration of an area which may result in zones being under-or over-treated. Gathering data on, and extracting useful management information from, within-field variability is the goal of Precision Agriculture

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

Data Acquisition Applications

Data acquisition systems have numerous applications. This book has a total of 13 chapters and is divided into three sections: Industrial applications, Medical applications and Scientific experiments. The chapters are written by experts from around the world, while the targeted audience for this book includes professionals who are designers or researchers in the field of data acquisition systems. Faculty members and graduate students could also benefit from the book

Applications and Experiences of Quality Control

The rich palette of topics set out in this book provides a sufficiently broad overview of the developments in the field of quality control. By providing detailed information on various aspects of quality control, this book can serve as a basis for starting interdisciplinary cooperation, which has increasingly become an integral part of scientific and applied research