Ecosystem Analysis Tools

The following courses are approved for Ecosystem Analysis Tools.


Introduction to radio-navigation techniques, using the Global Navigation Satellite System (GNSS); GNSS signal structures; satellite search and acquisition; satellite tracking; coordinate systems and time; observations; atmospheric effects; and positionvelocity-time (PVT) solutions. 3 credits.
Offers students in environmental and natural resources engineering programs an understanding of the hydrological processes and related design skills. Principles of soil erosion by water; drainage of agricultural lands; surface runoff; flood and reservoir routing; hydrodynamic and water quality in pipe network; nonpoint source pollution; and transport phenomenon are studied. Current computer models utilized in industry for decision support are applied using case studies to further enhance the understanding of the hydrological processes. Limitations and advantages of the models are discussed. Offered in alternate years. 3 credits.
Variable Title Course Number: Special Topics Engineering principles involved in assessment and management of nonpoint source (NPS) pollution. Effect of NPS pollution on ecosystem integrity. Use of GIS/mathematical models to quantify extent of pollution. Design/implementation of best management practices to improve water quality. Discussion of Total Maximum Daily Load (TMDL) principles and processes. 3 credits.
An introduction to the field of biosensors and an in-depth and quantitative view of device design and performance analysis. An overview of the current state of the art to enable continuation into advanced biosensor work and design. Topics emphasize biomedical, bioprocessing, environmental, food safety, and biosecurity applications. 3 credits.
This course will educate students in the use, manipulation and analysis of environmental data by introducing them to scripting languages (e.g. c-shell, python), data types (e.g. ASCII, binary, NetCDF), databases (e.g. XML, DBF) and data visualization software (e.g. GMT, ArcMap) as well as techniques for checking data quality, handling time series and spatial data, and filling in missing data. 3 credits.
Application of remote sensing and spatial databases for observing and managing land resources within the Earth System; analysis and interpretation of remotely sensed data in combination with field observations and other data sources; conceptualization and design of a global earth resources information system. 3 credits.
Introduction to SAS as a programming language, for students with no prior exposure to programming languages. Basics of programming languages, SAS concepts, data input and manipulation. Introduction to SAS for graphs, univariate statistics, simple statistics for classification data, analysis of variance, simple and multiple regression. 1 credit.
This course has required class trips. Students will pay individual lodging or meal expenses where necessary. The soil as a natural body; its characteristics and processes of formation; the principal soils of Indiana; their adaptations, limitations, productivity, and use; soil survey methods and airphoto interpretation of soil patterns. 3 credits.
Focuses on the application of various molecular genetic techniques for studying microorganisms from and in the environment. The method, theoretical basis of each method, and interpretation of results are covered. The major areas discussed are the application of molecular genetic techniques to study: (1) total microbial communities; (2) diversity of microorganisms in a community; and (3) biotechnological uses of microorganisms. Prerequisite: AGRY 32000 or 58000 or BCHM 56200 or BIOL 24100 or 43800 or 54900. 3 credits.
Fundamentals of GIS analysis applied to environmental, agricultural, and engineeringrelated problems. Topics include data sources, spatial analysis; projections; creating data and metadata, and conceptualizing and solving spatial problems using GIS. 3 credits.
This course covers topics that are useful for successfully designing and analyzing statistically observational and experimental studies in ecology, animal behavior, evolutionary biology, forestry, wildlife sciences, fisheries, etc. Some topics are: differences between hypotheses and predictions, design of an ecological study, general linear models, assumptions, different types of designs (factorial, nested, repeated measures, blocks, split-plots, etc.), fitting models to data, etc. The course will focus on the conceptual understanding of these topics (e.g., interpreting the results of statistical tests) and practice with statistical programs and real datasets. 3 credits.
Fundamental concepts and design procedures for the removal of particulates, gases, and toxic air pollutants from waste gas streams. Problem assessment; characterization of exhaust gas streams; fan characteristics. 3 credits.
Mathematical modeling of chemical and biological processes occurring in natural aquatic systems. Classical oxygen demand and nutrient processes are modeled, as well as chemical specific transport and fate processes. Emphasis is placed on deterministic models, mass balance approaches, and chemical specific coefficients or parameters. 3 credits.
We will explore models on how human and natural systems interact (or social-ecological systems), e.g., resource-harvest models. These will introduce the important concepts of stability, resilience, regime shift (an important phenomenon related to sustainability, in which apparently slow changes can lead to large, rapid consequences), and early warning signals of such regime shifts. Dynamical systems theory will be introduced to gain a mathematical understanding of these concepts. We will also cover game theory—an analysis of human conflicts—at the basic level. Building on this foundation, we will also cover a few simple models of other types of ‘coupled’ systems, e.g., sociotechnical systems and sociohydrological systems, in which the role of engineered or technical components is more clearly present. 3 credits.
Course teaches computing techniques including error analysis, line and surface fitting, interpolation, map projections, geospatial and temporal correlations, signal processing, and visualization with discussions on specific and practical geoscience applications. Lectures with computer exercises and team project reporting using open-source computer software. (EAPS 50700 has replaced EAPS 50900) 3 credits.
Origin and evolution of radar. Modern weather radar systems and their component parts. Propagation of microwave energy in the atmosphere. Rayleigh and Mie scattering theory, with application to scattering by precipitation. Utilization of radar systems in forecasting quantitative analyses and cloud physics research. Recent refinement and future potential. Prior course work in synoptic meteorology labs and atmospheric physics is required. 3 credits.
A course that introduces students to direct and remotely sensed observations of the atmosphere. Directly measured quantities discussed include temperature, pressure, moisture, wind, solar radiation, chemical properties of the atmosphere, etc. Remote sensing of cloud, precipitation, and air motion by weather radars, satellites, profilers, lidars, and other emerging technologies is reviewed. Students will gain experience in observation techniques and data interpretation, and will learn uncertainty and error assessment. 3 credits.
3 credits.
The course is presented in five units. Foundations: the review of continuous-time and discrete-time signals and spectral analysis; design of finite impulse response and infinite impulse response digital filters; processing of random signals. Speech processing; vocal tract models and characteristics of the speech waveform; short-time spectral analysis and synthesis; linear predictive coding. Image processing: two-dimensional signals, systems and spectral analysis; image enhancement; image coding; and image reconstruction. The laboratory experiments are closely coordinated with each unit. Throughout the course, the integration of digital signal processing concepts in a design environment is emphasized. 4 credits.
Theory and algorithms for processing of deterministic and stochastic signals. Topics include discrete signals, systems, and transforms, linear filtering, fast Fourier transform, nonlinear filtering, spectrum estimation, linear prediction, adaptive filtering, and array signal processing. 3 credits.
Introduction to the concepts of multispectral image data generation and analysis. Basic principles of optical radiation, reflection, and measurement in natural scenes. Fundamentals of multispectral sensor design and data analysis for complex scenes. Application of signal processing and signal design principles and of statistical pattern recognition to these problems. Spatial image processing methods and algorithms as appropriate to land scene data. Practice with analysis of actual aircraft and spacecraft data in a cross-disciplinary environment. 3 credits.
Introduction to computational methods for describign physical, chemical and microbiological processes that occur in natural and engineering aqueous systems, including rivers and lakes, water and wastewater treatment systems. 3 credits.
This area provides a foundation for understanding the philosophical and theoretical underpinnings and procedures used in conducting qualitative research. 3 credits.
Qualitative research methods at an advanced level beyond EDCI 61500. 3 credits.
Introduction to the principles of remote sensing, aerial photo interpretation, photogrammetry, geographic information systems, and global positioning systems. Primary applications of geospatial science and technology in forestry and natural resources. 3 credits.
Advanced topics in specialties of department members such as, but not limited to: biochemistry and physiology; biological control; insect pest management; veterinary entomology; nematode systematics; pathology; systematics. The field in which work is offered will appear on the student's record. Doctoral student standing. Permission of instructor required. 1 credit.
3 credits.
Tools for analysing the regenerating forested ecological systems. 3 credits.
Advanced course in the use of digital remote sensing techniques and geographic information systems (GIS) for renewable natural resources management. Emphasizes the physical principles behind the digital remote sensing of vegetative features, present-day instrument technology, spatial data processing and analysis algorithms, error analysis and accuracy assessment procedures, and multi-source data integration. Provides hands-on experience with forest canopy modeling, atmospheric modeling, image processing, and GIS software on microcomputer and workstation platforms. 3 credits.
Research methods for natural resource social science. 3 credits.
Training in the application of statistical techniques (principally multivariate) to analyze ecological data. 3 credits.
Individual-based computational modeling and analysis of ecosystems. 3 credits.
This course covers the principles of aerosol behavior and sampling with particular emphasis on applications in the health sciences. Topics include aerosol aerodynamics, particle size distributions, methods of particulate air sampling, operating principles of aerosol instrumentation, pulmonary deposition of aerosols, aerosol lung dosimetry, and environmental aerosol measurements. 3 credits.
Understanding the objective function, developing problem representation and system modeling based on the objective, developing your own solution approaches or adapting methods from a variety of disciplines integrated in a functional framework on to reduce problem complexity, solving simplified optimization problems with standard methods, designing experiments, implementation, market and IP strategy. 3 credits.
This course covers an introduction to mathematical concepts, exploratory data analysis, data preparation, data visualization, supervised learning, unsupervised learning, deep learning, and reinforcement learning with applications in thermal systems. 3 credits.
Descriptive statistics; elementary probability; sampling distributions; inference, testing hypotheses, and estimation; normal, binomial, Poisson, hypergeometric distributions; one-way analysis of variance; contingency tables; regression. 3 credits.
Descriptive statistics; elementary probability; sampling distributions; inference, testing hypotheses, and estimation; normal, binomial, Poisson, hypergeometric distributions; one-way analysis of variance; contingency tables; regression. 3 credits.
Inference in simple and multiple linear regression, residual analysis, transformations, polynomial regression, model building with real data, nonlinear regression. One-way and two-way analysis of variance, multiple comparisons, fixed and random factors, analysis of covariance. 3 credits.
Fundamentals, completely randomized design; randomized complete blocks; latin square; multi-classification; factorial; nested factorial; incomplete block and fractional replications for 2n, 3n, 2m x 3n; confounding; lattice designs; general mixed factorials; split plot; analysis of variance in regression models; optimum design. 3 credits.
A first course in stationary time series with applications in engineering, economics, and physical sciences. Stationarity, autocovariance function and spectrum; integral representation of a stationary time series and interpretation; linear filtering, transfer functions; estimation of spectrum; multivariate time series. Use of computer programs for covariance and spectral estimation. 3 credits.


*Students can have up to 6 credits of 300-400 level courses applied to their plan of study.