This on-line course provides students with a strong foundation of modern Geospatial Science theory and an assessment of the Geospatial Science industry including regularly used definitions, methods, tools, data, analysis, products, and professional resources. This course also explores the multiple ways in which the Geospatial Sciences are used across disciplines thus aligning students well for future classes, project work, and professional endeavors within the Geospatial Science field. Includes: Instructor lead and guest lecture lead on-line lectures, discussion board forums, assignments, and a final project report.

This class is a condensed intensive learning class that meets daily for two week. The goal of the course is to provide students with a solid foundation in the the concepts and applied techniques of a Geographic Information System (GIS) as a tool to solve real world problems across multiple disciplines. Technical topics covered range from spatial data quality, date conversion, database design, data management, analysis, and visualization. Special emphasis is placed on student hands-on lab exercises and the development of an independent GIS project using ESRI's ArcGIS software. Examples of the use of GIS in varied disciplines such as Geography, Business, Planning, Transportation, Natural Resources, Water Resources, Sociology, and Ecology will be discussed. Students can register for this class via the UNH summer session webpage at: http://www.unh.edu/summersession/registration.html

This course teaches Geographic Information System (GIS) topics in context to Earth and Environmental Science themes. Themes covered include Geology, natural hazards, environmental monitoring, planning and infrastructure, water resources, climate change, energy, and environmental engineering. GIS topics covered include spatial data quality, data conversion, database design, data management, analysis, and visualization. Course materials used include, slide set presentations, hands-on exercises, assignments, Earth and Environmental Science discussions, exams, and an independent GIS project.

This course provides students the opportunity for application of emerging technologies with a focus on Geographic Information Systems and remote sensing in water resources engineering and hydrology. Topics may include digital mapping of water resources information, spatial coordinate systems, river and watershed networks, soil and land use mapping, flood/hydrology modeling and flood plain mapping, terrain analysis for hydrologic modeling, and integration of time series and geospatial data. Additional GIS topics geared to the needs and interests of students may include Water/Sewer/Stormwater/CSO utilities (i.e. end users, CSO separation, daylighting) and environmentally regulated facilities (i.e. construction sites, municipal landfills).

Theory, concepts, and applications of geographic information systems (GIS) for use in natural resources and related fields. Discussion of database structures, sources of data, spatial data manipulation/analysis/modeling, data quality standards and assessment, and data display/map production including many examples and practical applications. Hands-on lab exercises using ArcGIS 10. Permission. Lab.

Design and analysis of biological and ecological research experiments. "Real world" studies used to discuss the identification of hypotheses, appropriate experimental design, and the application of statistical analyses including ANOVA, ANCOVA, correlation and regression, cluster analysis, classification and ordination techniques. Theoretical statistical concepts tailored to consider student's own thesis and dissertation research, allowing statistical problems to be addressed at various stages of the research process. Common computer packages used for analyses. Prereq: BIOL 528; permission.

This course will consider basic techniques used in time series and spectral analysis of random data, and prepare the student for analyzing and interpreting geophysical time series data for science and engineering applications. Course Content include Fourier Analysis – Fourier series, generalized functions, convolution, discrete, Fourier transform, fast Fourier transform, Sampling Theory – discrete sampling - infinite record length, aliasing; continuous sampling - finite record length, smearing and leakage; discrete sampling – finite record length, data windowing, Statistics & Probability – probability density functions, confidence intervals, correlation, regression, least-square-errors, hypothesis testing, Spectral Analysis – auto-spectrum, data windowing, reducing noise in spectra, confidence intervals, cross-spectrum, coherence, phase Empirical Orthogonal Functions – real, complex, and frequency-domain.

An introduction to multivariate statistical methods, including principal components, discriminate analysis, cluster analysis, factor analysis, multidimensional scaling, and MANOVA. Additional topics include generalized linear models, general additive models, depending on the interests of class participants. This course completes a solid grounding in modern applications of statistics used in most research applications. The use of statistical software, such as JMP, S PLUS, or R, is fully integrated into the course. Prereq: MATH 835 or MATH 839.

Statistical methods for the analysis of relationships between response and input variables: simple linear regression, multiple regression analysis, residual analysis model selection, multi-collinearity, nonlinear curve fitting, categorical predictors, introduction to analysis of variance, analysis of covariance, examination of validity of underlying assumptions, logistic regression analysis. Emphasizes real applications with use of statistical software. Prereq: basic introductory statistics.

Frequentist and Bayesian methods for estimation of characteristics measured in space (usually 2-dimensional Euclidean space). Spatial averaging. Spatial point processes: models for clustering and inhibition. Cluster detection. Point referenced data: varigram estimation, Kriging, spatial regression. Lattice based data: spatial auto-regression, Markov random field models. Spatial regression models. Non-Gaussian response variables. Hierarchical Bayesian spatial models and Markov chain Monte Carlo methods. Multivariable spatial models. Prereq: Intermediate statistics including basics of maximum likelihood estimation; linear regression modeling including familiarity with matrix notation, basic concepts of calculus including partial derivatives.

Application of statistical methods to the analysis of social data, with particular emphasis on multiple regression and related topics.

The science and technology of acquiring, managing, and displaying geographically-referenced information; the size and shape of the earth, datums and projections; determination of precise positioning of points on the earth and the sea , including classical terrestrial-based methods and satellite-based methods; shoreline mapping, nautical charting and electronic charts. One year of each calculus and physics required.

Remote sensing provides insight into spatial and temporal aspects of environmental and earth systems. Students will learn digital image processing techniques, understand different sensor and platform technologies, and discuss new trends in remote sensing science. Focus on applied research questions and projects will be addressed. The course will include hyperspectral, lidar analysis, and unmanned aerial systems. Work will be done using ImageJ, Google Earth Engine and python.

Within this course, students will learn concepts and applied methods of Unoccupied Aerial Vehicle (UAV) drone operations and remote sensing for use across multiple disciplines. Concepts to be covered include the types, costs, and uses of various UAVs and UAV sensor systems, flight operations, flight mission planning, aerial data collection, image mosaicing, image processing, data analysis, and visualization. Students will utilize class-provided UAV flight planning software, hands-on use of class-provided UAVs, and data processing software for the collection, analysis, and mapping of sample data.

This on-line course focuses on the application of locational crowdsourced information as well as qualitative information (e.g., digital photos) to use real-world examples, where we can apply the technology and applications anywhere. The course incorporates concepts from geography, history, anthropology, sociology, planning, information science, and disaster management. Students will learn about the ongoing process of data acquisition and problem conceptualization. Rather than work to produce a static result, students will be encouraged to think about how they will incorporate changes, update and refine their analyses, and successfully navigate a dynamic temporal and spatial setting. This course will be a hands-on and lab-based to introduce concepts of locational crowdsourced information, and teach them how to capture and use data.

This internship allows students to gain real-world GIS, GPS, Remote Sensing, or other Geospatial Science experience at professional organizations where they will build their skills and expand their network of colleagues . Students will work at an advisor approved organization at least eight hours per week for a semester, provide regular updates of their progress, and submit a final report outlining their experience at the end of their internship (Permission Required).

This internship allows students to gain real-world GIS, GPS, Remote Sensing, or other Geospatial Science experience at professional organizations where they will build their skills and expand their network of colleagues . Students will work at an advisor approved organization at least eight hours per week for a semester, provide regular updates of their progress, and submit a final report outlining their experience at the end of their internship (Permission Required).

This internship allows students to gain real-world GIS, GPS, Remote Sensing, or other Geospatial Science experience at professional organizations where they will build their skills and expand their network of colleagues . Students will work at an advisor approved organization at least eight hours per week for a semester, provide regular updates of their progress, and submit a final report outlining their experience at the end of their internship (Permission Required).

To review areas of mathematics essential to the JHC/CCOM core courses, in particular Geodesy (OE/ES 871). Review of Linear Algebra and Multivariate Calculus, Elementary Differential Geometry of Curves and Surfaces, Complex Numbers Ordinary differential equations (overview), Miscellaneous methods: Transform Methods. Prerequisites: Some familiarity with single-variable calculus at the level of Math 425/426, and linear algebra at the level of Math 545.

Practical and conceptual presentation of the use of remote sensing and other geospatial technologies for mapping and monitoring the environment. This course begins with the use of aerial photographs (photogrammetry, and photo interpretation) and includes measures of photo scale and area, parallax and stereo viewing, object heights, flight planning, photo geometry, the electromagnetic spectrum, camera systems and vegetation/land cover mapping. The course concludes with an introduction to other geospatial technologies including digital image analysis, global positioning (GPS), and geographic information systems (GIS). Conceptual lectures are augmented with practical homework assignments and hands-on lab exercises. Prereq: algebra. Special fee. Lab.

Introduction to digital remote sensing, including multispectral scanners (Landsat and SPOT) radar, and thermal imagery. Hands-on image processing including filtering, image display, ratios, classification, registration, and accuracy assessment. GIS as it applies to image processing. Discussion of practical applications. Use of ERDAS image-processing software. Knowledge of PCs required. Prereq: NR 857 or equivalent and permission.

Provides the field and remote sensing tools and experience needed by students to assess forest conditions at the individual tree and stand levels, as well as to conduct independent research projects on specific topics of interest. May include assessing change-over-time, landscape-level impacts of urban developments, severe weather events, and other natural and anthropogenic perturbations affecting the health of forests. Forest damage due to insects, air pollution (primarily ground-level ozone), drought, the 1998 ice storm, and others are investigated. Lab. Special fee. Permission.

Ocean mapping requires precise positioning and navigation. For this we need to precisely know Earth's shape, gravity field, and orientation in space. Data used for this purpose include satellite-based positioning, gravity measurements, and ground surveys. Reference frames can then be created allowing the integration of geometric observations for the creation of mapping products. This course will focus on this integration of measurements and the uncertainty associated to them.

This course will give hands-on experience with the complete process of designing, collecting, analyzing and writing about survey research. Work is mainly completed with large-scale and professionally conducted surveys. The course will cover three main steps of the survey research process. 1) From raw data to findings. Cleaning, labeling, weighting, recoding and generating new variables; the use of do-files to perform complex tasks; preliminary tables and graphs; identification of key findings; designing publishable tables and graphs. 2) How surveys are done. Questionnaire design; CATI scripts sampling; telephone interviewing; initial and translated data. 3) Writing up and presenting survey results. Design of slide shows, reports and articles; statistics and modeling; the process of peer review.