Welcome to the webpage of my classes. My courses are designed for a variety of student backgrounds, so feel free to contact me for further information. I can help you decide whether a class fits your needs. Keep in mind that typically my classes have a large project at the end to accommodate a variety of student backgrounds.
Note: All courses are using Blackboard for course delivery and student grading.
Current Semester (Spring 2015)
- Principles of Remote Sensing (4cr) ERE 365/ ERE 565 Sample Syllabus
Description: The class provides a qualitative and quantitative introduction to the fundamentals of acquiring, analyzing and utilizing remote sensing data. It is designed as the first exposure to remote sensing techniques, therefore no prior experience is necessary.
- Digital Image Analysis (3cr) ERE 622 Sample Syllabus
Description: Topics covered in this class include elements of digital image processing and analysis systems: Digital image representation, visual perception, sampling and quantization, pixel connectivity, Fourier transforms, image enhancement, filtering, edge detection, thresholding, morphology, recognition and interpretation. General programming experience and quantitative background are required. Assignments will use Matlab software package, though no prior knowledge of Matlab is required. In addition to the the regular laboratory exercises there are two synthesis labs and a class project. Students are expected to consult with the instructor to ensure that their project meets the course expectations.
- ESF Goes to Space (1cr) ERE 496 Sample Syllabus
Description: A new one credit course to design, build, launch and retrieve a high altitude sensor.
Spatial Analysis (3cr) ERE 621, Syllabus
Description: Topics covered in this course include elements of spatial statistics and modeling as applied to various data formats: single point data, continuous data and area data (if time permits). The triangle visualize-explore-model will be employed with emphasis in the modeling section. Examples of taught methods include: first/second order effects, complete spatial randomness, nearest neighbor and Ripley’s K statistics, tessellation, kernel, covariograms and variograms, and several kriging methods. General programming experience and quantitative background are required. Assignments will use Matlab software package, though no prior knowledge of Matlab is required. Students are expected to consult with the instructor to ensure that their class project meets the course expectations.
- Artificial Intelligence in Geography (1cr) ERE 796
Description: This course focuses on theory and applications of artificial intelligence (AI) methods to geographic problems. Methods discussed include decision trees, fuzzy logic, neural networks (backpropagation, radial basis function, self-organizing maps), genetic algorithms and agent-based modeling. This course is exclusively for graduate students with relevant background.
- Surveying for Engineers (4cr) ERE 371, Sample Syllabus
Description: Many programs at ESF aim at training students in designing solutions to problems associated with managing and developing land resources. A basic tenet of this training is an ability to locate and quantify the resource(s) being managed or problem(s) being solved. In addition professionals involved with the design and construction of facilities must acquire knowledge of construction surveying principles and practices. ERE 371 introduces surveying for these and other tasks associated with engineering or construction management practice.
Introduction to Global Positioning Systems (1cr) ERE 566, Sample Syllabus
Description: Global positioning systems (GPS) provide a means to collect location information for a variety of applications. This course provides an introduction to the theory and practice of performing GPS measurements.
Introduction to Spatial Information (1cr) ERE 553, Sample Syllabus
Description: Many courses at ESF require a fundamental background in spatial information. This course introduces basic spatial terminology and methods for determining and expressing position. The course also considers accuracy and precision in the context of horizontal measurements and explores issues with subsequent use of these measurements for producing maps and performing analysis.