GIS and Remote Sensing
Glenn Kroeger
Trinity University
Summary
An introduction to GIS, spatial analysis and remote sensing with an emphasis on natural science and environmental applications. The course begins by examining how all forms of information are digitally encoded. Topics covered include fundamentals of cartography, object and raster GIS analysis, GPS theory and practice, geostatistical analysis of data and interpolation and multispectral remote sensing and image classification.
Course Size:
15-30
Course Format:
Students enroll in one course that includes both lecture and lab. The lecture and the lab are both taught by the professor.
Course Context:
This is an upper division course with a general prerequisite of previous lab courses in natural sciences. Most of the students are majors in Geosciences, Biology or Environmental Studies although most cohorts also include a few students from Economics and Business Administration.
Course Content:
In this course, students learn the ways in which digital data are stored and the formats and hardware used to store and transfer digital spatial data such as images and maps. They study the basic concepts of cartography and the practical implications of the choices of datums and projections in mapping. They are introduced to the principles behind the operation of the Global Positioning System and carry out field work with differential GPS. They represent and manipulate vector and raster data in Geographic Information Systems to solve a variety of spatial problems. They employ a variety of deterministic techniques to interpolate irregularly samples data into regularly spaced samples, then examine the basic ideas of geostatistics including regionalized variables, analyze semivariances of data sets and employ Kriging to create statistically optimal interpolations of those data. Students the principles behind the acquisition of electromagnetic remotely-sensed data sets and process multispectral data for geologic and environmental applications. Finally, they employ several methods to classify image data into thematic maps.
Course Goals:
Students should be able to acquire GIS data sets and remote sensing imagery from a variety of online sources.
Students should be able to choose appropriate digital and file formats for storing, processing and transferring GIS data sets and remote sensing imagery.
Students should be able to identify and choose appropriate cartographic datums and projections to store, analyze and display spatial data.
Students should be able to carry out GIS analysis on object and raster based spatial data.
Students should be able to identify the equipment necessary to acquire high-precision GPS data.
Students should be able to carry out simple geostatistical analysis of irregularly sampled data sets and employ appropriate interpolation methods to analyze and display that data.
Students should be able to make useful color composite images from multispectral remote sensing imagery.
Student should be able to use band ratios and Principal Component Analysis to create color composite images that discriminate surface materials.
Students should be able to carry out simple unsupervised and supervised spectral classification of remote sensing imagery.
Students should be able to choose appropriate digital and file formats for storing, processing and transferring GIS data sets and remote sensing imagery.
Students should be able to identify and choose appropriate cartographic datums and projections to store, analyze and display spatial data.
Students should be able to carry out GIS analysis on object and raster based spatial data.
Students should be able to identify the equipment necessary to acquire high-precision GPS data.
Students should be able to carry out simple geostatistical analysis of irregularly sampled data sets and employ appropriate interpolation methods to analyze and display that data.
Students should be able to make useful color composite images from multispectral remote sensing imagery.
Student should be able to use band ratios and Principal Component Analysis to create color composite images that discriminate surface materials.
Students should be able to carry out simple unsupervised and supervised spectral classification of remote sensing imagery.
Course Features:
This course has a series of exercises that introduce GIS and remote sensing techniques in a fairly "cookbook" fashion. After each exercise, students complete a project which requires they apply and extend those techniques to different data sets and more complex situations.
Course Philosophy:
I chose this approach to make the focus of the course transferrable GIS techniques, not just ArcGIS menus and tools.
Assessment:
There are several short exams, but the primary assessment is based on student project reports.
Syllabus:
Syllabus (Microsoft Word 2007 (.docx) 25kB May1 19)