Spatial Analysis and Modeling (ENGR 180)

Over the past two decades, with the advancement of survey techniques such as Geographic Positional System (GPS), Geographic Information System (GIS), and Remote Sensing (RS), geodatasets have dramatically increased and become more widely distributed. Spatial analysis and modeling are important tools in exploring and analyzing the geodatasets, and linking the spatial data to decision making. The main objective of this course is to provide students with practical and theoretical aspects of spatial analysis and modeling. We will emphasize hands on exercises for students to implement and construct spatial models. The course consists of two major parts. The first part is the introduction of Visual Basic (VB) and ArcGIS. The second part includes a range of spatial modeling techniques and their implementations.

Advanced topics on geospatial analysis (ES 292)

The objective of this class is to provide students with the recent development on geospatial analysis methods (e.g. GIS, Remote Sensing, and GPS). Research topics include: high spatial resolution image analysis (e.g. object-based image analysis), hyperspectral image analysis, Lidar image processing, environmental niche modeling and GCM downscaling, sensor networks, webGIS, Python script for ArcGIS, Integrating R with GIS, Geostatistics etc.

Environmental Data Analysis with R (ENVE 105 and ES 205)

The objective of this class is to provide students with probabilistic and statistical methods to analyze environmental data. This class will emphasize on both theoretical and applied aspects of data analysis methods. Topics include: idistribution, hypothesis test, linear regression, multiple regression, uncertainty analysis, error prorogation, outlier detection, sample design, time series analysis, spatial interpolation, and Bayesian inference. Weekly homework assignments are from environmental applications, and we will mainly use the statistical software R.

Remote Sensing of Environment (ENVE 152 and ES 252)

Remote Sensing is a technique that acquires earth object information without physically contacting with the objects. It has the advantage of gathering information in very large study areas, or remote areas that humans are difficult to access. Remote sensing has been increasingly used in a variety of environment studies including climate change, water resources, land use / land cover change, hazard management etc. This class provides an introduction to the fundamentals of remote sensing. Class lectures will focus on a range of concepts and techniques key to understanding how remote sensing data are acquired, displayed, restored, enhanced, and analyzed. Topics include remote sensing principles, aerial photography, photogrammetry, image interpretation, image processing, and applications of remote sensing in a range of environmental applications. Weekly exercises will provide students hands-on experience of understanding and applying remote sensing techniques in solving real-world environmental problems.