Courses

This graduate-level course (hybrid format of seminar and coding labs) focused on understanding ecological processing and environmental modeling using advanced remote sensing approaches. We will focus on case studies that apply advance remote sensing techniques, including LiDAR, multispectral, and hyperspectral, and thermal imagery, at different spatial-temporal scales. We will focus on data management and coding using R and Google Earth Engine (GEE).

This undergraduate level course emphasizes the use of remote sensors and geographic information systems to study the Earth’s land surface. The course emphasizes the use of these technologies primarily in the context of forestry but also in the broader context of natural resources management. Remote sensing technologies that are emphasized include aircraft and satellite-borne sensors in the visible and near-infrared portions of the electromagnetic spectrum, as well as active sensing devices like lidar (Light-detection and Ranging) technology. Students gain proficiency in the use of advanced remote sensing/image processing and geographic information systems (GIS) software to display, analyze, and map land surface features.

This undergraduate-level course focused on quantitative methods in geography. Students frequently face the tasks to extract information from a pile of geographic data, to forecast what will happen based on what we know, or to design a scheme to collect data to test a given hypothesis. These tasks cannot be achieved without appropriate quantitative analysis. In the history of Geography, quantitative revolution occurred in the 1950s and 1960s which brought mathematic and statistical tools into the discipline that transformed it from a primarily descriptive and unscientific discipline to a scientific one. Today, quantitative methods are used in almost all areas of Geography. The goal of this course is to provide the fundamental quantitative skills that geographers need in scientific explorations. The topics of this course include basic concept in statistics, data visualization, descriptive statistics, probability distributions, point and interval estimation, hypothesis testing, and analysis of variance.