Advanced Remote Sensing and Image Processing
Course Description: This is an advanced course in optical remote sensing. The instructor assumes that you have taken Geog 477 or with equivalent knowledge. If you did not take Geog 477 or equivalent experience, you have read the recommended book chapters over the winter break. The goal of this course is to help students obtain a sound understanding of optical remote sensing physics, and research skills in land surface information extraction using space borne optical remote sensing imagery. Images from MODIS, Landsat, and Ikonos satellites will be the primary source of data in the lab exercises for this class. We will use ENVI/IDL as the primary image processing software in the labs. The underlying theme of the course is to extract vegetation information from remotely sensed data. Topics of the course include key preprocessing steps of digital images for data analysis, land-cover/land-use classification and change detection, mapping vegetation leaf area index, and modeling of land surface productivity using remotely sensed data, and a limited amount of fieldwork for vegetation structure sampling. We will learn how to use information from remotely sensed images in the spectral as well as temporal domains. We will also learn how to link ground observations with satellite images for model development or ground truthing using Global Positioning Systems. The formats of the course include instructor lectures, paper presentations and discussions by students, and hands-on lab exercises. All class material will be available through http://sakai.unc.edu. Each student need to have an 8GB memory stick available to use for this class.