This course is an introduction to photogrammetry and remote sensing of forested ecosystems. You will learn tools to gather spatial information about the Earth’s surface by remote sensing and how this information can be applied to map, monitor and manage forests and natural resources.

FE 444/544 is intended to provide you with the fundamentals of spatial data acquisition from airborne and spaceborne sensors. It will provide an introduction into the theory of spectral reflectance properties of vegetation, the principles of photographic analysis and aerial photo-interpretation, and new advances such as LIDAR. 

Syllabus

Measurable Student Learning Outcomes

Students completing this course will acquire fundamental knowledge of the techniques available to remotely sense vegetation. The course has two objectives:

1. analysis of remote sensed images obtained from airborne and spaceborn platforms
2. photogrammetric application in forestry, with focus on photo measurements. Emphasis will be placed on usage of lidar and satellite imagery in forestry.

Success in course objectives is measured by 70 percent or higher score average for exam, quiz, and lab problems.

The undergraduate learning outcomes (FE 444) are:

• Interpret the electromagnetic spectrum, and explain the spectral signature of vegetation.
• Place images in a coordinate system, and convert among systems.
• Georeference images and convert between coordinate systems.
• Orient stereoscopic images under a stereoscope.
• Extract patterns from images using unsupervised and supervised classifications.
• Evaluate the accuracy of a classification.
• Use radar images in forestry assessments
• Determine stand height and individual tree height from lidar
• Explain the spatial and spectral properties of the most common satellite sensors (such as Landsat, Sentinel, or MODIS) and recommend their usage to specific projects

The graduate learning outcomes (FE 544), in addition to the undergraduate learning outcomes, are:

• Interpret images classified with spatial, spectral, and combined algorithms to answer forest inventory, monitoring, and management issues
• Analyze the performances of image classification algorithms
• Integrate forest attributes derived from radar images with ground data
• Interpret the accuracy of lidar-derived estimates in two contexts: forest inventory and forest monitoring
• Synthesize the information needed for a particular project that is available from various satellites
• Integrate data supplied by passive and active sensors
• Design a sample strategy for assessing the accuracy of  remote sensing data