College of Forestry
Review of passive and active sensors used in remote sensing. Geospatial data manipulation in R. Image classification. Point clouds: lidar and photogrammetric. Point cloud manipulation for forestry application. Sampling for accuracy assessment. Lec/lab. PREREQS: FE 444 or GEOG 480 or equivalent.
All required learning resources are posted online on Canvas. However, the course requires understanding of the remote sensing techniques. Therefore, two textbook are recommended for the successful completion of this class:
Supplementary material: Lillesand, T.M., Kiefer, R.W. and Chipman, J (2015) Remote sensing and Image Interpretation (7th Ed). Wiley and Sons, New York.
Students completing this course will acquire advanced knowledge of the techniques available extract detailed information about vegetation from remotely sense data. The course has two objectives:
Course success is measured as 70 % or higher average for labs, midterm, and final exam.
Measureable learning outcomes:
Week |
Topic |
Lab |
1 |
Introduction to R
|
Lab 1: Gdal and raster packages Input/output data, Coordinate systems |
2 |
Remote sensing data |
Lab 2: manipulation of images and points clouds Registration and co-registration |
3 |
Image classification I |
Lab 3: supervised and unsupervised classification ISODAT, ML, SAM, & Confusion matrices |
4 |
Image classification II |
Lab 4: advanced image classification techniques Caret & Random forests |
5 |
Point clouds |
Lab 4: advanced image classification techniques (2) Bayesian Models & Support Vector Machine |
6 |
Point clouds |
Lab 6: lidar and photogrammetric point clouds lidR, pdal, & DTM extraction |
7 |
Forest measurements |
Lab 7: tree and stand attributes CHM, segmentation of individual trees |
8 |
Assessment of results accuracy |
Lab8: sampling for accuracy assessment |
9 |
Project topics |
Lab9: Individual / Group work on project |
10 |
|
Lab 10: Individual / Group work on project |
Finals |
|
Final Exam |