ESRM 430: Remote Sensing of the Environment
High-resolution Arial & Satellite Imagery, LiDAR, Hierarchical Feature Extraction and Object Based Classification, Data Fusion, Stereoscopy, Geovisualization, UAV's

Course objectives: To develop an understanding of remote sensing fundamentals and the ability to interpret and manipulate high-resolution remotely sensed images and datasets. Students will be presented with the traditional and ‘state of the art’ image processing techniques, and a firm theoretical and practical background in remote sensing applications. By the end of the course students will be expected to evaluate available remote sensing data sources and design simple projects related to environmental applications.

Dr. L. Monika Moskal
Hours: by appointment Bloedel 382
Jonnie Dunne
Hours: in Bloedel 357 1:30-2:30 T-Th and by appointment

TTh 12:30 – 1: 20 in More 220


A - T 2:30 – 3:50 in Bloedel Hall 261

B - T 4:00 – 5:20 in Bloedel Hall 261

C - Th 2:30 – 3:50 in Bloedel Hall 261

D - Th 4:00 – 5:20 in Bloedel Hall 261
Drop BOX


  • LABS


Course Summary
5 credits = 2 lecture credits + 3 lab credits
There are no course requirements to enroll in the course although you will find that prior statistics, GIS and Remote Sensing classes are of benefit.

Individual arrangments need to be made to take course as W credit; see Dr, Moskal in person.

Students will be exposed to the principles of photogrammetry, image and point cloud interpretation and hyperspatial (high spatial resolution) remote sensing applications in natural resource management. In the first half of the course, manual and computer based laboratory exercises emphasize conventional analysis of aerial photographs and high resolution satellite imagery. The second half of the course focuses on the application of active remotely sensed data, specifically LiDAR (Light Detection and Ranging). The uses of hyperspatial remotely sensed information for wetlands, watersheds, forest resources, wildlife habitat, point and non-point pollution, environmental monitoring, land use planning, urban-suburban-forestry interfaces, and outdoor recreation will be discussed and illustrated using research examples throughout the course. Students will have the opportunity to apply these principles and obtain hands-on experience. Students will come out of this course with a mastery of a wide variety of interpretation, measurement, environmental monitoring and map making skills specific to hyperspatial remote sensing. Practitioners and users from public and private institutions may be involved as guest lecturers. 

Teaching philosophy: I consider the student/faculty mentor relationship to be the centerpiece of the academic culture, and perhaps the most rewarding professional experience. At her best, a professor acts as a mentor to her students - be it a group of noisy first-years or an independent star graduate. The precise strategy of achieving this relationship will change with circumstance, but the overall approach remains the same. While I can't claim to have developed an exhaustive philosophy on the subject, I have listed some of my guiding principles below.

  1. Respect: I bring a high standard of respect to the classroom, taking care to run on time, organize clear, high-quality lectures and labs, and encourage thoughtful dialog among class participants. I have always made an effort to treat students as peers, responding carefully to suggestions, and being honest when confronted by questions that I cannot answer. However, respect is a two-way street. I expect prepared (do the readings) attentive, punctual students who participate in the learning process.
  2. Excellence: I set clear, high standards at the beginning of a course and challenge students to reach for pre-defined goals. I instruct all of my students to focus on extracting value from their education in the form of identifiable skills and knowledge areas. I do not reward lazy or sloppy thinking. I encourage students to think critically, use their resources effectively, and hone their problem-solving abilities.
  3. Independence: Where possible, I try to incorporate problem- or inquiry-based learning approaches into each of my courses. I do not design assignments that follow a 'cook book' approach to a singular conclusion, instead preferring complex-structured problems with more than one workable solution. While initially frustrating to some students, my experience is that this sort of approach encourages competent, independent thinking and results in a far more satisfying conclusion to the learning experience.


Week 1 -- Lecture Slides

  • What is Hyperspatial Remote Sensing?
  • Principles of Image Interpretation
  • Principles of  Remote Sensing
  • Readings: Campbell & Wynne Chapters 1, 5 and 10
  • Optional Readings: Chambers et al. 2007, Tatem et al. 2008, Adams et al. 2004, Melesse at al. 2007 -- can be used for Graduate Student Annotated Bibliography

Week 2 -- Lecture Slides

  • Aerial and high resolution imagery
  • Research examples using aerial photography and high resolution imagery
  • Readings: Campbell & Wynne Chapters 3 and 4; de Leeuw et al. 2011; Gordon, 2005

Week 3 -- Lecture Slides continued from week 2

Week 4 -- Lecture Slides

  • Natural Resource Management Remote Sensing Examples from Forestry, Landcover Change, Landscape Ecology, Geovisualization, Landuse Planning, and Wildlife Applications Continued
  • Readings: Campbell & Wynne Chapters 20 and 21; Moskal and Franklin 2004

Week 5 -- Midterm Prep Slides

Week 6 -- Lecture Slides continued from week 4

Week 7 -- Lecture Slides

  • Statistical Pattern Recognition and Image Segmentation
  • Per-pixel and Object Based Image Classification
  • Readings: Campbell & Wynne Chapters 11 and 12; Moskal et al. 2011; Myint et al. 2011

Week 8 -- Lecture Slides

  • Active Remote Sensing
  • Aerial and Terrestrail LiDAR
  • Readings: Campbell & Wynne Chapter 7; Vaughn et al. 2011; Erdody and Moskal 2010; Zheng and Moskal 2009; Richardson et al. 2009; Moskal et al. 2009; Kato et al. 2009

Week 9 -- Lecture Slides (continued from week 8)

  • LiDAR Applications
  • Class Discussion on Future Trends in Remote Sensing
  • Reading: Campbell & Wynne Chapter 8; Moskal and Zheng 2012

Week 10 -- Lecture Slides

Final Projects (Lab 10) Due
Week after last lab


Lab 1 Geo-wiki

Lab 2 Google Earth

Lab 3 UW Map Library with Matt Parsons (UW Geospatial Data and Map Librarian)

Meet at the main entrance to Suzzallo Library, the lab will take place at the UW Map Collection and Cartographic Information Services

Lab 4 Introduction to Computer Aided Image Segmentation -- SPRING Software

Lab 5 Advanced Computer Aided Image Segmentation and GIS Integration

Lab 6 Historical Change Detection and Accuracy Assessment

Lab 7 Mobile GIS
Meet in the SEFS Courtyard, guest lab instructor: Dr. Matthew Dunbar from the UW CSDE

Lab 8 Introduction to LiDAR Data Analysis -- FUSION Software

Lab 9 Advanced LiDAR Data Analysis and GIS Integration

Lab 10 - Final Project

Final Projects (Lab 10) Due
Week after the last lab

The course spans traditional and very new sub-branches of remote sensing, thus, there is no one textbook that would best fit this class content. Most of the readings you are expected to do are peer-reviewed literature reviews and research articles, the course readings are found below. Also below, are suggested textbooks that relate to the course content.

Optional Textbooks - --- copy of books on hold @ Odegard Library (2 hour loan)

  • James Campbell and Randolph Wynne, 2011. Introduction to Remote Sensing, 5th ed. The Guilford Press, p.667.
  • David P. Paine and James D. Kiser, 2003. Aerial Photography & Image Interpretation. 2nd ed. Wiley, p. 648
  • Thomas Blaschke, Stefan Lang and Geoffrey Hay, 2008. Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications (Lecture Notes in Geoinformation and Cartography). 1st ed. Springer, p. 836
  • Sorin C. Popescu, 2012. LiDAR: Remote Sensing of Terrestrial Environments. 1st ed. CRC Press, p. 300

UW - ESRM430 Hyperspatial Remote Sensing is a group in Environmental Sciences on Mendeley.

Grade Allocation

  • Midterm                                20%                         
  • Labs (9 @ 5% each)               45 %*
  • Final Project (Lab 10)             25%
  • Random Quizzes (3-5)            10 %

The actual number of labs and quizzes might be lower but not higher.
Approximate letter grades will be 93% (A=4.0), 82 % (B= 3.0), 71 % (C= 2.0), and 60% (D= 1.0).  You will fail the course if your cumulative % is below 59 % (F = 0.0).

Individual arrangments need to be made to take course as W credit; see Dr, Moskal in person.

*Annotated Bibliographies (Graduate Students ONLY)

Graduate students do not submit labs. Every week, starting week two, an annotated bibliographic reference based on a remote sensing - theme refereed journal article will be due at the beginning of each lab session; for a total of 9 annotated bibliographies. Thus, graduate student are expected to attend the labs, however, the annotated bibliographies will substitute for the lab and midterm grades. Annotated bibliographies can be submitted using the ESRM 430 Digital Dropbox. The whole graduate student grade is based on the annotated bibliographies.

Instructions on how to produce an annotated bibliography are available at Cornell Library Site.
Each bibliographic reference will be graded as follows: 5 pts = Excellent, 4 pts = Good, 3 pts = Fair, 2 pts = Poor, 0 pts = Late or did not hand in. 

Assignments, Lab, Exam Submissions:

ESRM 430 Digital Dropbox to submit your labs, midterm, final and annotated bibliography. Always use your name in the file name of your submission. Always assure that you are uploading files to the correct lab/assignment/exam folder. You will have till the start of the next lab session to submit your lab.

Course Related Resources

Free Software Used in the Course

Remotely Sensed Data


University of Washington

College of the Environment, School of Forest Resources  
Cell p hone: 206.225.1510
Seattle, WA 98195-2100