ESRM433 LIDAR: Remote Sensing

Course Information for ESRM433/SEFS533

Objectives

To develop an understanding of LiDAR remote sensing fundamentals and the ability to interpret and manipulate 3D point cloud remotely sensed data. Students will be presented with the operational and ‘state of the art’ processing techniques, and a firm theoretical and practical background in LiDAR remote sensing applications. By the end of the course, students will be expected to evaluate available LiDAR data sources and design simple projects related to environmental applications.

Is there a textbook?
The course spans some traditional and very new sub-branches of LiDAR remote sensing, thus, there is no one textbook that would best fit the class content. Most of the readings you are expected to do are peer-reviewed literature reviews and research articles and are listed on Canvas. Extra readings consisting of reports and project manuals are also provided most weeks.

Instructors

Instructors

  • Professor:  L. Monika Moskal she/her/hers lmmoskal@uw.edu Office Hours: Thursdays at 12:30-1:30 To learn more about Prof. Moskal in a less formal format watch my recent SEFS Seminars Talk.
  • Teaching Assistant: Yelyzaveta Ismatullayeva lismat@uw.edu

Unless specified the content below applies to both ESRM433/SEFS533.

Lecture

Tuesdays 3:00 – 4:50pm More Hall 220 (Zoom Link)

There will be in person lectures that will be streamed on zoom and recorded and posted on this canvas page. Students are encouraged to attend in person but not required. There will be numerous guest speakers throughout the quarter presenting on numerous lidar related topics as well as several in class demonstrations of lidar.

Online Lab Help Sessions

Thursdays 3:30 – 4:50pm (Zoom Link)

There will be no in person meetings for the labs. We will be using SEFS Madrona Virtual Desktop for all labs. Please review the FAQ.. Additional resources and links for the course are continuously added through on the discussion thread: Additional Resources. Labs are posted directly in the weekly modules. Students are STRONGLY encouraged to log onto the live lab help session to work on the lab material for that week so questions can be answered in real time as well as make sure of the weekly lab discussion boards.

Grading

Both ESRM433 and SEFS533 are 5 credit courses, which means you are expected to be putting in 15 hours/week Links to an external site. of work on these courses.

  • 90% of the grade are labs at 9 points each, 10 labs in total
    • SEFS 533 – For each lab, there is an additional step required for SEFS533 Students (Graduate Students)
    • Late assignments will have a 10% reduction in the score
  • 10% is the completion of 10 weekly Self-assessment Quizzes (1 point each)
  • No Graded Midterms, Exams or Quizzes

There are 100 points possible. 1 point = 1% of you grade.

Approximate letter grades: 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).

There will be opportunities for extra credit. Make sure you read all of your comments and the assignment answer keys as most extra credit will be located there.

Tentative Course Outline

We use Weekly Modules in this course. They will be published by  Tuesday noon of the start week of the module.

Weekly topics are:

Week: 1 Introduction to LiDAR Remote Sensing

  • Class structure and logistics
  • What lidar is and how it has been used
  • Basic lidar visualization
  • Where you can obtain lidar data

Week 2: Introduction to LiDAR analysis

  • Intro to Coordinate Refence Systems (CRS)
  • Lidar Metadata
  • Introduction to LidR and RStudio

Week 3: Surface and ground models

  • Create and assess LiDAR ground models
  • Generating output for further analysis in ArcGIS Pro
  • Projections and Coordinate Systems

Week 4: Canopy models

  • Create Canopy Height Models (CHMs) & Digital Surface Models (DSMs)
  • Individual Tree Detection (ITD)

Week 5: LiDAR metrics

  • Relating plot data to lidar data
  • Using R for linear regression analysis
  • This will be a crash course in simple linear regression and multiple linear regression

Week 6: 3D Point clouds from photos vs. LiDAR

  • Rumple index and canopy height models
  • Digital Aerial Photogrammetry (DAP)
  • Linear regression analysis with rasters
  • Creating forest characterization maps

Week 7: Ground Model Analysis

  • Locate lidar data from coordinate points
  • Model wetland boundaries using lidar DTMs
  • Compare outputs to national wetland database

Week 8: Terrestrial and mobile LiDAR

  • Terrestrial Mobile Lidar Scanning (TMLS)
    • Introduction
    • Comparison to ALS
    • Tree Segmentation
  • Terrestrial Lidar Scanning (TLS)
    • Comparison to drone Digital Aerial Photogrammetry (dDAP)
    • Quantifying coastal cliff erosion

Week 9: Spaceborn LiDAR

  • Introduction to full waveform lidar
  • Introduction to Global Ecosystem Dynamics Investigation (GEDI) Lidar
  • Downloading and using GEDI data
  • Characterizing forested landscapes with full waveform lidar

Week 10: Comparative LiDAR

  • Comparison of lidar
    • Cloud Metrics / Rumple / Tree Segmentation between ALS & MLS