Remote Sensing Course Syllabus, Spring 2022

Learning about GIS and remote sensing involves a mixture of education and training:

Help file (text) 



Readings.  You are responsible for all these pages, but not links on them.

Learning Outcomes

Practical Exercises/labs

1,2 What are GIS and Remote Sensing
Vector and raster data
Map elements
  • Describe the difference between GIS and remote sensing, and the ways they complement each other.
  • Differentiate raster and vector data, and what happens to each as the map scale changes
  • Recognize the pan, VIS/NIR, and TIR bands from their different spatial resolutions.
  • Use false color imagery for its depiction of vegetation and sharp land/water contrast because of the response to NIR radiation.
  • Create maps with legends, grids, and scalebars.
  • Recognize the common open data formats for imagery, grids, vector data, and point clouds
  • Understand how the number of points in a grid increases as the data spacing changes.



3,4 Landsat TM
  • Use the Stefan-Boltzmann and Wien's displacement laws to explain the differences we can get in spatial resolution in different parts of the spectrum.
  • Explains the tradeoffs among spatial, spectral, radiometric, and temporal resolution for satellite imagery.
  • Analsyze multispectral imagery from Landsat and Sentinel-2 satellites.
  • Differentiate atmospheric absorption bands and windows, and how they affect remote sensing
  • Use the different albedoes at different wavelengths to interpret multispectral imagery.


Black body radiation
Reflectance spectra

Annapolis TM8 scene



Eureka Valley Landslide

  • Interpret reflectance spectra in terms of albedo, wavelength, and surface materials.
  • Interpret color satellite imagery in terms of its use of three bands displayed in red, green, and blue.
  • Interpret grayscale satellite imagery in terms of the intensity is a single band.
  • Use contract enhancement to stretch colors in a particular range of DN's to bring out relevant details in the imagery.
  • Differentiate DN, radiance, reflectance, brightness temperature
  • Use sun position to interpret shadows in imagery, particularly the seasonal patterns for sun-synchronous satellites





RGB image separation

Grayscale game

Band computations

Band ratios and indexes


Kangaroo Island Fires
6,7 Lidar

This week and the next were shifted, so that we will have covered lidar before you pick your project topic.

  • Evaluate lidar data in terms of useful aspects almost always present in addition to the point elevation (return intensity and classification as ground/other) and data that may be present if desired by the customer paying for the survey (merged true or false color imagery, and more complete classification).
  • Use the wavelength of the laser to evaluate what the data show.
  • Understand what return number shows about the lidar return.
  • Describe how the lidar collects elevation and intensity, and how the ground classification is later accomplished.
  • Differentiate Map scale: small scale (large area, low detail) and large scale (small area, high detail)
  • Understand why we measure radiance and then compute brightness temperature in the TIR, and reflectance in the VIS, NIR, and SWIR.
  • Avoid the dangers of using screen pixel size to characterize spatial resolution when you are not viewing satellite imagery at full resolution.

Sentinel-2 Hub and Playground


Yosemite Rock Fall
  • Grid lidar for time series and change detection, understanding the choices made and the different land surfaces.
  • Understand how landcover uses data from Landsat and Sentinel to divide the land surface into about two dozer caterogories like forest, crops, developed.
  • Interpret lidar point clouds and grids.
  • Discuss the challenges of using a difference grid to track land cover changes.

US state sites
Country sites (including arctic)
Las Vegas NLCD

Data sources (US)

Data sources (international)

  • Earth Explorer for satellite imagery, and the SRTM DEM
  • If you want the Sentinel 2 land cover contact your instructor.  It is a huge download.


10.11 Hyperspectral





  • Only material above will be on the final exam
  • Describe how the radar altimeter can measure with geoid, SWH, and wind speed with a single active radar.
  • Explain why "sea level" measured by GPS and other satellite systems can be up to 100 m different from the water, and why you must consider whether elevations are referenced to the geoid or the ellipsoid.
  • Understand that a local tidal datum applies to nautical charts, and  that is can be substantially different from the national datum used on land maps.
  • Describe how laser altimeters work, and the tradeoffs with collecting full waveform data.
  • Differentiate multispectral and hyperspectral imagery.
  • Discuss the difference between supervised and unsupervised classification.

Key data sets, with directions for your projects.  Because you have choices, and each of the data sources is different, I anticipate that many of you will need EI to get through all the details.

Quick labs

12,13 Web viewing and processing



weather satellites