Remote Sensing
This course explores the theory, technology, and applications of remote sensing. It is designed for individuals with an interest in GIS and geospatial science who have no prior experience working with remotely sensed data. Lab exercises make use of the web and the ArcGIS Pro software. A few additional exercices have been provided that use QGIS. You will work with and explore a wide variety of data types including aerial imagery, satellite imagery, multispectral imagery, digital terrain data, light detection and ranging (LiDAR), thermal data, and synthetic aperture RaDAR (SAR). Remote sensing is a rapidly changing field influenced by big data, machine learning, deep learning, and cloud computing. In this course you will gain an overview of the subject of remote sensing, with a special emphasis on principles, limitations, and possibilities.
This course makes use of the ArcGIS Pro software package from the Environmental Systems Research Institute (ESRI). Directions for installing the software have been provided. If you are not a West Virginia University student, you can still complete the labs but you will need to obtain access to the software on your own. We assume prior experience with ArcGIS Pro. If you are working through our materials, we suggest completing the GIScience course prior to attempting this material.
If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course.
If you would like an introductory text on remote sensing, we would suggest Imagery and GIS: Best Practices for Extracting Information from Imagery by Green, Congalton, and Tukman.
After completing this course you will be able to:
- use the concept of the remote sensing system to explain the process by which remote sensing approaches can be used to gain information about the Earth.
- find remotely sensed data using the web.
- describe the spectral reflectance properties of major land cover types (vegetation, rocks, soils, snow, and clouds) and use this information in image interpretation and analysis.
- list the major sources of remote sensing data and describe their spatial, temporal, spectral, and radiometric characteristics.
- describe typical steps and procedures for enhancing, analyzing, and classifying images.
- use geospatial software to undertake basic image enhancement and analysis.
- summarize scientific knowledge in written form in a coherent and structured manner.
- apply critical thinking in reading and writing about scientific concepts.
This course was produced by West Virginia View (www.wvview.org) with support from AmericaView (americaview.org). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077 and G23AP00683. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey.
Course content generated by West Virginia View (WV View) are licensed under CC BY-NC-SA 4.0.
© [2026] West Virginia View (WV View). Unless otherwise noted, this
work is licensed under a Creative Commons
Attribution-NonCommercial-ShareAlike 4.0 International License (CC
BY-NC-SA 4.0). You are free to share and adapt these materials for
non-commercial purposes, provided you give appropriate credit and
distribute any derivative works under the same license. To view a
copy of this license, visit
https://creativecommons.org/licenses/by-nc-sa/4.0/.
Suggested attribution string: "[Title of Material]" by West Virginia View (WV View) is licensed under CC BY-NC-SA 4.0. Available at [URL].