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. 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. In addition, this course emphasizes information literacy, and will develop your skills in finding, evaluating, and using scholarly information.

You will be asked to work through a series of modules that present information relating to a specific topic. You will also complete a series of lab exercises to reinforce the material. Lastly, you will complete paper reviews and a term project. We have also provided additional bonus material and links associated with surface hydrologic analysis with TauDEM, geographic object-based image analysis (GEOBIA), Google Earth Engine (GEE), and the geemap Python library for Google Earth Engine. Please see the sequencing document for our suggested order in which to work through the material. We have also provided PDF versions of the lectures with the notes included.

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 are interested in using QGIS and associated tools for remote sensing and image analysis, check out our Open-Source GIScience course.

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 (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. 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.