Be able to articulate the differences between the different types of "resolutions" in real world contexts:
spatial resolution
temporal resolution
radiometric resolution
spectral resolution
Be familiar with the datasets and satellites that we have used and discussed in class. What applications could each be useful for?
What are some real world applications of spectral signatures? How might these be related to the process of supervised and unsupervised classifications?
Be able to articulate in detail what NDVI is, how it is calculated, what it is indicative, and why.
Be able to look up metadata (either in ArcGIS Online or GEE) in order to learn specifics about datasets
Apply the concepts of the electromagnetic spectrum and atmospheric windows to discuss how to design sensors for certain applications.
Know generally what a projection is, how it is created, and what you need to keep in mind when determining what type of projection to apply where.
Have a really good handle on "overlay layers" and how "union, intersect, and erase" can be used when trying to understand data layers
Know what a space-time cube is, how it is applied, and how it is structured
Be able to think through a case study in geospatial ethics
Be able to articulate why adding a geospatial component enriches data analysis and interpretation
What is a suitability analysis and when/why might you use it?
Be able to reflect on what you have learned and how you might use GIS / Remote Sensing in the future.
Be able to draw out a simple geoprocessing model for a simple geospatial task - or alternatively - be able to interpret a geoprocessing model provided to you
Know the difference between spatial operations (query, join, etc.) and attribute operations (query, join, etc.)
Know the unique advantages/disadvantages of raster and vector data.
If given a map, be able to critique it according to our Map Evaluation Guidelines