GIS 4035 - Remote Sensing and Photo Interpretation Student Spotlight

GIS4035/L Remote Sensing and Photo Interpretation, Instructor, Mr. Brian Fulfrost

Module 10 - Supervised Classification

Lab Description: For this laboratory exercise students used data that covers the County of Gray’s Harbor, located in Washington State. Assuming that the area is to be used as a camp for military exercises, students learned how to perform a supervised classification by collecting sets of pixels to define spectral signatures. Students also evaluated the accuracy of those signatures, and used them to classify the entire image.

Student Spotlight Award

The BLOG postings for Module 10 on supervised classification were overall very good! The Goal(s) for this BLOG post were:

• Create spectral signatures and AOI features
• Produce classified images from satellite data
• Recognize and eliminate spectral confusion between spectral signatures

Although many students maps and results demonstrated their growing expertise in automated digital image processing, we found Alicia Lindbom's BLOG post, whose BLOG posts have been consistently very good throughout the semester, demonstrated a high level of  understanding of the process and techniques if supervised image classification.

Alicia Lindbom

Congratulations to Alicia Lindbom for being selected as this week's student spotlight!  Alicia is a returning adult learner currently completing the GIS Online Graduate Certificate program. She has been an environmental educator and program coordinator for the past 7 years, as well as a scientific illustrator on a recent Mesa College laboratory manual.   Upon completion of the certificate, Alicia hopes that the new GIS skills paired with her outreach experience will move her into a higher level science education/research paired with field work.  Welcome back to the spotlight Alicia!

Why was Alicia selected? Alicia has approached each Module, including this module on supervised classification, with critical thinking. Her BLOG includes a concise introduction to the goals of the module - which is essential for the reader to best understand the remainder of the post. In addition, her description includes just the right amount of detail about the process of applying the techniques of supervised classification. This includes the development of spectral signatures, the choice of spectral bands and analysis of the spectral distance file, which identifies pixels "spectral distance", from the signatures - the brighter the pixel the more off it is from the training site. and ultimately the type of supervised classification .Alicia did a great job describing both the techniques she applied as well as the limitations she encountered developing spectral signatures. Alicia does a very good job describing the process for choosing spectral signatures that serve as the "best ft" for training sites that most comprehensively identify the desired level of Land Use Land Cover classifications She also does an excellent job at describing the iterative process of band choice and training site refinement that is crucial to producing more accurate supervised classifications. Her results, which did an admirable job distinguishing urban and roads which is very difficult with the chosen imagery, are presented in a visually compelling map. Overall, Alicia's BLOG posting was very good!

GIS6005- Communicating GIS - Student Spotlight

GIS 6005 - Communicating GIS, Instructor, Dr. Derek Morgan

Communicating GIS is the first course to be offered from the M.S.A. with a specialization in Geographic Information Science (GIS) degree plan. This course begins with the basic theory of graphic design, cartography, and map production and distribution. Students then learn to communicate specific types of spatial and analytical information through maps, written and oral explanations, graphs, tables, charts, and interactive web mapping applications.

During week 9, students built on previous exercises and worked with techniques for including multiple variables within a single map. When effectively designed multivariate mapping can be used to show relationships between variables, making this method particularly useful for exploration and confirmation purposes. The lab drew on previous material such as color, choropleth and map design in general.

STUDENT SPOTLIGHT AWARDS

We would like to recognize EmilyVandenheuvel for her multivariate module work. 
 

Emily has been a pleasure to have in the class. Emily comes to us with a BA in Geography & Psychology from Northern Michigan University. Her previous education has served her well in Communicating GIS, as she has thoughtfully contributed to course discussions both as a student and discussion leader. For instance during the week of lab 6 Emily asked clarifying questions regarding the usage of normalized variables within Choropleth mapping that fostered a subsequent dialog that was useful to her classmates. For the multivariate lab Emily successfully created a bivariate map illustrating obesity as it relates to physical activity rates across the USA. Her legend (shown here) demonstrates many of the design principles that we covered in the class. She makes use of different typography and colors effectively showing the relationship between these two variables. Also, her blog post is well written and shows the corresponding map she produced.

Great work Emily!  

Multivariate Mapping Module Learning Outcomes

• Recall map design choice for multivariate maps
• Explain how multivariate maps contribute to a better understanding of the relationship between variables.
• Carry out bivariate choropleth using GIS software