Landsat data preprocessing workshop

12:00 pm on Friday, May 9, 2014
3:00 pm on Friday, May 9, 2014
CAS 435, computer lab
Hello everyone, I will be hosting a short workshop on Landsat data preprocessing this Friday (Friday, May 9th) at 12:00PM - 3:00PM in room 435 (4th floor computer lab) and all interested are encouraged to attend. During these three hours I will explain how to assemble large Landsat datasets on the GEO computing cluster from the initial steps of finding and ordering imagery to organizing and summarizing atmospherically corrected and cloud masked image stacks. Please RSVP using this small survey: Overview: The Landsat family of satellites have been in space since 1972 and can provide a rich history of the land surface of the Earth over the last ~40 years. Recent advances in computing hardware, free access to the data, and new innovative cloud screening and atmospheric correction algorithms have enabled researchers to "data mine" this archive. Analyses that were previously only possible at a very coarse resolution (e.g., AVHRR and MODIS) are beginning to be possible at the 30m Landsat resolution. This workshop intends to teach good practices for acquiring, organizing, preprocessing, and documenting your dense Landsat time series before ingestion into the "data mining" algorithm of your choice. Requirements: 1. GEO user account 2. Basic familiarity with Linux file operations using the Bash shell (or enough familiarity with your shell of choice to translate). To determine which shell you're using, type "ps -p $$" in your terminal 3. Basic familiarity with a text editor on the GEO cluster (nedit, vim, emacs, etc.) 4. Approximately 50GB of space on the GEO cluster (email me if this is a problem and I can set you up with some temporary space) Topics: 1. Search for and filter Landsat acquisitions using EarthExplorer 2. Submit order for "Landsat Climate Data Record (CDR)" product using ESPA 3. Download completed order onto GEO cluster 4. Organize and extract imagery from archives 5. Combine dataset bands into "stacked" images of common geographic extent 6. Summarize and document dataset Advanced: 7. Run Fmask using custom parameters on images 8. Create spatial subset using extent or polygon ROI 9. Create "preview" images to help filter through preprocessed imagery Please email me with any questions. Thanks, Chris Holden