Every fall I ask my GIS students to answer the big questions in advance of their class projects. This year climate change, wildlife conservation, land use and water quality are important, as well as a number of other topics. Remote sensing continues to be important to GISers. Scientists, government and communities need to work together to solve problems. 

Why? 

• What does the proposed project hope to accomplish?
• What is the problem that needs to be addressed?
• What do you expect to happen?

How? 

• What analysis approach will be used?
• Why was this approach selected?
• What are alternative methods?
• Is the analysis reproducible?

What?

• What are the datasets that are needed?
• Where will they come from?
• Have you downloaded and checked this dataset?
• Do you have a backup dataset?

Who?

• Who will care about this? And why?
• How will they use the results?
• Will they be involved in the entire workflow?

Here are the responses from Fall 2017:

So much to learn! Here is my distillation of the main take-homes from last week. 

Notes about the workshop in general:

• Making participants do organized homework and install software is necessary for complicated workshop content: http://neondataskills.org/workshop-event/NEON-Data-Insitute-2017
• NEON used tips from software carpentry workshops, particularly the green-and-pink sticky tag tip. When you put up a pink sticky on your computer, you need help; when you put up a green sticky on your computer, you are all good. This makes everything go smoother and means participants don't have to hold up their hand to get attention.
• Having lots of helpful, friendly faces around to help during the coding bits, and having access to the code when you got behind was critical.
• The workshop content and daily schedule: 
  • http://neondataskills.org/data-institute-17/day1/
  • http://neondataskills.org/data-institute-17/day2/
  • http://neondataskills.org/data-institute-17/day3/
  • http://neondataskills.org/data-institute-17/day4/

NEON data and resources:

• http://www.neonscience.org/resources/data-tutorials
• All the NEON airborne data can be found here: http://www.neonscience.org/data/airborne-data
• For more on when data rolls out, sign up for the NEON eNews here: http://www.neonscience.org/

Other misc. tools:

• For cleaning messy data - check out OpenRefine - a FOS tool for cleaning messy data http://openrefine.org/
• Excel is cray-cray, best practices for spreadsheets: http://www.datacarpentry.org/spreadsheet-ecology-lesson/
• Morpho (from DataOne) to enter metadata: https://www.dataone.org/software-tools/morpho
• Pay attention to file size with your git repositories - check out: https://git-lfs.github.com/. Git is good for things you do with your hands (like code), not for large data.
• Markdown renderer: http://dillinger.io/
• MIT License, like Creative Commons for code: https://opensource.org/licenses/MIT
• There is a new project called "Feather" that allows compatibility between python and R: https://blog.rstudio.org/2016/03/29/feather/
• All the NEON airborne data can be found here: http://www.neonscience.org/data/airborne-data
• Information on the TIFF specification and TIFF tags here: http://awaresystems.be/, however their TIFF Tag Viewer is only for windows.
• All NEON point cloud classifications are done with LASTools. Go LASTools! https://rapidlasso.com/lastools/
• Check out pdal - like gdal for point clouds. It can be used from bash. Learned from my workshop neighbor Sergio Marconi https://www.pdal.io/
• Reflectance Tarps are made by GroupVIII http://www.group8tech.com/
• ATCOR http://www.rese.ch/products/atcor/ says we should be able to rely on 3-5% error on reflectance when atmospheric correction is done correctly (say that 10 times fast) with a well-calibrated instrument.
• NEON hyperspectral data is stored in HDF5 format. HDFView is a great tool for interrogating the metadata, among other things. https://support.hdfgroup.org/products/java/hdfview/

First of all, Pearl Street Mall is just as lovely as I remember, but OMG it is so crowded, with so many new stores and chains. Still, good food, good views, hot weather, lovely walk.

Welcome to Day 2! http://neondataskills.org/data-institute-17/day2/
Our morning session focused on reproducibility and workflows with the great Naupaka Zimmerman. Remember the characteristics of reproducibility - organization, automation, documentation, and dissemination. We focused on organization, and spent an enjoyable hour sorting through an example messy directory of misc data files and code. The directory looked a bit like many of my directories. Lesson learned. We then moved to working with new data and git to reinforce yesterday's lessons. Git was super confusing to me 2 weeks ago, but now I think I love it. We also went back and forth between Jupyter and python stand alone scripts, and abstracted variables, and lo and behold I got my script to run.

The afternoon focused on Lidar (yay!) and prior to coding we talked about discrete and waveform data and collection, and the opentopography (http://www.opentopography.org/) project with Benjamin Gross. The opentopography talk was really interesting. They are not just a data distributor any more, they also provide a HPC framework (mostly TauDEM for now) on their servers at SDSC (http://www.sdsc.edu/). They are going to roll out a user-initiated HPC functionality soon, so stay tuned for their new "pluggable assets" program. This is well worth checking into. We also spent some time live coding with Python with Bridget Hass working with a CHM from the SERC site in California, and had a nerve-wracking code challenge to wrap up the day.

Fun additional take-home messages/resources:

• ISO International standard for dates = YYYY-MM-DD
• Missing values in R = NA, in Python = -9999
• For cleaning messy data - check out OpenRefine - a FOS tool for cleaning messy data http://openrefine.org/
• Excel is cray-cray, best practices for spreadsheets: http://www.datacarpentry.org/spreadsheet-ecology-lesson/
• Morpho (from DataOne) to enter metadata: https://www.dataone.org/software-tools/morpho
• Pay attention to file size with your git repositories - check out: https://git-lfs.github.com/. Git is good for things you do with your hands (like code), not for large data.
• Funny how many food metaphors are used in tech teaching: APIs as a menu in a restaurant; git add vs git commit as a grocery cart before and after purchase; finding GIS data is sometimes like shopping for ingredients in a specialty grocery store (that one is mine)...
• Markdown renderer: http://dillinger.io/
• MIT License, like Creative Commons for code: https://opensource.org/licenses/MIT
• "Jupyter" means it runs with Julia, Python & R, who knew?
• There is a new project called "Feather" that allows compatibility between python and R: https://blog.rstudio.org/2016/03/29/feather/
• All the NEON airborne data can be found here: http://www.neonscience.org/data/airborne-data
• Information on the TIFF specification and TIFF tags here: http://awaresystems.be/, however their TIFF Tag Viewer is only for windows.

Thanks for everyone today! Megan Jones (our fearless leader), Naupaka Zimmerman (Reproducibility), Tristan Goulden (Discrete Lidar), Keith Krause (Waveform Lidar), Benjamin Gross (OpenTopography), Bridget Hass (coding lidar products).

Our home for the week

I left Boulder 20 years ago on a wing and a prayer with a PhD in hand, overwhelmed with bittersweet emotions. I was sad to leave such a beautiful city, nervous about what was to come, but excited to start something new in North Carolina. My future was uncertain, and as I took off from DIA that final time I basically had Tom Petty's Free Fallin' and Learning to Fly on repeat on my walkman. Now I am back, and summer in Boulder is just as breathtaking as I remember it: clear blue skies, the stunning flatirons making a play at outshining the snow-dusted Rockies behind them, and crisp fragrant mountain breezes acting as my Madeleine. I'm back to visit the National Ecological Observatory Network (NEON) headquarters and attend their 2017 Data Institute, and re-invest in my skillset for open reproducible workflows in remote sensing. 

Day 1 Wrap Up from the NEON Data Institute 2017
What a day! http://neondataskills.org/data-institute-17/day1/
Attendees (about 30) included graduate students, old dogs (new tricks!) like me, and research scientists interested in developing reproducible workflows into their work. We are a mix of ages and genders. The morning session focused on learning about the NEON program (http://www.neonscience.org/): its purpose, sites, sensors, data, and protocols. NEON, funded by NSF and managed by Battelle, was conceived in 2004 and will go online for a 30-year mission providing free and open data on the drivers of and responses to ecological change starting in Jan 2018. NEON data comes from IS (instrumented systems), OS (observation systems), and RS (remote sensing). We focused on the Airborne Observation Platform (AOP) which uses 2, soon to be 3 aircraft, each with a payload of a hyperspectral sensor (from JPL, 426, 5nm bands (380-2510 nm), 1 mRad IFOV, 1 m res at 1000m AGL) and lidar (Optech and soon to be Riegl, discrete and waveform) sensors and a RGB camera (PhaseOne D8900). These sensors produce co-registered raw data, are processed at NEON headquarters into various levels of data products. Flights are planned to cover each NEON site once, timed to capture 90% or higher peak greenness, which is pretty complicated when distance and weather are taken into account. Pilots and techs are on the road and in the air from March through October collecting these data. Data is processed at headquarters.

In the afternoon session, we got through a fairly immersive dunk into Jupyter notebooks for exploring hyperspectral imagery in HDF5 format. We did exploration, band stacking, widgets, and vegetation indices. We closed with a fast discussion about TGF (The Git Flow): the way to store, share, control versions of your data and code to ensure reproducibility. We forked, cloned, committed, pushed, and pulled. Not much more to write about, but the whole day was awesome!

Fun additional take-home messages:
- NEON is amazing. I should build some class labs around NEON data, and NEON classroom training materials are available: http://www.neonscience.org/resources/data-tutorials
- Making participants do organized homework is necessary for complicated workshop content: http://neondataskills.org/workshop-event/NEON-Data-Insitute-2017
- HDF5 as an possible alternative data format for Lidar - holding both discrete and waveform
- NEON imagery data is FEDExed daily to headquarters after collected
- I am a crap python coder
- #whofallsbehindstaysbehind
- Tabs are my friend

Thanks to everyone today, including: Megan Jones (Main leader), Nathan Leisso (AOP), Bill Gallery (RGB camera), Ted Haberman (HDF5 format), David Hulslander (AOP), Claire Lunch (Data), Cove Sturtevant (Towers), Tristan Goulden (Hyperspectral), Bridget Hass (HDF5), Paul Gader, Naupaka Zimmerman (GitHub flow).