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TEAM ACTIVITY Day 1: Make a plan

Instructions

Work through the prompts in order. Please use a decision-making method “to decide” before moving to a new section of the activity.

Brainstorming pictures

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Day 1 Objectives

  1. Get to know your group members.
  2. Decide on a research question and project title.
  3. Start exploring potential datasets.

Introductions (approx. time: 10 mins total or "1-2 breaths" per prompt)

Please share the following information about yourself. Each team member should type their response in the space below (create more as needed).

  • Name: Karem Meza
  • Pronouns: She
  • Expertise: Remote Sensing and water flux
  • Environmental Data Science Superpower: Machine learning
  • Reflection on Polarities Exercise: Stay at comfort zone
  • Name: Kimberly Thompson
  • Pronouns: she/her
  • Expertise: ecological modelling, time series analysis, spatial distributions
  • Environmental Data Science Superpower: quantitative analysis
  • Reflection on Polarities Exercise: My answers always depended on context
  • Name: Evan Gallant
  • Pronouns: he/him
  • Expertise: Sustainable food systems, but moreso the social side
  • Environmental Data Science Superpower: N/A, but an interest in connecting people with insights that can be gained from applying ML to remote sensing data
  • Reflection on Polarities Exercise: Flip flopped compared to what I would have answered a couple years ago
  • Name: Cove Sturtevant
  • Pronouns: he/him
  • Expertise: biometeorology, data science
  • Environmental Data Science Superpower: Centralized code
  • Reflection on Polarities Exercise: I gesture a lot
  • Name: Cameron Pittman
  • Pronouns: He/Him
  • Expertise: Biodiversity Infromation Sytems/Collection Management Systems
  • Environmental Data Science Superpower: Database Managemnet/Machine Learning
  • Reflection on Polarities Exercise: I use my hands a lot
  • Name: Moses Kiwanuka
  • Pronouns: He/Him
  • Expertise: Water resources/quality, Remote sensing, GIS
  • Environmental Data Science Superpower: Machine learning / Deaap Learning
  • Reflection on Polarities Exercise: I prefer abit of more silence but i can talk where need be.
  • Name: Ivan Oyege
  • Pronouns: He/Him/His
  • Expertise: Pesticide Chemistry/Sustaible agriculture
  • Environmental Data Science Superpower: Using Machine Learning and Deep Learning in modeling of environmental and agricultural data
  • Reflection on Polarities Exercise: I love short emails
  • Name: Youmi Oh
  • Pronouns: She/her
  • Expertise: Greenhouse gas monitoring and modeling
  • Environmental Data Science Superpower: atmosphere and biosphere modeling
  • Reflection on Polarities Exercise: I need to learn how to stand silence :)
  • Name: Jarrod Red Bird]
  • Pronouns: [He,him]
  • Expertise: [IT Specialist Rosebud Sioux Tribe]
  • Environmental Data Science Superpower: [learning more about data science, ]
  • Reflection on Polarities Exercise: [feel more alive and have a better sense being outdoors. traditional knowledge]
  • Name: William Blacksmith
  • Pronouns: He/Him
  • Expertise: IT Specialist/Network Administrator
  • Environmental Data Science Superpower: No Superpower in Data Science, But a fast learner
  • Reflection on Polarities Exercise: Shy and keep to myself
  • Name: Nathan Korinek
  • Pronouns: He/Him
  • Expertise: ML/NN, big data, data synthesis, python, wildfire
  • Environmental Data Science Superpower: I have used python to tackle large datasets and find meaningful results in collaboration with many others
  • Reflection on Polarities Exercise: Not AS shy as I thought

Research Question: Innovation for Inclusion or Computation (approx. time: 5-10 mins)

Write the research question your team selected in the space below. Feel free to revise the original question.

  • How do wildfire severity, human activity, and environmental factors effect water quality downstream from fire sites?

Project Title (approx. time: 5-10 mins)

Craft a catchy title for your team’s project. Think of something that would grab attention at a conference or in a headline.

  • Wildfire AI

Promoting Resilience and Adaptation

Describe how your proposed project aligns with the Summit's themes of resilience and adaptation. Please provide 1-2 sentences that clearly connect your project's goals or methods to these themes.

  • Using machine learning (AI) approaches to answer novel, difficult questions about how combined factors influence key ecosystem metrics is an important aspect of understanding complex environmental systems. Understanding these systems is crucial to managing them sustainably and increasing their resilience. Machine learning offers an approach to model and understand these complexities, paving the way for action. (PLEASE REVISE AS YOU SEE FIT - I'M TIRED)

Choosing Big Data Sets

Explore potential data sets for your project's topic from the data library. List your options below, organizing them by whether they represent the system you're studying (e.g., deciduous forests) or the disruption to it (e.g., wildfire). Then discuss your choices and indicate your final selections.

https://docs.google.com/document/d/10KbRaTRBYmO_Uwj4yUQRmzmvxG0NyPjgYAIZjx5g3Mg/edit?usp=sharing Flowchart (1)

Draft Potential Data Sets

  • System Being Perturbed/Disrupted:
    • Water:
    • USGS Stream Gauge data - water quality
    • DB Hydro (is this only for Florida?)
  • Perturbator/Disrupter:
    • Fire - severity, DNBI/DNBR:
    • In general, comparing remote sensing data from before and after the fire
    • Fire - extent:
    • MTBS (Monitoring Trends in Burn Severity)
    • FIRED
    • National Interagency Fire Center - active/live fires as well as historical data
    • Water:
    • USGS Stream Guage data
    • DB Hydro
    • Human activity:
    • NLCD (National land cover database)
    • Other:
    • Looking for meteorological, soil, vegetation (NDVI), ecosystem type

Severity - how it affects the area (this is the metric we want) Intensity - how hot the fire burned

Final Choice

  • System Being Perturbed/Disrupted (Final Choice):
    • USGS Stream Gauge data - for water quality
    • NHD (National Hydrologic Dataset) - for river confluences and boundaries
  • Perturbator/Disrupter (Final Choice):
    • MTBS - for burn severity

Brief Check-in: Definition of Resilience (approx. 5 mins)

Below is a working definition of the word "Resilience" for the Summit. Please edit the definition below based on your earlier discussion and chosen project.

"Resilience is the capacity of a system, community, organization, or individual to absorb stress, recover from disruptions, adapt to change, and continue to develop and thrive."

  • [Edit or reaffirm this definition here]

Day 1 Report Back

Select one representative from your group to present your proposed project. For the report back, each group will have 30-60 seconds to present their responses to the questions below. Keep it concise and focused. This is just a quick oral presentation - you will not be able to use slides/images.

  • Project Title:
  • Wildfire AI
  • Research Question:
  • How do wildfire severity, human activity, and environmental factors effect water quality downstream from fire sites?
  • Selected Data Sets:
  • Still finalizing
Last update: 2024-05-19