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visual map of the topics that will be discussed in this session can be previewed here (warning: very large image).
This session continues the synthesis of ideas contributed by individuals from various ESIP clusters (including Agriculture and Climate, Semantic Harmonization, EnviroSensing, Machine Learning, and Drones) applied to wildfire management. This session focuses on the challenge of ingesting heterogeneous data from in-situ and remotely-sensed systems into models and applications between the pre-fire and fire containment phases. Scenarios include (a) using heterogeneous data for better planning prescribed burns by using data before and after a burn for ingestion into fire behavior models, and (b) using heterogeneous data to recommend both strategic fuel break siting during pre-fire planning and optimal containment line location in the course of active wildfire fighting.
This synthesis session directly extends two of the key takeaways proposed by discussants during the 2021 ESIP summer meeting session “Identifying technology capabilities that meet wildfire science and practitioner requirements”: (a) “...improve fusion among near-term fire behavior model data, values-at-risk data, and sensor data that can be represented and visualized in a Common Operating Picture”, and (b) “...better estimate burn severity by fusing data from various sources (in-situ, remote, model)”.
High-level agenda for this session:
- Preview and synthesis of session concepts (Brian Wee | Massive Connections)
- Stakeholder perspective: Keeping your eyes on the big picture (Genny Biggs | Gordon and Betty Moore Foundation)
- Stakeholder perspective: Challenges from the wildfire frontlines (Chief Dave Winnacker | Fire Chief at Moraga-Orinda Fire District)
- Technical solution perspective: In-situ EnviroSensing: challenges and opportunities in the (wild)fire continuum (Scotty Strachan | Nevada EPSCoR | ESIP EnviroSensing Cluster)
- Technical solution perspective: sUAS data use/reuse/repurpose for science and management (Andrea Thomer | University of Michigan | ESIP Drones Cluster)
- Technical solution perspective: Wildland Fire Simulation and Data Assimilation using UAS data (Xiaolin Hu | Georgia State University)
- Technical solution perspective: AI/ML for Wildfire: Limits and Opportunities (Ziheng Sun | George Mason University | ESIP Machine Learning Cluster)
- Breakout groups for (1) In-situ and remote data fusion, (2) UAS data ingest into models.
- Breakout groups present on (1) Barriers to implementation, (2) What is achievable in the short-term
- Synthesis and looking ahead
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