Difference between revisions of "ESIP 2022 January Meeting Materials for the session 'In-situ and remotely-sensed data integration for wildfire management'"

Latest revision as of 10:48, January 21, 2022

Purpose of this page

This page provides a summary of the session ESIP 2022 January Meeting session 'In-situ and remotely-sensed data integration for wildfire management' held on 2022-01-19.

Pointers to essential documents

Big picture synthesis diagram with slide excerpts from workshop presenters updated with participant input
ESIP meeting webpage for this workshop session
Google notes document used to document the proceedings of the workshop session
Presentations from this session are located under the respective speakers in the "Agenda" section below.

People involved

Session organizers:

Presenters:

Overview

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)”..

Agenda

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

Three main takeaways

Connect early with stakeholders to co-design and co-develop solutions so as to include appropriate scale of application and intended audience/user, and to be useful and usable within stakeholder environments.
Need to extend /apply science-based observation architectures (sensors, communications, data management, compute, etc) to operational needs, by appropriately aggregating and packaging the data for use in operational environments.
Co-designed and co-developed solutions must take into consideration operational constraints (e.g. logistical, legal, citizen expectations) especially in wildfire response (as opposed to pre-fire planning) contexts.

@@ Line 30: / Line 30: @@
 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)”..
+This synthesis session directly extends two of the key takeaways proposed by discussants during the [https://tinyurl.com/2021julyESIPworkshop 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)”..
 ==Agenda==