ESIP 2022 January Meeting Materials for the session 'In-situ and remotely-sensed data integration for wildfire management'

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

• ESIP meeting webpage for this workshop session
• Presentations from this session are located under the respective speakers in the "Agenda" section below.

People involved

• Session organizers:
  • Brian Wee
  • Bill Teng
  • Scotty Strachan
  • Martha Apple
  • Lindsay Barbieri
  • Rustem 'Arif' Albayrak

• Presenters:
  • Fire Chief Dave Winnacker (Moraga-Orinda Fire District)
  • Xiaolin Hu (Professor of Computer Science, Georgia State University)

• Session attendees: <to be released as a Google doc>

Overview

The proposed 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

<to be published>

Three main takeaways

<to be published>