Feasibility of Remote Sensing Data Sets for Evaluation of Next Generation Fire Behavior Models
U.S. Geological Survey (USGS) scientists are working with the U.S. Department of Defense (DoD) Environmental Security Technology Certification Program (ESTCP) to advance efforts to deploy next generation fire behavior models through a research-to-operations transition to enable land managers to use advanced modeling tools for real-time decision making. As part of this effort, USGS is leading an effort to assess the feasibility of using existing classified, commercial, and publicly available wildfire remote sensing datasets for model evaluation.
Background
Following an increasing need to understand and predict wildland fire behavior and effects under a broad range of burning conditions, scientists have been developing a new generation of fire behavior models that use advanced computer simulation techniques to represent and model the various processes driving fire behavior in wildland systems. These models typically require a large amount of precise environmental data to simulate a real burning environment. Thus, most efforts to date for evaluating these models have focused on prescribed and laboratory fires rather than on wildfires. However, there exists a wide range of classified, commercial, and publicly available wildfire remote sensing datasets that may offer new opportunities for evaluating these fire behavior models.
Objectives and Methods
The USGS is leading an effort to assess the feasibility of using state-of-the-art remotely sensed data sources for evaluating next generation fire behavior models. To this end, USGS convened a virtual workshop with experts in the fields of remote sensing and fire modeling. Collectively, these experts compared available remotely sensed data for three test-case wildfires that burned in Arizona in 2022 against the capabilities and needs of a suite of next generation fire behavior models.
Specifically, the workshop addressed three goals:
- To assess the suitability of a variety of classified, commercial, and publicly available remotely sensed datasets for advancing fire model evaluation.
- To develop ideas to integrate remotely sensed data products with fire model inputs and outputs.
- To identify barriers and limitations to performing an evaluation of next-generation fire behavior models.
A publicly available report that documents the results of this workshop is forthcoming. This report will support information exchange between remote sensing and fire modeling experts and help both groups meet their respective and interdependent objectives.
Assessing the potential for evaluation of wildland fire models using remotely sensed data—Summary proceedings from a U.S. Geological Survey workshop in 2024 Assessing the potential for evaluation of wildland fire models using remotely sensed data—Summary proceedings from a U.S. Geological Survey workshop in 2024
U.S. Geological Survey (USGS) scientists are working with the U.S. Department of Defense (DoD) Environmental Security Technology Certification Program (ESTCP) to advance efforts to deploy next generation fire behavior models through a research-to-operations transition to enable land managers to use advanced modeling tools for real-time decision making. As part of this effort, USGS is leading an effort to assess the feasibility of using existing classified, commercial, and publicly available wildfire remote sensing datasets for model evaluation.
Background
Following an increasing need to understand and predict wildland fire behavior and effects under a broad range of burning conditions, scientists have been developing a new generation of fire behavior models that use advanced computer simulation techniques to represent and model the various processes driving fire behavior in wildland systems. These models typically require a large amount of precise environmental data to simulate a real burning environment. Thus, most efforts to date for evaluating these models have focused on prescribed and laboratory fires rather than on wildfires. However, there exists a wide range of classified, commercial, and publicly available wildfire remote sensing datasets that may offer new opportunities for evaluating these fire behavior models.
Objectives and Methods
The USGS is leading an effort to assess the feasibility of using state-of-the-art remotely sensed data sources for evaluating next generation fire behavior models. To this end, USGS convened a virtual workshop with experts in the fields of remote sensing and fire modeling. Collectively, these experts compared available remotely sensed data for three test-case wildfires that burned in Arizona in 2022 against the capabilities and needs of a suite of next generation fire behavior models.
Specifically, the workshop addressed three goals:
- To assess the suitability of a variety of classified, commercial, and publicly available remotely sensed datasets for advancing fire model evaluation.
- To develop ideas to integrate remotely sensed data products with fire model inputs and outputs.
- To identify barriers and limitations to performing an evaluation of next-generation fire behavior models.
A publicly available report that documents the results of this workshop is forthcoming. This report will support information exchange between remote sensing and fire modeling experts and help both groups meet their respective and interdependent objectives.