Program Areas
Bridging the gap between cutting-edge research and on-the-ground management. The ReSHAPE program, an initiative of SWERI provides the mapped, accessible data needed to inform strategies that reduce the risk of catastrophic wildfire.
What is ReSHAPE?
A Multidisciplinary Approach: To understand how fuel treatments interact with wildfires ReSHAPE applies a holistic research framework that integrates ecological, social science, data science, and economic perspectives.
- Evidence-Based Insights: We evaluate interactions between treatments and wildfires to validate what works and identify where improvements are needed.
- User-Centric Development: By studying how practitioners use decision support tools, we ensure our research and tools meet the needs of end users.
- Data Synthesis: Our team “cross-walks” complex federal and state systems of record, transforming millions of records into a single, searchable national database.
- Economic Value: We provide the frameworks necessary to estimate return on investment for forest restoration, helping to justify critical funding for community and ecosystem protection.
This integrated research doesn’t just live in reports – it is used to iteratively advance TWIG, ensuring every map and dataset we provide is informed by rigorous, actionable science.
CONNECT (Social Science)
Our social science research program is evaluating the use and effectiveness of decision-support tools, including TWIG. Those engaged in making decisions about reducing unwanted wildfire impacts to social and ecological values are confronted with an overwhelming volume of geospatial data and decision-support products. The products are targeted at different types of decisions, objectives, and spatial scales.
CONNECT is working to:
- Inventory and document existing decision-support tools (DSTs)
- Evaluate use of existing DSTs and current applications
- Evaluate TWIG with identified audiences
- Prepare and support workshops, trainings, etc.
- Create a tool use evaluation method and support appropriate application of DSTs
- Understand how treatment goals and effects are conceptualized and measured throughout the lifespan of a treatment project.
Biophysical Effects
The biophysical effects program area provides the research needed to help understand how fuel treatments perform when interacting with wildfire. By analyzing recent fires and using remote sensing data, our researchers evaluate how ecologically appropriate management – such as thinning and prescribed fire – can successfully reduce fire risks and burn severity. These findings do more than just validate existing work; they provide actionable knowledge needed to make decisions about future fuel treatments.
Economics
Our economics research program, conducted by our partner, the Conservation Economics Institute, is aware that there is an overarching need to also include a focus on the economic effects of fuel treatments. In fact, the economic effects of fuel treatments – enhancing ecosystem services and avoiding numerous wildfire costs – are a rationale for fuel treatments.
Examining the benefits, cost effectiveness, and return on investments in fuel treatments can provide economic findings that can help explain the importance of fuel treatments, add to the justification of landscape-level approaches, and identify beneficiaries that might pay for ecosystem services and greater funding for treatments.
Data Science
Data related to wildfire fuel treatment and management is collected by multiple agencies. However, the data is often not designed to address key land management questions, lacks comparability, is not easily accessible, and suffers from inconsistent data entry strategies.
There is a necessity to clarify what data is included in the federal systems of record, integrate and visualize various datasets related to fuel treatments and wildfire incidents, and establish a process for comparing similar data while informing users of differences in terminology and data measurements.
Additionally, developing predictive models and leveraging big data analytics will enhance confidence in analyses, identify potential data gaps, and improve the identification of high-risk areas for targeted fuel treatments. Quantifying and reducing uncertainty in data and models will further support informed decision-making.
Frequently Asked Questions
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