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Abstract Forest fire frequency, extent, and severity have rapidly increased in recent decades across the western United States (US) due to climate change and suppression‐oriented wildfire management. Fuels reduction treatments are an increasingly popular management tool, as evidenced by California's plan to treat 1 million acres annually by 2050. However, the aggregate efficacy of fuels treatments in dry forests at regional and multi‐decadal scales is unknown. We develop a novel fuels treatment module within a coupled dynamic vegetation and fire model to study the effects of dead biomass removal from forests in the Sierra Nevada region of California. We ask how annual treatment extent, stand‐level treatment intensiveness, and spatial treatment placement alter fire severity and live carbon loss. We find that a ∼30% reduction in stand‐replacing fire was achieved under our baseline treatment scenario of 1,000 km2 year−1after a 100‐year treatment period. Prioritizing the most fuel‐heavy stands based on precise fuel distributions yielded cumulative reductions in pyrogenic stand‐replacement of up to 50%. Both removing constraints on treatment location due to remoteness, topography, and management jurisdiction and prioritizing the most fuel‐heavy stands yielded the highest stand‐replacement rate reduction of ∼90%. Even treatments that succeeded in lowering aggregate fire severity often took multiple decades to yield measurable effects, and avoided live carbon loss remained negligible across scenarios. Our results suggest that strategically placed fuels treatments are a promising tool for controlling forest fire severity at regional, multi‐decadal scales, but may be less effective for mitigating live carbon losses.
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Leveraging the next generation of spaceborne Earth observations for fuel monitoring and wildland fire management
Abstract Managing fuels is a key strategy for mitigating the negative impacts of wildfires on people and the environment. The use of satellite‐based Earth observation data has become an important tool for managers to optimize fuel treatment planning at regional scales. Fortunately, several new sensors have been launched in the last few years, providing novel opportunities to enhance fuel characterization. Herein, we summarize the potential improvements in fuel characterization at large scale (i.e., hundreds to thousands of km2) with high spatial and spectral resolution arising from the use of new spaceborne instruments with near‐global, freely‐available data. We identified sensors at spatial resolutions suitable for fuel treatment planning, featuring: lidar data for characterizing vegetation structure; hyperspectral sensors for retrieving chemical compounds and species composition; and dense time series derived from multispectral and synthetic aperture radar sensors for mapping phenology and moisture dynamics. We also highlight future hyperspectral and radar missions that will deliver valuable and complementary information for a new era of fuel load characterization from space. The data volume that is being generated may still challenge the usability by a diverse group of stakeholders. Seamless cyberinfrastructure and community engagement are paramount to guarantee the use of these cutting‐edge datasets for fuel monitoring and wildland fire management across the world.
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- Award ID(s):
- 2153040
- PAR ID:
- 10566592
- Publisher / Repository:
- Wiley Online Library
- Date Published:
- Journal Name:
- Remote Sensing in Ecology and Conservation
- ISSN:
- 2056-3485
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/rse2.416
