Scientific Research

Modelling chamise fuel moisture content across California: a machine learning approach

2022. Scott B. Capps, Wei Zhuang, Rui Liu, Tom Rolinski, Xin Qu

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← Previous Characterizing the Rate of Spread of Wildfires in Emerging Fire Environments of Northwestern Europe Next → Rapid attribution analysis of the extraordinary heat wave on the Pacific coast of the US and Canada in June 2021.
A large wildfire burns on a hillside behind a residential neighborhood with red-roofed houses, sending thick gray smoke into the sky; overlaid text describes an “urban conflagration” where fire spreads into a wildland-urban interface and destroys buildings with high intensity.

Scientific Research

Technosylva’s Urban Conflagration Model Helps Utilities and Fire Agencies Prepare for Fires in Populated Areas

Technosylva’s Urban Conflagration Model addresses that gap by simulating how fires spread through the wildland-urban interface, where structure density, vegetation encroachment, and fuel types create fire behavior that looks fundamentally different from wildland fires.

Scientific Research

Assessing the suppression difficulty of wildland fires for initial attack response

2025 • Adrián Cardil, Adrián Jiménez-Ruano, Santiago Monedero, Phillip SeLegue, Macarena Ortega, Raúl Quilez, Jeff Fuentes, Geoff Marshall, Robert Clark, Tim Chavez, Sergio de-Miguel, Tomás Quiñones, Fernando Bezares, Scott Purdy, Kristen Allison, Tim Metzger, Chris Waters, Midhun Mohan, Joaquín Ramirez

Scientific Research

FIRE-RES Geo-Catch: a mobile application to support reliable fuel mapping at a pan-European scale

2023 • Erico Kutchartt, José R. González-Olabarria, Antoni Trasobares, Sergio de-Miguel, Adrián Cardil, Brigite Botequim, Vassil Vassilev, Palaiologos Palaiologou, Martino Rogai, Francesco Pirotti
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