Mapping wildland fuels for fire management across multiple scales: Integrating remote sensing, GIS, and biophysical modeling

TLDR

Fuel maps are essential for computing spatial fire hazard and risk and simulating fire growth and intensity, yet mapping them is highly complex, requiring expertise in remote sensing, fire behavior, fuels modeling, ecology, and GIS, and future efforts must improve field data, fuel models, GIS layers, satellite imagery, and ecosystem models. The paper outlines challenges in mapping fuels—such as canopy concealment, fuelbed complexity, fuel type diversity, fuel variability, and model generalization—and proposes a method that uses current remote sensing and image processing technology. Four approaches to mapping fuels are discussed—field reconnaissance, direct mapping, indirect mapping, and gradient modeling—and a method using current remote sensing and image processing technology is proposed.

Abstract

This paper was presented at the conference ‘Integrating spatial technologies and ecological principles for a new age in fire management’, Boise, Idaho, USA, June 1999 Fuel maps are essential for computing spatial fire hazard and risk and simulating fire growth and intensity across a landscape. However, fuel mapping is an extremely difficult and complex process requiring expertise in remotely sensed image classification, fire behavior, fuels modeling, ecology, and geographical information systems (GIS). This paper first presents the challenges of mapping fuels: canopy concealment, fuelbed complexity, fuel type diversity, fuel variability, and fuel model generalization. Then, four approaches to mapping fuels are discussed with examples provided from the literature: (1) field reconnaissance; (2) direct mapping methods; (3) indirect mapping methods; and (4) gradient modeling. A fuel mapping method is proposed that uses current remote sensing and image processing technology. Future fuel mapping needs are also discussed which include better field data and fuel models, accurate GIS reference layers, improved satellite imagery, and comprehensive ecosystem models.