With an increase in the risk of large fires across much of the Western United States, along with a growing variety of fuel types that result from changes in the landscape and management strategies, there has never been a more pressing need for accurate, cost-efficient, large scale forest fuel maps. Emerging remote sensing technologies may yield exactly the kind of large scale maps needed to more accurately predict forest fuel loads, fire risk, and fire behavior. With the Greater Yellowstone Ecosystem as their backdrop, Don Despain, Sasaan Saatchi, Kerry Halligan, Richard Aspinall, and Robert Crabtree worked together to acquire a detailed catalogue of remote sensing data for estimating forest fuel load, and creating subsequent maps. They retrieved passive (optical) and active (radar and LiDar) remote sensing data from a variety of sensors, interpreted the data, combined the data, and created maps—all with the intent of fi nding the most accurate remote sensing data in terms of its correlation with their “on the ground” field data. They found remarkably close accuracy with their airplane-retrieved radar data, showing that particular sensors could achieve about 70 percent accuracy compared to field data in predicting fuel load. This work helps mark a new era of potentially more accurate and cost-effective remote sensing technology specifi cally in regards to estimating forest fuel load, and related mapmaking
