Enhanced Wildland Fire Management Decision Support Using Lidar-Infused LANDFIRE Data

This proposal seeks to develop a prototype application that integrates nationally available LANDFIRE data with locally available lidar data to produce high quality vegetation structure and fuels information to support strategic and tactical wildland fire decision-making at incident/local scales. The project focuses specifically on the integration of Earth observations into decision-making activities and cross-cuts two of NASAs Applied Sciences Program Priority Topics, e.g., Fire Risk Assessment Prediction, and Planning; and Fuels Inventory, Characterization, and Mapping. The application will initially exist within the Wildland Fire Assessment System, a decision support system developed and managed by the USDA Forest Service to support prototype applications. The proposed work addresses two significant shortcomings in fuels mapping. First, the vegetation structure inputs to LANDFIRE are a known weakness in the Wildland Fire Decision Support System at local scales with important implications for accurately predicting crown-fire initiation and fire spread in forests. Second, although active remote sensing techniques like lidar have long been recognized as potentially valuable for improving structural characterizations of forests, they have not systematically been integrated in fuels mapping efforts because they have historically not been widely available and were often difficult to work with. The proposed application addresses both of these shortcomings by integrating existing LANDFIRE data with consistent measurements of height, cover, and fuels from lidar for a diversity of landscapes. The application will work with airborne lidar collections and will extrapolate the structural information to larger areas using spaceborne GLAS lidar data. Additionally, given the increasing frequency of lidar acquisitions in the wildlands for a variety of purposes, it is anticipated that the proposed application will provide a framework for more widespread adoption of active remote sensing techniques such as lidar in operational vegetation/fuels mapping and natural resource decision support.