Assessing forest fuel models using LIDAR remote sensing Conference Paper uri icon

abstract

  • The overall aim of this paper is to develop the use of LIDAR remote sensing to accurately and effectively assess fuel models in east Texas. This paper presents methods for using airborne LIDAR (LIght Detection And Ranging) to regain forest parameters critical for fire behavior modeling. The ground truth data collected over 62 plots were compared with the LIDAR data. Several data fusion approaches, LIDAR-Multispectral stack, Principal Component Analysis, and Minimum Noise Fraction, were used for assessing fuel models. In addition, a supervised image classification was applied to a multispectral QuickBird image and LIDAR derived images. Seven initial classes were considered and classification accuracy was evaluated using confusion matrices and K-hat statistics. This study achieved a detailed mapping of fuels for input into fire behavior models such as FARSITE, FlamMap, and DEVS.

published proceedings

  • American Society for Photogrammetry and Remote Sensing - Annual Conference of the American Society for Photogrammetry and Remote Sensing 2006: Prospecting for Geospatial Information Integration

author list (cited authors)

  • Mutlu, M., & Popescu, S.

complete list of authors

  • Mutlu, M||Popescu, S

publication date

  • December 2006