Scientists and managers alike need timely, cost-effective, and technically appropriate fire-related information to develop functional strategies for the diverse fire communities. "Remote Sensing Modeling and Applications to Wildland Fires" addresses wildland fire management needs by presenting discussions that link ecology and the physical sciences from local to regional levels, views on integrated decision support data for policy and decision makers, new technologies and techniques, and future challenges and how remote sensing might help to address them. While creating awareness of wildland fire management and rehabilitation issues, hands-on experience in applying remote sensing and simulation modeling is also shared. This book will be a useful reference work for researchers, practitioners and graduate students in the fields of fire science, remote sensing and modeling applications. Professor John J. Qu works at the Department of Geography and GeoInformation Science at George Mason University (GMU), USA. He is the Founder and Director of the Environmental Science and Technology Center (ESTC) and EastFIRE Lab at GMU.

The application of satellite remote sensing to the detection and study of wildfires has grown rapidly in recent years as new tools have become available and are put into use. Spaceborne imagery can provide a unique perspective to viewing the fire, giving space/time coverage not available with any other observational system. One aspect of fires that can both be detected with satellite imagery and modeled numerically is the smoke plume produced by the fire. Surprisingly, most models designed to study smoke plumes were created to study controlled burns and not wildfires. We use one such model to compare model simulations with a suite of different types of satellite imagery to study a major wildfire. The 2003 Aspen Fire in the mountains north of Tucson, Arizona is used as a case study for the analysis of satellite imagery of a wildfire smoke plume in conjunction with model simulations of this plume. We clearly demonstrate that this plume model can be used to adequately simulate the fire plume as depicted in the satellite imagery when the plume achieves a sufficient altitude. For weak fires and low wind conditions the plumes often follow the local surface topography.

The book presents a wide range of techniques for extracting information from satellite remote sensing images in forest fire danger assessment. It covers the main concepts involved in fire danger rating, and analyses the inputs derived from remotely sensed data for mapping fire danger at both the local and global scale. The questions addressed concern the estimation of fuel moisture content, the description of fuel structural properties, the estimation of meteorological danger indices, the analysis of human factors associated with fire ignition, and the integration of different risk factors in a geographic information system for fire danger management.

The book provides a systematic review of the different applications for remote sensing and geographical information system techniques in research and management of forest fires. The authors have been involved in this field of research for several years. The book also benefits from data generated within the Megafires project, founded under the DG-XII of the European Union. A clear integration of research and experience is provided. New data gathered from fires affecting European countries between 1991 and 1997 are included as well as satellite images and auxiliary cartographic information. Geographic Information System files have been included in the attached CD-ROM depicting land cover, elevation, Koeppen classification climates and NOAA-AVHRR data of all European Mediterranean Europe at 1 sq km resolution. All these files are in Idrisi format and can be easily accessed from any GIS program. An Idrisi viewer has also been included in the CD-ROM.

Remote sensing techniques were investigated as an alternative for documenting selected preattack fire planning information. Locations of fuel models, road systems, and water i sources were recorded by Landsat satellite imagery and aerial photography for a portion of the Six Rivers National Forest in northwestern California. The two fuel model groups used were from the 1978 National Fire Danger Rating System and the Northern Forest Fire Laboratory. Landsat-derived fuel model data were digitized and computer analyzed by unsupervised and guided clustering techniques to produce a fuel model map of the area. Overall Landsat classification accuracies of fuel models were moderate-71 percent. This was mainly due to the incompatibilities found between fuel model descriptions and remote sensing capabilities. The results suggest, however, that a basic preattack plan that is moderately reliable, quickly attainable, and easily updated is feasible by applying remote sensing techniques.

Global Biomass Burning provides a convenient and current reference on such topics as the remote sensing of biomass burning from space, the geographical distribution of burning; the combustion products of burning in tropical, temperate, and boreal ecosystems; burning as a global source of atmospheric gases and particulates; the impact of biomass burning gases and particulates on global climate; and the role of biomass burning on biodiversity and past global extinctions."--Pub. desc.