Fire Science

Fire Danger
Fire Fuel Mapping
Large Fire Forecasts: Vegetation conditions are determined by satellite imagery and combined with forecasted weather information to produce daily large fire potential outlooks.

Fuel Mapping
Fire Danger
LANDFIRE: Over 20 geospatial layers are developed describing vegetation, disturbance, fuel and fire regimes for the entire US.

CHISLIC: A research project to develop an automated tool for updating LANDFIRE fuel layers using lidar data.

Improving rangeland fuel maps: A research project to combine satellite data with biogeochemical models for improving shrub and grassland fuel maps and fire risk assessments.

Lidar potential for fuels mapping: A research project to develop surface and canopy fuels mapping methodologies using lidar data.

Burn Mapping
Burn Mapping
MTBS: A project to map burn severity and fire perimeters of all large fires in the US from 1984 to present.

Burned Area Emergency Response: Rapid mapping of burned areas immediately post-fire to support landscape stabilization and rehabilitation.

Multi-sensor Fire Detection: A research project to automate detection of wildfires in the US using multiple satellite data sources.

Mojave Region Fire Atlas: A project to develop an atlas of all wildfire activity in the Mojave Region from 1972-present.

National Wildlife Refuge Fire Mapping: A project to map smaller fires on US Fish & Wildlife Refuges across the US through time.

 Fire Science Publications

Eidenshink, J., B. Schwind, K. Brewer, Z.-L. Zhu, B. Quayle, and S. Howard. 2007. A project for monitoring trends in burn severity. Fire Ecology 3: 3-21.

Howard, S.M., and J.M. Lacasse. 2004. An Evaluation of Gap-Filled Landsat SLC-Off Imagery for Wildland Fire Burn Severity Mapping. Photogrammetric Engineering & Remote Sensing 70: 877-880.

Nelson, K.J., J. Connot, B. Peterson, and C. Martin. 2013. The LANDFIRE Refresh strategy: updating the national dataset. Fire Ecology 9: 80-101.

Nelson, K.J., J. Connot, B. Peterson, and J.J. Picotte. 2013. LANDFIRE 2010- updated data to support wildfire and ecological management. Earthzine

Nelson, K.J., and D. Steinwand. 2015 A Landsat data tiling and compositing approach optimized for change detection in the conterminous United States. Photogrammetric Engineering & Remote Sensing 81: 573-586.

Peterson, B., K. Nelson, and B. Wylie. 2013. Towards integration of GLAS into a national fuel mapping program. Photogrammetric Engineering & Remote Sensing 79: 175-183.

Peterson, B., and K.J. Nelson. 2014. Mapping Forest Height in Alaska Using GLAS, Landsat Composites, and Airborne LiDAR. Remote Sensing 6: 12409-12426.

Peterson, B., K. J. Nelson, C. Seielstad, J. Stoker, W. M. Jolly, and R. Parsons. 2015. Automated integration of lidar into the LANDFIRE products suite. Remote Sensing Letter 6: 247-256.

Rollins, M.G. 2009. LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment. International Journal of Wildland Fire 18: 235-249.

Vogelmann, J.E., J.R. Kost, B. Tolk, S. Howard, K. Short, X. Chen, C. Huang, K. Pabst, and M.G. Rollins. 2011. Monitoring landscape change for LANDFIRE using multi-temporal satellite imagery and ancillary data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4: 252-264.

Vogelmann, J.E., B. Tolk, and Z. Zhu. 2009. Monitoring forest changes in the southwestern United States using multitemporal Landsat data. Remote Sensing of Environment 113: 1739-1748.

Zahn, S. G. 2015. LANDFIRE: US Geological Survey Fact Sheet 2015-3047, 2 p.,