You are here

Keyword: Landfire

A simulation of probabilistic wildfire risk components for the continental United States

Publications Posted on: October 07, 2011
This simulation research was conducted in order to develop a large-fire risk assessment system for the contiguous land area of the United States. The modeling system was applied to each of 134 Fire Planning Units (FPUs) to estimate burn probabilities and fire size distributions. To obtain stable estimates of these quantities, fire ignition and growth was simulated for 10,000 to 50,000 "years" of artificial weather.

The national tree-list layer

Publications Posted on: January 31, 2011
The National Tree-List Layer (NTLL) project used LANDFIRE map products to produce the first national tree-list map layer that represents tree populations at stand and regional levels. The NTLL was produced in a short time frame to address the needs of Fire and Aviation Management for a map layer that could be used as input for simulating fire-caused tree mortality across landscapes.

Integrating Landsat-derived disturbance maps with FIA inventory data: Applications for state-Level forest resource assessments

Publications Posted on: July 15, 2009
Landsat images have been widely used for assessing forest characteristics and dynamics. Recently, significant progress has been made towards indepth exploration of the rich Landsat archive kept by the U.S. Geological Survey to improve our under standing of forest disturbance and recovery processes. In this study, we used Landsat images to map forest disturbances at biennial intervals from 1984 to 2007 for the State of Mississippi.

Using simulated historical time series to prioritize fuel treatments on landscapes across the United States: The LANDFIRE prototype project

Publications Posted on: March 14, 2008
Canopy and surface fuels in many fire-prone forests of the United States have increased over the last 70 years as a result of modern fire exclusion policies, grazing, and other land management activities. The Healthy Forest Restoration Act and National Fire Plan establish a national commitment to reduce fire hazard and restore fire-adapted ecosystems across the USA.

Climate change effects on historical range and variability of two large landscapes in western Montana, USA

Publications Posted on: March 14, 2008
Quantifying the historical range and variability of landscape composition and structure using simulation modeling is becoming an important means of assessing current landscape condition and prioritizing landscapes for ecosystem restoration. However, most simulated time series are generated using static climate conditions which fail to account for the predicted major changes in future climate.

Habitat classification modelling with incomplete data: Pushing the habitat envelope

Publications Posted on: November 07, 2007
Habitat classification models (HCMs) are invaluable tools for species conservation, land-use planning, reserve design, and metapopulation assessments, particularly at broad spatial scales. However, species occurrence data are often lacking and typically limited to presence points at broad scales. This lack of absence data precludes the use of many statistical techniques for HCMs.

Fuels Products of the LANDFIRE Project

Publications Posted on: February 02, 2007
The LANDFIRE project is a collaborative interagency effort designed to provide seamless, nationally consistent, locally relevant geographic information systems (GIS) data layers depicting wildland fuels, vegetation and fire regime characteristics. The LANDFIRE project is the first of its kind and offers new opportunity for fire management and research activities.

Landfire: Landscape Fire and Resource Management Planning Tools Project

Publications Posted on: February 02, 2007
Managers are faced with reducing hazardous fuel, restoring fire regimes, and decreasing the threat of catastrophic wildfire. Often, the comprehensive, scientifically-credible data and applications needed to test alternative fuel treatments across multi-ownership landscapes are lacking.