JFSP Projects in Progress
You may search JFSP Project Information by the following: Project Number, Title, Principal Investigator, Cooperators or key words contained in a brief description of the project.
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05-1-1-12: Burn Severity Mapping Using Simulation Modeling and Satellite Imagery |
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Robert Keane |
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As wildland fire becomes an increasingly important issue affecting our nation’s landscapes, land managers must be able to quickly assess fire effects to efficiently allocate rehabilitation resources to areas under their custody. Satellite image-based burn maps can be quickly generated to provide a landscape view of relative fire severity, while fire effects simulation models provide biotic context to the effects of the burn. These techniques could be used synergistically to improve burn severity mapping capabilities of land managers, enabling them to quickly and effectively meet rehabilitation objectives. This proposal addresses AFP 2005-1 Task 1 in two ways: 1. We seek to evaluate two tools that managers can use to assess burn severity immediately post-fire, and 2. We seek to demonstrate the predictive utility of a landscape scale fire effects model in prioritization of fuels reduction treatments. Considering results from a previous investigation, we propose to expand our exploration and demonstration of these tools through testing and evaluation of the FIREHARM fire effects model, and comparison of satellite burn severity images to field and modeled data. We will develop a set of tools and procedures for running the model and generating burn severity images. Additionally, we will hold an informational workshop that will teach managers how to use these tools. |
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04-1-2-02: Mapping and analysis of pre-fire fuels loading and burn intensity using pre-fire interferometric synthetic aperture radar data combined with burn intensity derived from post-fire multispectral imagery for the 2003 southern California fires |
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Hans-Erik Andersen |
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In 2003 15 large fires consumed more than 750,000 acres in southern California. Coincidentally, a pre-fire, high-resolution interferometric radar dataset was collected in 2002/2003 by NOAA that covers most of the major burned areas from Santa Barbara to the Mexican border (~10 million acres). These data were collected with the GeoSAR interferometric synthetic aperture (IFSAR) radar system and are available from NOAA for analysis. Also, high-resolution multispectral imagery was flown over all major burned areas (~2 million acres) and used to produce burned area orthophotographs immediately after the fires. This post-fire imagery (along with BAER team fire intensity maps) is also available from ERSI / San Bernardino National Forest. In this project, we propose to: install and measure a limited number of vegetation field plots in unburned areas surrounding 3 major burned areas to augment existing national forest, CDF, FIA and NRI pre-fire inventory plot data; develop regression relationships between these ground plot vegetation data; use these regressions to map vegetation density, structure, and fuels throughout the burned and unburned areas of the 3 major fires; compare these vegetation density/structure maps with burned area assessment maps (vegetation mortality, soil severity) developed by the BAER program; conduct an analysis to determine if the IFSAR vegetation density/structure maps are well-correlated with the post-fire high-resolution multispectral orthophotography images and finally, make recommendations on whether or not it is desirable to process the entire NOAA IFSAR dataset to develop fuels maps throughout the 5 county area. |
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03-2-3-18: Using Lidar to identify sediment and forest structure change in the Hayman burn, Colorado |
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Merrill Kaufmann |
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Small-footprint multiple-return Lidar data collected in the Cheesman lake property prior to the 2002 Hayman fire in Colorado provides an excellent opportunity to evaluate Lidar as a tool to predict and analyze fire effects on both soil erosion and overstory structure. Remeasuring this area and applying change detection techniques will allow analysis at a high resolution not possible before. Our primary objectives focus on the use of change detection techniques with pre-and post-fire small-footprint multiple-return Lidar data to: (1) evaluate the effectiveness of change detection to identify and quantify areas of erosion or deposition caused by post-fire rain events and rehab activities: (2) identify and quantify areas of biomass loss or forest structure change due to the Hayman fire: and (3) examine effects of pre-fire fuels and vegetation structure derived from Lidar data on patterns of burn severity. The proposed study will use existing Lidar and field data and post-fire Lidar data in the same area. |
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03-S-01: Demonstration and integration of systems for fire remote sensing, ground-based fire measurement, and fire modeling |
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Colin Hardy |
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This project will conduct a proof-of-concept research to compare state-of-science space-borne, airborne, and ground-based fire measurement systems; begin evaluation of two fire-behavior simulation models; test the utility of the airborne remote sensing for incident management; and investigate the development of a common database architecture. Significant and complementary linkages exist between this proposal and those previously submitted under JFSP RFP-2003-2 Task 1, by Finney and others (Modeling surface winds in complex terrain for wildland fire incident support) and Morgan and others (Assessing the causes, consequences and spatial variability of burn severity: a rapid response proposal). |
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01-1-4-02: Fuel Classification for the Southern Appalachian Mountains using Hyperspectral Image Analysis and Landscape Ecosystem Classification |
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Tom Waldrop |
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Fire managers in the southern Appalachian Mountains region are gaining skills for using prescribed burning to reduce fuel loads but they lack basic fuels information that is readily available for other regions. Researchers are taking a two-phase approach to modeling fuel loading in the region. First they are developing a classification of fuels using ground measurements and an existing Landscape Ecosystem Classification system to define fuel categories. Second they are determining if each class can be detected from aerial images using hyperspectral image analysis. Analysis of these images will identify areas on the ground containing each category of fuel loading. Ground crews will validate this information. Resulting information will reduce costs associated with mapping fuels throughout the region. |
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01-1-4-07: The use of high resolution remotely sensed data in estimating crown fire behavior variables |
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Gerard Schreuder |
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Fire researchers and managers are dependent upon accurate, reliable, and efficiently obtained data for the development and application of crown fire behavior models. In particular, reliable estimates of critical crown characteristics, including crown bulk density, canopy height, crown base height, and canopy closure are required to accurately map fuel loading and model fire behavior over the landscape. The emergence of a new generation of high-resolution remote sensing systems in recent years, as well as the development of more accurate field measurement techniques, could allow for more accurate and efficient estimation of crown fire behavior variables. With spatial resolutions often less then one meter, the spatial data provided by these sensors can support more detailed measurement of the forest canopy structure. However, there is a need for the development of analytical techniques to automatically and efficiently extract the required information from the enormous quantity of data provided by these high-resolution remote sensing systems, as well as to assess their utility and cost-effectiveness for the application of fire behavior modeling. We propose to carry out an extensive investigation of the utility of A) active infrared (LIDAR) sensor data and B) active microwave (IFSAR) sensor data for this application, and C) to compare remote sensing estimates with field-based techniques for the estimation of crown fire fuel density, type and condition. |
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01-1-4-09: A novel approach to regional fuel mapping: linking inventory plots with satellite imagery and GIS databases using the Gradient Nearest Neighbor method |
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Janet Ohmann |
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Knowing the types and amounts of fuels at a site is an important prerequisite to evaluating fire risk, predicting fire behavior, and assessing fire effects. Aerial photographs and satellite imagery have been used extensively to develop maps of vegetation and fuels at a range of spatial scales. However, many fuel attributes cannot be directly measured using remote sensing and instead ecological inferences about fuels based on overstory vegetation are made. Researchers will examine an alternative approach to fuel mapping called Gradient Nearest Neighbor (GNN) that uses multivariate statistics to link ground data, satellite imagery, and GIS maps of environmental variables. Fuel maps for three prototype landscapes in Oregon, Washington, and California will be produced. A user-friendly software interface will be developed to facilitate use of the GNN method by managers. |
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01-1-4-12: Evaluate sensitivities of burn-severity mapping algorithms for different ecosystems and fire histories in the United States |
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Zhiliang Zhu |
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Spatial data collected via remote sensing techniques provide valuable information on the effects of wildland fires that burn millions of hectares within America’s forest, shrub lands, and rangelands each year. Such data also provide insights into many of today’s science issues such as carbon cycle, biodiversity, and land cover and land use changes. Current burn mapping efforts often lack systematic validation and comparability across large regions. Researchers will evaluate a robust, consistent burn-severity mapping algorithm for baseline inventory and mapping. They seek to ensure a sound science basis for the overall goal of operational, standardized burn mapping in support of land management and scientific investigations. |
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01-1-4-14: Advanced Remote Sensing Technologies for Monitoring Postburn Vegetation Trends and Conditions |
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Raymond Kokaly |
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For at least 45 years, natural resource managers have been employing prescribed fire as a range management tool in grassland-shrub ecosystems. Although generally effective, more precise information is needed for comparing actual response of the ecological landscape to the objectives set out in prescribed fire plans. Scientists will apply newly developed remote sensing techniques (imaging spectroscopy) to accurately describe the temporal dynamics of vegetation community composition in a grassland-shrub ecosystem following prescribed fire treatments. This information could then be input into fire behavior and fire danger rating models such as BEHAVE, FARSITE, and NFDRS. |
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01-1-4-15: Mapping horizontal and vertical distribution of fuel by fusing high-resolution hyperspectral and polarimetric data |
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Don Despain |
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To help fire managers deal with today’s fires, fuels information is needed on different levels and for different purposes including planning for prescribed burns or fire suppression activities and planning fuels management activities. Most fuel loading information currently used in the field and in fire spread models comes from painstakingly obtained fuel measurements in field plots or from 1970s efforts to create a fire danger index based on a mechanistic model. The utility of this information for larger regional scale fuels mapping is limited. Researchers will test the utility of combining optical (hyperspectral) and radar (SAR) data to create vegetation-specific fuel load maps suitable for planning on regional scales. |
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01-1-4-23: Quantitative comparison of spectral indices and transformations with multi-resolution remotely sensed data using ground measurements: Implications for fire severity modeling |
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Jennifer Rechel |
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Satellite imagery at 1-kilometer resolution (Advanced Very High Resolution Radiometer) has been used to model and map fire severity using continental scale land cover classes and ecoregions. In California and Nevada this technique involves using only the spectral Normalized Difference Vegetation Index (NDVI) to develop a series of greenness maps to predict fire occurrence. However, to reflect the unique vegetation characteristics of communities with sparsely covered or diverse surfaces, satellite imagery at more appropriate landscape and regional scales and indices other than NDVI are needed. Researchers will compare vegetation indices and spectral transformations at 20 meters, 30 meters, 250 meters and 1 kilometer. Ground measurements will be taken in the Sierra Nevada and Rocky Mountains to validate these vegetation spectral indices. Results of this work will enhance current maps and vegetation greenness estimates to provide fire managers with more accurate daily fire severity ratings for regional and local operations planning efforts. |
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01B-2-1-01: Field Measurements for the Training and Validation of Burn Severity Maps from Spaceborne, Remotely Sensed Imagery |
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Thomas Bobbe |
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Rehabilitation activities conducted after wildfires occur are geared toward minimizing erosion and facilitating re-establishment of vegetation. A key factor in making decisions about what actions to take is burn severity, which determines the impact of the fire on the hydraulic properties of the soil. Current methods for developing burn severity maps involve conducting field or helicopter surveys that are both time-consuming and expensive. Researchers are working to refine and validate information received from the newest satellite imagery so that it can be used to create burn severity maps. Results of this work will speed the generation and accuracy of maps thus facilitating the work of managers in developing rehabilitation prescriptions and implementing rehabilitation plans. |
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01C-2-1-02: Evaluating high resolution hyperspecteral images for determining postfire burn severity |
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Peter Robichaud |
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Mapping burn severity after wildfire events has been the focus of burn rehabilitation crews for decades. Burn severity can vary depending upon the type of fuel present and the duration of the fire in a given location, and is typically mapped as high, medium or low. While these designations have been useful for rehabilitation efforts, they have been difficult to accurately map. Recently, remote sensing in the form of airborne digital color infrared photography and multi-spectral satellite imagery have been used to map burn severity. Although these tools have been useful for delineating burn extent and vegetation condition after a fire, their link to burn severity is not direct. As new remote sensing tools become available, it is necessary to test their capabilities on the ground and in the air to determine how they can improve upon existing methods. The new hyperspectral sensors show promise for improving upon capabilities of collecting direct, meaningful measurements of burn severity. |
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01C-2-1-08: Real-time evaluation of effects of fuel-treatments on other previous land management activities on fire behavior during wildfires |
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JoAnn Fites-Kaufman |
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Current wildland fire suppression, fuel treatment programs, and fire planning efforts require quantitative information on how fuel treatments and other past land-use activities influence fire behavior. The best means to obtain this information is through direct measurement of fuel conditions and fire behavior as fire passes through areas of the landscape with different treatment histories and fuel configurations. A rapid response team will be utilized to measure fuel conditions pre- and post-fire, and fire behavior during wildland fire in areas with various fuel treatments and other past land-use management activities. These types of directly measured relationships will improve predictions of fire behavior during wildfire events, ensuring firefighter safety, and our ability to model and predict effectiveness of fuel reduction treatments. The latter will increase the scientific basis for planning and implementing fuel reduction programs. |
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00-1-3-01: The use of Landsat 7 (ETM+) and AVIRIS data to map fuel characteristic classes in western ecosystems |
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Jan W. van Wagtendonk |
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Land managers need to have access to accurate, high resolution, and relatively cost effective maps of fuel conditions to set priorities, design fuel treatments, and use new tools that have been developed for predicting fire behavior and effects. This project will evaluate maps derived from satellite (Landsat 7) and airborne (Airborne Visible Infrared Imaging Spectrometer or AVIRIS) imagery coupled with data collected in the field for accuracy and cost effectiveness using forest, range, and grassland test sites in California, Montana, Nevada, and North Dakota. |
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00-1-3-05: Testing an approach to improve fire fuel mapping by modeling fuel structure and types based on combined satellite imagery and field data |
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Zhiliang Zhu |
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Key to successful fire prevention, management, and fire behavior prediction is large area, high quality, spatially explicit datasets that depict fuel characteristics. Characterizing fuel types require specialized field data, such effort is very difficult to sustain over large geographic areas. Current fuel maps are usually based on conversion of vegetation maps developed for other purposes, which often result in low spatial accuracy. This research is to develop and test a methodology to map the spatial distribution of several key fire fuel attributes over large geographic areas taking advantages of remote sensing data and ground measurements (such as that collected by the Forest Service Forest Inventory and Analysis program). |
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00-1-3-19: Monitoring fire effects at multiple scales: Integrating standardized field data collection with remote sensing to assess fire effects |
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Robert Keane |
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Monitoring ecosystems after fire enables us to understand the consequences of fire on vegetation, wildlife, soils and other ecosystem components. Monitoring is also the critical feedback loop that allows fire managers to constantly improve prescriptions and fire plans based on new knowledge gained from field measurements. The information needed to assess fire effects at the landscape level is often difficult to obtain especially when fire size, remoteness, and rugged terrain hinder direct observation. Researchers are working to develop and test a comprehensive Fire Effects Monitoring System (FIREMON) that integrates new and current field sampling methods and remote sensing of satellite imagery. web site: http://www.fire.org/firemon/default.htm |
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00-1-3-21: Validation of Crown Fuel Amount and Configuration Measured by Multispectral Fusion of Remote Sensors |
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JoAnn Fites-Kaufman |
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The fire susceptible mixed conifer and ponderosa pine ecosystems in the Sierra Nevada bioregion put this area at risk from wildfire. Currently, different sub-regions of the Sierra Nevada are mapped during different years using different methodologies making it difficult to plan on a larger scale. A reliable, cost-effective process for evaluating and monitoring fuel levels and potential fire behavior across the Sierra Nevada bioregion would benefit fire managers in development of vegetation treatment plans. NASA has sponsored research to obtain remote sensing information for two landscapes in this region. Researcher effort here is targeted at installing a large network of sample sites across these landscapes to provide a robust statistical analysis of the relationships between ground-based measurements of crown dimensions and remotely sensed predictions. The remote sensing data includes radar, LIDAR, and Landsat TM. This will be the first time that radar and LIDAR have been applied to map fuel conditions. These results will be applicable to crown fuel mapping in other parts of the country. |
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