JFSP Completed Projects
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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Using Lidar to identify sediment and forest structure change in the Hayman burn, Colorado | |
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Project # 03-2-3-18; Principal Investigator: 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. Final Report |
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Field Measurements for the Training and Validation of Burn Severity Maps from Spaceborne Remotely Sensed Imagery | |
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Project # 01B-2-1-01; Principal Investigator: Thomas Bobbe | |
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This project supports the refinement and validation of a next generation of imagery-based, burn severity maps that are used by Burn Area Emergency Response (BAER) teams. Funds from the proposed project were used to collect geo-referenced validating remote sensing classifications. Processes have been established to rapidly acquire and process satellite imagery at low to moderate cost for BAER teams. Further info at: http://www.fs.fed.us/eng/rsac/baer/jfs.html |
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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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Project # 01-1-4-23; Principal Investigator: 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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Mapping horizontal and vertical distribution of fuel by fusing high-resolution hyperspectral and polarimetric data | |
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Project # 01-1-4-15; Principal Investigator: 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. Final Report VIPER Tools™ free software is an add-on for ENVI®, the image processing software from ITT Visual Information Solutions. |
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Advanced Remote Sensing Technologies for Monitoring Postburn Vegetation Trends and Conditions | |
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Project # 01-1-4-14; Principal Investigator: 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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Evaluate sensitivities of burn-severity mapping algorithms for different ecosystems and fire histories in the United States | |
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Project # 01-1-4-12; Principal Investigator: 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. Final Report |
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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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Project # 01-1-4-09; Principal Investigator: 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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The use of high resolution remotely sensed data in estimating crown fire behavior variables | |
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Project # 01-1-4-07; Principal Investigator: 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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Fuel Classification for the Southern Appalachian Mountains using Hyperspectral Image Analysis and Landscape Ecosystem Classification | |
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Project # 01-1-4-02; Principal Investigator: 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. PRELIMINARY FUEL CHARACTERIZATION OF THE CHAUGA RIDGES A COMPARISON OF THREE METHODS FOR CLASSIFYING FUEL LOADS |
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Validation of Crown Fuel Amount and Configuration Measured by Multispectral Fusion of Remote Sensors | |
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Project # 00-1-3-21; Principal Investigator: 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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