Kurtis Nelson
Kurtis Nelson is a Scientist with the USGS Earth Resources Observation and Science (EROS) Center in Sioux Falls, SD.
Science and Products
Monitoring Trends in Burn Severity Thematic Burn Severity Mosaic (ver. 12.0, April 2025) Monitoring Trends in Burn Severity Thematic Burn Severity Mosaic (ver. 12.0, April 2025)
The Monitoring Trends in Burn Severity (MTBS) Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period 1984 and beyond. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the...
Filter Total Items: 19
An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data
Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling data usage strategy for any dataset and identify an appropriate number of rules in the regression tree model that...
Authors
Yingxin Gu, Bruce K. Wylie, Stephen P. Boyte, Joshua J. Picotte, Danny Howard, Kelcy Smith, Kurtis Nelson
Enhanced canopy fuel mapping by integrating lidar data Enhanced canopy fuel mapping by integrating lidar data
Background The Wildfire Sciences Team at the U.S. Geological Survey’s Earth Resources Observation and Science Center produces vegetation type, vegetation structure, and fuel products for the United States, primarily through the Landscape Fire and Resource Management Planning Tools (LANDFIRE) program. LANDFIRE products are used across disciplines for a variety of applications. The...
Authors
Birgit E. Peterson, Kurtis J. Nelson
LANDFIRE 2010—Updates to the national dataset to support improved fire and natural resource management LANDFIRE 2010—Updates to the national dataset to support improved fire and natural resource management
The Landscape Fire and Resource Management Planning Tools (LANDFIRE) 2010 data release provides updated and enhanced vegetation, fuel, and fire regime layers consistently across the United States. The data represent landscape conditions from approximately 2010 and are the latest release in a series of planned updates to maintain currency of LANDFIRE data products. Enhancements to the...
Authors
Kurtis J. Nelson, Donald G. Long, Joel A. Connot
A comparison of NLCD 2011 and LANDFIRE EVT 2010: Regional and national summaries. A comparison of NLCD 2011 and LANDFIRE EVT 2010: Regional and national summaries.
In order to provide the land cover user community a summary of the similarity and differences between the 2011 National Land Cover Dataset (NLCD) and the Landscape Fire and Resource Management Planning Tools Program Existing Vegetation 2010 Data (LANDFIRE EVT), the two datasets were compared at a national (conterminous U.S.) and regional (Eastern, Midwestern, and Western) extents (Figure...
Authors
Alexa McKerrow, Jon Dewitz, Donald G. Long, Kurtis Nelson, Joel A. Connot, Jim Smith
A landsat data tiling and compositing approach optimized for change detection in the conterminous United States A landsat data tiling and compositing approach optimized for change detection in the conterminous United States
Annual disturbance maps are produced by the LANDFIRE program across the conterminous United States (CONUS). Existing LANDFIRE disturbance data from 1999 to 2010 are available and current efforts will produce disturbance data through 2012. A tiling and compositing approach was developed to produce bi-annual images optimized for change detection. A tiled grid of 10,000 × 10,000 30 m pixels...
Authors
Kurtis Nelson, Daniel R. Steinwand
Automated integration of lidar into the LANDFIRE product suite Automated integration of lidar into the LANDFIRE product suite
Accurate information about three-dimensional canopy structure and wildland fuel across the landscape is necessary for fire behaviour modelling system predictions. Remotely sensed data are invaluable for assessing these canopy characteristics over large areas; lidar data, in particular, are uniquely suited for quantifying three-dimensional canopy structure. Although lidar data are...
Authors
Birgit Peterson, Kurtis Nelson, Carl Seielstad, Jason M. Stoker, W. Matt Jolly, Russell Parsons
Science and Products
Monitoring Trends in Burn Severity Thematic Burn Severity Mosaic (ver. 12.0, April 2025) Monitoring Trends in Burn Severity Thematic Burn Severity Mosaic (ver. 12.0, April 2025)
The Monitoring Trends in Burn Severity (MTBS) Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period 1984 and beyond. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the...
Filter Total Items: 19
An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data
Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling data usage strategy for any dataset and identify an appropriate number of rules in the regression tree model that...
Authors
Yingxin Gu, Bruce K. Wylie, Stephen P. Boyte, Joshua J. Picotte, Danny Howard, Kelcy Smith, Kurtis Nelson
Enhanced canopy fuel mapping by integrating lidar data Enhanced canopy fuel mapping by integrating lidar data
Background The Wildfire Sciences Team at the U.S. Geological Survey’s Earth Resources Observation and Science Center produces vegetation type, vegetation structure, and fuel products for the United States, primarily through the Landscape Fire and Resource Management Planning Tools (LANDFIRE) program. LANDFIRE products are used across disciplines for a variety of applications. The...
Authors
Birgit E. Peterson, Kurtis J. Nelson
LANDFIRE 2010—Updates to the national dataset to support improved fire and natural resource management LANDFIRE 2010—Updates to the national dataset to support improved fire and natural resource management
The Landscape Fire and Resource Management Planning Tools (LANDFIRE) 2010 data release provides updated and enhanced vegetation, fuel, and fire regime layers consistently across the United States. The data represent landscape conditions from approximately 2010 and are the latest release in a series of planned updates to maintain currency of LANDFIRE data products. Enhancements to the...
Authors
Kurtis J. Nelson, Donald G. Long, Joel A. Connot
A comparison of NLCD 2011 and LANDFIRE EVT 2010: Regional and national summaries. A comparison of NLCD 2011 and LANDFIRE EVT 2010: Regional and national summaries.
In order to provide the land cover user community a summary of the similarity and differences between the 2011 National Land Cover Dataset (NLCD) and the Landscape Fire and Resource Management Planning Tools Program Existing Vegetation 2010 Data (LANDFIRE EVT), the two datasets were compared at a national (conterminous U.S.) and regional (Eastern, Midwestern, and Western) extents (Figure...
Authors
Alexa McKerrow, Jon Dewitz, Donald G. Long, Kurtis Nelson, Joel A. Connot, Jim Smith
A landsat data tiling and compositing approach optimized for change detection in the conterminous United States A landsat data tiling and compositing approach optimized for change detection in the conterminous United States
Annual disturbance maps are produced by the LANDFIRE program across the conterminous United States (CONUS). Existing LANDFIRE disturbance data from 1999 to 2010 are available and current efforts will produce disturbance data through 2012. A tiling and compositing approach was developed to produce bi-annual images optimized for change detection. A tiled grid of 10,000 × 10,000 30 m pixels...
Authors
Kurtis Nelson, Daniel R. Steinwand
Automated integration of lidar into the LANDFIRE product suite Automated integration of lidar into the LANDFIRE product suite
Accurate information about three-dimensional canopy structure and wildland fuel across the landscape is necessary for fire behaviour modelling system predictions. Remotely sensed data are invaluable for assessing these canopy characteristics over large areas; lidar data, in particular, are uniquely suited for quantifying three-dimensional canopy structure. Although lidar data are...
Authors
Birgit Peterson, Kurtis Nelson, Carl Seielstad, Jason M. Stoker, W. Matt Jolly, Russell Parsons