Satellite-Based Fire Progression Mapping: A Comprehensive Assessment for Large Fires in Northern California

Satellite-based active fire (AF) products provide opportunities for constructing continuous fire progression maps, a critical dataset needed for improved fire behavior modeling and fire management. This study aims to investigate the geospatial interpolation techniques in mapping the daily fire progression and assess the accuracy of the derived maps from multisensor AF products. We focused on 42 large wildfires greater than 5000 acres in Northern California from 2017 to 2018, where the USDA Forest Service National Infrared Operations (NIROPS) daily fire perimeters were available for the comparison. The standard AF products from the moderate resolution imaging spectroradiometer (MODIS), the visible infrared imaging radiometer suite (VIIRS), and the combined products were used as inputs. We found that the estimated fire progression areas generated by the natural neighbor method with the combined MODIS and VIIRS AF input layers performed the best, with R2 of 0.7 ± 0.31 and RMSE of 1.25 ± 1.21 (103 acres) at a daily time scale; the accuracy was higher when assessed at a two-day rolling window, e.g., R2 of 0.83 ± 0.20 and RMSE of 0.74 ± 0.94 (103 acres). A relatively higher spatial accuracy was found using the 375 m VIIRS AF product as inputs, with a kappa score of 0.55 and an overall accuracy score of 0.59, when interpolated with the natural neighbor method. Furthermore, the locational pixel-based comparison showed 61% matched to a single day and an additional 25% explained within ±1 day of the estimation, revealing greater confidence in fire progression estimation at a two-day moving time interval. This study demonstrated the efficacy and potential improvements of daily fire progression mapping at local and regional scales.

[1]  Yu. N. Blagoveshchenskii,et al.  Use of empirical Bayesian kriging for revealing heterogeneities in the distribution of organic carbon on agricultural lands , 2017, Eurasian Soil Science.

[2]  Christopher D. Elvidge,et al.  VIIRS Nightfire: Satellite Pyrometry at Night , 2013, Remote. Sens..

[3]  R. Cohen,et al.  Characterization of wildfire NO x emissions using MODIS fire radiative power and OMI tropospheric NO 2 columns , 2011 .

[4]  Jeffrey T. Morisette,et al.  Validation of active fire detection from moderate-resolution satellite sensors: the MODIS example in northern eurasia , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[5]  D. Roy,et al.  The Collection 6 MODIS burned area mapping algorithm and product , 2018, Remote sensing of environment.

[6]  Robert E. Wolfe,et al.  Suomi NPP VIIRS prelaunch and on‐orbit geometric calibration and characterization , 2013 .

[7]  C. Justice,et al.  Evaluation of global fire detection algorithms using simulated AVHRR infrared data , 1999 .

[8]  K. Krivoruchko.  Empirical Bayesian Kriging Implemented in ArcGIS Geostatistical Analyst , 2012 .

[9]  E. Knapp,et al.  Drought, Tree Mortality, and Wildfire in Forests Adapted to Frequent Fire , 2018 .

[10]  J. Randerson,et al.  Mapping the Daily Progression of Large Wildland Fires Using MODIS Active Fire Data , 2014 .

[11]  S. Stephens,et al.  Spatial patterns of large natural fires in Sierra Nevada wilderness areas , 2007, Landscape Ecology.

[12]  D. Roy,et al.  Global assessment of the temporal reporting accuracy and precision of the MODIS burned area product , 2010 .

[13]  Xiaoyang Zhang,et al.  A preliminary evaluation of GOES-16 active fire product using Landsat-8 and VIIRS active fire data, and ground-based prescribed fire records , 2020 .

[15]  Wei Li,et al.  The development and first validation of the GOES Early Fire Detection (GOES-EFD) algorithm , 2016 .

[16]  M. Turner,et al.  THE INFLUENCE OF FIRE INTERVAL AND SEROTINY ON POSTFIRE LODGEPOLE PINE DENSITY IN YELLOWSTONE NATIONAL PARK , 2003 .

[17]  Scott L. Stephens,et al.  Prehistoric fire area and emissions from California's forests, woodlands, shrublands, and grasslands , 2007 .

[18]  Jennifer K. Balch,et al.  Human-started wildfires expand the fire niche across the United States , 2017, Proceedings of the National Academy of Sciences.

[19]  C. Silva,et al.  Use of ordinary kriging and Gaussian conditional simulation to interpolate airborne fire radiative energy density estimates , 2018 .

[20]  R. Rothermel.  A Mathematical Model for Predicting Fire Spread in Wildland Fuels , 2017 .

[21]  S. Parks.  Mapping day-of-burning with coarse-resolution satellite fire-detection data , 2014 .

[22]  T. Spies,et al.  Factors associated with crown damage following recurring mixed-severity wildfires and post-fire management in southwestern Oregon , 2010, Landscape Ecology.

[23]  D. Roy,et al.  Quantification of MODIS fire radiative power (FRP) measurement uncertainty for use in satellite‐based active fire characterization and biomass burning estimation , 2014 .

[24]  D. E. Hall,et al.  Integrated Active Fire Retrievals and Biomass Burning Emissions Using Complementary Near-Coincident Ground, Airborne and Spaceborne Sensor Data , 2014 .

[25]  Christopher C. Schmidt,et al.  Near-Real-Time Global Biomass Burning Emissions Product from Geostationary Satellite Constellation , 2012 .

[26]  C. Justice,et al.  The collection 6 MODIS active fire detection algorithm and fire products , 2016, Remote sensing of environment.

[27]  Ian C. Fuller,et al.  Assessing DEM interpolation methods for effective representation of upland stream morphology for rapid appraisal of bed stability , 2012 .

[28]  M. A. Fosberg.  Weather in wildland fire management: the Fire Weather Index , 1978 .

[29]  Jun Wang,et al.  First Global Analysis of Saturation Artifacts in the VIIRS Infrared Channels and the Effects of Sample Aggregation , 2015, IEEE Geoscience and Remote Sensing Letters.

[30]  D. Roy,et al.  An active-fire based burned area mapping algorithm for the MODIS sensor , 2009 .

[31]  W. Schroeder,et al.  Active fire detection using Landsat-8/OLI data , 2016 .

[32]  R. Sibson,et al.  A brief description of natural neighbor interpolation , 1981 .

[33]  C. Justice,et al.  Active fires from the Suomi NPP Visible Infrared Imaging Radiometer Suite: Product status and first evaluation results , 2014 .

[34]  Fotini-Niovi Pavlidou,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE SYSTEMS JOURNAL 1 A Comparative Review on Wildfire Simulators , 2022 .

[35]  Ambrish Sharma,et al.  Intercomparison of MODIS and VIIRS Fire Products in Khanty-Mansiysk Russia: Implications for Characterizing Gas Flaring from Space , 2017 .

[36]  A. P. Williams,et al.  Impact of anthropogenic climate change on wildfire across western US forests , 2016, Proceedings of the National Academy of Sciences.

[37]  T. Swetnam,et al.  Warming and Earlier Spring Increase Western U.S. Forest Wildfire Activity , 2006, Science.

[38]  W. Schroeder,et al.  The New VIIRS 375 m active fire detection data product: Algorithm description and initial assessment , 2014 .

[39]  D. Roy,et al.  Global validation of the collection 6 MODIS burned area product , 2019, Remote sensing of environment.

[40]  C. Justice,et al.  Potential global fire monitoring from EOS‐MODIS , 1998 .

[41]  W. Schroeder,et al.  Assessment of VIIRS 375 m active fire detection product for direct burned area mapping , 2015 .

[42]  C. O. Justicea,et al.  The MODIS fire products , 2002 .

[43]  Yan Guo,et al.  A Method for Forest Fire Detection Using UAV , 2015 .

[44]  Wen Xiao,et al.  Performance of Three MODIS Fire Products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a Mountainous Area of Northwest Yunnan, China, Characterized by Frequent Small Fires , 2017, Remote. Sens..

[45]  Jun Wang,et al.  Improving Nocturnal Fire Detection With the VIIRS Day–Night Band , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[46]  Diane K. Davies,et al.  Fire Information for Resource Management System: Archiving and Distributing MODIS Active Fire Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[47]  M. R. Holdaway,et al.  Spatial modeling and interpolation of monthly temperature using kriging , 2006 .

[48]  Yoram J. Kaufman,et al.  An Enhanced Contextual Fire Detection Algorithm for MODIS , 2003 .