apping burn severity in a disease-impacted forest landscape using andsat and MASTER imagery

Abstract Global environmental change has increased forest vulnerability to the occurrence of interacting disturbances, including wildfires and invasive diseases. Mapping post-fire burn severity in a disease-affected forest often faces challenges because burned and infested trees may exhibit a high similarity in spectral reflectance. In this study, we combined (pre- and post-fire) Landsat imagery and (post-fire) high-spectral resolution airborne MASTER data [MODIS (moderate resolution imaging spectroradiometer)/ASTER (advanced spaceborne thermal emission and reflection radiometer)] to map burn severity in a California coastal forest environment, where a non-native forest disease sudden oak death (SOD) was causing substantial tree mortality. Results showed that the use of Landsat plus MASTER bundle performed better than using the individual sensors in most of the evaluated forest strata from ground to canopy layers (i.e., substrate, shrubs, intermediate-sized trees, dominant trees and average), with the best model performance achieved at the dominant tree layer. The mid to thermal infrared spectral bands (3.0–12.5 μm) from MASTER were found to augment Landsat’s visible to shortwave infrared bands in burn severity assessment. We also found that infested and uninfested forests similarly experienced moderate to high degrees of burns where CBI (composite burn index) values were higher than 1. However, differences occurred in the regions with low burn severity (CBI values lower than 1), where uninfested stands revealed a much lower burn effect than that in infested stands, possibly due to their higher resilience to small fire disturbances as a result of higher leaf water content.

[1]  W. Kurz,et al.  Mountain pine beetle and forest carbon feedback to climate change , 2008, Nature.

[2]  F. Davis,et al.  Pre-impact forest composition and ongoing tree mortality associated with sudden oak death in the Big Sur region; California. , 2010 .

[3]  Geoffrey J. Hay,et al.  A multiscale geographic object-based image analysis to estimate lidar-measured forest canopy height using Quickbird imagery , 2011, Int. J. Geogr. Inf. Sci..

[4]  A. K. Milne,et al.  The use of remote sensing in mapping and monitoring vegetational change associated with bushfire events in Eastern Australia , 1986 .

[5]  P. Chavez.  Image-Based Atmospheric Corrections - Revisited and Improved , 1996 .

[6]  R. DeFries,et al.  Understorey fire frequency and the fate of burned forests in southern Amazonia , 2013, Philosophical Transactions of the Royal Society B: Biological Sciences.

[7]  Aster Gdem Srtm Dted,et al.  ASTER Global DEM Validation Summary Report , 2009 .

[8]  P. Mausel,et al.  Assessment of vegetation change in a fire-altered forest landscape. , 1990 .

[9]  R. B. Jackson,et al.  A Large and Persistent Carbon Sink in the World’s Forests , 2011, Science.

[10]  J. Keeley.  Fire intensity, fire severity and burn severity: a brief review and suggested usage , 2009 .

[11]  Jay D. Miller,et al.  Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR) , 2007 .

[12]  Jiyuan Liu,et al.  Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data , 2002 .

[13]  Lorraine Remer,et al.  Detection of forests using mid-IR reflectance: an application for aerosol studies , 1994, IEEE Trans. Geosci. Remote. Sens..

[14]  R. Meentemeyer,et al.  Unexpected redwood mortality from synergies between wildfire and an emerging infectious disease. , 2013, Ecology.

[15]  Ross K Meentemeyer,et al.  Landscape epidemiology of emerging infectious diseases in natural and human-altered ecosystems. , 2012, Annual review of phytopathology.

[16]  Jay D. Miller,et al.  Mapping forest post-fire canopy consumption in several overstory types using multi-temporal Landsat TM and ETM data , 2002 .

[17]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[18]  Raymond F. Kokaly,et al.  Characterization of post-fire surface cover, soils, and burn severity at the Cerro Grande Fire, New Mexico, using hyperspectral and multispectral remote sensing , 2007 .

[19]  N. Benson,et al.  Landscape Assessment: Ground measure of severity, the Composite Burn Index; and Remote sensing of severity, the Normalized Burn Ratio , 2006 .

[20]  Wesley G. Page,et al.  Bark beetles, fuels, fires and implications for forest management in the Intermountain West , 2008 .

[21]  S. Hook,et al.  Evaluating spectral indices for burned area discrimination using MODIS/ASTER (MASTER) airborne simulator data , 2011 .

[22]  R. Meentemeyer,et al.  Object-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery , 2015 .

[23]  Joanne C. White,et al.  Lidar calibration and validation for geometric-optical modeling with Landsat imagery , 2012 .

[24]  D. Roberts,et al.  Multiple Endmember Spectral Mixture Analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries , 2013 .

[25]  David P. Roy,et al.  Remote sensing of fire severity: assessing the performance of the normalized burn ratio , 2006, IEEE Geoscience and Remote Sensing Letters.

[26]  Yves Bergeron,et al.  Change of fire frequency in the eastern Canadian boreal forests during the Holocene: does vegetation composition or climate trigger the fire regime? , 2001 .

[27]  Lars Eklundh,et al.  A new invasive insect in Sweden – Physokermes inopinatus: Tracing forest damage with satellite based remote sensing , 2012 .

[28]  R. Meentemeyer,et al.  Spatial estimation of the density and carbon content of host populations for Phytophthora ramorum in California and Oregon , 2011 .

[29]  Daniel G. Neary,et al.  Wildland fire in ecosystems: effects of fire on soils and water , 2005 .

[30]  K. Zhao,et al.  A comparison of Gaussian process regression, random forests and support vector regression for burn severity assessment in diseased forests , 2014 .

[31]  P. Teillet,et al.  On the Slope-Aspect Correction of Multispectral Scanner Data , 1982 .

[32]  W. Kurz,et al.  Could increased boreal forest ecosystem productivity offset carbon losses from increased disturbances? , 2008, Philosophical Transactions of the Royal Society B: Biological Sciences.

[33]  David M Rizzo,et al.  Phytophthora ramorum: integrative research and management of an emerging pathogen in California and Oregon forests. , 2005, Annual review of phytopathology.

[34]  F. Maselli,et al.  Using SPOT images and field sampling to map burn severity and vegetation factors affecting post forest fire erosion risk , 2008 .

[35]  Andrew T. Hudak,et al.  Mapping fire scars in a southern African savannah using Landsat imagery , 2004 .

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

[37]  B. Markham,et al.  Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors , 2009 .

[38]  Michael A. Wulder,et al.  Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities , 2006 .

[39]  R. Meentemeyer,et al.  Impact of sudden oak death on tree mortality in the Big Sur ecoregion of California , 2008, Biological Invasions.

[40]  G. Hulley,et al.  An alternative spectral index for rapid fire severity assessments , 2012 .

[41]  D. Hall,et al.  Landsat digital analysis of the initial recovery of burned tundra at Kokolik River, Alaska , 1980 .

[42]  Myoung-Soo Won,et al.  Relationship between landscape structure and burn severity at the landscape and class levels in Samchuck, South Korea , 2009 .

[43]  D. Verbyla,et al.  Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM , 2005 .

[44]  R. Meentemeyer,et al.  An emergent disease causes directional changes in forest species composition in coastal California , 2012 .

[45]  M. Turner,et al.  Factors Influencing Succession: Lessons from Large, Infrequent Natural Disturbances , 1998, Ecosystems.

[46]  R. Meentemeyer,et al.  Interacting disturbances: wildfire severity affected by stage of forest disease invasion. , 2011, Ecological applications : a publication of the Ecological Society of America.

[47]  S. Hook,et al.  The MODIS/ASTER airborne simulator (MASTER) - a new instrument for earth science studies , 2001 .

[48]  S. A. Lewis,et al.  Remote sensing techniques to assess active fire characteristics and post-fire effects , 2006 .

[49]  Sander Veraverbeke,et al.  Evaluating Spectral Indices for Assessing Fire Severity in Chaparral Ecosystems (Southern California) Using MODIS/ASTER (MASTER) Airborne Simulator Data , 2011, Remote. Sens..

[50]  M. Déqué,et al.  Simulation of potential range expansion of oak disease caused by Phytophthora cinnamomi under climate change , 2004 .

[51]  S. Running,et al.  Remote Sensing of Forest Fire Severity and Vegetation Recovery , 1996 .

[52]  E. Chuvieco,et al.  Burn severity estimation from remotely sensed data: Performance of simulation versus empirical models , 2007 .

[53]  Gregory P Asner,et al.  Geography of forest disturbance , 2013, Proceedings of the National Academy of Sciences.