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Title: Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques

Abstract

Understanding post-fire forest recovery is pivotal to the study of forest dynamics and global carbon cycle. Field-based studies indicated a convex response of forest recovery rate to burn severity at the individual tree level, related with fire-induced tree mortality; however, these findings were constrained in spatial/temporal extents, while not detectable by traditional optical remote sensing studies, largely attributing to the contaminated effect from understory recovery. For this work, we examined whether the combined use of multi-sensor remote sensing techniques (i.e., 1m simultaneous airborne imaging spectroscopy and LiDAR and 2m satellite multi-spectral imagery) to separate canopy recovery from understory recovery would enable to quantify post-fire forest recovery rate spanning a large gradient in burn severity over large-scales. Our study was conducted in a mixed pine-oak forest in Long Island, NY, three years after a top-killing fire. Our studies remotely detected an initial increase and then decline of forest recovery rate to burn severity across the burned area, with a maximum canopy area-based recovery rate of 10% per year at moderate forest burn severity class. More intriguingly, such remotely detected convex relationships also held at species level, with pine trees being more resilient to high burn severity and having a higher maximummore » recovery rate (12% per year) than oak trees (4% per year). These results are one of the first quantitative evidences showing the effects of fire adaptive strategies on post-fire forest recovery, derived from relatively large spatial-temporal domains. Our study thus provides the methodological advance to link multi-sensor remote sensing techniques to monitor forest dynamics in a spatially explicit manner over large-scales, with important implications for fire-related forest management, and for constraining/benchmarking fire effect schemes in ecological process models.« less

Authors:
ORCiD logo [1];  [1];  [2];  [3];  [4];  [1]
  1. Brookhaven National Laboratory (BNL), Upton, NY (United States). Environmental and Climate Sciences Dept.
  2. Univ. of Maryland, College Park, MD (United States). Dept. of Geographical Sciences
  3. NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States). Biospheric Sciences Branch
  4. USDA Forest Service, Northeastern Area State and Private Forestry (NA S&PF), Durham, NH (United States)
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES); National Aeronautics and Space Administration (NASA); New York Statewide Digital Orthoimagery Program (NYSDOP)
OSTI Identifier:
1430853
Alternate Identifier(s):
OSTI ID: 1582891
Report Number(s):
BNL-203373-2018-JAAM
Journal ID: ISSN 0034-4257
Grant/Contract Number:  
SC0012704
Resource Type:
Accepted Manuscript
Journal Name:
Remote Sensing of Environment
Additional Journal Information:
Journal Volume: 210; Journal Issue: C; Journal ID: ISSN 0034-4257
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 47 OTHER INSTRUMENTATION; 59 BASIC BIOLOGICAL SCIENCES; Burn severity; Species-specific post-fire responses; Fire adaptive strategies; WorldView-2; Vegetation classification; Hyperspectral data; Forest composition and structure

Citation Formats

Meng, Ran, Wu, Jin, Zhao, Feng, Cook, Bruce D., Hanavan, Ryan P., and Serbin, Shawn P. Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques. United States: N. p., 2018. Web. https://doi.org/10.1016/j.rse.2018.03.019.
Meng, Ran, Wu, Jin, Zhao, Feng, Cook, Bruce D., Hanavan, Ryan P., & Serbin, Shawn P. Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques. United States. https://doi.org/10.1016/j.rse.2018.03.019
Meng, Ran, Wu, Jin, Zhao, Feng, Cook, Bruce D., Hanavan, Ryan P., and Serbin, Shawn P. Fri . "Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques". United States. https://doi.org/10.1016/j.rse.2018.03.019. https://www.osti.gov/servlets/purl/1430853.
@article{osti_1430853,
title = {Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques},
author = {Meng, Ran and Wu, Jin and Zhao, Feng and Cook, Bruce D. and Hanavan, Ryan P. and Serbin, Shawn P.},
abstractNote = {Understanding post-fire forest recovery is pivotal to the study of forest dynamics and global carbon cycle. Field-based studies indicated a convex response of forest recovery rate to burn severity at the individual tree level, related with fire-induced tree mortality; however, these findings were constrained in spatial/temporal extents, while not detectable by traditional optical remote sensing studies, largely attributing to the contaminated effect from understory recovery. For this work, we examined whether the combined use of multi-sensor remote sensing techniques (i.e., 1m simultaneous airborne imaging spectroscopy and LiDAR and 2m satellite multi-spectral imagery) to separate canopy recovery from understory recovery would enable to quantify post-fire forest recovery rate spanning a large gradient in burn severity over large-scales. Our study was conducted in a mixed pine-oak forest in Long Island, NY, three years after a top-killing fire. Our studies remotely detected an initial increase and then decline of forest recovery rate to burn severity across the burned area, with a maximum canopy area-based recovery rate of 10% per year at moderate forest burn severity class. More intriguingly, such remotely detected convex relationships also held at species level, with pine trees being more resilient to high burn severity and having a higher maximum recovery rate (12% per year) than oak trees (4% per year). These results are one of the first quantitative evidences showing the effects of fire adaptive strategies on post-fire forest recovery, derived from relatively large spatial-temporal domains. Our study thus provides the methodological advance to link multi-sensor remote sensing techniques to monitor forest dynamics in a spatially explicit manner over large-scales, with important implications for fire-related forest management, and for constraining/benchmarking fire effect schemes in ecological process models.},
doi = {10.1016/j.rse.2018.03.019},
journal = {Remote Sensing of Environment},
number = C,
volume = 210,
place = {United States},
year = {2018},
month = {6}
}

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Cited by: 8 works
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    • DOI: 10.3390/rs11202433

    A Review of the Applications of Remote Sensing in Fire Ecology
    journal, November 2019

    • Szpakowski, David; Jensen, Jennifer
    • Remote Sensing, Vol. 11, Issue 22
    • DOI: 10.3390/rs11222638

    Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing
    journal, June 2018

    • Li, Xuedong; Zhang, Hongyan; Yang, Guangbin
    • Remote Sensing, Vol. 10, Issue 7
    • DOI: 10.3390/rs10071000

    Examining post-fire vegetation recovery with Landsat time series analysis in three western North American forest types
    journal, April 2019