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

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 co...

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Published in	Remote sensing of environment Vol. 210; no. C; pp. 282 - 296
Main Authors	Meng, Ran, Wu, Jin, Zhao, Feng, Cook, Bruce D., Hanavan, Ryan P., Serbin, Shawn P.
Format	Journal Article
Language	English
Published	New York Elsevier Inc 01.06.2018 Elsevier BV Elsevier
Subjects	Airborne sensing BASIC BIOLOGICAL SCIENCES Burn severity burning Canopies canopy Carbon cycle Coniferous forests Detection Ecological effects ENVIRONMENTAL SCIENCES Fire adaptive strategies Fire effects Forest & brush fires Forest composition and structure forest dynamics Forest ecosystems Forest fires Forest management Forests Hyperspectral data image analysis Lidar Measurement multispectral imagery Oak OTHER INSTRUMENTATION Pine Pine trees Pinus Quercus Recovery Remote monitoring Remote sensing Remote sensing systems Remote sensing techniques Remote sensors Satellite imagery satellites Sensing techniques Species-specific post-fire responses Spectroscopy tree mortality Trees Understory Vegetation classification WorldView-2 Hyperspectral data Burn severity Fire adaptive strategies Species-specific post-fire responses Forest composition and structure WorldView-2 Vegetation classification
Online Access	Get full text
ISSN	0034-4257 1879-0704
DOI	10.1016/j.rse.2018.03.019

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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. Here, we examined whether the combined use of multi-sensor remote sensing techniques (i.e., 1 m simultaneous airborne imaging spectroscopy and LiDAR and 2 m 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 scales. 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 constraining/benchmarking fire effect schemes in ecological process models. •Novel remote sensing advances quantitations of post-fire forest recovery.•A convex relationship exists between forest recovery rate and burn severity.•The detected convex relationship holds at species level.•Studying species-specific post-fire responses to different levels of burn severity
AbstractList	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. Here, we examined whether the combined use of multi-sensor remote sensing techniques (i.e., 1 m simultaneous airborne imaging spectroscopy and LiDAR and 2 m 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 scales. 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 constraining/benchmarking fire effect schemes in ecological process models. •Novel remote sensing advances quantitations of post-fire forest recovery.•A convex relationship exists between forest recovery rate and burn severity.•The detected convex relationship holds at species level.•Studying species-specific post-fire responses to different levels of burn severity 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. Here, we examined whether the combined use of multi-sensor remote sensing techniques (i.e., 1 m simultaneous airborne imaging spectroscopy and LiDAR and 2 m 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 scales. 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 constraining/benchmarking fire effect schemes in ecological process models. 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.
Author	Hanavan, Ryan P. Serbin, Shawn P. Meng, Ran Wu, Jin Cook, Bruce D. Zhao, Feng
Author_xml	– sequence: 1 givenname: Ran surname: Meng fullname: Meng, Ran email: ranmeng@bnl.gov organization: Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, New York, NY 11973, USA – sequence: 2 givenname: Jin surname: Wu fullname: Wu, Jin organization: Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, New York, NY 11973, USA – sequence: 3 givenname: Feng surname: Zhao fullname: Zhao, Feng organization: Department of Geographical Sciences, University of Maryland, 1165 Lefrak Hall, College Park, MD 20742, USA – sequence: 4 givenname: Bruce D. surname: Cook fullname: Cook, Bruce D. organization: Biospheric Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, MD 20742, USA – sequence: 5 givenname: Ryan P. surname: Hanavan fullname: Hanavan, Ryan P. organization: USDA Forest Service, Northeastern Area State & Private Forestry, 271 Mast Rd., Durham, NH 03824, USA – sequence: 6 givenname: Shawn P. surname: Serbin fullname: Serbin, Shawn P. organization: Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, New York, NY 11973, USA
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Snippet	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...
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SubjectTerms	Airborne sensing BASIC BIOLOGICAL SCIENCES Burn severity burning Canopies canopy Carbon cycle Coniferous forests Detection Ecological effects ENVIRONMENTAL SCIENCES Fire adaptive strategies Fire effects Forest & brush fires Forest composition and structure forest dynamics Forest ecosystems Forest fires Forest management Forests Hyperspectral data image analysis Lidar Measurement multispectral imagery Oak OTHER INSTRUMENTATION Pine Pine trees Pinus Quercus Recovery Remote monitoring Remote sensing Remote sensing systems Remote sensing techniques Remote sensors Satellite imagery satellites Sensing techniques Species-specific post-fire responses Spectroscopy tree mortality Trees Understory Vegetation classification WorldView-2
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
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