Allometry and structural volume change of standing dead southern pine trees using non-destructive terrestrial LiDAR

Paul A. Klockow; Eric B. Putman; Jason G. Vogel; Georgianne W. Moore; Christopher B. Edgar; Sorin C. Popescu

doi:10.1016/j.rse.2020.111729

Allometry and structural volume change of standing dead southern pine trees using non-destructive terrestrial LiDAR

Paul A. Klockow, Eric B. Putman, Jason G. Vogel, Georgianne W. Moore, Christopher B. Edgar, Sorin C. Popescu

Forest Resources

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

Tree mortality from major disturbances can greatly increase dead wood in forested areas, affecting fire intensity and behavior, wildlife habitat, and carbon dynamics. Accurately quantifying regional standing dead tree (SDT) pools, as conducted by the U.S. Forest Service Forest Inventory and Analysis (FIA) program, remains a prominent challenge. Little empirical work has been done accounting for structural changes in SDT volume across decay classes due to measurement and sampling challenges associated with SDT. Light-detection-and-ranging (LiDAR) represents a remote sensing technology with the potential to improve sampling efficacy and provide volume estimates of SDT via non-destructive sampling. Following this, the goal of this study was to explore the feasibility of empirically quantifying and assessing structural volume in southern pine SDT by decay class using terrestrial LiDAR. To meet this goal, we addressed three objectives, 1) construct empirical volume estimates of SDT by decay class using terrestrial LiDAR and a voxel-based, volume calculation algorithm capable of accounting for occlusion and point cloud quality, 2) develop allometric relationships of aboveground SDT component volumes by decay class and assess error in models and predictions, and 3) quantify proportion-remaining volume of SDT components from terrestrial LiDAR-derived volumes relative to predicted intact tree volumes. This study represents the first to develop empirically-based, terrestrial LiDAR-derived allometric volume relationships and proportion-remaining volume of SDT by decay class. Results indicate that terrestrial LiDAR-derived volumes of SDT produced robust allometric equations by decay class for total above-stump and stem-plus-bark components (adjusted R² = 0.94–0.98). Allometric relationships for tops-and-branches comprised more variability, likely impacted by scan quality, having adjusted R² values of ~0.52–0.59. Notably, the inclusion of height in allometric relationships for total above-stump volume precluded the need for decay class as a covariate, accounting for the variability inherent in each decay class. Importantly, this means that total above-stump allometric equations could be effective under different decay class systems or, more broadly, where no decay classes were measured, thus providing broad utility. Empirically-derived proportion-remaining volume of SDT components followed expected decreasing trends by decay class. Interestingly, proportion-remaining volume for tops-and-branches closely matched theoretically-derived values from a previous FIA-related study. Ultimately, terrestrial LiDAR was critical for efficiently measuring volume of southern pine SDT by decay class and for developing SDT-specific allometric relationships of volume and estimates of structural change by decay class. This study showcases the feasibility of LiDAR-derived, SDT-specific tools for improved accounting of SDT resources in FIA and other inventories.

Original language	English (US)
Article number	111729
Journal	Remote Sensing of Environment
Volume	241
DOIs	https://doi.org/10.1016/j.rse.2020.111729
State	Published - May 2020

Bibliographical note

Funding Information:
Funding for this study was provided in part by a Texas A&M University Merit Fellowship and the National Aeronautics and Space Administration Rapid Response and Novel Research in Earth Science program (Grant number NNX14AN99G ). We would like to thank Warren Oja and Justin Seaborn of the Sam Houston National Forest for allowing access to SDT. We are grateful to Jordan Herrin, Crockett Pegoda, and Justin Branch from the Texas A&M Forest Service for coordinating assistance with field work. We greatly appreciate additional field and lab assistance from Marco Minor, Chase Brooke, Kathryn Benson, Mollie Bender, and Sascha Lodge. Reconstruction of SDT volumes was conducted with the use of Texas A&M University High Performance Research Computing (HPRC) supercomputing resources. We greatly appreciate their assistance in training, troubleshooting, and maintaining these resources. We thank John Zobel for advice on aspects of the allometric analyses. Finally, we thank three anonymous reviewers and the Associate Editor for providing valuable feedback and suggestions that helped improve the content of this manuscript. The contents of this manuscript are solely the responsibility and creation of the authors and do not necessarily represent the official views of the National Aeronautics and Space Administration.

Funding Information:
Funding for this study was provided in part by a Texas A&M University Merit Fellowship and the National Aeronautics and Space Administration Rapid Response and Novel Research in Earth Science program (Grant number NNX14AN99G). We would like to thank Warren Oja and Justin Seaborn of the Sam Houston National Forest for allowing access to SDT. We are grateful to Jordan Herrin, Crockett Pegoda, and Justin Branch from the Texas A&M Forest Service for coordinating assistance with field work. We greatly appreciate additional field and lab assistance from Marco Minor, Chase Brooke, Kathryn Benson, Mollie Bender, and Sascha Lodge. Reconstruction of SDT volumes was conducted with the use of Texas A&M University High Performance Research Computing (HPRC) supercomputing resources. We greatly appreciate their assistance in training, troubleshooting, and maintaining these resources. We thank John Zobel for advice on aspects of the allometric analyses. Finally, we thank three anonymous reviewers and the Associate Editor for providing valuable feedback and suggestions that helped improve the content of this manuscript. The contents of this manuscript are solely the responsibility and creation of the authors and do not necessarily represent the official views of the National Aeronautics and Space Administration.

Publisher Copyright:
© 2020 Elsevier Inc.

Keywords

Allometry
Decay class
Forest Inventory and Analysis (FIA)
Loblolly pine
Southern pine
Standing dead tree (SDT)
Structural loss
Terrestrial LiDAR
Terrestrial laser scanning (TLS)
Volume estimation
Voxel

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@article{ee2a8d2ab229469fbe9593cfaf66200d,

title = "Allometry and structural volume change of standing dead southern pine trees using non-destructive terrestrial LiDAR",

abstract = "Tree mortality from major disturbances can greatly increase dead wood in forested areas, affecting fire intensity and behavior, wildlife habitat, and carbon dynamics. Accurately quantifying regional standing dead tree (SDT) pools, as conducted by the U.S. Forest Service Forest Inventory and Analysis (FIA) program, remains a prominent challenge. Little empirical work has been done accounting for structural changes in SDT volume across decay classes due to measurement and sampling challenges associated with SDT. Light-detection-and-ranging (LiDAR) represents a remote sensing technology with the potential to improve sampling efficacy and provide volume estimates of SDT via non-destructive sampling. Following this, the goal of this study was to explore the feasibility of empirically quantifying and assessing structural volume in southern pine SDT by decay class using terrestrial LiDAR. To meet this goal, we addressed three objectives, 1) construct empirical volume estimates of SDT by decay class using terrestrial LiDAR and a voxel-based, volume calculation algorithm capable of accounting for occlusion and point cloud quality, 2) develop allometric relationships of aboveground SDT component volumes by decay class and assess error in models and predictions, and 3) quantify proportion-remaining volume of SDT components from terrestrial LiDAR-derived volumes relative to predicted intact tree volumes. This study represents the first to develop empirically-based, terrestrial LiDAR-derived allometric volume relationships and proportion-remaining volume of SDT by decay class. Results indicate that terrestrial LiDAR-derived volumes of SDT produced robust allometric equations by decay class for total above-stump and stem-plus-bark components (adjusted R2 = 0.94–0.98). Allometric relationships for tops-and-branches comprised more variability, likely impacted by scan quality, having adjusted R2 values of ~0.52–0.59. Notably, the inclusion of height in allometric relationships for total above-stump volume precluded the need for decay class as a covariate, accounting for the variability inherent in each decay class. Importantly, this means that total above-stump allometric equations could be effective under different decay class systems or, more broadly, where no decay classes were measured, thus providing broad utility. Empirically-derived proportion-remaining volume of SDT components followed expected decreasing trends by decay class. Interestingly, proportion-remaining volume for tops-and-branches closely matched theoretically-derived values from a previous FIA-related study. Ultimately, terrestrial LiDAR was critical for efficiently measuring volume of southern pine SDT by decay class and for developing SDT-specific allometric relationships of volume and estimates of structural change by decay class. This study showcases the feasibility of LiDAR-derived, SDT-specific tools for improved accounting of SDT resources in FIA and other inventories.",

keywords = "Allometry, Decay class, Forest Inventory and Analysis (FIA), Loblolly pine, Southern pine, Standing dead tree (SDT), Structural loss, Terrestrial LiDAR, Terrestrial laser scanning (TLS), Volume estimation, Voxel",

author = "Klockow, {Paul A.} and Putman, {Eric B.} and Vogel, {Jason G.} and Moore, {Georgianne W.} and Edgar, {Christopher B.} and Popescu, {Sorin C.}",

note = "Funding Information: Funding for this study was provided in part by a Texas A&M University Merit Fellowship and the National Aeronautics and Space Administration Rapid Response and Novel Research in Earth Science program (Grant number NNX14AN99G ). We would like to thank Warren Oja and Justin Seaborn of the Sam Houston National Forest for allowing access to SDT. We are grateful to Jordan Herrin, Crockett Pegoda, and Justin Branch from the Texas A&M Forest Service for coordinating assistance with field work. We greatly appreciate additional field and lab assistance from Marco Minor, Chase Brooke, Kathryn Benson, Mollie Bender, and Sascha Lodge. Reconstruction of SDT volumes was conducted with the use of Texas A&M University High Performance Research Computing (HPRC) supercomputing resources. We greatly appreciate their assistance in training, troubleshooting, and maintaining these resources. We thank John Zobel for advice on aspects of the allometric analyses. Finally, we thank three anonymous reviewers and the Associate Editor for providing valuable feedback and suggestions that helped improve the content of this manuscript. The contents of this manuscript are solely the responsibility and creation of the authors and do not necessarily represent the official views of the National Aeronautics and Space Administration. Funding Information: Funding for this study was provided in part by a Texas A&M University Merit Fellowship and the National Aeronautics and Space Administration Rapid Response and Novel Research in Earth Science program (Grant number NNX14AN99G). We would like to thank Warren Oja and Justin Seaborn of the Sam Houston National Forest for allowing access to SDT. We are grateful to Jordan Herrin, Crockett Pegoda, and Justin Branch from the Texas A&M Forest Service for coordinating assistance with field work. We greatly appreciate additional field and lab assistance from Marco Minor, Chase Brooke, Kathryn Benson, Mollie Bender, and Sascha Lodge. Reconstruction of SDT volumes was conducted with the use of Texas A&M University High Performance Research Computing (HPRC) supercomputing resources. We greatly appreciate their assistance in training, troubleshooting, and maintaining these resources. We thank John Zobel for advice on aspects of the allometric analyses. Finally, we thank three anonymous reviewers and the Associate Editor for providing valuable feedback and suggestions that helped improve the content of this manuscript. The contents of this manuscript are solely the responsibility and creation of the authors and do not necessarily represent the official views of the National Aeronautics and Space Administration. Publisher Copyright: {\textcopyright} 2020 Elsevier Inc.",

year = "2020",

month = may,

doi = "10.1016/j.rse.2020.111729",

language = "English (US)",

volume = "241",

journal = "Remote Sensing of Environment",

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TY - JOUR

T1 - Allometry and structural volume change of standing dead southern pine trees using non-destructive terrestrial LiDAR

AU - Klockow, Paul A.

AU - Putman, Eric B.

AU - Vogel, Jason G.

AU - Moore, Georgianne W.

AU - Edgar, Christopher B.

AU - Popescu, Sorin C.

N1 - Funding Information: Funding for this study was provided in part by a Texas A&M University Merit Fellowship and the National Aeronautics and Space Administration Rapid Response and Novel Research in Earth Science program (Grant number NNX14AN99G ). We would like to thank Warren Oja and Justin Seaborn of the Sam Houston National Forest for allowing access to SDT. We are grateful to Jordan Herrin, Crockett Pegoda, and Justin Branch from the Texas A&M Forest Service for coordinating assistance with field work. We greatly appreciate additional field and lab assistance from Marco Minor, Chase Brooke, Kathryn Benson, Mollie Bender, and Sascha Lodge. Reconstruction of SDT volumes was conducted with the use of Texas A&M University High Performance Research Computing (HPRC) supercomputing resources. We greatly appreciate their assistance in training, troubleshooting, and maintaining these resources. We thank John Zobel for advice on aspects of the allometric analyses. Finally, we thank three anonymous reviewers and the Associate Editor for providing valuable feedback and suggestions that helped improve the content of this manuscript. The contents of this manuscript are solely the responsibility and creation of the authors and do not necessarily represent the official views of the National Aeronautics and Space Administration. Funding Information: Funding for this study was provided in part by a Texas A&M University Merit Fellowship and the National Aeronautics and Space Administration Rapid Response and Novel Research in Earth Science program (Grant number NNX14AN99G). We would like to thank Warren Oja and Justin Seaborn of the Sam Houston National Forest for allowing access to SDT. We are grateful to Jordan Herrin, Crockett Pegoda, and Justin Branch from the Texas A&M Forest Service for coordinating assistance with field work. We greatly appreciate additional field and lab assistance from Marco Minor, Chase Brooke, Kathryn Benson, Mollie Bender, and Sascha Lodge. Reconstruction of SDT volumes was conducted with the use of Texas A&M University High Performance Research Computing (HPRC) supercomputing resources. We greatly appreciate their assistance in training, troubleshooting, and maintaining these resources. We thank John Zobel for advice on aspects of the allometric analyses. Finally, we thank three anonymous reviewers and the Associate Editor for providing valuable feedback and suggestions that helped improve the content of this manuscript. The contents of this manuscript are solely the responsibility and creation of the authors and do not necessarily represent the official views of the National Aeronautics and Space Administration. Publisher Copyright: © 2020 Elsevier Inc.

PY - 2020/5

Y1 - 2020/5

N2 - Tree mortality from major disturbances can greatly increase dead wood in forested areas, affecting fire intensity and behavior, wildlife habitat, and carbon dynamics. Accurately quantifying regional standing dead tree (SDT) pools, as conducted by the U.S. Forest Service Forest Inventory and Analysis (FIA) program, remains a prominent challenge. Little empirical work has been done accounting for structural changes in SDT volume across decay classes due to measurement and sampling challenges associated with SDT. Light-detection-and-ranging (LiDAR) represents a remote sensing technology with the potential to improve sampling efficacy and provide volume estimates of SDT via non-destructive sampling. Following this, the goal of this study was to explore the feasibility of empirically quantifying and assessing structural volume in southern pine SDT by decay class using terrestrial LiDAR. To meet this goal, we addressed three objectives, 1) construct empirical volume estimates of SDT by decay class using terrestrial LiDAR and a voxel-based, volume calculation algorithm capable of accounting for occlusion and point cloud quality, 2) develop allometric relationships of aboveground SDT component volumes by decay class and assess error in models and predictions, and 3) quantify proportion-remaining volume of SDT components from terrestrial LiDAR-derived volumes relative to predicted intact tree volumes. This study represents the first to develop empirically-based, terrestrial LiDAR-derived allometric volume relationships and proportion-remaining volume of SDT by decay class. Results indicate that terrestrial LiDAR-derived volumes of SDT produced robust allometric equations by decay class for total above-stump and stem-plus-bark components (adjusted R2 = 0.94–0.98). Allometric relationships for tops-and-branches comprised more variability, likely impacted by scan quality, having adjusted R2 values of ~0.52–0.59. Notably, the inclusion of height in allometric relationships for total above-stump volume precluded the need for decay class as a covariate, accounting for the variability inherent in each decay class. Importantly, this means that total above-stump allometric equations could be effective under different decay class systems or, more broadly, where no decay classes were measured, thus providing broad utility. Empirically-derived proportion-remaining volume of SDT components followed expected decreasing trends by decay class. Interestingly, proportion-remaining volume for tops-and-branches closely matched theoretically-derived values from a previous FIA-related study. Ultimately, terrestrial LiDAR was critical for efficiently measuring volume of southern pine SDT by decay class and for developing SDT-specific allometric relationships of volume and estimates of structural change by decay class. This study showcases the feasibility of LiDAR-derived, SDT-specific tools for improved accounting of SDT resources in FIA and other inventories.

AB - Tree mortality from major disturbances can greatly increase dead wood in forested areas, affecting fire intensity and behavior, wildlife habitat, and carbon dynamics. Accurately quantifying regional standing dead tree (SDT) pools, as conducted by the U.S. Forest Service Forest Inventory and Analysis (FIA) program, remains a prominent challenge. Little empirical work has been done accounting for structural changes in SDT volume across decay classes due to measurement and sampling challenges associated with SDT. Light-detection-and-ranging (LiDAR) represents a remote sensing technology with the potential to improve sampling efficacy and provide volume estimates of SDT via non-destructive sampling. Following this, the goal of this study was to explore the feasibility of empirically quantifying and assessing structural volume in southern pine SDT by decay class using terrestrial LiDAR. To meet this goal, we addressed three objectives, 1) construct empirical volume estimates of SDT by decay class using terrestrial LiDAR and a voxel-based, volume calculation algorithm capable of accounting for occlusion and point cloud quality, 2) develop allometric relationships of aboveground SDT component volumes by decay class and assess error in models and predictions, and 3) quantify proportion-remaining volume of SDT components from terrestrial LiDAR-derived volumes relative to predicted intact tree volumes. This study represents the first to develop empirically-based, terrestrial LiDAR-derived allometric volume relationships and proportion-remaining volume of SDT by decay class. Results indicate that terrestrial LiDAR-derived volumes of SDT produced robust allometric equations by decay class for total above-stump and stem-plus-bark components (adjusted R2 = 0.94–0.98). Allometric relationships for tops-and-branches comprised more variability, likely impacted by scan quality, having adjusted R2 values of ~0.52–0.59. Notably, the inclusion of height in allometric relationships for total above-stump volume precluded the need for decay class as a covariate, accounting for the variability inherent in each decay class. Importantly, this means that total above-stump allometric equations could be effective under different decay class systems or, more broadly, where no decay classes were measured, thus providing broad utility. Empirically-derived proportion-remaining volume of SDT components followed expected decreasing trends by decay class. Interestingly, proportion-remaining volume for tops-and-branches closely matched theoretically-derived values from a previous FIA-related study. Ultimately, terrestrial LiDAR was critical for efficiently measuring volume of southern pine SDT by decay class and for developing SDT-specific allometric relationships of volume and estimates of structural change by decay class. This study showcases the feasibility of LiDAR-derived, SDT-specific tools for improved accounting of SDT resources in FIA and other inventories.

KW - Allometry

KW - Decay class

KW - Forest Inventory and Analysis (FIA)

KW - Loblolly pine

KW - Southern pine

KW - Standing dead tree (SDT)

KW - Structural loss

KW - Terrestrial LiDAR

KW - Terrestrial laser scanning (TLS)

KW - Volume estimation

KW - Voxel

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JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

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ER -

Allometry and structural volume change of standing dead southern pine trees using non-destructive terrestrial LiDAR

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