Quantifying wetland microtopography with terrestrial laser scanning

Atticus E.L. Stovall, Jacob S. Diamond, Robert A Slesak, Daniel L. McLaughlin, Hank Shugart

Research output: Contribution to journalArticle

Abstract

Wetlands hold the highest density of belowground carbon stocks on earth, provide myriad biogeochemical and habitat functions, and are at increasing risk of degradation due to climate and land use change. Microtopographic variation is a common and functionally important feature of wetlands but is challenging to quantify, constraining estimates of the processes and functions (e.g., habitat diversity, carbon storage) that it regulates. We introduce a novel method of quantifying fine-scale microtopographic structure with Terrestrial Laser Scanning using 10 black ash (Fraxinus nigra) wetlands in northern Minnesota, USA as test cases. Our method reconstructs surface models with fine detail on the order of 1 cm. Our independent validation verifies the surface models capture hummock (local high points) and hollow (local low points) features with high precision (RMSE = 3.67 cm) and low bias (1.26 cm). A sensitivity analysis of surface model resolution showed a doubling of model error between 1 cm and 50 cm resolutions, suggesting high-resolution reconstructions most precisely capture surface variation. We also compared five classification methods at resolutions ranging from 1 cm to 1 m and determined that maximum likelihood classification at 25 cm resolution most accurately (78.7%) identifies hummock and hollow features, but a simple thresholding of surface model elevation and slope was ideal for hummock feature delineation, retaining over 91% of hummock areas. Finally, we test and validate a novel microtopographic delineation method (TopoSeg) that accurately (Bias = 0.2–11.9%, RMSE = 19.6–24.1%) estimates the height, area, volume, and perimeter of individual hummock features. For the first time, we introduce an accurate and automated approach for quantifying fine-scale microtopography through high resolution surface models, feature classification, and feature delineation, enabling geospatial statistics that can explain spatial heterogeneity of habitat structure, soil processes, and carbon storage in wetland systems.

Original languageEnglish (US)
Article number111271
JournalRemote Sensing of Environment
Volume232
DOIs
StatePublished - Oct 1 2019

Fingerprint

microtopography
microrelief
Wetlands
lasers
wetlands
laser
wetland
Scanning
Lasers
Fraxinus nigra
Ashes
carbon sequestration
Carbon
habitats
habitat structure
habitat
methodology
soil structure
soil carbon
carbon sinks

Keywords

  • 3D
  • Algorithm
  • Automatic
  • Classification
  • Hollow
  • Hummock
  • Hydrology
  • Machine learning
  • Model resolution
  • Sensitivity
  • Terrestrial LiDAR

Cite this

Quantifying wetland microtopography with terrestrial laser scanning. / Stovall, Atticus E.L.; Diamond, Jacob S.; Slesak, Robert A; McLaughlin, Daniel L.; Shugart, Hank.

In: Remote Sensing of Environment, Vol. 232, 111271, 01.10.2019.

Research output: Contribution to journalArticle

Stovall, Atticus E.L. ; Diamond, Jacob S. ; Slesak, Robert A ; McLaughlin, Daniel L. ; Shugart, Hank. / Quantifying wetland microtopography with terrestrial laser scanning. In: Remote Sensing of Environment. 2019 ; Vol. 232.
@article{e7f7baa77f3d473f9bf5db28e63b6dd1,
title = "Quantifying wetland microtopography with terrestrial laser scanning",
abstract = "Wetlands hold the highest density of belowground carbon stocks on earth, provide myriad biogeochemical and habitat functions, and are at increasing risk of degradation due to climate and land use change. Microtopographic variation is a common and functionally important feature of wetlands but is challenging to quantify, constraining estimates of the processes and functions (e.g., habitat diversity, carbon storage) that it regulates. We introduce a novel method of quantifying fine-scale microtopographic structure with Terrestrial Laser Scanning using 10 black ash (Fraxinus nigra) wetlands in northern Minnesota, USA as test cases. Our method reconstructs surface models with fine detail on the order of 1 cm. Our independent validation verifies the surface models capture hummock (local high points) and hollow (local low points) features with high precision (RMSE = 3.67 cm) and low bias (1.26 cm). A sensitivity analysis of surface model resolution showed a doubling of model error between 1 cm and 50 cm resolutions, suggesting high-resolution reconstructions most precisely capture surface variation. We also compared five classification methods at resolutions ranging from 1 cm to 1 m and determined that maximum likelihood classification at 25 cm resolution most accurately (78.7{\%}) identifies hummock and hollow features, but a simple thresholding of surface model elevation and slope was ideal for hummock feature delineation, retaining over 91{\%} of hummock areas. Finally, we test and validate a novel microtopographic delineation method (TopoSeg) that accurately (Bias = 0.2–11.9{\%}, RMSE = 19.6–24.1{\%}) estimates the height, area, volume, and perimeter of individual hummock features. For the first time, we introduce an accurate and automated approach for quantifying fine-scale microtopography through high resolution surface models, feature classification, and feature delineation, enabling geospatial statistics that can explain spatial heterogeneity of habitat structure, soil processes, and carbon storage in wetland systems.",
keywords = "3D, Algorithm, Automatic, Classification, Hollow, Hummock, Hydrology, Machine learning, Model resolution, Sensitivity, Terrestrial LiDAR",
author = "Stovall, {Atticus E.L.} and Diamond, {Jacob S.} and Slesak, {Robert A} and McLaughlin, {Daniel L.} and Hank Shugart",
year = "2019",
month = "10",
day = "1",
doi = "10.1016/j.rse.2019.111271",
language = "English (US)",
volume = "232",
journal = "Remote Sensing of Environment",
issn = "0034-4257",
publisher = "Elsevier",

}

TY - JOUR

T1 - Quantifying wetland microtopography with terrestrial laser scanning

AU - Stovall, Atticus E.L.

AU - Diamond, Jacob S.

AU - Slesak, Robert A

AU - McLaughlin, Daniel L.

AU - Shugart, Hank

PY - 2019/10/1

Y1 - 2019/10/1

N2 - Wetlands hold the highest density of belowground carbon stocks on earth, provide myriad biogeochemical and habitat functions, and are at increasing risk of degradation due to climate and land use change. Microtopographic variation is a common and functionally important feature of wetlands but is challenging to quantify, constraining estimates of the processes and functions (e.g., habitat diversity, carbon storage) that it regulates. We introduce a novel method of quantifying fine-scale microtopographic structure with Terrestrial Laser Scanning using 10 black ash (Fraxinus nigra) wetlands in northern Minnesota, USA as test cases. Our method reconstructs surface models with fine detail on the order of 1 cm. Our independent validation verifies the surface models capture hummock (local high points) and hollow (local low points) features with high precision (RMSE = 3.67 cm) and low bias (1.26 cm). A sensitivity analysis of surface model resolution showed a doubling of model error between 1 cm and 50 cm resolutions, suggesting high-resolution reconstructions most precisely capture surface variation. We also compared five classification methods at resolutions ranging from 1 cm to 1 m and determined that maximum likelihood classification at 25 cm resolution most accurately (78.7%) identifies hummock and hollow features, but a simple thresholding of surface model elevation and slope was ideal for hummock feature delineation, retaining over 91% of hummock areas. Finally, we test and validate a novel microtopographic delineation method (TopoSeg) that accurately (Bias = 0.2–11.9%, RMSE = 19.6–24.1%) estimates the height, area, volume, and perimeter of individual hummock features. For the first time, we introduce an accurate and automated approach for quantifying fine-scale microtopography through high resolution surface models, feature classification, and feature delineation, enabling geospatial statistics that can explain spatial heterogeneity of habitat structure, soil processes, and carbon storage in wetland systems.

AB - Wetlands hold the highest density of belowground carbon stocks on earth, provide myriad biogeochemical and habitat functions, and are at increasing risk of degradation due to climate and land use change. Microtopographic variation is a common and functionally important feature of wetlands but is challenging to quantify, constraining estimates of the processes and functions (e.g., habitat diversity, carbon storage) that it regulates. We introduce a novel method of quantifying fine-scale microtopographic structure with Terrestrial Laser Scanning using 10 black ash (Fraxinus nigra) wetlands in northern Minnesota, USA as test cases. Our method reconstructs surface models with fine detail on the order of 1 cm. Our independent validation verifies the surface models capture hummock (local high points) and hollow (local low points) features with high precision (RMSE = 3.67 cm) and low bias (1.26 cm). A sensitivity analysis of surface model resolution showed a doubling of model error between 1 cm and 50 cm resolutions, suggesting high-resolution reconstructions most precisely capture surface variation. We also compared five classification methods at resolutions ranging from 1 cm to 1 m and determined that maximum likelihood classification at 25 cm resolution most accurately (78.7%) identifies hummock and hollow features, but a simple thresholding of surface model elevation and slope was ideal for hummock feature delineation, retaining over 91% of hummock areas. Finally, we test and validate a novel microtopographic delineation method (TopoSeg) that accurately (Bias = 0.2–11.9%, RMSE = 19.6–24.1%) estimates the height, area, volume, and perimeter of individual hummock features. For the first time, we introduce an accurate and automated approach for quantifying fine-scale microtopography through high resolution surface models, feature classification, and feature delineation, enabling geospatial statistics that can explain spatial heterogeneity of habitat structure, soil processes, and carbon storage in wetland systems.

KW - 3D

KW - Algorithm

KW - Automatic

KW - Classification

KW - Hollow

KW - Hummock

KW - Hydrology

KW - Machine learning

KW - Model resolution

KW - Sensitivity

KW - Terrestrial LiDAR

UR - http://www.scopus.com/inward/record.url?scp=85069636005&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85069636005&partnerID=8YFLogxK

U2 - 10.1016/j.rse.2019.111271

DO - 10.1016/j.rse.2019.111271

M3 - Article

VL - 232

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

M1 - 111271

ER -