Hierarchical integration of individual tree and area-based approaches for savanna biomass uncertainty estimation from airborne LiDAR

Grigorijs Goldbergs, Shaun R. Levick, Michael Lawes, Andrew Edwards

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Understanding the role that the vast north Australian savannas play in the continental carbon cycle requires reliable quantification of their carbon stock at landscape and regional scales. LiDAR remote sensing has proven efficient and accurate for the fine-scale estimation of above-ground tree biomass (AGB) and carbon stocks in many ecosystems, but tropical savanna remain under studied. We utilized a two-phase LiDAR analysis procedure which integrates both individual tree detection (ITC) and area-based approaches (ABA) to better understand how the uncertainty of biomass estimation varies with scale. We used estimations from individual tree LiDAR measurements as training/reference data, and then applied these data to develop allometric equations related to LIDAR metrics. We found that LiDAR individual tree heights were strongly correlated with field-estimated AGB (R2 = 0.754, RMSE = 90 kg), and that 63% of individual trees crowns (ITC) could be accurately delineated with a canopy maxima approach. Area-based biomass estimation (ABA), which incorporated errors from the ITC steps, identified the quadratic mean of canopy height (QMCH) as the best single independent variable for different plot sample sizes (e.g. for 4 ha plots: R2 = 0.86, RMSE = 3.4 Mg ha− 1; and 1 ha plots: R2 = 0.83, RMSE = 4.0 Mg ha− 1). Our results show how ITC and ABA approached can be integrated to understand how biomass uncertainty varies with scale across broad landscapes. Understanding these scaling relationships is critical for operationalizing regional savanna inventories, monitoring and mapping.

Original languageEnglish
Pages (from-to)141-150
Number of pages10
JournalRemote Sensing of Environment
Volume205
DOIs
Publication statusPublished - 1 Feb 2018

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savanna
savannas
Biomass
uncertainty
biomass
Carbon
carbon sinks
canopy
Ecosystems
Remote sensing
tree and stand measurements
tree crown
remote sensing
Uncertainty
carbon
carbon cycle
Monitoring
ecosystems
monitoring
detection

Cite this

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title = "Hierarchical integration of individual tree and area-based approaches for savanna biomass uncertainty estimation from airborne LiDAR",
abstract = "Understanding the role that the vast north Australian savannas play in the continental carbon cycle requires reliable quantification of their carbon stock at landscape and regional scales. LiDAR remote sensing has proven efficient and accurate for the fine-scale estimation of above-ground tree biomass (AGB) and carbon stocks in many ecosystems, but tropical savanna remain under studied. We utilized a two-phase LiDAR analysis procedure which integrates both individual tree detection (ITC) and area-based approaches (ABA) to better understand how the uncertainty of biomass estimation varies with scale. We used estimations from individual tree LiDAR measurements as training/reference data, and then applied these data to develop allometric equations related to LIDAR metrics. We found that LiDAR individual tree heights were strongly correlated with field-estimated AGB (R2 = 0.754, RMSE = 90 kg), and that 63{\%} of individual trees crowns (ITC) could be accurately delineated with a canopy maxima approach. Area-based biomass estimation (ABA), which incorporated errors from the ITC steps, identified the quadratic mean of canopy height (QMCH) as the best single independent variable for different plot sample sizes (e.g. for 4 ha plots: R2 = 0.86, RMSE = 3.4 Mg ha− 1; and 1 ha plots: R2 = 0.83, RMSE = 4.0 Mg ha− 1). Our results show how ITC and ABA approached can be integrated to understand how biomass uncertainty varies with scale across broad landscapes. Understanding these scaling relationships is critical for operationalizing regional savanna inventories, monitoring and mapping.",
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Hierarchical integration of individual tree and area-based approaches for savanna biomass uncertainty estimation from airborne LiDAR. / Goldbergs, Grigorijs; Levick, Shaun R.; Lawes, Michael; Edwards, Andrew.

In: Remote Sensing of Environment, Vol. 205, 01.02.2018, p. 141-150.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Hierarchical integration of individual tree and area-based approaches for savanna biomass uncertainty estimation from airborne LiDAR

AU - Goldbergs, Grigorijs

AU - Levick, Shaun R.

AU - Lawes, Michael

AU - Edwards, Andrew

PY - 2018/2/1

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N2 - Understanding the role that the vast north Australian savannas play in the continental carbon cycle requires reliable quantification of their carbon stock at landscape and regional scales. LiDAR remote sensing has proven efficient and accurate for the fine-scale estimation of above-ground tree biomass (AGB) and carbon stocks in many ecosystems, but tropical savanna remain under studied. We utilized a two-phase LiDAR analysis procedure which integrates both individual tree detection (ITC) and area-based approaches (ABA) to better understand how the uncertainty of biomass estimation varies with scale. We used estimations from individual tree LiDAR measurements as training/reference data, and then applied these data to develop allometric equations related to LIDAR metrics. We found that LiDAR individual tree heights were strongly correlated with field-estimated AGB (R2 = 0.754, RMSE = 90 kg), and that 63% of individual trees crowns (ITC) could be accurately delineated with a canopy maxima approach. Area-based biomass estimation (ABA), which incorporated errors from the ITC steps, identified the quadratic mean of canopy height (QMCH) as the best single independent variable for different plot sample sizes (e.g. for 4 ha plots: R2 = 0.86, RMSE = 3.4 Mg ha− 1; and 1 ha plots: R2 = 0.83, RMSE = 4.0 Mg ha− 1). Our results show how ITC and ABA approached can be integrated to understand how biomass uncertainty varies with scale across broad landscapes. Understanding these scaling relationships is critical for operationalizing regional savanna inventories, monitoring and mapping.

AB - Understanding the role that the vast north Australian savannas play in the continental carbon cycle requires reliable quantification of their carbon stock at landscape and regional scales. LiDAR remote sensing has proven efficient and accurate for the fine-scale estimation of above-ground tree biomass (AGB) and carbon stocks in many ecosystems, but tropical savanna remain under studied. We utilized a two-phase LiDAR analysis procedure which integrates both individual tree detection (ITC) and area-based approaches (ABA) to better understand how the uncertainty of biomass estimation varies with scale. We used estimations from individual tree LiDAR measurements as training/reference data, and then applied these data to develop allometric equations related to LIDAR metrics. We found that LiDAR individual tree heights were strongly correlated with field-estimated AGB (R2 = 0.754, RMSE = 90 kg), and that 63% of individual trees crowns (ITC) could be accurately delineated with a canopy maxima approach. Area-based biomass estimation (ABA), which incorporated errors from the ITC steps, identified the quadratic mean of canopy height (QMCH) as the best single independent variable for different plot sample sizes (e.g. for 4 ha plots: R2 = 0.86, RMSE = 3.4 Mg ha− 1; and 1 ha plots: R2 = 0.83, RMSE = 4.0 Mg ha− 1). Our results show how ITC and ABA approached can be integrated to understand how biomass uncertainty varies with scale across broad landscapes. Understanding these scaling relationships is critical for operationalizing regional savanna inventories, monitoring and mapping.

KW - Biomass

KW - Carbon

KW - LiDAR

KW - Savanna

KW - Tropics

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U2 - 10.1016/j.rse.2017.11.010

DO - 10.1016/j.rse.2017.11.010

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SP - 141

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JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

ER -