A tree-based approach to biomass estimation from remote sensing data in a tropical agricultural landscape

Sarah J. Graves, T. Trevor Caughlin, Gregory P. Asner, Stephanie A. Bohlman

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Agricultural land now exceeds forests as the dominant global biome. Because of their global dominance, and potential expansion or loss, methods to estimate biomass and carbon in agricultural areas are necessary for monitoring global terrestrial carbon stocks and predicting carbon dynamics. Agricultural areas in the tropics have substantial tree cover and associated above ground biomass (AGB) and carbon. Active remote sensing data, such as airborne LiDAR (light detection and ranging), can provide accurate estimates of biomass stocks, but common plot-based methods may not be suitable for agricultural areas with dispersed and heterogeneous tree cover. The objectives of this research are to quantify AGB of a tropical agricultural landscape using a tree-based method that directly incorporates the size of individual trees, and to understand how landscape estimates of AGB from a tree-based method compare to estimates from a plot-based method. We use high-resolution (1.12 m) airborne LiDAR data collected on a 9280-ha region of the Azuero Peninsula of Panama. We model individual tree AGB with canopy dimensions from the LiDAR data. We apply the model to individual tree crown polygons and aggregate AGB estimates to compare with previously developed plot-based estimates. We find that agricultural trees are a distinct and dominant part of our study site. The tree-based approach estimates greater AGB in pixels with low forest cover than the plot-based approach, resulting a 2-fold difference in landscape AGB estimates between the methods for non-forested areas. Additionally, one third of the total landscape AGB exists in areas having <10% cover, based on a global tree cover product. Our study supports the continued use and development of allometric models to predict individual tree biomass from LiDAR-derived canopy dimensions and demonstrates the potential for spatial information from high-resolution data, such as relative isolation of canopies, to improve allometric models.

Original languageEnglish (US)
Pages (from-to)32-43
Number of pages12
JournalRemote Sensing of Environment
Volume218
DOIs
StatePublished - Dec 1 2018
Externally publishedYes

Fingerprint

aboveground biomass
remote sensing
Remote sensing
Biomass
agricultural land
lidar
biomass
carbon
canopy
Carbon
methodology
polygon
biome
Panama
forest cover
carbon sinks
Tropics
tree crown
method
tropics

Keywords

  • Agricultural carbon
  • Agroforestry
  • Allometric model
  • Land-sharing
  • LiDAR
  • Panama
  • Silvopasture
  • Trees on farms
  • Trees outside forests
  • Tropical trees

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

Cite this

A tree-based approach to biomass estimation from remote sensing data in a tropical agricultural landscape. / Graves, Sarah J.; Caughlin, T. Trevor; Asner, Gregory P.; Bohlman, Stephanie A.

In: Remote Sensing of Environment, Vol. 218, 01.12.2018, p. 32-43.

Research output: Contribution to journalArticle

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abstract = "Agricultural land now exceeds forests as the dominant global biome. Because of their global dominance, and potential expansion or loss, methods to estimate biomass and carbon in agricultural areas are necessary for monitoring global terrestrial carbon stocks and predicting carbon dynamics. Agricultural areas in the tropics have substantial tree cover and associated above ground biomass (AGB) and carbon. Active remote sensing data, such as airborne LiDAR (light detection and ranging), can provide accurate estimates of biomass stocks, but common plot-based methods may not be suitable for agricultural areas with dispersed and heterogeneous tree cover. The objectives of this research are to quantify AGB of a tropical agricultural landscape using a tree-based method that directly incorporates the size of individual trees, and to understand how landscape estimates of AGB from a tree-based method compare to estimates from a plot-based method. We use high-resolution (1.12 m) airborne LiDAR data collected on a 9280-ha region of the Azuero Peninsula of Panama. We model individual tree AGB with canopy dimensions from the LiDAR data. We apply the model to individual tree crown polygons and aggregate AGB estimates to compare with previously developed plot-based estimates. We find that agricultural trees are a distinct and dominant part of our study site. The tree-based approach estimates greater AGB in pixels with low forest cover than the plot-based approach, resulting a 2-fold difference in landscape AGB estimates between the methods for non-forested areas. Additionally, one third of the total landscape AGB exists in areas having <10{\%} cover, based on a global tree cover product. Our study supports the continued use and development of allometric models to predict individual tree biomass from LiDAR-derived canopy dimensions and demonstrates the potential for spatial information from high-resolution data, such as relative isolation of canopies, to improve allometric models.",
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