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 language | English (US) |
|---|---|
| Pages (from-to) | 32-43 |
| Number of pages | 12 |
| Journal | Remote Sensing of Environment |
| Volume | 218 |
| DOIs | |
| State | Published - Dec 1 2018 |
| Externally published | Yes |
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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 journal › Article
}
TY - JOUR
T1 - A tree-based approach to biomass estimation from remote sensing data in a tropical agricultural landscape
AU - Graves, Sarah J.
AU - Caughlin, T. Trevor
AU - Asner, Gregory P.
AU - Bohlman, Stephanie A.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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.
AB - 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.
KW - Agricultural carbon
KW - Agroforestry
KW - Allometric model
KW - Land-sharing
KW - LiDAR
KW - Panama
KW - Silvopasture
KW - Trees on farms
KW - Trees outside forests
KW - Tropical trees
UR - http://www.scopus.com/inward/record.url?scp=85053814869&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053814869&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2018.09.009
DO - 10.1016/j.rse.2018.09.009
M3 - Article
AN - SCOPUS:85053814869
VL - 218
SP - 32
EP - 43
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
SN - 0034-4257
ER -