14 January 2015 Science Briefs

SNAMP Pub #29: Airborne Lidar-derived volume metrics for aboveground biomass estimation: a comparative assessment for conifer stands

Article Title: Airborne Lidar-derived volume metrics for aboveground biomass estimation: a comparative assessment for conifer stands

Authors: Shengli Tao, Qinghua Guo, Le Li, Baolin Xue, Maggi Kelly, Wenkai Li, GuangCai Xu, and Yanjun Su

Research Highlights:


  • We estimated aboveground biomass using a range of metrics derived from airborne Lidar data.

  • We examined how overlapping crowns in the forest can complicate our ability to map individual trees, and developed a method to account for horizontal crown overlap in the Lidar data.

  • Above ground biomass was most accurately estimated with an equation using tree crown volume, and a model that included overlapping crowns.

Background:
Estimating forest aboveground biomass (AGB) is critical to understand terrestrial carbon cycling in the context of global warming and climate change. The conventional method for estimating AGB relies on estimating biomass using field data and allometric (tree allometry establishes species specific quantitative relations between field-gathered height and dbh of trees and their total biomass) equations. This is an accurate method, yet it is labor and time intensive. Compared with the conventional approach, passive remote sensing techniques can reduce the amount of field work required and improve efficiency. Airborne light detection and ranging (Lidar) is an active remote-sensing technique that has proven to have great capability in estimating forest inventory parameters. The ability of Lidar to measure the vertical distribution of the tree canopy yields three-dimensional (3D) information on forest structure, and has facilitated new approaches to AGB estimation.

In this paper we provide a comprehensive comparison of methods to estimate above ground biomass. We compared (using regression) stem volume using field-based allometry with a range of different volume metrics calculated from airborne Lidar data. We paid attention to the impact of horizontal tree crown overlap on AGB estimation, and so compared estimates with and without consideration of crown overlap.

Methods:
We developed several regression models to estimate aboveground biomass; in each case, the dependent variable is the field-derived AGB (calculated using allometry), and the independent variable is plot volume. Plot volume was calculated with different approaches including estimated stem volume from lidar-derived individual trees; estimated crown volume from a 3D reconstruction of lidar-derived individual trees; estimated crown volume from the canopy height model (CHM); and estimated crown volume from the canopy height model (CHM) when overlapping trees are accounted for. All models were developed at plot level by totaling all the individual tree metrics in each plot. The models were evaluated using correlation coefficient (R2) and root-mean-square error (RMSE) values. This work was performed in the southern Sierra Nevada Adaptive Management Project study site, and used field data from 120 plots (0.05 ha each, with a radius of 12.62 m). The Sierra Nevada Adaptive Management Project has been formed to develop, implement and test Adaptive Management processes through testing the efficacy of Strategically Placed Landscape Treatments (SPLATs) in two study areas in the Sierra Nevada.

Results:
The best regression model (with the highest R2 of 0.77) and lowest root-mean-square error (RMSE) (of 179.0 Mg/ha) to estimate aboveground biomass used lidar volume metrics derived from the canopy height model (CHM) that considered overlapping crowns.

Conclusions:


  1. The most accurate regression model was the one that used metrics that accounted for overlapping crowns.

  2. Although airborne Lidar data is useful for estimating biomass, there are still limitations, mainly due to the inability of airborne Lidar to penetrate dense forest canopy. The most accurate ways to quantify the biomass of dense forests likely will require a fusion of terrestrial and airborne Lidar.

Full Reference:
Tao, Shengli; Qinghua Guo; Le Li; Baolin Xue; Maggi Kelly; Wenkai Li; GuangCai Xu; and YanJun Su. 2014. Airborne Lidar-derived volume metrics for aboveground biomass estimation: a comparative assessment for conifer stands. Agricultural and Forest Meteorology 198-199: 24-32. Spatial Team.

The full paper is available here.

For more information about the SNAMP project and the Spatial team, please see:
Spatial Team.

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