Abstract
Developing accurate but inexpensive methods for estimating above-ground carbon biomass is an important technical challenge that must be overcome before a carbon offset market can be successfully implemented in the United States. Previous studies have shown that LiDAR (light detection and ranging) is well-suited for modeling above-ground biomass in mature forests; however, there has been little previous research on the ability of LiDAR to model above-ground biomass in areas with young, aggrading vegetation. This study compared the abilities of discrete-return LiDAR and high resolution optical imagery to model above-ground carbon biomass at a young restored forested wetland site in eastern North Carolina. We found that the optical imagery model explained more of the observed variation in carbon biomass than the LiDAR model (adj-R2 values of 0.34 and 0.18 respectively; root mean squared errors of 0.14 Mg C/ha and 0.17 Mg C/ha respectively). Optical imagery was also better able to predict high and low biomass extremes than the LiDAR model. Combining both the optical and LiDAR improved upon the optical model but only marginally (adj-R2 of 0.37). These results suggest that the ability of discrete-return LiDAR to model above-ground biomass may be rather limited in areas with young, small trees and that high spatial resolution optical imagery may be the better tool in such areas.
Highlights
The destruction of forests and wetlands is a primary contributor to global climate change [1], [2], [3]
At the Timberlake study site, the absolute performance of the Light detection and ranging (LiDAR) model could be improved, by collecting the LiDAR data during a leaveon period and by developing methods for collecting more reliable field data
The results of this study suggest that optical imagery may prove to be the more reliable tool
Summary
The destruction of forests and wetlands is a primary contributor to global climate change [1], [2], [3]. The United Nation’s REDD program as well as recent proposals in the United States for a carbon offset market are examples of such programs. Speaking, they aim to promote conservation by placing a monetary value on the carbon sequestration services provided by healthy ecosystems [4], [5], [6]. Methods have been developed for estimating carbon biomass using remotely sensed data This approach involves creating empirical models that relate variables extracted from the remotely sensed data to sample biomass data. The use of remotely sensed data has the potential for producing more accurate estimates of total biomass; remote sensing methods can be more expensive due to the cost of acquiring and processing the remotely sensed data
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