Title: Estimating aboveground carbon stocks of a forest affected by mountain pine beetle in Idaho using lidar and multispectral imagery
Author: Bright, Benjamin C.; Hicke, Jeffrey A.; Hudak, Andrew T.
Source: Remote Sensing of Environment. 124: 270-281.
Publication Series: Journal/Magazine Article (JRNL)
Description: Mountain pine beetle outbreaks have caused widespread tree mortality in North American forests in recent decades, yet few studies have documented impacts on carbon cycling. In particular, landscape scales intermediate between stands and regions have not been well studied. Remote sensing is an effective tool for quantifying impacts of insect outbreaks on forest ecosystems at landscape scales. In this study, we developed and evaluated methodologies for quantifying aboveground carbon (AGC) stocks affected by mountain pine beetle using field observations, lidar data, and multispectral imagery. We evaluated methods at two scales, the plot level and the tree level, to ascertain the capability of each for mapping AGC impacts of bark beetle infestation across a forested landscape. In 27 plots across our 5054-ha study area in central Idaho, we measured tree locations, health, diameter, height, and other relevant attributes. We used allometric equations to estimate AGC content of individual trees and, in turn, summed tree AGC estimates to the plot level. Tree-level and plot-level AGC were then predicted from lidar metrics using separate statistical models. At the tree level, cross-validated additive models explained 50-54% of the variation in tree AGC (rootmean square error (RMSE) values of 26-42 kg AGC, or 32-48%). At the plot level, a cross-validated linearmodel explained 84% of the variation in plot AGC (RMSE of 9.2 Mg AGC/ha, or 12%).
Keywords: carbon, mountain pine beetle, insect outbreak, tree mortality, aerial imagery, lidar
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Bright, Benjamin C.; Hicke, Jeffrey A.; Hudak, Andrew T. 2012. Estimating aboveground carbon stocks of a forest affected by mountain pine beetle in Idaho using lidar and multispectral imagery. Remote Sensing of Environment. 124: 270-281.
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