Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/39498
Title: Quantifying biomass consumption and carbon release by the California Rim Fire by integrating airborne LiDAR and Landsat-OLI data
Authors: Garcia, Mariano
Saatchi, Sassan
Koltunov, Angeles
Koltunov, Alexander
Ustin, Susan
Ramirez, Carlos
Garcia-Gutierrez, Jorge
Balzter, Heiko
First Published: 18-Feb-2017
Publisher: American Geophysical Union (AGU)
Citation: Journal of Geophysical Research: Biogeosciences, 2017, 122 (2), pp. 340-353
Abstract: Quantifying biomass consumption and carbon release is critical to understanding the role of fires in the carbon cycle and air quality. We present a methodology to estimate the biomass consumed and the carbon released by the California Rim fire by integrating postfire airborne LiDAR and multitemporal Landsat Operational Land Imager (OLI) imagery. First, a support vector regression (SVR) model was trained to estimate the aboveground biomass (AGB) from LiDAR-derived metrics over the unburned area. The selected model estimated AGB with an R2 of 0.82 and RMSE of 59.98 Mg/ha. Second, LiDAR-based biomass estimates were extrapolated to the entire area before and after the fire, using Landsat OLI reflectance bands, Normalized Difference Infrared Index, and the elevation derived from LiDAR data. The extrapolation was performed using SVR models that resulted in R2 of 0.73 and 0.79 and RMSE of 87.18 (Mg/ha) and 75.43 (Mg/ha) for the postfire and prefire images, respectively. After removing bias from the AGB extrapolations using a linear relationship between estimated and observed values, we estimated the biomass consumption from postfire LiDAR and prefire Landsat maps to be 6.58 ± 0.03 Tg (1012 g), which translate into 12.06 ± 0.06 Tg CO2e released to the atmosphere, equivalent to the annual emissions of 2.57 million cars.
DOI Link: 10.1002/2015JG003315
ISSN: 2169-8953
eISSN: 2169-8961
Links: http://onlinelibrary.wiley.com/doi/10.1002/2015JG003315/abstract;jsessionid=A413586E3785B011F9099F04817B0BD5.f04t04
http://hdl.handle.net/2381/39498
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © the authors, 2017. This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Appears in Collections:Published Articles, Dept. of Geography

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