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Garcia_et_al_2017c_CanopyFuelsRimFire.pdf (2.91 MB)

Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data

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posted on 2017-06-27, 15:28 authored by Mariano García, Sassan Saatchi, Angeles Casas, Alexander Koltunov, Susan L. Ustin, Carlos Ramirez, Heiko Balzter
Accurate, spatially explicit information about forest canopy fuel properties is essential for ecosystem management strategies for reducing the severity of forest fires. Airborne LiDAR technology has demonstrated its ability to accurately map canopy fuels. However, its geographical and temporal coverage is limited, thus making it difficult to characterize fuel properties over large regions before catastrophic events occur. This study presents a two-step methodology for integrating post-fire airborne LiDAR and pre-fire Landsat OLI (Operational Land Imager) data to estimate important pre-fire canopy fuel properties for crown fire spread, namely canopy fuel load (CFL), canopy cover (CC), and canopy bulk density (CBD). This study focused on a fire prone area affected by the large 2013 Rim fire in the Sierra Nevada Mountains, California, USA. First, LiDAR data was used to estimate CFL, CC, and CBD across an unburned 2 km buffer with similar structural characteristics to the burned area. Second, the LiDAR-based canopy fuel properties were extrapolated over the whole area using Landsat OLI data, which yielded an R2 of 0.8, 0.79, and 0.64 and RMSE of 3.76 Mg·ha−1, 0.09, and 0.02 kg·m−3 for CFL, CC, and CBD, respectively. The uncertainty of the estimates was estimated for each pixel using a bootstrapping approach, and the 95% confidence intervals are reported. The proposed methodology provides a detailed spatial estimation of forest canopy fuel properties along with their uncertainty that can be readily integrated into fire behavior and fire effects models. The methodology could be also integrated into the LANDFIRE program to improve the information on canopy fuels.

History

Citation

Remote Sensing, 2017, 9 (4), pp. 394-394

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Geography

Version

  • VoR (Version of Record)

Published in

Remote Sensing

Publisher

MDPI

eissn

2072-4292

Acceptance date

2017-04-19

Copyright date

2017

Available date

2017-06-27

Publisher version

http://www.mdpi.com/2072-4292/9/4/394

Language

en

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