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Remote Sens. 2017, 9(4), 394; doi:10.3390/rs9040394

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

Centre for Landscape and Climate Research, Department of Geography, University of Leicester, Leicester LE1 7RH, UK
Jet Propulsion Laboratory (JPL), California Institute of Technology, Pasadena, CA 91109, USA
The Climate Corporation, 201 Third Street, Suite 1100, San Francisco, CA 94103, USA
Center for Spatial Technologies and Remote Sensing (CSTARS), University of California Davis, Davis, CA 95618, USA
Region 5 Remote Sensing Lab, McClellan, USDA Forest Service, Vallejo, CA 95652, USA
National Centre for Earth Observation, University of Leicester, Leicester LE1 7RH, UK
Author to whom correspondence should be addressed.
Academic Editors: Juha Hyyppä and Prasad S. Thenkabail
Received: 8 March 2017 / Revised: 11 April 2017 / Accepted: 19 April 2017 / Published: 22 April 2017
View Full-Text   |   Download PDF [2981 KB, uploaded 22 April 2017]   |  


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. View Full-Text
Keywords: LiDAR; Landsat OLI; data integration; canopy fuel load; canopy cover; canopy bulk density; megafires LiDAR; Landsat OLI; data integration; canopy fuel load; canopy cover; canopy bulk density; megafires

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

García, M.; Saatchi, S.; Casas, A.; Koltunov, A.; Ustin, S.L.; Ramirez, C.; Balzter, H. Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data. Remote Sens. 2017, 9, 394.

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