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


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


Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

1

Centre for Landscape and Climate Research, Department of Geography, University of Leicester, Leicester LE1 7RH, UK

2

Jet Propulsion Laboratory JPL, California Institute of Technology, Pasadena, CA 91109, USA

3

The Climate Corporation, 201 Third Street, Suite 1100, San Francisco, CA 94103, USA

4

Center for Spatial Technologies and Remote Sensing CSTARS, University of California Davis, Davis, CA 95618, USA

5

Region 5 Remote Sensing Lab, McClellan, USDA Forest Service, Vallejo, CA 95652, USA

6

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

Abstract 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





Autor: Mariano García 1,2,* , Sassan Saatchi 2, Angeles Casas 3, Alexander Koltunov 4, Susan L. Ustin 4, Carlos Ramirez 5 and Heiko Balzter 1,6

Fuente: http://mdpi.com/



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