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.
Highlights
Forest fuels, the organic matter available for fire ignition and combustion, are an essential component of fire management activities [1]
The models based on the metrics selected by regression and the genetic algorithms showed overfitting issues when applied to the independent stepwise regression and the genetic algorithms showed overfitting issues when applied to the validation datasets, with a decrease of R2 by 21% and 54% and an increase of RRMSE by 27% and 30%, independent validation datasets, with a decrease of R2 by 21% and 54% and an increase of RRMSE by respectively
This research demonstrated how LiDAR-based canopy fuel properties can be extrapolated to larger regions using Landsat data
Summary
The organic matter available for fire ignition and combustion, are an essential component of fire management activities [1]. Spatially explicit information on forest fuels is Remote Sens. Fire suppression policies adopted during the last century and demographic factors like rural abandonment have led to fuel accumulation and spatial homogenization of forest fuels that can unintentionally foster extreme fire events exceeding the recovery capacity of the ecosystems and reducing their resilience [2]. These megafires can adversely change fundamental processes of energy exchange, water fluxes, and nutrient and carbon cycling [3]. Climate change projections indicate their frequency and intensity are expected to further increase as a result of longer and drier fire seasons [4]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
