Forest wildfires in the Philippines are all human-caused. Resource managers are
dependent on accurate estimates and spatially accurate forest structure information
to govern this kind of phenomena.This study presents methods of using airborne
laser scanning data to acquire forest and… More
Forest wildfires in the Philippines are all human-caused. Resource managers are
dependent on accurate estimates and spatially accurate forest structure information
to govern this kind of phenomena.This study presents methods of using airborne
laser scanning data to acquire forest and tree parameters that are critical in
modelling forest fire behavior. The study aimed on assessing fuel models of a tree
plantation through LiDAR (Light Detection and Ranging) point cloud data. The tree
plantation under this study is located in the Municipality of Malitbog, Bukidnon
that is managed by Bukidnon Forest Incorporated (BFI). Canopy fuel of Caribbean
pine (Pinus caribaea) was determined through calculation of canopy bulk density
(CBD), canopy base height (CBH), canopy fuel weight (CFW) and canopy height
(CH), which are essential in mapping the spatial distribution and modelling fire
behavior. The Canopy Fuel Estimator (CFE) software, developed by researchers from
USDA Forest Service, was used in this study. The plantation’s mean value of canopy
fuel was extracted from available LiDAR data, which was also compared to the field
data. Findings show that Caribbean pine plantation has high CBH (11.8 m), CH
(31.5 m), and CBD (1.5 kg/m2). This indicates higher risk of forest fire in the area.
Furthermore, field data for canopy fuel (CBH = 17.9 m, CH = 27.9 m, CFW = 2.9
kg/ha, CBD = 0.6 kg/m2) was observed to be close to LiDAR data (CBH = 11.8 m,
CH = 31.5, CFW = 1.5 kg/ha, CBD = 1.5 kg/m2). Therefore, this study indicates the
reliability of LiDAR data in modeling canopy fuel in a homogenous tree plantation
through CFE. The maps produced can be used in fire behavior prediction, fuel
reduction treatment prioritization and during active fire elimination. Less