LiDAR Application in Forest Fuel Measurements for Bushfire Hazard Mitigation
Creators: name: Chen, Yang
Publisher: name: Monash University
Year: 2017
Subjects: classificationCode: 419999
subject: Other environmental sciences not elsewhere classified

description: Australia’s
native Eucalypt forests are the most fire-prone in the world due to high rates
of fuel accumulation, high flammability of fuel, and seasonally hot and dry
weather conditions. Projected changes in the frequency and intensity of extreme
climate and weather could increase the occurrence of ‘mega-fires’, extreme fire
events with catastrophic impacts on people and the environment. Current methods
for fire risk mitigation and prediction such as fire danger rating systems,
fire behaviour models, and hazard reduction treatments require an accurate
description of forest fuel. However, fire management authorities share a common
challenge to efficiently and accurately quantify forest fuel properties (e.g.
fuel load and fuel structure) at a landscape scale. A landscape includes the
physical elements of geo-physically defined landforms, such as forests,
grasslands, and lakes. This thesis investigates the application of the Light
Detection and Ranging (LiDAR) technique in quantifying forest fuel properties,
including fuel structural characteristics and litter-bed fuel load at a
landscape scale.<br> <br>
   Currently, fire fighters and land managers still rely on
empirical knowledge to visually assess forest fuel characteristics of distinct
fuel layers. The visual assessment method provides a subjective description of
fuel properties that can lead to unreliable fire behaviour prediction and
hazard estimation. This study developed a novel method to classify understorey
fuel layers in order to quantify fuel structural characteristics more
accurately and efficiently by integrating terrestrial LiDAR data and Geographic
Information Systems (GIS). The GIS-based analysis and processing procedures
allow more objective descriptions of fuel covers and depths for individual fuel
layers. The more accurate forest fuel structural information derived from
terrestrial LiDAR data can be used to prescribe fire hazard-reduction burns,
predict fire behaviour potentials, monitor fuel growth, and conserve forest
habitats and ecosystems in multilayered Eucalypt forests.<br> <br>
   Traditionally, litter-bed fuel load is directly measured through
destructive sampling, sorting, and immediate weighing after oven drying for 24
hours at 105 °C. This direct measurement of fuel load on a landscape scale
requires extensive field sampling, post laboratory work and statistical
analysis, which is labour intensive and time consuming. This study found new
relationships among forest litter-bed fuel load, surface fuel depth, fire
history and environmental factors through multiple regressions with airborne
and terrestrial LiDAR data. The fuel load models established in this study
indicate that litter-bed depth and fire history are the primary predictors in
estimating litter-bed fuel load, while canopy density and terrain features are
secondary predictors.<br> <br>
   Current fuel models are constrained to estimate spatial
variations in fuel load within homogeneous vegetation that previously
experienced the same fire events. This study developed a predictive model
through multiple regression to estimate the spatial distribution of litter-bed
fuel load in multilayered eucalypt forests with various fire histories and
forest fuel types. This model uses forest structural indices and terrain
features derived from airborne LiDAR data as predictors, which can be applied
when data on forest fuel types and previous fire disturbances are absent. It
can be used to map the litter-bed fuel load distribution at a landscape scale
to support regional wildland fire management and planning.<br> <br>
   This study indicates that LiDAR allows a more efficient and
accurate description of fuel structural characteristics and estimation of
litter-bed fuel load. The results from this study can assist fire hazard
assessment, fuel reduction treatment, and fire behaviour prediction, and
therefore may reduce the impact to communities and environment.