@article {bnh-6056, title = {Floods, bushfires and sectoral economic output in Australia, 1978-2014}, journal = {Economic Record}, volume = {95}, year = {2018}, month = {11/2018}, pages = {58-80}, abstract = {

Using state-level annual variation in natural disasters and economic output in Australia, we estimate the direct effects of floods and bushfires on sectoral gross value added during the period 1978{\textendash}2014. We find that floods exert an adverse and persistent effect on the outputs of agriculture, mining, construction and financial services sectors. For example, our estimates indicate that a state that experienced a flood in a given year encountered, on average, 5{\textendash}6 per cent lower agricultural output in both that year and the following year, compared to another state with no such flood experience. Sectoral responses to bushfires are more nuanced.

}, keywords = {economic, Floods, Natural hazards}, doi = { https://doi.org/10.1111/1475-4932.12446}, url = {https://onlinelibrary.wiley.com/doi/full/10.1111/1475-4932.12446}, author = {Mehmet Ulubasoglu and Muhamman Rahman and Yasin Onder and Chen, Yang and Abbas Rajabifard} } @article {bnh-3919, title = {Development of a predictive model for estimating forest surface fuel load in Australian eucalypt forests with LiDAR data}, journal = {Environmental Modelling \& Software}, volume = {97}, year = {2017}, month = {11/2017}, pages = {61-71}, chapter = {61}, abstract = {

Accurate description of forest surface fuel load is important for understanding bushfire behaviour and suppression difficulties, predicting ongoing fires for operational activities, assessing potential fire hazards and assisting in fuel hazard-reduction burns to reduce fire risks to the community and the environment. Bushfire related studies and current operational activities have a common challenge in quantifying fuels, since the fuel load varies across the landscape. This paper developed a predictive model that efficiently and accurately estimates quantities of surface fuel in Australian southeast Eucalypt forests. Model coefficients were determined through a three-step process that attempts to evaluate how the spatial variation in surface fuel load relates to litter-bed depth, fuel characteristics, topography and previous fire disturbance. First, the forest surface fuel depth-to-load relationship was established; second, key quantitative variables of environmental factors were added; and third, important qualitative variables of fuel characteristics were included. The verification of model prediction was conducted through leave-one-out cross-validation (CV). Light Detection and Ranging was used to quantify forest structural characteristics and terrain features. The calibrated model had a\ R2\ of 0.89 (RMSE\ =\ 20.7\ g) and performed better than the currently used surface fuel load models, including McArthur{\textquoteright}s (R2\ =\ 0.61 and\ RMSE\ =\ 39.6\ g) and Gilroy and Tran{\textquoteright}s (R2\ =\ 0.69 and\ RMSE\ =\ 36.5\ g) models. This study describes a novel approach to forest surface fuel load modelling using forest characteristics and environmental factors derived from LiDAR data through statistical analysis. The model established in this study can be used as an efficient approach to assist in forest fuel management and fire related operational activities.

}, doi = {10.1016/j.envsoft.2017.07.007}, url = {http://www.sciencedirect.com/science/article/pii/S1364815216304418}, author = {Chen, Yang and Xuan Zhou and Marta Yebra and Sarah Harris and Nigel Tapper} } @conference {bnh-3232, title = {Estimation of forest surface fuel load using airborne LiDAR data}, booktitle = {SPIE Remote Sensing}, year = {2017}, month = {09/2017}, publisher = {SPIE}, organization = {SPIE}, address = {Warsaw, Poland}, abstract = {

Accurately describing forest surface fuel load is significant for understanding bushfire behaviour and suppression difficulties, predicting ongoing fires for operational activities, as well as assessing potential fire hazards. In this study, the Light Detection and Ranging (LiDAR) data was used to estimate surface fuel load, due to its ability to provide threedimensional
information to quantify forest structural characteristics with high spatial accuracies. Firstly, the multilayered eucalypt forest vegetation was stratified by identifying the cut point of the mixture distribution of LiDAR point density through a non-parametric fitting strategy as well as derivative functions. Secondly, the LiDAR indices of heights, intensity, topography, and canopy density were extracted. Thirdly, these LiDAR indices, forest type and previous fire disturbances were then used to develop two predictive models to estimate surface fuel load through multiple regression analysis. Model 1 was developed based on LiDAR indices, which produced a R2 value of 0.63. Model 2 (R2 = 0.8) wasderived from LiDAR indices, forest type and previous fire disturbances. The accurate and consistent spatial variation in surface fuel load derived from both models could be used to assist fire authorities in guiding fire hazard-reduction burns and fire suppressions in the Upper Yarra Reservoir area, Victoria, Australia.

}, author = {Chen, Yang and Xuan Zhou and Marta Yebra and Sarah Harris and Nigel Tapper} } @mastersthesis {bnh-3596, title = {LiDAR Application in Forest Fuel Measurements for Bushfire Hazard Mitigation}, volume = {Doctor of Philosophy}, year = {2017}, month = {01/2017}, pages = {134}, school = {Monash University}, type = {PhD}, address = {Clayton}, abstract = {

Australia{\textquoteright}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 {\textquoteleft}mega-fires{\textquoteright}, 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.

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.

Traditionally, litter-bed fuel load is directly measured through destructive sampling, sorting, and immediate weighing after oven drying for 24 hours at 105 {\textdegree}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.

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.

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.

}, author = {Chen, Yang} } @article {bnh-1661, title = {Using LiDAR for forest and fuel structure mapping: options, benefits, requirements and costs}, number = {64}, year = {2015}, month = {03/2015}, institution = {Bushfire and Natural Hazards CRC}, abstract = {

Understanding fuel structure is important for assessing suppression difficulty, risk of damage from bushfires, monitoring fuel build up and planning hazard reduction programs. Technologies such as airborne Light Detection and Ranging (LiDAR) can provide precise information about fuel structure over larger areas. However, the use of LiDAR by fire managers is still in the early stages and has not been implemented through any routine operational program in Australia.

This report aims to address this situation by describing and evaluating the maturity and suitability of airborne LiDAR to derive the different types of information needed in forest fuel assessment. It does so through a set of questions scoped in consultation with fire managers through the Bushfire and Natural Hazards CRC project {\textquoteleft}Mapping bushfire hazard and impacts{\textquoteright}. The language and technical detail is aimed at a wide audience with fields of expertise outside LiDAR.

This report first covers some of the basic principles on LiDAR and then focuses on the analysis of the information content and accuracy of airborne LiDAR to retrieve the forest fuel attributes that are important for fire management. The information that can be derived about the height, cover fraction and density of different over- and understorey layers is assessed, along with other useful information that may be derived. Additional measurements that help to make more optimal use of airborne LiDAR data are presented, including terrestrial laser scanning, UAV-borne LiDAR, and airborne imaging. Guidance is provided on discovering existing LiDAR data, factors determining the cost of new LiDAR data acquisition, and options for processing the data. Finally, the current and future development in the use of LiDAR for fire management are discussed.

Summarising, airborne LiDAR may be considered a mature data product that is commercially available, using established data standards. However, standardised data specifications and processing methods for applications in fuel mapping do not yet exist. Essential aspects to consider are the type of fuel information, accuracy and spatial detail desired. Greater data density can increase accuracy and spatial detail, but will also increase the cost of acquisition. In forests with a dense overstorey canopy high data density may be the only way to obtain information on the understorey. In small-scale applications, field or UAV-mounted LiDAR systems may be a suitable alternative for airborne LiDAR.

Priority areas for research and development to achieve more cost-effective and successful use of LiDAR by the fire management community were identified. This includes the development of standardised methods to acquire and process airborne LiDAR data for fuel mapping, the validation of these methods using field measurements, and investigation of full-waveform airborne LiDAR as a promising alternative to current LiDAR data collection methods. The Bushfire and Natural Hazards CRC project {\textquoteleft}Mapping bushfire hazard and impacts{\textquoteright} is working with end users to pursue each of these lines of enquiry.

}, issn = {64}, author = {Marta Yebra and Marselis, S and Albert van Dijk and Geoffrey J. Cary and Chen, Yang} }