Sarah Harris

Evaluating Spectral Indices for Assessing Fire Severity in Chaparral Ecosystems (Southern California) Using MODIS/ASTER (MASTER) Airborne Simulator Data

Wildland fires are a yearly recurring phenomenon in many terrestrial ecosystems. Accurate fire severity estimates are of paramount importance for modeling fire-induced trace gas emissions and rehabilitating post-fire landscapes. We used high spatial and high spectral resolution MODIS/ASTER (MASTER) airborne simulator data acquired over four 2007 southern California burns to evaluate the effectiveness of 19 different spectral indices, including the widely used Normalized Burn Ratio (NBR), for assessing fire severity in southern California chaparral. Ordinal logistic regression was used to assess the goodness-of-fit between the spectral index values and ordinal field data of severity. The NBR and three indices in which the NBR is enhanced with surface temperature or emissivity data revealed the best performance. Our findings support the operational use of the NBR in chaparral ecosystems by Burned Area Emergency Rehabilitation (BAER) projects, and demonstrate the potential of combining optical and thermal data for assessing fire severity. Additional testing in more burns, other ecoregions and different vegetation types is required to fully understand how (thermally enhanced) spectral indices relate to fire severity.

The relationship between fire behaviour measures and community loss: an exploratory analysis for developing a bushfire severity scale

Current fire danger scales do not adequately reflect the potential destructive force of a bushfire in Australia and, therefore, do not provide fire prone communities with an adequate warning for the potential loss of human life and property. To determine options for developing a bushfire severity scale based on community impact and whether a link exists between the energy release rate (power) of a fire and community loss, this paper reviewed observations of 79 wildfires (from 1939 to 2009) across Victoria and other southern states of Australia. A methodology for estimating fire power based on fuel loading, fire size and progression rate is presented. McArthur’s existing fire danger indices (FDIs) as well as fuel- and slope-adjusted FDIs were calculated using fire weather data. Analysis of possible relationships between fire power, FDIs, rate of spread and Byram’s fireline intensity and community loss was performed using exposure as a covariate. Preliminary results showed that a stronger relationship exists between community loss and the power of the fire than between loss and FDI, although fuel-adjusted FDI was also a good predictor of loss. The database developed for this study and the relationships established are essential for undertaking future studies that require observations of past fire behaviour and losses and also to form the basis of developing a new severity scale.

Forecasting fire activity in Victoria, Australia, using antecedent climate variables and ENSO indices

Climatic variability alters precipitation and temperature patterns globally, and presumably has an effect on regional fire regimes, yet relationships between climate variation and bushfire activity are poorly understood in many fire-prone regions. Such relationships may facilitate forecasting of the potential fire risk for an upcoming season. This paper reviews the interaction between the El Nino–Southern Oscillation (ENSO), and the weather and fire activity in the fire-prone state of Victoria, Australia. Linear correlations were used to analyse the relationships between ENSO indices, spatially averaged climate variables and fire activity in Victoria from 1972 to 2012. Data were analysed using monthly and seasonal averages and both antecedent and concurrent relationships were explored. Significant relationships were identified between fire activity and climate variables, especially in September to November, the months immediately preceding the fire season. In order of strength, these climate variables are vapour pressure at 1500 hours, vapour pressure at 0900 hours, maximum temperature, rainfall and minimum temperature. Additionally, significant relationships between ENSO indices and fire activity were identified for this period, although these relationships were not as strong as those with climate variables. The potential exists to use ENSO indices and climate variables to forecast an upcoming season’s potential bushfire activity. This is the first study to analyse the ENSO–fire relationship specifically for Victoria and also the first to use Victorian fire activity data (rather than indices of fire weather) as a basis for the comparison with climate and ENSO.

Variability and drivers of extreme fire weather in fire-prone areas of south-eastern Australia

Most of the life and property losses due to bushfires in south-eastern Australia occur under extreme fire weather conditions – strong winds, high temperatures, low relative humidity (RH) and extended drought. However, what constitutes extreme, and the values of the weather ingredients and their variability, differs regionally. Using a gridded dataset to identify the highest 10 fire weather days from 1972 to 2012, as defined by McArthur’s Forest Fire Danger Index (FFDI), for 24 sites across Victoria and nearby, we analyse the extent and variability of these highest 10 FFDI days, and of the contributing temperature, RH, wind speed, wind direction and drought indices. We document the occurrence of these events by time of day, month of occurrence and inter-annual variability. We find there is considerable variability among regions in the highest FFDI days and also the contributing weather and drought parameters, with some regional groupings apparent. Many major fire events occurred on these highest 10 fire weather days; however there are also days in which extreme fire weather occurred yet no known major fires are recorded. The results from this study will be an additional valuable resource to fire agencies in fire risk planning by basing fire management decisions on site-specific extreme fire weather conditions.

Corrigendum to: Forecasting fire activity in Victoria, Australia, using antecedent climate variables and ENSO indices

Climatic variability alters precipitation and temperature patterns globally, and presumably has an effect on regional fire regimes, yet relationships between climate variation and bushfire activity are poorly understood in many fire-prone regions. Such relationships may facilitate forecasting of the potential fire risk for an upcoming season. This paper reviews the interaction between the El Nino–Southern Oscillation (ENSO), and the weather and fire activity in the fire-prone state of Victoria, Australia. Linear correlations were used to analyse the relationships between ENSO indices, spatially averaged climate variables and fire activity in Victoria from 1972 to 2012. Data were analysed using monthly and seasonal averages and both antecedent and concurrent relationships were explored. Significant relationships were identified between fire activity and climate variables, especially in September to November, the months immediately preceding the fire season. In order of strength, these climate variables are vapour pressure at 1500 hours, vapour pressure at 0900 hours, maximum temperature, rainfall and minimum temperature. Additionally, significant relationships between ENSO indices and fire activity were identified for this period, although these relationships were not as strong as those with climate variables. The potential exists to use ENSO indices and climate variables to forecast an upcoming seasons potential bushfire activity. This is the first study to analyse the ENSO–fire relationship specifically for Victoria and also the first to use Victorian fire activity data (rather than indices of fire weather) as a basis for the comparison with climate and ENSO.

A bias corrected WRF mesoscale fire weather dataset for Victoria, Australia 1972-2012

Climatology data of fire weather across the landscape can provide science-based evidence for informing strategic decisions to ameliorate the impacts (at times extreme) of bushfires on community socio-economic wellbeing and to sustain ecosystem health and functions. A longterm climatology requires spatial and temporal data that are consistent to represent the landscape in sufficient detail to be useful for fire weather studies and management purposes. To address this inhomogeneity problem for analyses of a variety of fire weather interests and to provide a dataset for management decision-support, a homogeneous 41-year (1972-2012), hourly interval, 4 km gridded climate dataset for Victoria has been generated using a combination of mesoscale modelling, global reanalysis data, surface observations, and historic observed rainfall analyses. Hourly near-surface forecast fields were combined with Drought Factor (DF) fields calculated from the Australian Water Availability Project (AWAP) rainfall analyses to generate fields of hourly fire danger indices for each hour of the 41-year period. A quantile mapping (QM) bias correction technique utilizing available observations during 19962012 was used to ameliorate any model biases in wind speed, temperature and relative humidity. Extensive evaluation was undertaken including both quantitative and case study qualitative assessments. The final dataset includes 4-km surface hourly temperature, relative humidity, wind speed, wind direction, Forest Fire Danger Index (FFDI), and daily DF and Keetch-Byram Drought Index (KBDI), and a 32-level full three-dimensional volume atmosphere.

Strata-based forest fuel classification for wild fire hazard assessment using terrestrial LiDAR

Abstract. Fuel structural characteristics affect fire behavior including fire intensity, spread rate, flame structure, and duration, therefore, quantifying forest fuel structure has significance in understanding fire behavior as well as providing information for fire management activities (e.g., planned burns, suppression, fuel hazard assessment, and fuel treatment). This paper presents a method of forest fuel strata classification with an integration between terrestrial light detection and ranging (LiDAR) data and geographic information system for automatically assessing forest fuel structural characteristics (e.g., fuel horizontal continuity and vertical arrangement). The accuracy of fuel description derived from terrestrial LiDAR scanning (TLS) data was assessed by field measured surface fuel depth and fuel percentage covers at distinct vertical layers. The comparison of TLS-derived depth and percentage cover at surface fuel layer with the field measurements produced root mean square error values of 1.1 cm and 5.4%, respectively. TLS-derived percentage cover explained 92% of the variation in percentage cover at all fuel layers of the entire dataset. The outcome indicated TLS-derived fuel characteristics are strongly consistent with field measured values. TLS can be used to efficiently and consistently classify forest vertical layers to provide more precise information for forest fuel hazard assessment and surface fuel load estimation in order to assist forest fuels management and fire-related operational activities. It can also be beneficial for mapping forest habitat, wildlife conservation, and ecosystem management.

Dryness thresholds for fire occurrence vary by forest type along an aridity gradient: evidence from Southern Australia

ContextWildfires are common in localities where there is sufficient productivity to allow the accumulation of biomass combined with seasonality that allows this to dry and transition to a flammable state. An understanding of the conditions under which vegetated landscapes become flammable is valuable for assessing fire risk and determining how fire regimes may alter with climate change.ObjectivesWeather based metrics of dryness are a standard approach for estimating the potential for fires to occur in the near term. However, such approaches do not consider the contribution of vegetation communities. We aim to evaluate differences in weather-based dryness thresholds for fire occurrence between vegetation communities and test whether these are a function of landscape aridity.MethodsWe analysed dryness thresholds (using Drought Factor) for fire occurrence in six vegetation communities using historic fires events that occurred in South-eastern Australia using logistic regression. These thresholds were compared to the landscape aridity for where the communities persist.ResultsWe found that dryness thresholds differed between vegetation communities, and this effect could in part be explained by landscape aridity. Dryness thresholds for fire occurrence were lower in vegetation communities that occur in arid environments. These communities were also exposed to dry conditions for a greater proportion of the year.ConclusionsOur findings suggest that vegetation driven feedbacks may be an important driver of landscape flammability. Increased consideration of vegetation properties in fire danger indices may provide for better estimates of landscape fire risk and allow changes to fire regimes to be anticipated.

Determining the minimum sampling frequency for ground measurements of burn severity

Understanding burn severity is essential to provide an overview of the precursory conditions leading to fires as well as understanding the constraints placed on fire management services when mitigating their effects. Determining the minimum sampling frequency for ground measurements is not only essential for accurately assessing burn severity, but also for fire managers to better allocate resources and reduce the time and costs associated with sampling. In this study, field sampling methods for assessing burn severity are analysed statistically for 10 burn sites across Victoria, Australia, with varying spatial extents, topography and vegetation. Random and transect sampling methods are compared against each other using a Monte Carlo simulation to determine the minimum sample size needed for a difference of 0.02 (2%) in the severity classes proportions relative to the population proportions. We show that, on average, transect sampling requires a sampling rate of 3.16% compared with 0.59% for random sampling. We also find that sites smaller than 400 ha require a sampling rate of between 1.4 and 2.8 times that of sites larger than 400 ha to achieve the same error. The information obtained from this study will assist fire managers to better allocate resources for assessing burn severity.

Estimation of forest surface fuel load using airborne lidar data

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 three-dimensional 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) was derived 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.