Kerrie M. Tomkins

Fire severity and vegetation indices are widely used to understand fire disturbance and vegetation recovery in heterogenous landscapes. Multispectral sensors are used to understand fire severity and post-fire vegetation dynamics. We utilised four fire severity (dNBR, RBR, RdNBR and MIRBI) and four vegetation indices (Chlorophyll Index of Red-Edge, dNDVI, EVI and NDRE) derived from Sentinel-2 images to understand the spatio-temporal variability of fire severity and vegetation recovery. The Masonite Road fire burned 2321.32 hectares of mixed Eucalyptus and wetland forests in the southwestern Telligerry State Conservation Area of New South Wales, Australia, in January 2018. Spectral and spatial analysis of different fire severity and vegetation indices revealed the blaze affected the landscape with moderate to high level of severity. Fire severity indices showed similar patterns in spatio-temporal analysis for a year, but Chlorophyll Index of Red-Edge and EVI did not show any pattern of fire impacts on vegetation. Among the analysed indices, two combinations of fire severity and vegetation indices (dNBR or RBR or RdNBR with NDVI and MIRBI withNDRE) were significantly correlated and can be effective in assessing fire severity, burned area and vegetation recovery.

Fire severity indices have been using to assess the level of fire severity and extent of burned areas. Three established fire severity indices - differenced Normalized Burn Ratio (dNBR), Relativized dNBR (RdNBR) and Relativized Burn Ratio (RBR) - from three optical satellite sensors (MODIS, Landsat 8 and Sentinel-2A) were compared in this study. Fire Severity Indices were derived for initial (immediately after the fire) and extended (a certain time after the fire) fire severity assessment. During the Kains Flat Fire, New South Wales, Australia, Callitris, Eucalypt Medium Woodland and Other Native Forest were the three-major fire affected forest types. Sentinel-2A derived fire indices showed better assessment of fire severity compared to the MODIS (Moderate Resolution Imaging Spectroradiometer) and Landsat 8 sensor. Landsat 8 and Sentinel-2A derived fire severity indices showed higher similarities in fire severity indices inter-comparisons. This study will facilitate the selection of appropriate fire indices and indices combinations for fire severity assessment in heterogeneous landscapes, burn severity mapping and monitoring.

Forest fire is one of the major environmental disturbances for the Australian continent. Identification of occurrence patterns of large fires, fire mapping, determination of fire spreading mechanisms, and fire effect modeling are some of the best measures to plan and mitigate fire effects. This chapter describes fire occurrence in New South Wales (Australia), the Australian National Bushfire Model Project (ANBMP), fire propagation modeling methods, the McArthur’s model and current forest fire modeling approaches in the state of New South Wales of Australia. Among the established fire models, PHOENIX Rapidfire predicts fire spread and facilitates loss and damage assessments as the model considers many environmental and social variables. Two fire spread models, SPARK and Amicus, have been developed and facilitated fire spread mapping and modeling in Australia.

This study aims to establish a proof-of-concept of using optical sensors to detect and determine the spectral properties of weed and native species in Kosciusko National Park (KNP), Australia. This involves interpretation of the spectral profile of the two weeds, namely Orange Hawkweed and Ox-Eye Daisy (in field and in lab), in contrast to their surrounding native vegetation. This paper presents a case study into the applicability of hyperspectral sensors, and discusses the collected spectral profiles. The preliminary results indicate that difference in profiles between the weeds and natives is large, and opens further research into determining if the profile is unique enough to then pick-up with remotely-sensed imagery collected by drones, aircraft, or satellites.

The rainfall‐runoff events following five fires that occurred within a 40‐year period in eucalypt forests of the Nattai catchment, southeastern Australia, were investigated to quantify the postwildfire hydrological response and to provide context for lower than expected erosion and sediment transport rates measured after wildfires in 2001. Daily rainfall and hourly instantaneous discharge records were used to examine rainfall‐runoff events in two gauged subcatchments (>100 km²) for up to 3 years after fire and compared with nonfire periods. Radar imagery, available from 2001, was used to determine the intensity and duration of rainfall events. Wildfires in the study catchment appear to have no detectable impact on surface runoff at the large catchment scale, regardless of fire severity, extent or time after fire. Instead, the magnitude of postfire runoff is related to the characteristics of rainfall after fire. Rainfall is highly variable in terms of annual totals and the number, size, and type of events. Rainfall events that cause substantial surface runoff are characterized by moderate‐high intensity falls lasting one or more days (≥1 year average recurrence interval). These are triggered by synoptic‐scale weather patterns, which do not reliably occur in the postfire window and are independent of broad‐scale climate dominated by the El Niño–Southern Oscillation (ENSO). This study highlights the importance of considering the characteristics of rainfall, as well as local factors, in interpreting the postfire hydrological response.