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Many previous vegetation description and mapping programs have highlighted the need for a hierarchical classification that easily translates from regional through to international scales. Often programs have not selected appropriate levels of vegetation classification and mapping for management purposes. Here, we provide an alternative broad vegetation classification at the macrogroup level in the international vegetation classification for eastern New South Wales (NSW) that could be applied in regional and local environmental planning and management. Existing aerial photographic interpretation (API) floristic and canopy maps available for eastern NSW were collated and reclassified and incorporated into a unifying dataset of macrogroups, which also included types dominated by exotic vegetation such as exotic plantations. There we identified, in total, 71 macrogroups across eastern New South Wales (NSW) of a total of 85 identified for NSW mapped at 1:25000 scale, or finer, in the following three sections: north-east, Sydney basin and central west, and southern coast and Snowy Mountains. These were then amalgamated into a single remnant vegetation map covering 23 525 280 ha. This alternative broad vegetation hierarchy provides a reliable and overt vegetation classification and mapping dataset that can easily incorporate future revisions as new mapping data become available.

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<p>The weather and its climatic evolution play the main role in generating hazard profiles of forest fires. The increased in magnitude and damage of last forest fire seasons has caused a larger concern of the insurance sector for this peril. Due to the lack of knowledge of this risk, there is a widespread low level of insurance coverage of forest fire risk. A first step forward is clearly needed to (1) propose simplified approaches showing how the risk links with its main weather drivers, and (2) re-incentivize the use of insurance by forest managers.</p><p>To answer this objective, ARIA Technologies and its partners have developed a geospatial web-based decision tool to support both forest owners and forest insurance actors in managing the vulnerability of their asset/portfolios to fire risk. RiskFP includes:</p><ul><li>A “realistic disaster scenarios generator module” that allows the generation of hundreds of scenarios of extreme wildfires to complete information from historical fires databases. This information can be used in damage and loss modelling to improve the estimation of the probable maximum loss (PML). In addition, the risk FP “impact module” provides to the users information on the different potential impact like the amount of biomass burnt or the economic losses.</li> <li>A precise mapping of the local forest fire risk through the graphical representation of an index including five risk levels (from low to extreme) that provides an overview of the most critical locations regarding the potential behavior of the fire in case of an hypothetical ignition.</li> <li>A forecasting/projection module to inform the users on the frequency of the severe-extreme days in the mid- and long-term horizons. It can be used by the forestry sector to better anticipate and prepare the next fire season and as a planning tool for long-term operation/investment.</li> </ul><p>At the heart of the platform lies the concept of critical landscape weather patterns (CLP), an empirical fire weather index that identifies severe-extreme weather days derived from hourly records of a representative weather station (Gellie, 2019). It could be computed from past records, seasonal forecast or climate projection allowing to provide fire risk assessment for these different time scales. The CLP module is coupled with a propagation model, the Wildfire Analyst® forest fire simulator at the resolution of about 40m, that is used to estimate the progression and behavior of the fire in space and time. It is based on the standardized and validated semi-empirical Rothermel propagation model (1972).</p><p><strong>Acknowledgements:</strong></p><p>We acknowledge the European Commission for sponsoring this work in the framework of the H2020-insurance project (Grant Agreement number 730381).</p>