About
Shahriar Rahman
Shahriar is developing a stochastic fire effect model to predict the impacts of fire severity on the vegetation of selected national parks around Sydney. The model will integrate environmental parameters, geo-spatial fire information and climatic data as the model inputs, which will help to develop statistically reliable future environmental scenarios for the post-fire impacts on vegetation. He is also a mapping city and country expert at Cognizant.
Student project
The aim of this research is to develop a stochastic fire effect model to predict the impacts of fire severity on the vegetation of selected national parks around Sydney. The main aim of the research will be achieved through the following research objectives: understanding the spatio-temporal patterns of bush fires considering important environmental variables, assessment and of burn severity using field sampling and geo-spatial fire indices and parameterising, calibrating, verification and validation of a stochastic fire effect model with the derived from geo-sampling (Geo-CBI) and geospatial information.
The outcome of this research will be a geo-spatial fire effect model integrating environmental parameters, geo-spatial fire information and climatic data as the model inputs, which will help to develop statistically reliable future environmental scenarios for the post-fire impacts on vegetation. The developed fire effect model will be developed with the spatio-temporal satellite image analysis and robust geo-processing techniques, so, a minimal data upgradation will be required to simulate the short and long-term impacts of bushfires. This model will be validated on the selected National Parks around Sydney, but, there will be a chance to improve and validate the developed algorithm for the whole Australia, which will minimize the time and cost of fire impact prediction.
