Land cover change detection using Landsat TM imagery of the 2009 Victorian bushfires

The 2009 Victorian bushfires, also called the Black Saturday bushfires, ignited across the Australian state of Victoria on Saturday 7th February 2009, resulting in Australia’s highest ever loss of life from a bushfire. According to the Victorian Police, the bushfires caused at least 173 known deaths of people and 414 people injured. And many towns located in the north-east of the state capital Melbourne were badly damaged or almost completely destroyed. These devastating consequences of the bushfires point out that it requires enough information about bushfires to ensure to control them in time and minimise the potential risk for society. Traditional field survey techniques such as using total station and GPS has proven to be difficult due to the factors such as lack of accessibility and large scales assessment in the bushfire case. Therefore, satellite remote sensing was introduced as an alternative technique to overcome the problems above. Furthermore, the use of multispectral Landsat Thematic Mapper (TM) data has been evaluated to detect and analyse the land cover changes caused by the bushfires in a rapid and cost-effective way without actual physical contact. And the 2009 Victorian bushfires detection was performed by using two Landsat TM data, one pre-fire and one post-fire, acquired on 1st March 2008 and 21st April 2009, respectively.Two digital change detection techniques, including image differencing and post-classification comparison, were used for the detection of 2009 Victorian bushfires. And the strength and weakness of each change detection method was also examined and results derived from each method were assessed for accuracy against the ground truth data provided by the Victorian Country Fire Authority (CFA). During the study of image differencing method, a new bushfire detection procedure combined the different remotely sensed indices, which integrated the Earth’s surface information including brightness, greenness and wetness, was proposed to efficiently appraise the devastated destruction in the 2009 Victorian bushfires. To conclude, the image differencing method integrated the remotely sensed indices including NBR, NDVI and MNDWI offers the best bushfire detection results. It generates the KHAT value about 84.44%, indicating a strong agreement. Furthermore, it’s omission error, such as the actual bushfire pixels are not detected, and commission error, such as the change pixels but not belong to bushfires, are 14.07% and 13.38%, respectively. On the other hand, the approach of post-classification comparison provides the lowest KHAT value (i.e. 59.84%), which represents moderate agreement. And the omission error and commission error are 15.96% and 46.15%, respectively