Abstract:
Wildfires are a common phenomenon on earth and can have disastrous effects on the environment,
infrastructure and surrounding communities. At the same time, many ecosystems are fire prone and
require burning at regular intervals, in order to maintain the health of the ecosystems. It is necessary
to minimise the negative effects of fires where possible. Information needs to be provided to fire
management officials to facilitate efficient planning and mitigation in order to minimise the negative
effects. Wildfires are influenced by many variables including vegetation type, fuel load, fuel
moisture, proximity to roads, proximity to settlements, elevation, slope, aspect, temperature,
precipitation, wind and relative humidity. These variables can be used to build a fire potential index
that determines the probability of a fire occurrence and the possibility of the fire to become an out
of control fire. Fire potential indices provide information on where fire potential is high so fire
management officials can plan resources accordingly and thus minimise negative impacts of
wildfires. Many fire potential indices have been developed but their usefulness in South Africa has
not been verified. The aim of the research was to implement and evaluate different fire potential
indices utilising geographic information, including remote sensing products, to predict fire potential
in South Africa. The Mpumalanga and the Western Cape provinces were used as case studies. The
time periods included February to December 2015 for Mpumalanga and August 2014 to June 2015
for the Western Cape. A number of candidate fire potential indices were implemented in the Python
scripting language. A variety of data sources were used to implement the fire potential indices. The
fire potential indices were evaluated along with a few fire danger indices. The performance
evaluation compared satellite detected active fire events to the fire potential indices in the study
areas based on statistical metrics including Pseudo R2, C-Index, Eastaugh’s Two-Part Parametric,
Bhattacharyya Coefficient and Percentile Shift. The evaluation was performed per pixel for the entire
date range. A performance ranking was then calculated for all the indices based on the pixel
performance and a final ranking was assigned to each index. The Fire Potential Index performed best
amongst the implemented candidate fire potential indices. The Canadian Fire Weather Index
performed well in Mpumalanga and the Fine Fuel Moisture Code performed well in the Western
Cape. The overall performance of the indices was not very high. This is due to the fact that even
though fire potential is high in an area, an ignition source might not be present to cause an actual
fire event. The performance of fire potential indices and fire danger indices were different in the two
provinces. Future work can be done to develop an index based on South African conditions or
calibrate the indices implemented in this research for an area.
