A research report submitted to the Faculty of Science, University of the Witwatersrand, and Johannesburg in partial fulfillment of the requirements for the degree Master o f Science (Geographical Information System and Remote Sensing) Johannesburg, 2017. / The purpose of this analysis was to map the frequency of veld fires using remote sensing data from 2012 to 2016. The analysis successfully answered three objectives which are mapping the spatio-temporal pattern of veld fires in Mazowe district from 2012 to 2016, assessing the strength of association between burnt area and land cover classes and to determine the degree of veld fires in Mazowe district. The analysis of the veld fire frequency was undertaken in ENVI 5.3 and Arc Map software. Landsat imagery and MODIS fire products were collected and processed. For each year two Landsat images were used, one image before the season of fire starts which was used to classify the land cover classes and one image after fire season which was used to classify the burnt and unburnt classes. The MODIS fire products data were used to validate the burnt and the unburnt classification. The evaluations of the classifiers were done through accuracy assessment using confusion matrix and the results ranged from 85 to 95%.The study quantified the relationship between burnt areas land cover classes. The study also calculated the fire frequency. The results revealed that the veld fire frequency was high in A1 farms which measures 5 hectares, A2 farms which measures average of 318 hectares and grasslands compared to other land tenure and land cover classes. Areas with high frequency were observed in south, south west and some central parts of Mazowe district. There was high fire occurrence in 2012 and 2014. The study also noted that the fire occurrence was gradually decreasing, however the levels of fire occurrence remains high. The study concluded that A1, A2 farms and grasslands are prone to veld fires. The study recommends adaption of remote sensing techniques in eradicating veld fires. / MT 2017