The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1477–1482, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1477-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1477–1482, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1477-2020

  22 Aug 2020

22 Aug 2020

MODELLING OF NATURAL FIRE OCCURRENCES: A CASE OF SOUTH AFRICA

T. Moyo1, W. Musakwa2, N. A. Nyathi3, E. Mpofu2, and T. Gumbo4 T. Moyo et al.
  • 1Department of Operations and Quality Management, University of Johannesburg. Corner Siemert & Beit Streets, Doornfontein 0184 Johannesburg, South Africa
  • 2Future Earth and Ecosystem Services Research Group, Department of Urban and Regional Planning, University of Johannesburg, Corner Siemert & Beit Streets, Doornfontein 0184 Johannesburg, South Africa
  • 3Department of Geography, Environmental Management and Energy Studies, University of Johannesburg, University Road, Auckland Park, Johannesburg, Sout South Africa
  • 4Department of Urban and Regional Planning, University of Johannesburg, Corner Siemert & Beit Streets, Doornfontein 0184 Johannesburg, South Africa

Keywords: Natural fire, Global warming, Local Moran I, clusters, LISA, VIIRS and MODIS data, South Africa

Abstract. In contemporary literature there have been growing concerns regarding preservations of natural ecosystems. Given the global growth in awareness of global warming, the need for natural fire prediction models has grown rapidly. Using South Africa as a case study, we evaluate the potential of integrating several natural fire prediction models and geographical information system (GIS) platforms. Initially, natural fire prone regions in South Africa were spatially demarcated basing on municipal historical data records. Thereafter, the natural fire prediction models were applied/tested in parallel to identify the best prediction models that give optimum results in predicting natural fires. The models were assessed for accuracy using historical data. Preliminary results reveal locations in the North West, Mpumalanga and Limpopo province had the highest recorded potential for natural fires. In conclusion, the work demonstrates huge potential of prediction models in informing the likelihood of natural fire outbreaks. Lastly, the work recommends the adoption of natural fire prediction models and the subsequent formulation and use of relevant future natural fire mitigation policies and techniques to avert disasters in time.