Fire scar mapping for disaster response in KwaZulu-Natal South Africa using Landsat 8 imagery

Z Oumar

doi:10.4314/sajg.v4i3.11

Abstract

This study assessed the potential of the new Landsat 8 multispectral imagery in rapidly mapping fire scars to aid disaster management response teams in emergency efforts. Maximum likelihood and iso cluster algorithms where used to classify burnt and unburnt areas in KwaZulu-Natal, South Africa. The Landsat 8 sensor successfully classified burnt and unburnt areas with overall accuracies ranging from 80% to 93.33% on independent test datasets. Farms and communities affected by the wildfires were overlaid with the classified maps in order to determine the extent of each farm burnt. Maps were created for disaster management response teams in order to identify critical farms and communities in need of assistance. The study indicates the operational use of the new Landsat 8 data in fire scar mapping for disaster response. The result is critical for fire scar mapping in South Africa using freely available Landsat 8 multispectral data.

Highlights

The mapping of fire scars plays an important role in the disaster management cycle and forms part of the response and recovery stages of disaster management (Joyce et al, 2009)
This sudden decrease in reflectance in the visible and near-infrared bands and increase in the shortwave infrared bands is due to the replacement of healthy senesced vegetation by char which is picked up by Landsat bands 3, 4, 5 and 7 which contain most of the spectral information for burn scars (Pereira and Setzer, 1993; Trigg and Flasse, 2000)
The following conclusions can be drawn: 1) Landsat 8 data successfully mapped burnt and unburnt areas with an overall classification accuracy of 93.33% and a k statistic of 0.87 using a maximum likelihood classification algorithm on an independent test dataset

Summary

Introduction

The mapping of fire scars plays an important role in the disaster management cycle and forms part of the response and recovery stages of disaster management (Joyce et al, 2009). Burnt vegetation and organic matter generally results in optically darker scar features in imagery due to the presence of ash thereby resulting in different reflectance values as compared to unburnt surfaces (Joyce et al 2009) These reflectance differences are used to separate burnt and unburnt areas using individual spectral bands, normalized indices and band ratios (Trigg et al, 2005). Higher spatial resolution data which is often costly is used for the prediction of areas likely to burn, suppression as fires burn and post fire rehabilitation efforts (Lentile et al, 2006) Moderate resolution data such as Landsat is used to quantify the areas burnt, separate burnt from unburnt areas, and has more local applicability (Bastarrika, 2011; Lentile et al, 2006). With the free availability of the new Landsat 8 data which was launched in February 2013, remote sensing researchers in Africa are able to assist emergency response services in wildfire mapping which is one of the major disasters facing African countries (Guha-Sapir, et al, 2014)

Objectives

Results

Conclusion