Evaluating satellite and climate data-derived indices as fire risk indicators in savanna ecosystems

Abstract

The repeated occurrence of severe wildfires has highlighted the need for development of effective vegetation monitoring tools. We compared the performance of indices derived from satellite and climate data as a first step toward an operational tool for fire risk assessment in savanna ecosystems. Field collected fire activity data were used to evaluate the potential of the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and the meteorological Keetch-Byram drought idex (KBDI) to assess fire risk. Performance measures extracted from the binary logistic regression model fit were used to quantitatively rank indices in terms of their effectiveness as fire risk indicators. NDWI performed better when compared to NDVI and KBDI based on the results from the ranking method. The c-index, a measure of predictive ability, indicated that the NDWI can be used to predict seasonal fire activity (c=0.78). The time lag at the start of the fire season between time-series of fire activity data and the selected indices also was studied to evaluate the ability to predict the start of the fire season. The results showed that NDVI, NDWI, and KBDI can be used to predict the start of the fire season. NDWI consequently had the highest capacity to monitor fire activity and was able to detect the start of the fire season in savanna ecosystems. It is shown that the evaluation of satellite- and meteorological fire risk indices is essential before the indices are used for operational purposes to obtain more accurate maps of fire risk for the temporal and spatial allocation of fire prevention or fire management.