Modelling spatiotemporal variability in fires in semiarid savannas: a satellite-based assessment around Africa’s largest protected area
Niti B. Mishra A D , Kumar P. Mainali B and Kelley A. Crews C
+ Author Affiliations
- Author Affiliations
A Geography and Earth Science, University of Wisconsin–La Crosse, WI 54601, USA.
B Department of Biology, University of Maryland, College Park, MD 20742, USA.
C Department of Geography and the Environment, University of Texas at Austin, Austin, TX 78712, USA.
D Corresponding author. Email: nmishra@uwlax.edu
International Journal of Wildland Fire 25(7) 730-741 https://doi.org/10.1071/WF15152
Submitted: 16 August 2015 Accepted: 1 April 2016 Published: 11 May 2016
Abstract
The relative importance of various drivers of fire regimes in savanna ecosystems can be location-specific. We utilised satellite-derived time-series burned area (2001–13) to examine how spatiotemporal variations in burned area and fire frequency were determined by rainfall, vegetation morphology and land use in semiarid savanna. Mean precipitation of the rainy season (Nov–Apr) had a strong and positive relationship with burned area in the following dry season (variance explained 63%), with the relationship being strongest inside protected areas (variance explained 73%). Burned area and fire frequency were higher in vegetation types with higher herbaceous cover, indicating a causal link between herbaceous load and fire. Among land use, fire frequency was highest in protected areas and lowest in farms and ranches. Spatial models (generalised linear models with Poisson and negative binomial distribution) accounting for spatial autocorrelation showed that land-use classes and vegetation types together explained approximately half of the deviance in null model (48%). Existence of fences and boreholes resulted in finer-scale spatial differences in fire frequency. There was minimal dependence of vegetation types on land-use classes in determining fire frequency (interaction between the two predictors was minimal). These results have significant implications for understanding drivers of fire activity in savanna ecosystems.
Additional keywords: generalised liner model, Kalahari, MODIS.
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