Abstract

BackgroundFire is an important process that shapes the structure and functioning of African savanna ecosystems, and managers of savanna protected areas use fire to achieve ecosystem goals. Developing appropriate fire management policies should be based on an understanding of the determinants, features, and effects of prevailing fire regimes, but this information is rarely available. In this study, we report on the use of remote sensing to develop a spatially explicit dataset on past fire regimes in Majete Wildlife Reserve, Malawi, between 2001 and 2019. Moderate Resolution Imaging Spectroradiometer (MODIS) images were used to evaluate the recent fire regime for two distinct vegetation types in Majete Wildlife Reserve, namely savanna and miombo. Additionally, a comparison was made between MODIS and Visible Infrared Imager Radiometer Suite (VIIRS) images by separately evaluating selected aspects of the fire regime between 2012 and 2019.ResultsMean fire return intervals were four and six years for miombo and savanna vegetation, respectively, but the distribution of fire return intervals was skewed, with a large proportion of the area burning annually or biennially, and a smaller proportion experiencing much longer fire return intervals. Variation in inter-annual rainfall also resulted in longer fire return intervals during cycles of below-average rainfall. Fires were concentrated in the hot-dry season despite a management intent to restrict burning to the cool-dry season. Mean fire intensities were generally low, but many individual fires had intensities of 14 to 18 times higher than the mean, especially in the hot-dry season. The VIIRS sensors detected many fires that were overlooked by the MODIS sensors, as images were collected at a finer scale.ConclusionsRemote sensing has provided a useful basis for reconstructing the recent fire regime of Majete Wildlife Reserve, and has highlighted a current mismatch between intended fire management goals and actual trends. Managers should re-evaluate fire policies based on our findings, setting clearly defined targets for the different vegetation types and introducing flexibility to accommodate natural variation in rainfall cycles. Local evidence of the links between fires and ecological outcomes will require further research to improve fire planning.

Highlights

  • Fire is an important process that shapes the structure and functioning of African savanna ecosystems, and managers of savanna protected areas use fire to achieve ecosystem goals

  • Imágenes del satélite Moderate Resolution Imaging Spectroradiometer (MODIS) (Espectroradiómetro de Imágenes de Resolución Moderada) fueron usadas para evaluar el régimen reciente de fuego para dos tipos distintos de vegetación en la Reserva de vida Silvestre de Majete, denominadas savanna y miombo

  • Adicionalmente fue hecha una comparación entre imágenes de MODIS y Visible Infrared Imager Radiometer Suite (VIIRS) (Radiómetro de Imágenes en el Infrarrojo Visible), mediante la evaluación por separado de aspectos seleccionados del régimen de fuego entre 2012 y 2019

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Summary

Introduction

Fire is an important process that shapes the structure and functioning of African savanna ecosystems, and managers of savanna protected areas use fire to achieve ecosystem goals. Managers of savanna ecosystems in sub-Saharan Africa have used fire to achieve a range of management goals for at least the past 100 years (van Wilgen 2009), and detailed information on fire regimes has been documented for a few African protected areas (PAs; Scholes and Walker 1993; van Wilgen et al 2000; Brockett et al 2001; Eby et al 2015; Archibald et al 2017). The majority of PAs, those smaller in size, have very little or no information on past fire regimes or fire management practices This lack of information means that decisions on whether, how, and when to use fire can only be based on general principles, rather than on an informed understanding of prevailing fire regimes and their effects in any given area (Forsyth and van Wilgen 2008). Remote sensing provides a way to significantly improve our understanding of past fire regimes in data-sparse environments and of the relationship between fire, climate, and vegetation (Goodwin and Collett 2014; Archibald and Hempson 2016)

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