Savanna fire regimes assessment with MODIS fire data: Their relationship to land cover and plant species distribution in western Burkina Faso (West Africa)

Abstract

a b s t r a c tThe West African savannas are subject to changes in fire regimes related to land use intensification, which may infer significant biological modifications. We investigated the efficiency of MODIS fire products to account for the variability of fire regimes in relation to changes in land cover and savanna vegetation. The specificity and complementarities of both MODIS active fire (MOD14A2 and MYD14A2) and burnt area (MCD45A1) products were assessed in order to characterize fire regimes and to relate them with land cover. In addition the distribution of plant species among landscape units characterised by specific fire regimes was assessed. The calculation of the mutual information made it possible to set apart species more frequent in landscapes little or not at all subject to fire and species more frequent in burnt areas. The first group is the expression of the nature of the unburnt land-cover units, made up mainly of the agricultural areas dominated by fields and fallows. It consists of more potential threats (weeds, encroachers, invasive species etc.) than the second group, which is more typical of sudanian savannas, where fire provides the long-term stability needed to preserve the ecosystem.

Pastoralists’ Perception and Ecological Knowledge on Savanna Ecosystem Dynamics in Semi-arid Botswana

“…According to Chanda (1996), pastoral farmers tend to evade blame for overgrazing, and this could explain why most of the respondents failed to mention overgrazing as a cause of vegetation changes in this study. Although fire is also known to influence the composition of savanna vegetation (Furley et al 2008, Devineau et al 2010, it was hardly mentioned by respondents in this study, except at Xanagas. The possible explanation could be that the laws of Botswana do not allow the use of fire as a management tool in rangelands.…”

Integrating Land Change Science and Savanna Fire Models in West Africa

“…In his study of fire regimes in neighboring Burkina Faso, Devineau [48] also found that the fire regime closely aligns with the vegetation pattern. Similarly, Caillault [49] found that the fire regime follows the vegetation pattern which he determined to be a function of topography and agricultural density.…”

A Decade Long, Multi-Scale Map Comparison of Fire Regime Parameters Derived from Three Publically Available Satellite-Based Fire Products: A Case Study in the Central African Republic

“…A decrease in the size or intensity of subpixel combustion components, as indicated by a decreasing trend in the mean FRP, would hinder the active fire detection performance of both MODIS and (A)ATSR, whilst a decrease in the proportion of night time fire activity would have a greater impact on the capability of (A)ATSR to detect evidence of a fire in a given location. Although burn scars surrounding active fire pixels could influence the measurement of background brightness temperatures, and thus influence the detection of active fires, it is more likely that a decreasing trend in the mean cluster size of BA pixels is indicative of more patch-like agricultural and pastoral burning practices, or possibly a shift in fire activity into areas with greater tree canopy cover, both of which are more difficult to detect based on changes in surface reflectance alone [19,39]. Given that both the (A)ATSR active fire product and the MODIS burned area product indicate a decreasing trend in biomass burning across the CAR, and given that these results agree with a decreasing trend in burned area found in the northern hemisphere of Africa by Giglio et al [56] using a different MODIS burned area detection algorithm [57], we suggest that changes in measured active fire characteristics and burned area patterns (Figure 4) are less attributed to drifts in instrument responses, and more attributed to a genuine shift in the timing, intensity, and extent of true fire activity.…”

“…For example, satellite-derived fire chronologies have been used to determine distributions of fire return intervals from which fire frequency can be calculated (e.g., [18][19][20][21]), and fire seasonality has been characterized using a variety of satellite-derived metrics including the peak [22], the mid-point [23,24], the length of fire period [25], the fire season duration [26], the core burning season [27,28], and the classification of early and late season fires [29,30].…”