Landscape fires are substantial sources of (greenhouse) gases and aerosols. Fires in savanna landscapes represent more than half of global fire carbon emissions. Quantifying emissions from fires relies on accurate burned area, fuel load and burning efficiency data. Of these, fuel load remains the source of the largest uncertainty. In this study, we used high spatial resolution images from an Unmanned Aircraft System (UAS) mounted multispectral camera, in combination with meteorological data from the ERA-5 land dataset, to model instantaneous pre-fire above-ground biomass. We constrained our model with ground measurements taken in two locations in savanna-dominated regions in Southern Africa, one low-rainfall region (660 mm year${}^{-1}$) in the North-West District (Ngamiland), Botswana, and one high-rainfall region (940 mm year${}^{-1}$) in Niassa Province (northern Mozambique). We found that for fine surface fuel classes (live grass and dead plant litter), the model was able to reproduce measured Above-Ground Biomass (AGB) (R${}^2$ of 0.91 and 0.77 for live grass and total fine fuel, respectively) across both low and high rainfall areas. The model was less successful in representing other classes, e.g., woody debris, but in the regions considered, these are less relevant to biomass burning and make smaller contributions to total AGB.

Anthropogenic mortality is a pervasive threat to global biodiversity. African lions (Panthera leo) are particularly vulnerable to these threats due to their wide-ranging behaviour and substantial energetic requirements, which typically conflict with human activities, often resulting in population declines and even extirpations. Mozambique supports the 7^th largest lion population in Africa, which is recovering from decades of warfare, while ongoing conflicts and broad-scale socio-economic fragility continue to threaten these populations. Moreover, there are concerns that Mozambique represents a regional hotspot for targeted poaching of lions which fuels a transnational illegal wildlife trade. This study aimed to quantify the longitudinal impact of anthropogenic mortality on lion populations in Mozambique. Using national population estimates and monitoring records, we performed forward simulation population viability modelling incorporating detection-dependent population trends and varying scales of anthropogenic mortality. Between 2010–2023, 326 incidents of anthropogenic mortality involving 426 lions were recorded. Bushmeat bycatch and targeted poaching for body parts were the greatest proximate causes of lion mortality (i.e., 53% of incidents), increasing significantly over time and acting as cryptic suppressors of regional population recovery, followed by legal trophy hunting (i.e., 33%), and retaliatory killing (i.e., 13%). Our findings suggest that resilience to anthropogenic threats is largely a function of lion population size as well as resource and management capacity. For instance, projections suggest that the lion population in Niassa Special Reserve will likely remain stable despite comparatively high levels of anthropogenic mortality, although further escalation may precipitate decline. Conversely, the lion population in Limpopo National Park is projected to become extirpated by 2030 without the buffering effect of its neighbouring source population in Kruger National Park. These unsustainable levels of anthropogenic mortality threaten the long-term viability of lion populations in Mozambique, requiring urgent national-level action and public-private partnerships to support site security, monitoring, and policy enforcement.