Integrating Remote Sensing and GIS to Model Forest Fire Rik in Virunga Massif, Central - Eastern Africa

This study aimed at developing a forest fire risk model using a combination of GIS and Remote sensing techniques, which helped to identify the level of forest fire vulnerability in Virunga Massif, located at the edge of central and eastern Africa. The Analytic Hierarchical Process (AHP) approach was employed to rank and weigh the key variables and combine them into different fire risk input factors which were later integrated into the main forest fire risk model. The main input datasets, which were linked with a potential source of a forest fire, include the land cover (specifically vegetation type data generated through the Landsat 8 image classification); topographic variables such as slope, elevation and aspect retrieved from the existing Digital Elevation Model (DEM) of Rwanda; the concentration of illegal activities and proximity to beehives sites; as well as visibility from the road and human settlements. Input factor maps were generated, assigned weights and combined in a GIS environment to produce a Virunga massif fire risk model map, which was validated using the existing burnt areas map, and ground truth points recorded using GPS. The study found that the ignition factors are the most forest fire triggering factors in Virunga massif, followed by topographic factors which play a major role in the fire spreading across the ecosystem. The high forest fire risk areas were found in steep slope location around the peaks of the volcanoes, whereas areas with the lowest risk of forest fire were found inside the forest at gentle slopes. The model was validated at 75% accuracy using ground truth data. The study proposes measure to halt the ignition factors through prevention of illegal activities in the Virunga massif for the successful prevention of the forest fire risk in the ecosystem, with much effort invested during the dry season, along with the relocation of beehives to a farther distance from the ecosystem’s edge. Keywords: Forest Fire Risk Modelling, Biodiversity, Illegal Activities, Ignition Factors, Topographic Factors, Analytic Hierarchy Process

Effect of mountain gorilla (Gorilla beringei beringei) population growth to their key food plant biomass in Volcanoes National Park, Rwanda

High densities of large herbivores can have detrimental effects on plant biomass. Understanding the relationship between animal densities and plant distribution and abundance is essential for the conservation of endangered species and ecosystems. Mountain gorilla censuses conducted for different periods in the last three decades have revealed a steady increase of gorilla population in Virunga Massif whereby the recent number of gorillas has doubled compared to their number in the 1980s. It is unclear whether the continuous population growth of the herbivorous Virunga gorilla within an isolated forest ‘island’ has been affecting gorilla food plant biomass. This study investigated the effect of varying mountain gorilla densities on the biomass of the five key food plant species (Galium spp., Carduus nyassanus, Peucedanum linderi, Rubus spp., Laportea alatipes) that make up >70% of the mountain gorilla diet. We used plant biomass data collected in a central part of the Virunga massif, commonly known as Karisoke sector from 2009 to 2011, and GPS records of gorilla groups ranging in the same area nine months prior biomass assessment. Gorilla densities were estimated using the Kernel Utilization Distribution (KDE) analysis (functions: ‘kernelUD’ and ‘getvolumeUD’) from the Adehabitat package in R software, which provides the probability density of gorilla occurrence at each coordinate (x, y) of the study area. Analyses using GLMs suggest that gorilla densities (a proxy of previous gorilla utilization intensity) did neither affect the total biomass of key food plant species nor the biomass of each key food plant species (p>0.05). These results may indicate that current revisit rates of feeding sites by gorillas allow for complete plant regeneration, and no signs of overharvesting. Alternatively, feeding sites characterized by very high biomass may be preferred by gorillas and remain sites with the highest biomass even after being frequently used by gorillas. Findings also suggest that carrying capacity of the gorilla population in the study areas may not yet be reached if food is the driving constraint. However, monitoring of the relationship between gorilla densities and food plant biomass must continue while the Virunga population continues growing. Future studies also need to incorporate other sympatric large herbivores in the Virungas who share food plants with mountain gorillas.Keywords: habitat use, gorilla density, plants biomass