This study was conducted in Democratic Republic of Congo, in the province of Kwango, chiefdom of Pelende Nord in order to inventory the floristic diversity of forest islands. To study the vegetation, 15 transects 5 meters wide and 60 meters long were set up at random, with 5 transects per forest island. The inventoried plant species were identified using the combination of various identification keys, the botanical classification taking into account Angiosperm Phylogeny Group (APG IV). The results obtained reveal 129 plant species divided into 35 families including 48 species with an average of 25.5 ± 14.4 in the first forest island, 39 species with an average of 20 ± 11.4 in the second forest island and 42 species with an average of 21.5±12.3 in the third forest island. The most represented families are those of Rubiaceae with 45.7%, Euphorbiaceae come second with 42.9% and Fabaceae in third position with 28.6%. This floristic richness shows that if these forest islands are well preserved, they can evolve into forest ecosystems by providing the same ecosystem services.

This study is carried out with the aim of collating the local impacts of climate change in the coastal area of ​​Muanda in the DRC. Three pilot villages are selected because of their accessibility and ease of communication. Resource persons are sorted on the basis of level of study. For each village, 25 people are chosen, making a total of 75 for the three villages. Interviews and plenary sessions supplemented information on the local perception of climate change, as well as the vulnerability assessment.To assess the impacts of climate change, climate scenarios at the local (Muanda area) and regional (Africa) scales were evaluated using the Magicc/Scengen 5.3 software over a period of one hundred years, at the beginning of the century, in the middle of the century and towards the end of the century. Only the temperature variable is taken into account. The results revealed that the neighbouring inhabitants locally perceives and interprets the impacts of climate change through sea level rise, coastal erosion, the decline in fish and agricultural products, and drought. The projections also show that the temperature will only increase over time.

This study is carried in Kinshasa, provincial city in Democratic Republic of Congo, with the aim of inventorying the floristic richness of certain protected sites in order to preserve nature. The transect method with a double advantage, that of saving time and considerable cost, made it possible to collect data by direct observation (visual contact with the tree) and to determine the plant species inventoried. The plant species were identified using the combination of various determination keys. The results obtained were then supplemented by information concerning the ecological types, which enabled us to identify 80 genera and 89 plant species divided into 48 families according to the APG IV classification. The most represented families in number of species are those of Rubiaceae and Fabaceae visibly with 8 species or 9% against 7 species or 8%. The most represented species in number of individuals are Markhamia tomentosa (40 feet) or 8.6%, Strychnos variabilis (33 feet) or 6.4%, Oncoba welwitchii (30 feet) or 5.8%, Rhabdophyllum arnoldianum (25 feet) or 4.9%, Allophylus africanus (25 feet) or 4.9%, Hymenocardia ulmoides (21 feet) or 4.1% and Pentaclethra eetveldeana (17 feet) or 3.3%.

Millettia laurentii De Wild. is the high value species most exploited for timber in the Democratic Republic of Congo. For this, the species suffers from natural regeneration deficit to overcome this drawback, this study aims to develop relatively simple and efficient methods of vegetative multiplication of this species: the macrocuting. Four silvicultural techniques have been tested on a plot of a quarter of hectare (n=196). Gathering respectively macrocuting up to 1.50, 1.30, 0.75 and 1 meter above ground. After 12 months of observation, the treatments 1 and 0.75 meter height showed the highest percentage by number of the granulations is 75.5% with a mean of 9.8