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Context and background: In Cameroon, the pressure on wetlands, which cover nearly 70% of the national territory, appears to be increasing, whether for subsistence needs, firewood, grazing, logging or expansion of development projects. Currently, in terms of land use, forest has decreased by 619 km² and cultivated land has increased by 321 km². The surface area of degraded forests and land is estimated at around 12 million hectares, with a general trend towards an increase in the phenomenon due to both natural and anthropogenic factors. It is obvious that approaches in favour of wetlands are widely engaged in the different basins of Cameroon. However, the management strategies for these areas are based on knowledge that is certainly extensive, but still patchy. There is a serious lack of overview of the location of degraded lands, the intensity of this degradation and the way farmers are addressing this problem through sustainable land management. Goal and objectives: The objective of this study is to characterize the dynamics of land cover from multi)date satellite data in the Noun floodplain of Cameroon. Methodology The methodology relies on the use of remote sensing and GIS to identify spatial units and detect changes over a twenty-two year period (1999 to 2021). The land cover maps were produced from an unsupervised classification with maximum likelihood. Results: The results identified eight classes: herbaceous savannahs with shrubs, forest galleries, fields and plantations, herbaceous tannas, young fallows, mineralized and built-up soils, bare soils and surface waters. It appears that in 1999, the landscape was dominated by natural vegetation (72.6%) located from north to south of the Noun plain. However, since 2004, the landscape has been dominated by agricultural areas (56.8%). Natural formations have been progressively reduced in space over time. The evolution of the Noun floodplain landscape reveals that 14.3% of the space has remained stable. These are fields and plantations, young fallows, mineralized soils and surface water. This space has not migrated to other classes. While about 73.9% of the area has moved to higher classes, of which 35.6% to herbaceous tannas and 26% to fields and plantations. On the other hand, 72.6% of the area (herbaceous savannahs and forests gallery) has been heavily degraded. These results indicate that the landscape of the Noun floodplain is marked by a high level of agricultural activity, which is at the origin of the progressive degradation of the land. They suggest an effective awareness of the level of land degradation and a better integrated management of land at the local level. [ABSTRACT FROM AUTHOR]

Context and background Forest ecosystems play a key role in the regulation of carbon cycle. The sustainability of This ecosystemic service is a necessity in the context of global changes. The spatial distribution of carbon stock is a useful tool of decisionmaking. Which contribute to improve forest management practices. Goal and Objectives: The aim was to model the spatial distribution of the aboveground carbon stock in the Goungoun Forest Reserve and its surrounding lands in northern Benin. Methodology: Systematic stratified sampling was used for the forest inventory. One hundred and fifty circular sampling plots with a radius of 18 m were inventoried. In each plot, all trees with a DBH ≥ 10 cm were identified at species level and measured using compasses. The structural parameters of trees were calculated. The spatial data analysis tool was used to extract spectral indices from remote sensing images. In addition, road density, distance to road network and distance to water stream were calculated. Aboveground biomass was obtained using the allometric model. The conversion factor (CF = 0.50) was used to obtain the carbon stock. The Pearson correlation test was used to select the best environmental variable for carbon distribution modelling. The Radom Forest algorithm was used to predict the spatial distribution of aboveground carbon. The performance of model prediction was evaluated by cross-validation with 100 iterations. The coefficient of determination (R²), root mean square error (RMSE), prediction bias and Akaike Information Criterion (AIC) were used to measure model performance. Results: Results showed that the spatial distribution of carbon stock was strongly influenced by basal area, mean diameter, tree density, forest strats and water stress index. The relative importance of these variables was ranged from 4 to 96%. The model was very accurate (bias = -0.03%, RMSE = 3.24 t/ha and R² = 98.93%). The quantity of carbon observed was ranged from 0.26 to 76.52 t/ha. While, the carbon predicted was varied from 0.24 to 64.66 t/ha for all stands. The carbon stock of forest stands was 2.11 times higher than that of the forest surrounding lands. The sites with high carbon stock were located in savannah woodlands. Anthropogenic pressure triggered the carbon storage potential of the FCGTR. Therefore, we had to strengthen the monitoring of plant species for sustainable forest management. [ABSTRACT FROM AUTHOR]