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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]

The role of forests in carbon storage in a climate change context is relevance. In this paper, we will assess the carbon stock in the Libreville area using remote sensing data over a 2001-2018 period. This assessment of carbon stocks in forests is mainly based on Landsat optical imagery. A first analysis aimed at producing a land use mapping between 2001 and 2018 was carried out. These preliminary results were then used as a proxy for the assessment of carbon stocks using terrestrial and mangrove forests. The average value of carbon stock contained in each class comes from the bibliographical work carried out mainly in the same study area. The total value of the carbon stock was estimated at 70 913 018 ± 896 and 68 340 383 ± 927 tons in 2001 and 2018 respectively. [ABSTRACT FROM AUTHOR]

The organic matter (OM) is an important element of the fertility of cultivated soils. Reflectance measurements have been successfully used to predict soil organic carbon (OC) content of agricultural soils, measured in the laboratory on samples of dried soil or directly in the field using field spectro-radiometer or satellite imagery. This study proposes a method for spatial prediction of soil OC, from UAV imagery. An agricultural plot of 13 ha "Grande Borne" located in the plain of Versailles (78) was analysed in April 2013, shortly before sowing while it was still bare soil. The AIRINOV® UAV used was equipped with a four channels multispectral camera MultiSPEC 4C® (550nm, 660nm, 735 nm and 790 nm) and flying at an altitude of 150m. Twenty three ground control points, distributed in the plot were delineated by target located with a centimetric DGPS and different measures have been made synchronously with the drone flight: Spectral Measurements (spectroradiometer ASD FieldSpec 3®), roughness measurements by photogrammetric method, samples of soil samples for analysis of chemical composition and water content. The images obtained with a resolution of 15cm have been georeferenced with an infra-pixel accuracy. The field spectra were aggregated on the spectral bands of the Multispec 4C for validation by linear regression. The obtained R2, between 0.8 and 0.9 by band, with an error between 1 and 3%, have shown good reliability of the Multispec 4C® sensor. The OC content was then estimated by partial least squares regression (PLSR) from spectral measurements of the 23 plots. The mean squared error of cross validation (RMSECV) by LOO (Leave One Out) method was 1.97 g of OC per kg of soil. A second correction of the model incorporating the effects of moisture and roughness on reflectance, has improved the quality of the prediction by 18% and a RMSECV of 1.61 g / kg. The model was finally spatialized in GIS and compared to simple spatial interpolations of soil analyses. [ABSTRACT FROM AUTHOR]

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