The availability of freely available moderate-to-high spatial resolution (10–30 m) satellite imagery received a major boost with the recent launch of the Sentinel-2 sensor by the European Space Agency. Together with Landsat, these sensors provide the scientific community with a wide range of spatial, spectral, and temporal properties. This study compared and explored the synergistic use of Landsat-8 and Sentinel-2 data in mapping land use and land cover (LULC) in rural Burkina Faso. Specifically, contribution of the red-edge bands of Sentinel-2 in improving LULC mapping was examined. Three machine-learning algorithms – random forest, stochastic gradient boosting, and support vector machines – were employed to classify different data configurations. Classification of all Sentinel-2 bands as well as Sentinel-2 bands common to Landsat-8 produced an overall accuracy, that is 5% and 4% better than Landsat-8. The combination of Landsat-8 and Sentinel-2 red-edge bands resulted in a 4% accuracy improvement over that of Landsat-8. It was found that classification of the Sentinel-2 red-edge bands alone produced better and comparable results to Landsat-8 and the other Sentinel-2 bands, respectively. Results of this study demonstrate the added value of the Sentinel-2 red-edge bands and encourage multi-sensoral approaches to LULC mapping in West Africa.

Africa’s need to double food production and feed the burgeoning human population, without compromising its natural resource base, has raised the momentum for sustainable agricultural intensification on the continent. Many studies describe agronomic practices that can increase productivity on existing agricultural land without damaging the environment and without increasing the agricultural carbon footprint. However, there is limited information on specific practices with the greatest potential to contribute to sustainable intensification on smallholder farms in sub-Saharan Africa, while simultaneously keeping the carbon footprint low. The objectives of this review were to (1) identify good agronomic practices with potential for contributing to sustainable intensification across sub-Saharan Africa, (2) synthesize available information on benefits and synergies from these technologies, and (3) discuss bottlenecks in their adoption in order to obtain insights that inform the formulation of supportive policies. Agroforestry, cereal-legume intercropping, conservation agriculture, doubled-up legume cropping, fertilizer micro-dosing, planting basins, and push-pull technology were identified as key agronomic innovations widely promoted in sub-Saharan Africa. We show that these innovations can build synergies and increase resource use efficiency while reducing agricultural carbon footprint. We outline the benefits, trade-offs, and limitations of these practices and discuss their potential role in strengthening food sovereignty and climate change adaptation and mitigation.

In Africa, protected areas can play an important role in mitigating the effects of climate change through carbon sequestration but they are threatened due to increasing land degradation and deforestation (LDD). The Total Wildlife Reserve of Bontioli (TWRB) in Burkina Faso is one of the country’s refuges with high biodiversity. This reserve is seriously threatened by human activities, and little information is available about the on-site causes of degradation extent. This study was carried out to investigate drivers and extent of LDD in the TWRB. Household surveys, focus group discussions and field observations were used to identify socio-economic factors that influence land use and land cover (LULC) changes. The socio-economic data were analyzed using rankings and binary logistic regression techniques. Logistic regression model was used to establish the relationship between socio-economic drivers and land cover change. Remote sensing and GIS techniques were used to analyze land use and LULC changes over 29 years, employing Landsat images of 1984, 2001 and 2013. We performed a supervised classification based on the maximum likelihood algorithm to derive vegetation maps. The results revealed significant (p <0.05) LULC change from one class of LULC to another. From 1984 to 2001, tree savannas, bare soils and agricultural lands increased by 17.55%, 18.79% and 21778.79%, respectively, while woodland, gallery forest, shrub savannas and water bodies decreased by 22.02%, 5.03%, 40.08% and 31.2%, respectively. From 2001 to 2013, gallery forests decreased by 14.33%, tree savannas by 22.30% and shrub savannas by 5.14%, while agricultural lands increased by 167.87% and woodlands by 3.21%. LDD occurred at a higher rate in areas bordering the reserve compared to the core-protected area and the inaccessible areas. Agricultural expansion and wood cutting activities were the main direct causes of LDD. Extensive land utilization for agriculture is a major threat to the conservation of biodiversity in this reserve. The research highlighted the soundness of GIS and remote sensing practical application to assess vegetation change extent in Burkina Faso. Understanding the signal extent of vegetation change is particularly important to support efforts by policy makers to halt or at least to slow down the deforestation in the country. There is a need to control the immediate causes of LULC dynamics by limiting agricultural land expansion and wood cutting in the study area

Aim: Tree crowns determine light interception, carbon and water exchange. Thus, understanding the factors causing tree crown allometry to vary at the tree and stand level matters greatly for the development of future vegetation modelling and for the calibration of remote sensing products. Nevertheless, we know little about large‐scale variation and determinants in tropical tree crown allometry. In this study, we explored the continental variation in scaling exponents of site‐specific crown allometry and assessed their relationships with environmental and stand‐level variables in the tropics.Location: Global tropics.Time period: Early 21st century.Major taxa studied: Woody plants.Methods: Using a dataset of 87,737 trees distributed among 245 forest and savanna sites across the tropics, we fitted site‐specific allometric relationships between crown dimensions (crown depth, diameter and volume) and stem diameter using power‐law models. Stand‐level and environmental drivers of crown allometric relationships were assessed at pantropical and continental scales.Results: The scaling exponents of allometric relationships between stem diameter and crown dimensions were higher in savannas than in forests. We identified that continental crown models were better than pantropical crown models and that continental differences in crown allometric relationships were driven by both stand‐level (wood density) and environmental (precipitation, cation exchange capacity and soil texture) variables for both tropical biomes. For a given diameter, forest trees from Asia and savanna trees from Australia had smaller crown dimensions than trees in Africa and America, with crown volumes for some Asian forest trees being smaller than those of trees in African forests.Main conclusions: Our results provide new insight into geographical variability, with large continental differences in tropical tree crown allometry that were driven by stand‐level and environmental variables. They have implications for the assessment of ecosystem function and for the monitoring of woody biomass by remote sensing techniques in the global tropics.