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Even though considerable parts of the global tropical forests are located in Africa, reliable data on African forest resources is limited. While this is widely recognized for tropical moist forests, it also holds for tropical dry forests. To partially fill the gap a forest inventory was carried out in Burkina Faso, West Africa. In this paper we present a methodological approach and sample based estimates of the tree and forest resources including estimates of (1) land cover classes, (2) species composition, and (3) above ground tree carbon stocks. Following the land classification of the Food and Agriculture Organization of the United Nations (FAO), the forest cover of Burkina Faso was estimated as 42.6% (116,847km²). For the classes “other wooded land”, “other land” and “other land with tree cover” the estimates were 1.6%, 53.6%, and 9.1%, respectively. We found notable differences to the estimates published by FAO, in particular when considering the classes “forest” and “other wooded land” separately, but lesser so when the two classes are combined. That points to a major issue in applying these class definitions in semiarid environments. Given the relatively small sample size (n=46 field observed plots), relative standard errors (SE%) of area estimates are high (around 9% for the larger area classes). Aboveground tree carbon stocks were estimated to be 6.640, 5.580 and 7.222Mgha⁻¹ for “forest”, “other wooded land” and “other land with tree cover”, respectively (SE% around 18% for all three estimates). Availability of biomass models is very limited for all classes, in particular when it comes to shrubs. Furthermore, it was estimated that the most abundant tree species in Burkina Faso is Vittelaria paradoxa, the “shea butter tree” which is a multi-use tree species of high relevance for rural livelihoods. To our knowledge this study is the first field-based forest inventory on national level in Burkina Faso where the estimation of errors was possible on statistical grounds, and done. The results of this study revealed major issues that should be taken into account when doing similar studies, including carbon monitoring and accounting: increasing the sample size will lead to smaller standard errors (at a higher costs, of course), but will not solve the crucial points (1) of non-availability of suitable biomass models, in particular for shrub lands and (2) of implementation issues regarding the definition of land cover types.
