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International audience; Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement.

International audience; Slope Stability Models (SSMs) are valuable tools used as decision support in land management to mitigate catastrophic effects caused by rainfall-induced shallow landslides. In particular, SSMs incorporating the presence and influence of vegetation allow for the evaluation of how trees influence relative slope stability and how forest management could ensure the root reinforcement effect in space and time. By implementing empirical knowledge about complex mechanical and hydrological processes, SSMs have been realized by employing different modeling approaches and methods, becoming suitable for different contexts and scales of analysis. Recent SSMs increasingly consider vegetation both as a mechanism to counteract the triggering process of shallow landslides and as a manageable and modifiable tool for mitigating hazards. This review aims to analyze the state-of-the-art of SSMs applicable to vegetated slope areas, investigating those that consider root reinforcement and some of the most cited SSMs in the literature that neglect this effect instead. After classification and exposition on the spatial and temporal dimension of the analysis, modeling approaches, and complexity, we discuss the identification of the most suitable Slope Stability Model (SSM) for individual applications considering four fundamental aspects: modeling approaches, the analysis scale, and purpose, and the output data. Although all SSMs allow for risk analysis by quantifying the factor of safety, only a few allow for an accurate assessment of how changes in vegetation structure, due to the occurrence of natural and human disturbances, also affect the stability of a studied area. Such information is critical to identifying land management criteria to preserve and enhance the protection effect. The improvement of data collection and measurement techniques to obtain parameters for stability analysis required the development of new SSMs able to exploit the improved detail of information, thus allowing for increasingly accurate analyses.

So far, root reinforcement on soil has been primarily evaluated through direct shear and roots pull-out tests, while the effect of other stress paths and the behaviour at the soil–root¿ interface are still poorly investigated. In this regard, an apparatus with the facility to test soil and roots jointly under uniaxial extension is presented in the paper, together with its first results. Vegetated samples with Cynodon dactilon were tested after one and three months of growth. Soil exhibited a ductile response when close to saturation and a brittle one at drier states within the field capacity domain. The presence of roots increased the material’s tensile strength and enhanced its post-peak ductility. Measurements of matric suction and degree of saturation allowed interpreting the results in terms of constitutive stresses within a shear strength failure criterion for partially saturated soils. Even if plant roots critically impacted soil hydraulics, a positive strengthening effect was noticed on its mechanical behaviour. Roots mechanical and morphological features were characterised after tests. Two well-established root reinforcement models in the literature were used to interpret the results at the phenomenological scale while considering the hydro-mechanical behaviour at the soil–root interface, different root’s reinforcement mechanisms and the effect of soil’s hydro-mechanical states. The authors wish to acknowledge the support of the European Commission via the Marie Skłodowska-Curie Innovative Training Networks (ITN-ETN) project TERRE ‘Training Engineers and Researchers to Rethink geotechnical Engineering for a low carbon future’ (H2020-MSCA-ITN-2015- 675762). Furthermore, Alessandro Fraccica wishes to thank Mercedes Sondon (Universitat Politècnica de Catalunya, UPC), Luis Gandarillas, Ylenia Bianchi, Ferran Parera (support with PIV software) and Stefano Collico (support with statistical analysis). The H2020-ITN project TERRE ‘Training Engineers and Researchers to Rethink geotechnical Engineering for a low carbon future’ (H2020-MSCA-ITN-2015-675762) provided funding.