Abstract: Very high spatial resolution (VHSR) optical satellite imagery has shown good potential to provide non-saturating proxies of tropical forest aboveground biomass (AGB) from the analysis of canopy texture, for instance through the Fourier Transform Textural Ordination method. Empirical case studies however showed that the relationship between Fourier texture features and forest AGB varies across forest types and regions of the world, limiting model transferability. A better understanding of the biophysical mechanisms on which canopy texture – forest AGB relation relies is a prerequisite to move toward broad scale applications. Here we simulated VHSR optical canopy scenes in identical sun-sensor geometry for 279 1-ha tropical forest inventory plots distributed across the tropics. Our aim was to assess the respective merits and complementarity of two types of texture analysis techniques (i.e. Fourier and lacunarity) on a set of forests with contrasted structure and geographical origin, and develop a general texture-based approach for tropical forest AGB mapping. Across forests, Fourier texture captured a gradient of stands mean crown size reflecting well the progressive changes in stand structure throughout forest aggradation phase (e.g. Pearson's r = − 0.42 with basal area) while lacunarity texture captured a gradient of canopy openness (, i.e. Pearson's r = − 0.57 with stand gap fraction). Both types of texture indices were highly complementary for predicting forest AGB at the global level (so-called FL-model). The residual error of the FL-model was structured across sites and could be partially captured with a bioclimatic proxy, further improving the performance of the global model (so-called FLE-model) and reducing site-level biases. The FLE model was tested on a set of real Pleiades images covering a mosaic of high-biomass forests in the Congo basin (mean AGB over 49 field plots: 359 ± 98 Mg ha− 1), leading to a significant relationship (R2 = 0.47 on validation data) with reasonable error levels (< 25% rRMSE). The increasing availability of VHSR optical sensors (such as from constellations of small satellite platforms) raises the possibility of routine repeated imaging of the world's tropical forests and suggests that texture-based analyses could become an essential tool in international efforts to monitor carbon emissions from deforestation and forest degradations (REDD +). © 2017 Elsevier Inc.

Abstract: Although tropical forests harbour most of the terrestrial carbon and biological diversity on Earth they continue to be deforested or degraded at high rates. In Amazonia, the largest tropical forest on Earth, a sixth of the remaining natural forests is formally dedicated to timber extraction through selective logging. Reconciling timber extraction with the provision of other ecosystem services (ES) remains a major challenge for forest managers and policy-makers. This study applies a spatial optimisation of logging in Amazonian production forests to analyse potential trade-offs between timber extraction and recovery, carbon storage, and biodiversity conservation. Current logging regulations with unique cutting cycles result in sub-optimal ES-use efficiency. Long-term timber provision would require the adoption of a land-sharing strategy that involves extensive low-intensity logging, although high transport and road-building costs might make this approach economically unattractive. By contrast, retention of carbon and biodiversity would be enhanced by a land-sparing strategy restricting high-intensive logging to designated areas such as the outer fringes of the region. Depending on management goals and societal demands, either choice will substantially influence the future of Amazonian forests. Overall, our results highlight the need for revaluation of current logging regulations and regional cooperation among Amazonian countries to enhance coherent and trans-boundary forest management.

Abstract: One third of contemporary tropical forests is designated by national forest services for timber production. Tropical forests are also increasingly affected by anthropogenic disturbances. However, there is still much uncertainty around the capacity of tropical forests to recover their timber volume after logging as well as other disturbances such as fires, large blow-downs and extreme droughts, and thus on the long-term sustainability of logging. We developed an original Bayesian hierarchical model of Volume Dynamics with Differential Equations (VDDE) to infer the dynamic of timber volumes as the result of two ecosystem processes: volume gains from tree growth and volume losses from tree mortality. Both processes are expressed as explicit functions of the forest maturity, i.e. the overall successional stage of the forest that primarily depends on the frequency and severity of the disturbances that the forest has undergone. As a case study, the VDDE model was calibrated with data from Paracou, a long-term disturbance experiment in a neotropical forest where over 56 ha of permanent forest plots were logged with different intensities and censused for 31 years. With this model, we could predict timber recovery at Paracou at the end of a cutting cycle depending on the logging intensity, the rotation cycle length, and the proportion of commercial volume. The VDDE modelling framework developed presents three main advantages: (i) it can be calibrated with large tree inventories which are widely available from national forest inventories or logging concession management plans and are easy to measure, both on the field and with remote sensing; (ii) it depends on only a few input parameters, which can be an advantage in tropical regions where data availability is scarce; (iii) the modelling framework is flexible enough to explicitly include the effect of other types of disturbances (both natural and anthropogenic: e.g. blow-downs, fires and climate change) on the forest maturity, and thus to predict future timber provision in the tropics in a context of global changes. © 2018 Elsevier B.V.

Abstract: This study compares variability in the longitudinal Modulus of Elasticity (MOE) values, measured by three different methods, for eight tropical wood species covering a wide range of densities, a property that has been little described in the literature for some of the species studied. The modulus of elasticity in wood species is one of the main mechanical properties measured to characterize wood materials. However, this property is seldom described for the tropical wood species studied here, and the method used is often variable. The aim is to answer the following questions. In the methods used, what are the main variability factors which influence modulus measurement? Is the modulus different with regard to the solicitation direction (radial or tangential)? Which relationship exists between modulus and density for these species?
The samples were subjected to the four-point bending test, then to the free vibration test and to the forced-vibration test (which allows tests on small samples).The samples were subjected to stress in radial and tangential directions. The modulus values obtained by the different methods were well correlated for most of the species. The relationship between modulus and density was very good at inter-specific level because sampling covered a wide range of densities. But this relationship was not so good for each of the species sampled.
This kind of test was not appropriate for detecting differences in behavior between the two directions of solicitation for these species. The main features of the three methods were summarized, highlighting the advantages of each for the species studied.

Abstract: When ordinating plots of tropical rain forests using stand-level structural attributes such as biomass, basal area and the number of trees in different size classes, two patterns often emerge: a gradient from poorly to highly stocked plots and high positive correlations between biomass, basal area and the number of large trees. These patterns are inherited from the demographics (growth, mortality and recruitment) and size allometry of trees and tend to obscure other patterns, such as site differences among plots, that would be more informative for inferring ecological processes. Using data from 133 rain forest plots at nine sites for which site differences are known, we aimed to filter out these patterns in forest structural attributes to unveil a hidden pattern. Using a null model framework, we generated the anticipated pattern inherited from individual allometric patterns. We then evaluated deviations between the data (observations) and predictions of the null model. Ordination of the deviations revealed site differences that were not evident in the ordination of observations. These sites differences could be related to different histories of large-scale forest disturbance. By filtering out patterns inherited from individuals, our model analysis provides more information on ecological processes