• Energy Research

AbstractAimLarge tropical trees form the interface between ground and airborne observations, offering a unique opportunity to capture forest properties remotely and to investigate their variations on broad scales. However, despite rapid development of metrics to characterize the forest canopy from remotely sensed data, a gap remains between aerial and field inventories. To close this gap, we propose a new pan‐tropical model to predict plot‐level forest structure properties and biomass from only the largest trees.LocationPan‐tropical.Time periodEarly 21st century.Major taxa studiedWoody plants.MethodsUsing a dataset of 867 plots distributed among 118 sites across the tropics, we tested the prediction of the quadratic mean diameter, basal area, Lorey's height, community wood density and aboveground biomass (AGB) from the ith largest trees.ResultsMeasuring the largest trees in tropical forests enables unbiased predictions of plot‐ and site‐level forest structure. The 20 largest trees per hectare predicted quadratic mean diameter, basal area, Lorey's height, community wood density and AGB with 12, 16, 4, 4 and 17.7% of relative error, respectively. Most of the remaining error in biomass prediction is driven by differences in the proportion of total biomass held in medium‐sized trees (50–70 cm diameter at breast height), which shows some continental dependency, with American tropical forests presenting the highest proportion of total biomass in these intermediate‐diameter classes relative to other continents.Main conclusionsOur approach provides new information on tropical forest structure and can be used to generate accurate field estimates of tropical forest carbon stocks to support the calibration and validation of current and forthcoming space missions. It will reduce the cost of field inventories and contribute to scientific understanding of tropical forest ecosystems and response to climate change.

Intact tropical rainforests have been exposed to severe droughts in recent decades, which may threaten their integrity, their ability to sequester carbon, and their capacity to provide shelter for biodiversity. However, their response to droughts remains uncertain due to limited high-quality, long-term observations covering extensive areas. Here, we examined how the upper canopy of intact tropical rainforests has responded to drought events globally and during the past 3 decades. By developing a long pantropical time series (1992 to 2018) of monthly radar satellite observations, we show that repeated droughts caused a sustained decline in radar signal in 93%, 84%, and 88% of intact tropical rainforests in the Americas, Africa, and Asia, respectively. Sudden decreases in radar signal were detected around the 1997–1998, 2005, 2010, and 2015 droughts in tropical Americas; 1999–2000, 2004–2005, 2010–2011, and 2015 droughts in tropical Africa; and 1997–1998, 2006, and 2015 droughts in tropical Asia. Rainforests showed similar low resistance (the ability to maintain predrought condition when drought occurs) to severe droughts across continents, but American rainforests consistently showed the lowest resilience (the ability to return to predrought condition after the drought event). Moreover, while the resistance of intact tropical rainforests to drought is decreasing, albeit weakly in tropical Africa and Asia, forest resilience has not increased significantly. Our results therefore suggest the capacity of intact rainforests to withstand future droughts is limited. This has negative implications for climate change mitigation through forest-based climate solutions and the associated pledges made by countries under the Paris Agreement.

Abstract Tropical forests are characterized by large carbon stocks and high biodiversity, but they are increasingly threatened by human activities. Since structure strongly influences the functioning and resilience of forest communities and ecosystems, it is important to quantify it at fine spatial scales. Here, we propose a new simulation-based approach, the “Canopy Constructor”, with which we quantified forest structure and biomass at two tropical forest sites, one in French Guiana, the other in Gabon. In a first step, the Canopy Constructor combines field inventories and airborne lidar scans to create virtual 3D representations of forest canopies that best fit the data. From those, it infers the forests' structure, including crown packing densities and allometric scaling relationships between tree dimensions. In a second step, the results of the first step are extrapolated to create virtual tree inventories over the whole lidar-scanned area. Across the French Guiana and Gabon plots, we reconstructed empirical canopies with a mean absolute error of 3.98 m [95% credibility interval: 3.02, 4.98], or 14.4%, and a small upwards bias of 0.66 m [−0.41, 1.8], or 2.7%. Height-stem diameter allometries were inferred with more precision than crown-stem diameter allometries, with generally larger heights at the Amazonian than the African site, but similar crown-stem diameter allometries. Plot-based aboveground biomass was inferred to be larger in French Guiana with 400.8 t ha−1 [366.2–437.9], compared to 302.2 t ha−1 in Gabon [267.8–336.8] and decreased to 299.8 t ha−1 [275.9–333.9] and 251.8 t ha−1 [206.7–291.7] at the landscape scale, respectively. Predictive accuracy of the extrapolation procedure had an RMSE of 53.7 t ha−1 (14.9%) at the 1 ha scale and 87.6 t ha−1 (24.2%) at the 0.25 ha scale, with a bias of −17.1 t ha−1 (−4.7%). This accuracy was similar to regression-based approaches, but the Canopy Constructor improved the representation of natural heterogeneity considerably, with its range of biomass estimates larger by 54% than regression-based estimates. The Canopy Constructor is a comprehensive inference procedure that provides fine-scale and individual-based reconstructions even in dense tropical forests. It may thus prove vital in the assessment and monitoring of those forests, and has the potential for a wider applicability, for example in the exploration of ecological and physiological relationships in space or the initialisation and calibration of forest growth models.