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Uncovering the mechanisms that lead to Amazon forest resilience variations is crucial to predict the impact of future climatic and anthropogenic disturbances. Here, we apply a previously used empirical resilience metrics, lag‐1 month temporal autocorrelation (TAC), to vegetation optical depth data in C‐band (a good proxy of the whole canopy water content) in order to explore how forest resilience variations are impacted by human disturbances and environmental drivers in the Brazilian Amazon. We found that human disturbances significantly increase the risk of critical transitions, and that the median TAC value is ~2.4 times higher in human‐disturbed forests than that in intact forests, suggesting a much lower resilience in disturbed forests. Additionally, human‐disturbed forests are less resilient to land surface heat stress and atmospheric water stress than intact forests. Among human‐disturbed forests, forests with a more closed and thicker canopy structure, which is linked to a higher forest cover and a lower disturbance fraction, are comparably more resilient. These results further emphasize the urgent need to limit deforestation and degradation through policy intervention to maintain the resilience of the Amazon rainforests. [ABSTRACT FROM AUTHOR]

In the Amazon, deforestation and climate change lead to increased vulnerability to forest degradation, threatening its existing carbon stocks and its capacity as a carbon sink. We use satellite L-Band Vegetation Optical Depth (L-VOD) data that provide an integrated (top-down) estimate of biomass carbon to track changes over 2011–2019. Because the spatial resolution of L-VOD is coarse (0.25°), it allows limited attribution of the observed changes. We therefore combined high-resolution annual maps of forest cover and disturbances with biomass maps to model carbon losses (bottom- up) from deforestation and degradation, and gains from regrowing secondary forests. We show an increase of deforestation and associated degradation losses since 2012 which greatly outweigh secondary forest gains. Degradation accounted for 40% of gross losses. After an increase in 2011, old-growth forests show a net loss of above-ground carbon between 2012 and 2019. The sum of component carbon fluxes in our model is consistent with the total biomass change from L-VOD of 1.3 Pg C over 2012-2019. Across nine Amazon countries, we found that while Brazil contains the majority of biomass stocks (64%), its losses from disturbances were disproportionately high (79% of gross losses). Our multi-source analysis provides a pessimistic assessment of the Amazon carbon balance and highlights the urgent need to stop the recent rise of deforestation and degradation, particularly in the Brazilian Amazon. [ABSTRACT FROM AUTHOR]

Spatially heterogeneous plant mortality rates are an important predictor of the distribution of vegetation carbon in Amazonia. Reproducing the spatial gradients of vegetation carbon in Amazonia and the observed decline in the intact Amazonian carbon sink since 1990 is a challenge faced by dynamic global vegetation models (DGVMs). In this paper, we implement spatially variable mortality rates in TRIFFID, the DGVM currently coupled to the Joint UK Land Environment Simulator (JULES), and compare with the standard model which assumes a homogeneous mortality rate. Spatially variable gridded fields of Amazonian tree mortality are created using a well-known relationship between mortality and wood density, and three independent wood density maps. The diversified mortality scheme substantially improves the representation of vegetation carbon in TRIFFID when compared to observations, with a 90% reduction in model bias and an increase in the Pearson correlation coefficient with observed biomass. JULES now captures the observed variability of both mortality and vegetation carbon to a greater extent, demonstrating the potential of using easily-measured traits, like wood density, to add spatial and functional diversity into DGVMs. Despite this, the spatial variation of vegetation carbon simulated with the new mortality fields (with standard deviation 15 MgCha-1) is still less than half of the variation in the observed data (standard deviation 35 MgCha-1). Future work should consider the effects of additional processes, like fire, drought and the phosphorus cycle, on the simulated distribution of vegetation carbon in the Amazon. [ABSTRACT FROM AUTHOR]

Quantifying the timing of vegetation phenology is critical for monitoring and modelling ecosystem responses to environmental change. Phenological processes have been studied from landscape to global scales using Earth observing satellite data, and at local scale by in situ surveys of individual plants. Now, data acquired from multi‐spectral sensors on drone platforms provide flexible opportunities for monitoring phenology from individual plants to small ecosystem scales efficiently, allowing community and species level information to be derived. We captured a time‐series of drone‐acquired normalized difference vegetation index (NDVI) data with a multi‐spectral sensor (Parrot Sequoia, (Parrot, France)) over a highly heterogeneous ecosystem in Cornwall, UK, during a period of spring green‐up. We monitored NDVI trajectories at the individual crown and species' level. For deciduous crowns, we derived metrics representative of spring phenological stages: Start‐of‐spring (SOS), middle‐of‐spring green‐up (MOG) and start‐of‐peak greenness (SOP) using a logistic function. While the exact timing of SOS, MOG and SOP appeared susceptible to understorey effects and saturation of the NDVI, relative timing of green‐up for a subset of species was plausible in relation to phenological observations from an extended geographic region and in situ plant area index (PAI) measurements. In evergreen vegetation (Pinus spp.) subtle changes were also detected through the growing season. The impact of illumination differences was analysed for image pairs during leaf‐off and leaf‐on conditions. While significant, these effects were small (mean absolute NDVI deviation of up to 0.034 for leaf‐off, 0.013 for leaf‐on conditions), meaning that data captured under both constant direct and diffuse irradiance conditions can be used together and that cloudy conditions should not lead to data gaps. We conclude that the capability of drone‐mounted multi‐spectral instruments for spatio‐temporal characterization of crown‐level phenology shows great promise for improving the understanding of intra‐ and inter‐species differences in strategy, and offers an efficient means of doing so over areas of a few hectares. [ABSTRACT FROM AUTHOR]