Mapping plant species at the regional scale to provide information for ecologists and forest managers is a challenge for the remote sensing community. Here, we use a deep learning algorithm called U-net and very high-resolution multispectral images (0.5 m) from GeoEye satellite to identify, segment and map canopy palms over ~3000 km2 of Amazonian forest. The map was used to analyse the spatial distribution of canopy palm trees and its relation to human disturbance and edaphic conditions. The overall accuracy of the map was 95.5% and the F1-score was 0.7. Canopy palm trees covered 6.4% of the forest canopy and were distributed in more than two million patches that can represent one or more individuals. The density of canopy palms is affected by human disturbance. The post-disturbance density in secondary forests seems to be related to the type of disturbance, being higher in abandoned pas...

The Amazon basin hosts half the planet's remaining moist tropical forests, but they may be threatened in a warming world. Nevertheless, climate model predictions vary from rapid drying to modest wetting. Here we report that the catchment of the world's largest river is experiencing a substantial wetting trend since approximately 1990. This intensification of the hydrological cycle is concentrated overwhelmingly in the wet season driving progressively greater differences in Amazon peak and minimum flows. The onset of the trend coincides with the onset of an upward trend in tropical Atlantic sea surface temperatures (SST). This positive longer‐term correlation contrasts with the short‐term, negative response of basin‐wide precipitation to positive anomalies in tropical North Atlantic SST, which are driven by temporary shifts in the intertropical convergence zone position. We propose ...