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Abstract Tropical ecosystems are undergoing unprecedented rates of degradation from deforestation, fire, and drought disturbances. The collective effects of these disturbances threaten to shift large portions of tropical ecosystems such as Amazon forests into savanna‐like structure via tree loss, functional changes, and the emergence of fire (savannization). Changes from forest states to a more open savanna‐like structure can affect local microclimates, surface energy fluxes, and biosphere–atmosphere interactions. A predominant type of ecosystem state change is the loss of tree cover and structural complexity in disturbed forest. Although important advances have been made contrasting energy fluxes between historically distinct old‐growth forest and savanna systems, the emergence of secondary forests and savanna‐like ecosystems necessitates a reframing to consider gradients of tree structure that span forest to savanna‐like states at multiple scales. In this Innovative Viewpoint, we draw from the literature on forest–grassland continua to develop a framework to assess the consequences of tropical forest degradation on surface energy fluxes and canopy structure. We illustrate this framework for forest sites with contrasting canopy structure that ranges from simple, open, and savanna‐like to complex and closed, representative of tropical wet forest, within two climatically distinct regions in the Amazon. Using a recently developed rapid field assessment approach, we quantify differences in cover, leaf area vertical profiles, surface roughness, albedo, and energy balance partitioning between adjacent sites and compare canopy structure with adjacent old‐growth forest; more structurally simple forests displayed lower net radiation. To address forest–atmosphere feedback, we also consider the effects of canopy structure change on susceptibility to additional future disturbance. We illustrate a converse transition—recovery in structure following disturbance—measuring forest canopy structure 10 yr after the imposition of a 5‐yr drought in the ground‐breaking Seca Floresta experiment. Our approach strategically enables rapid characterization of surface properties relevant to vegetation models following degradation, and advances links between surface properties and canopy structure variables, increasingly available from remote sensing. Concluding, we hypothesize that understanding surface energy balance and microclimate change across degraded tropical forest states not only reveals critical atmospheric forcing, but also critical local‐scale feedbacks from forest sensitivity to additional climate‐linked disturbance. 

Summary Seasonal dynamics in the vertical distribution of leaf area index (LAI) may impact the seasonality of forest productivity in Amazonian forests. However, until recently, fine‐scale observations critical to revealing ecological mechanisms underlying these changes have been lacking.To investigate fine‐scale variation in leaf area with seasonality and drought we conducted monthly ground‐based LiDAR surveys over 4 yr at an Amazon forest site. We analysed temporal changes in vertically structuredLAIalong axes of both canopy height and light environments.Upper canopyLAIincreased during the dry season, whereas lower canopyLAIdecreased. The low canopy decrease was driven by highly illuminated leaves of smaller trees in gaps. By contrast, understoryLAIincreased concurrently with the upper canopy. Hence, tree phenological strategies were stratified by height and light environments. Trends were amplified during a 2015–2016 severe El Niño drought.Leaf area low in the canopy exhibited behaviour consistent with water limitation. Leaf loss from short trees in high light during drought may be associated with strategies to tolerate limited access to deep soil water and stressful leaf environments. Vertically and environmentally structured phenological processes suggest a critical role of canopy structural heterogeneity in seasonal changes in Amazon ecosystem function. 

Amazon forests are characterized by rich structural diversity. However, the influence of factors such as topography, soil attributes, and external disturbances on structural variability is not always well characterized, and traditional structural metrics may be inadequate to capture this type of complexity. While LiDAR offers expanded structural insights, traditional parameters used in LiDAR analysis, such as mean or maximum canopy height, are not always well directly linked to environmental variables like topography. Emerging approaches merge LiDAR with machine learning to uncover deeper structural complexities. However, work to date may fail to fully utilize the potential of fine-scale LiDAR information. Here we introduce a novel approach, leveraging 2D point cloud images derived from a profiling canopy LiDAR (PCL). The technique targets intricate details within LiDAR point clouds by using deep learning algorithms. With a dataset from the Central Amazon comprising 18 multitemporal transects of 450 m in length, our objective was to detect structural "fingerprints" of varied topographical types along a hillslope, comprising: Riparian, White-sand, and Plateau, and to detect any gradient of structural shifts based on terrain variations here represented by the height above the nearest drainage (HAND). The dataset was trained and tested using a leave-one-group-out approach (LOGO) in which, for each iteration, a complete 450 m multitemporal transect was excluded from training and tested after each iteration. The fast.ai platform and a ResNet-34 architecture, coupled with transfer learning, were used to perform a classification to distinguish between three topographical types. Furthermore, a hybrid model combining a Convolutional Autoencoder, and Partial Least Square (PLS) regression was designed to detect forest structural gradient correlations with HAND variation. Cross-validation achieved a promising high weighted F1 score of 0.83 to classify forests based on the topographical types. Additionally, a combined Convolutional Autoencoder and PLS regression revealed a strong correlation (R2 = 0.76) between actual and predicted HAND. Innovatively combining deep learning with ground-based PCL LiDAR, our study revealed unique Amazon Forest structures connected to topographic variation. Our findings underscore the transformative potential of such integrative approaches for investigating forest dynamics and promise a powerful new tool for understanding climate-related forest structure change. 

Abstract. In humid tropical regions, irregular illumination and cloud shadows can complicate near-surface optical remote sensing. This could lead to costly and repetitive surveys to maintain geographical and spectral consistency. This could have a significant impact on the regular monitoring of forest ecosystems. A novel correction method using deep learning is presented here to address the issue in high-resolution canopy images. Our method involves training a deep learning model on one or a few well-illuminated/homogeneous reference images augmented with artificially generated cloud shadows. This training enables the model to predict illumination and cloud shadow patterns in any image and ultimately mitigate these effects. Using images captured by multispectral and RGB cameras, we evaluated the method across multiple sensors and conditions. These included nadir-view images from two sensors mounted on a drone and tower-mounted RGB Phenocams. The technique effectively corrects uneven illumination in near-infrared and true-color RGB images, including non-forested areas. This improvement was also evident in more consistent normalized difference vegetation index (NDVI) patterns in areas affected by uneven illumination. By comparing corrected RGB images to the original in a binary classification task, we evaluated the method's accuracy and Kappa values. Our goal was to detect non-photosynthetic vegetation (NPV) in a mosaic. The overall accuracy and Kappa were both significantly improved in corrected images, with a 2.5% and 1.1% increase, respectively. Moreover, the method can be generalized across sensors and conditions. Further work should focus on refining the technique and exploring its applicability to satellite imagery and beyond. 

Abstract Predictions of the magnitude and timing of leaf phenology in Amazonian forests remain highly controversial. Here, we use terrestrial LiDAR surveys every two weeks spanning wet and dry seasons in Central Amazonia to show that plant phenology varies strongly across vertical strata in old-growth forests, but is sensitive to disturbances arising from forest fragmentation. In combination with continuous microclimate measurements, we find that when maximum daily temperatures reached 35 °C in the latter part of the dry season, the upper canopy of large trees in undisturbed forests lost plant material. In contrast, the understory greened up with increased light availability driven by the upper canopy loss, alongside increases in solar radiation, even during periods of drier soil and atmospheric conditions. However, persistently high temperatures in forest edges exacerbated the upper canopy losses of large trees throughout the dry season, whereas the understory in these light-rich environments was less dependent on the altered upper canopy structure. Our findings reveal a strong influence of edge effects on phenological controls in wet forests of Central Amazonia.