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1.Climate extremes such as drought can trigger large-scale tree die-off, reducing overstorey canopy and thereby increasing near-ground solar radiation. This directly affects biotic and abiotic processes, including plant physiology, reproduction, phenology, soil evaporation and nutrient cycling, which themselves affect understory facilitation, productivity and diversity, and land surface-atmosphere fluxes of energy, carbon and water. 2.Although important, assessing extreme-event solar radiation responses regionally following die-off is complex compared with characterizing patch-scale inputs. Estimating regional-scale changes requires integration of broad-scale downward-looking shading patterns due to canopy and topography with fine-scale upward-looking canopy details (e.g. live vs. dead trees, height, diameter, spatial pattern and foliar diffusivity). 3.We quantified increases in near-ground solar radiation following overstorey loss of pinon pine cover in response to a recent extreme drought event (2002-2003). We evaluated 211km2 in south-western USA seasonally and annually using high-spatial resolution satellite imagery, hemispherical ground photography, GIS (Geographic Information System)-based solar radiation modelling tools, in situ meteorological data and tree measurements. 4.Overstorey loss due to die-off produced increases in near-ground solar radiation regionally each season - up to 28Wm-2, an increase of 9.1%, in summer - while simultaneously decreasing spatial variation. Annually the increase was c. 17Wm-2. Larger increases occurred where initial canopy cover was greater or at higher elevations, by as much as c. 80Wm-2 (a 40% increase). 5.Synthesis. Our results are notable in that they quantify increases regionally in near-ground solar radiation in response to a climate extreme triggering widespread tree die-off. The substantial increases quantified are expected to have primary direct effects on processes such as plant physiology, reproduction, phenology, soil evaporation and nutrient cycling, and secondary effects on understory facilitation, productivity and diversity, and land surface-atmosphere fluxes of energy, carbon and water. Consequently, extreme event-induced changes in near-ground solar radiation need to be considered by both ecologists and physical scientists in assessing global change impacts. More generally, our results highlight an important but sometimes overlooked aspect of plant ecology - that plants not only respond to their physical environment and other plants, but also directly modify their physical environment from individual plant to regional scales.

In a temperate climate, understory trees leaf out earlier than canopy trees, but the cause of this discrepancy remains unclear. This study aims to investigate whether this discrepancy results from ontogenic changes or from microclimatic differences. Seedlings of five deciduous tree species were grown in spring 2012 in the understory and at canopy height using a 45-m-high construction crane built into a mature mixed forest in the foothills of the Swiss Jura Mountains. The leaf development of these seedlings, as well as conspecific adults, was compared, taking into account the corresponding microclimate. The date of leaf unfolding occurred 10–40 d earlier in seedlings grown at canopy level than in conspecific adults. Seedlings grown in the understory flushed c. 6 d later than those grown at canopy height, which can be attributed to the warmer temperatures recorded at canopy height (c. 1°C warmer). This study demonstrates that later leaf emergence of canopy trees compared with understory trees results from ontogenic changes and not from the vertical thermal profile that exists within forests. This study warns against the assumption that phenological data obtained in warming and photoperiod experiments on juvenile trees can be used for the prediction of forest response to climate warming.

• Leaf angle and leaf area index together influence canopy light interception and canopy photosynthesis. However, so far, there is no effective method to identify the optimal combination of these two parameters for canopy photosynthesis. • In this study, first a robust high-throughput method for accurate segmentation of maize organs based on 3D point clouds data was developed, then the segmented plant organs were used to generate new 3D point clouds for the canopy of altered architectures. With this, we simulated the synergistic effect of leaf area and leaf angle on canopy photosynthesis. • The results show that, compared to the traditional parameters describing the canopy photosynthesis including leaf area index, facet angle and canopy coverage, a new parameter – the canopy occupation volume (COV) – can better explain the variations of canopy photosynthetic capacity. Specifically, COV can explain > 79% variations of canopy photosynthesis generated by changing leaf angle and > 84% variations of canopy photosynthesis generated by changing leaf area. • As COV can be calculated in a high-throughput manner based on the canopy point clouds, it can be used to evaluate canopy architecture in breeding and agronomic research.

Canopy temperature T can is a key driver of plant function that emerges as a result of interacting biotic and abiotic processes and properties. However, understanding controls on T can and forecasting canopy responses to weather extremes and climate change are difficult due to sparse measurements of T can at appropriate spatial and temporal scales. Burgeoning observations of T can from thermal cameras enable evaluation of energy budget theory and better understanding of how environmental controls, leaf traits and canopy structure influence temperature patterns. The canopy scale is relevant for connecting to remote sensing and testing biosphere model predictions. We anticipate that future breakthroughs in understanding of ecosystem responses to climate change will result from multiscale observations of T can across a range of ecosystems.

The expansion of an urban tree canopy is a commonly proposed nature-based solution to combat excess urban heat. The influence trees have on urban climates via shading is driven by the morphological characteristics of trees, whereas tree transpiration is predominantly a physiological process dependent on environmental conditions and the built environment. The heterogeneous nature of urban landscapes, unique tree species assemblages, and land management decisions make it difficult to predict the magnitude and direction of cooling by transpiration. In the present article, we synthesize the emerging literature on the mechanistic controls on urban tree transpiration. We present a case study that illustrates the relationship between transpiration (using sap flow data) and urban temperatures. We examine the potential feedbacks among urban canopy, the built environment, and climate with a focus on extreme heat events. Finally, we present modeled data demonstrating the influence of transpiration on temperatures with shifts in canopy extent and irrigation during a heat wave.

River restoration projects often involve vegetation planting to retain sediment and stabilize riverbanks. Laboratory experiments have explored the impact of rigid emergent vegetation canopies on bed morphology. Inside canopies, bed erosion is attributed to vegetation‐induced turbulent kinetic energy (TKE). Based on the in‐canopy local TKE and the criteria for sediment movement, a method is established and validated for predicting the length of the bed erosion region. In the bare channel, bed erosion is related to the ratio of canopy length to flow adjustment distance, L/LI, and exhibits two trends. At L/LI < 1, the maximum depth, ds(bare), and length, Ls(bare), of the bed erosion region increase with increasing canopy length. At L/LI ≥ 1, ds(bare) and Ls(bare) are not influenced by the canopy length and remain constant. In vegetated regions with the same length and plant density, discontinuous canopies (streamwise interval s ≥ canopy width D) yield weaker bed erosion than continuous canopies. The mutual influence between two canopies must be considered if the canopy interval satisfies s < 3D. These results provide insights for designing vegetation canopies for river restoration projects. Key Points The impact of canopy length on bed morphology inside and outside the canopy is clarified A method for predicting the length of the bed erosion region inside canopies is established Discontinuous canopies produce weaker bed erosion than continuous canopies for the same vegetated region length and plant density

Lidar remote sensing has proven to be a powerful tool for estimating ground elevation, canopy height, and additional vegetation parameters, which in turn are valuable information for the investigation of ecosystems. Spaceborne lidar systems, like the Global Ecosystem Dynamics Investigation (GEDI), can deliver these height estimates on a near global scale. This paper analyzes the accuracy of the first version of GEDI ground elevation and canopy height estimates in two study areas with temperate forests in the Free State of Thuringia, central Germany. Digital terrain and canopy height models derived from airborne laser scanning data are used as reference heights. The influence of various environmental and acquisition parameters (e.g., canopy cover, terrain slope, beam type) on GEDI height metrics is assessed. The results show a consistently high accuracy of GEDI ground elevation estimates under most conditions, except for areas with steep slopes. GEDI canopy height estimates are less accurate and show a bigger influence of some of the included parameters, specifically slope, vegetation height, and beam sensitivity. A number of relatively high outliers (around 9–13% of the measurements) is present in both ground elevation and canopy height estimates, reducing the estimation precision. Still, it can be concluded that GEDI height metrics show promising results and have potential to be used as a basis for further investigations.

Satellite observations of Amazon forests show seasonal and interannual variations, but the underlying biological processes remain debated. Here we combined radiative transfer models (RTMs) with field observations of Amazon forest leaf and canopy characteristics to test three hypotheses for satellite-observed canopy reflectance seasonality: seasonal changes in leaf area index, in canopy-surface leafless crown fraction and/or in leaf demography. Canopy RTMs (PROSAIL and FLiES), driven by these three factors combined, simulated satellite-observed seasonal patterns well, explaining c. 70% of the variability in a key reflectance-based vegetation index (MAIAC EVI, which removes artifacts that would otherwise arise from clouds/aerosols and sun–sensor geometry). Leaf area index, leafless crown fraction and leaf demography independently accounted for 1, 33 and 66% of FLiES-simulated EVI seasonality, respectively. These factors also strongly influenced modeled near-infrared (NIR) reflectance, explaining why both modeled and observed EVI, which is especially sensitive to NIR, captures canopy seasonal dynamics well. Our improved analysis of canopy-scale biophysics rules out satellite artifacts as significant causes of satellite-observed seasonal patterns at this site, implying that aggregated phenology explains the larger scale remotely observed patterns. This work significantly reconciles current controversies about satellite-detected Amazon phenology, and improves our use of satellite observations to study climate–phenology relationships in the tropics.

Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, and carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission has monitored global canopy structure using a satellite Light Detection and Ranging (LiDAR) instrument. While GEDI has collected billions of LiDAR shots across a near-global range (between 51.6°N and >51.6°S), their spatial distribution remains dispersed, posing challenges for achieving complete forest coverage. This study proposes and evaluates an approach that generates high-resolution canopy height maps by integrating GEDI data with Sentinel-1, Sentinel-2, and topographical ancillary data through three machine learning (ML) algorithms: random forests (RF), gradient tree boost (GB), and classification and regression trees (CART). To achieve this, the secondary aims included the following: (1) to assess the performance of three ML algorithms, RF, GB, and CART, in predicting canopy heights, (2) to evaluate the performance of our canopy height maps using reference canopy height from canopy height models (CHMs), and (3) to compare our canopy height maps with other two existing canopy height maps. RF and GB were the top-performing algorithms, achieving the best 13.32% and 16% root mean squared error for broadleaf and coniferous forests, respectively. Validation of the proposed approach revealed that the 100th and 98th percentile, followed by the average of the 75th, 90th, 95th, and 100th percentiles (AVG), were the most accurate GEDI metrics for predicting real canopy heights. Comparisons between predicted and reference CHMs demonstrated accurate predictions for coniferous stands (R-squared = 0.45, RMSE = 29.16%).