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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.

AIM: To test the Canopy Cover (CaCo) index for forest vegetation research estimation of canopy cover from hemispherical photographs, and introduce a new Android smartphone application for use of this index. METHODS: The original and modified CaCo index were evaluated using a data set of 234 hemispherical photographs taken in 78 plots in coniferous, mixed and broad‐leaved deciduous forests. The results of the CaCo analysis of these photographs were compared with expert field visual estimation of canopy cover of these plots. For each hemispherical photograph, several CaCo values were calculated based on the photograph being restricted to different degrees by artificial horizon masking. The CaCo index was also tested with respect to precision of canopy cover estimation and sensitivity to different photographic equipment, using a different set of 93 canopy photographs taken in mixed and coniferous forests. Calculation of the CaCo index was done with the newly developed GLAMA – Gap Light Analysis Mobile Application software. RESULTS: Linear regression showed a close relationship between the CaCo index and visually observed canopy cover data. A proposed calculation modification improved the stability of the CaCo index in cases in which no horizon masking was applied. The best fit, zero‐intercept and a regression slope close to1 were found in cases in which an artificial horizon mask that extended higher than 45° restricted the bottom part of the sky hemisphere. Low sensitivity of the CaCo index to type of photographic equipment used was shown. CONCLUSIONS: The CaCo index is robust and can be used for precise canopy cover estimation, comparable to visual canopy cover estimation and unaffected by observer bias. Not only can it be used on already‐captured photographs, but the index can also be employed on smartphones to rapidly capture hemispherical photographs and immediately calculate their index values. This application is freely available on the Internet and can serve as a powerful research and educational tool that can not only calculate CaCo values, but also standardize forest canopy visual estimates.

Background: In forests subject to stand-replacing disturbances, conventional models of succession typically overlook early-seral stages as a simple re-organization/establishment period. These models treat structural development in essentially 'relay floristic' terms, with structural complexity (three-dimensional heterogeneity) developing primarily in old-growth stages, only after a closedcanopy 'self-thinning' phase and subsequent canopy gap formation. However, is it possible that early-successional forests can sometimes exhibit spatial complexity similar to that in old-growth forests — i.e. akin to an 'initial floristic' model of structural development? Hypothesis: Based on empirical observations, we present a hypothesis regarding an important alternative pathway in which protracted or sparse forest establishment and interspecific competition thin out tree densities early on — thereby precluding overstorey canopy closure or a traditionally defined self-thinning phase. Although historically viewed as an impediment to stand development, we suggest this process may actually advance certain forms of structural complexity. These young stands can exhibit qualities typically attributed only to old forests, including: (1) canopy gaps associated with clumped and widely spaced tree stems; (2) vertically heterogeneous canopies including under- and midstories, albeit lower stature; (3) co-existence of shade-tolerant and intolerant species; and (4) abundant dead wood. Moreover, some of these qualities may persist through succession, meaning that a significant portion of eventual old-growth spatial pattern may already be determined in this early stage. Implications: The relative frequency of this open-canopy pathway, and the degree to which precocious complexity supports functional complexity analogous to that of old forests, are largely unknown due to the paucity of naturally regenerating forests in many regions. Nevertheless, recognition of this potential is important for the understanding and management of early-successional forests.

This study presents a novel approach in the application of Unmanned Aerial Vehicle (UAV) imaging for the conjoint assessment of the snow depth and winter leaf area index (LAI), a structural property of vegetation, affecting the snow accumulation and snowmelt. The snow depth estimation, based on a multi-temporal set of high-resolution digital surface models (DSMs) of snow-free and of snow-covered conditions, taken in a partially healthy to insect-induced Norway spruce forest and meadow coverage area within the Šumava National Park (Šumava NP) in the Czech Republic, was assessed over a winter season. The UAV-derived DSMs featured a resolution of 0.73–1.98 cm/pix. By subtracting the DSMs, the snow depth was determined and compared with manual snow probes taken at ground control point (GCP) positions, the root mean square error (RMSE) ranged between 0.08 m and 0.15 m. A comparative analysis of UAV-based snow depth with a denser network of arranged manual snow depth measurements yielded an RMSE between 0.16 m and 0.32 m. LAI assessment, crucial for correct interpretation of the snow depth distribution in forested areas, was based on downward-looking UAV images taken in the forest regime. To identify the canopy characteristics from downward-looking UAV images, the snow background was used instead of the sky fraction. Two conventional methods for the effective winter LAI retrieval, the LAI-2200 plant canopy analyzer, and digital hemispherical photography (DHP) were used as a reference. Apparent was the effect of canopy density and ground properties on the accuracy of DSMs assessment based on UAV imaging when compared to the field survey. The results of UAV-based LAI values provided estimates were comparable to values derived from the LAI-2200 plant canopy analyzer and DHP. Comparison with the conventional survey indicated that spring snow depth was overestimated, and spring LAI was underestimated by using UAV photogrammetry method. Since the snow depth and the LAI parameters are essential for snowpack studies, this combined method here will be of great value in the future to simplify snow depth and LAI assessment of snow dynamics.

Cocoa-based agroforests are promoted to replace monocultures for the provision of ecosystem services. However, shade tree pruning, an important tool to sustain cocoa yields, is not commonly implemented. This study investigates the effect of pruning on both agronomic and economic performance. In Bolivia, four famers’ sites were divided in half, and shade trees pruned in one of the two plots. Pruning resulted in a significant increase in cocoa yield, from an average of 430 to 710 kg ha−1 by boosting flowering and pod production, but not reducing the proportion of damaged pods, and of those lost to cherelle wilt. Additionally, scenario calculations using international and organic premium cocoa prices were conducted to evaluate the economic feasibility of pruning. The minimum, mean and maximum yield of 22 local cocoa-based agroforestry farms were used as reference for 25, 50 and 75% yield increase scenarios. Offsetting the pruning costs highly depended on the initial yield levels. Using the minimum yield, all scenarios led to a lower net income compared with no pruning. For the mean yield level, the net income was equal to that obtained without pruning when the yield increase was above 51%. At the maximum yield level, all increase scenarios resulted in a higher net income. Our results prove the importance of pruning agroforestry trees to increase cocoa yields. However, with current farm-gate prices for cocoa, farmers alone cannot cover the extra management costs. The cocoa sector should discuss different strategies to support pruning for a broader adoption of agroforests.

Canopy closure, which is the ratio of the vertical projection area of the crowns to the area of forest land, can indicate the growth and tending situation of a forest and is of great significance for forest management planning. In this study, a geometric-optical model (GOST model) was used to simulate the canopy gap fraction of a forest. Then, a canopy closure estimation method using the gap fraction was discussed. In this study, three typical planted forest farms (the Mengjiagang (MJG), Gaofeng (GF), and Wangyedian (WYD) forest farms) containing the most commonly planted tree species in the north and south regions of China were selected, and field measurements were executed. The results show that the gap fraction (Pvg-c) had a higher correlation with the average projected area of the tree crowns, and the relationship was an exponential function, with R2 and RMSE values of 0.5619 and 0.0723, respectively. Finally, the applicability and accuracy of this method were evaluated using line transects, and a fisheye camera measured the canopy closure. The accuracy of the canopy closure estimated by the Pvg-c was 86.69%. This research can provide a reference for canopy closure estimation using a geometric-optical model.

Forest canopy closure (FCC) is an important biological parameter to evaluate forest resources and biodiversity, and the use of multi-source remote sensing synergy to achieve high-accuracy estimate regional FCC at low cost is a current research hotspot. In this study, Shangri-La City, a mountainous area in southwest China, was considered as the research area. The satellite-borne LiDAR ICESat-2/ATLAS data were used as the main information source. Combined with 54 measured plot data, the improved machine learning model of the Bayesian optimization (BO) algorithm was used to obtain the FCC in the footprint-scale ATLAS footprint. Then, the multi-source remote sensing image Sentinel-1/2 and terrain factors were combined to perform regional-scale FCC remote sensing estimation based on the geographically weighted regression (GWR) model. The research results showed that (1) among the 50 extracted ATLAS LiDAR feature indices, the best footprint-scale modeling factors are Landsat_perc, h_dif_canopy, asr, h_min_canopy, toc_roughness, and n_touc_photons after random forest (RF) feature variable optimization; (2) among the BO-RFR, BO-KNN, and BO-GBRT models developed at the footprint scale, the FCC results estimated by the BO-GBRT model were the best ( R 2 = 0.65, RMSE = 0.10, RS = 0.079, and P = 79.2%), which was used as the FCC estimation model for 74,808 footprints in the study area; (3) taking the FCC value of ATLAS footprint scale in forest land as the training sample data of the regional-scale GWR model, the model accuracy was R 2 = 0.70, RMSE = 0.06, and P = 88.27%; and (4) the R ² between the FCC estimates from regional-scale remote sensing and the measured values is 0.70, with a correlation coefficient of 0.784, indicating strong agreement. Additionally, the average FCC is 0.50, predominantly distributed between 0.3 and 0.6, comprising 68.43%. These findings highlight the advantages of mountain FCC estimation using ICESat-2/ATLAS high-density, high-precision footprints and the fact that small-sample estimation results at the footprint scale can serve as training data for the regional-scale GWR model, offering a reference for low-cost, high-precision FCC estimation from footprint scale to regional scale.

Mangrove ecosystem responses to tropical cyclones have been well-documented over the last half century. Variability in tree mortality, aboveground biomass accumulation, canopy closure, and subsequent recovery has been explained by species, size, and geomorphology. This study gauges the initial response and short-term recovery of Puerto Rico’s mangroves following the 2017 hurricane season. Survival probability of tagged trees decreased with time, and the mean mortality across all sites was 22% after eleven months. Mean canopy closure loss was 51% one month after the hurricanes, and closure recovery rates decreased with time following the storms. Aboveground biomass accumulation decreased by 3.5 kg yr⁻¹ per tree, corresponding to a reduction of 8.6 Mg ha⁻¹ yr⁻¹ at the stand level. Eleven months later, the mangroves recovered to 72% canopy closure and to nearly 60% of their pre-storm growth rates. Species, size, and geomorphology were found to play a role, while an influence of surrounding land cover and urbanization could not be detected. Larger trees suffered 25% more mortality than smaller size classes, and Laguncularia racemosa suffered 11% less mortality than other species. Forests in tidally restricted canals experienced more canopy loss but faster recovery than open embayment systems. Canopy closure for some forests is not forecasted to return to pre-storm levels in the next 20 years, which when combined with changes in hurricane frequencies may or may not be sooner than the next extreme hurricane disturbance. These findings suggest that size, species, and geomorphology are important in mangrove resilience to tropical storms and that urbanization does not play a role. Managing mangrove ecosystems for optimal shoreline protection will depend upon knowing which forests are at greatest risk in a future of changing tropical cyclone strength and frequency.

Light is a key resource for plant growth and is of particular importance in forest ecosystems, because of the strong vertical structure leading to successive light interception from canopy to forest floor. Tree species differ in the quantity and heterogeneity of light they transmit. We expect decreases in both the quantity and spatial heterogeneity of light transmittance in mixed stands relative to monocultures, due to complementarity effects and niche filling. We tested the degree to which tree species identity and diversity affected, via differences in tree and shrub cover, the spatiotemporal variation in light availability before, during, and after leaf expansion. Plots with different combinations of three tree species with contrasting light transmittance were selected to obtain a diversity gradient from monocultures to three species mixtures. Light transmittance to the forest floor was measured with hemispherical photography. Increased tree diversity led to increased canopy packing and decreased spatial light heterogeneity at the forest floor in all of the time periods. During leaf expansion, light transmittance did differ between the different tree species and timing of leaf expansion might thus be an important source of variation in light regimes for understory plant species. Although light transmittance at the canopy level after leaf expansion was not measured directly, it most likely differed between tree species and decreased in mixtures due to canopy packing. A complementary shrub layer led, however, to similar light levels at the forest floor in all species combinations in our plots. Synthesis. We find that a complementary shrub layer exploits the higher light availability in particular tree species combinations. Resources at the forest floor are thus ultimately determined by the combined effect of the tree and shrub layer. Mixing species led to less heterogeneity in the amount of light, reducing abiotic niche variability. While there is clear evidence for increased canopy packing in more diverse forests, we showed that a complementary shrub layer exploits the higher light availability in particular tree species combinations, leading to similar light levels at the forest floor in all species combinations. Resources at the forest floor are thus ultimately determined by the combined effect of the tree and shrub layer. Mixing species led to less heterogeneity in the amount of light, reducing abiotic niche variability.

Canopy closure is a critical indicator reflecting forest structure, biodiversity, and ecological balance. This study proposes an estimation method integrating U-Net segmentation with machine learning, significantly improving accuracy through multi-source remote sensing data and feature selection. Covering eight U.S. continental states, the study utilized 13,000 stratified samples equally split for model training and validation. Four states were used to train models based on XGBoost, random forest (RF), LightGBM, and support vector machine (SVM), while the remaining four states served for validation. The results indicate that (1) U-Net effectively extracted tree crowns from aerial imagery to construct the sample dataset; (2) among the tested algorithms, XGBoost achieved the highest accuracy of 0.88 when incorporating Sentinel-1, Sentinel-2, vegetation indices, and land cover features, outperforming models using only Sentinel-2 data by 25.7%; and (3) XGBoost-estimated tree canopy cover (Model TCC) showed finer spatial details than the National Land Cover Database Tree Canopy Cover (NLCD TCC), with R2 against the true tree canopy closure from U-Net (True TCC) up to 49.1% higher. This approach offers a cost-effective solution for regional-scale canopy monitoring.