Continuous monitoring of forest canopy structure and phenology is pivotal for the assessment of ecosystem responses to environmental variability and changes. The present study evaluated the use of repeat digital trail cameras as a low-cost, flexible, and accessible in situ monitoring solution for quantifying daily canopy attributes, including effective leaf area index (Le) and canopy cover. A trial camera monitoring network (CrowNet) was established encompassing 20 forest stands in Italy, under different management and environmental conditions, resulting in over 44,000 daily images collected over three years. We demonstrated that taking the mean daily canopy attribute allowed to obtain smooth time series from trail cameras, from which phenological transition dates can be inferred. Daily canopy attributes were validated against manual digital cover photography measurement. To further explore the applicability of this monitoring solution, we performed a comparison between daily Le time series derived from a subset of trail cameras located in beech forests and data collected by multitemporal UAV LiDAR. Results demonstrated the close agreement between the two methods across the entire phenological period (start and end of season). We also illustrated use of continuous trail camera estimates to calibrate a vegetation index (NDVI) to infer leaf area and canopy cover from optical multi-temporal UAV data. We further investigated use of trail camera to detect species-specific differences in tree phenology from time series acquired in a mixed oak-hornbeam forest. We found different canopy structure and phenological transition dates in three broadleaved species (oak, ash, hornbeam), supporting the effectiveness of trail cameras for species-oriented phenology monitoring. We conclude that trail cameras provide a reliable solution for daily canopy monitoring, offering a significant cost-effective and flexible alternative to traditional field methods and providing potential to calibrate, validate or integrate remotely-sensed information. However, camera failures during adverse weather, and the need for more efficient image data quality checking procedures, still represent open challenges. Future improvements, such as weatherproof housing and automated pre-processing screening procedures, are therefore recommended for making trail camera fully operational in ground canopy and phenology monitoring. © 2025 Elsevier B.V.

Leaf area index (LAI) is an important biophysical parameter describing vegetation. LAI is typically retrieved from optical remote sensing by empirical models relating LAI to vegetation indices, such as the Normalized Difference Vegetation Index (NDVI). As the relationship between LAI and NDVI is non-linear and crop type dependant, several specific empirical equations relating LAI to NDVI have been developed using field data. This study presented LAIr, an R package to derive LAI from NDVI data from the most comprehensive library of conversion equations. In the package, the range of functions differs on environmental factors, sensors, and vegetation types, allowing flexibility in choosing appropriate options based on specific application, scale of investigation and data availability. We illustrated the use of the package with a case study to compare a generic LAI product with specific NDVI-based LAI estimations. By leveraging empirical knowledge, LAIr enables accurate and context-specific estimation of LAI. The deployment of an open-source R package serves as a valuable tool for aiding researchers in selecting the most appropriate equations for conducting NDVI-to-LAI conversion. © 2024

Different satellite datasets, including multispectral Sentinel 2 and synthetic aperture radar Sentinel 1 and ALOS2, were tested to estimate the Leaf Area Index (LAI) in the Zagros forests, Ilam province, in Iran. Field data were collected in 61 sample plots by hemispherical photographs, to train and validate the LAI estimation models. Different satellite data combinations were used as input in regression models built with the following algorithms: Multiple Linear Regression, Random Forests, and Partial Least Square Regression. The results indicate that Leaf Area Index can be best estimated using integrated ALOS2 and Sentinel 2 data; these inputs generated the model with higher accuracy (R² = 0.84). The combination of a single band and a vegetation index from Sentinel 2 also led to successful results (R² = 0.81). Lower accuracy was obtained when using only ALOS 2 (R² = 0.72), but this dataset is helpful where cloud coverage affects optical data. Sentinel 1 data was not useful for LAI predic-tion. The optimal model was based on the traditional Multiple Linear Regression algorithm, using a preliminary input selection step to exclude multi-collinearity effects. To avoid this step, the use of Partial Least Square Regression may be an alternative, as this algorithm was able to produce estimates similar to those obtained with the best model. © SISEF.

The dataset reports data collected in 38 square (50 x 50m) 0.25 ha plots representative of poplar plantations in Lombardy Region (Northern Italy), which were used to calibrate optical information derived from unmanned aerial vehicle (UAV) and satellite (Sentinel-2) sensors. In each plot, the diameter at breast height was measured using a caliper; height, stem and crown volume of each tree were then derived from diameter using allometric equations developed in an independent study. Additional canopy attributes (foliage and crown cover, crown porosity, leaf area index) were derived in each plot from 12-20 optical images collected using digital cover photography (DCP). The collected data allows characterizing the assessment of structure of these plantations, along with their variation over the rotation time. Canopy and crown data also enable the evaluation of optimal rotation and tree spacing, as well as the relationship between stand and canopy structure. The raw datasets consist of 2,591 records (trees) associated with inventory measurements and 616 records (images) associated with optical canopy measurements. An R code was also provided to calculate plot-level attributes from raw data. Dataset and associated metadata are freely available at http://dx.doi.org/10.17632/ycr7w5pvkt.1. © 2021 Centro di Ricerca per la Selvicoltura, Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria. All rights reserved.

Key message: A method was proposed to remove the subjectivity of gap size analyses approaches implemented by default in cover photography. The method yielded robust and replicable measurements of forest canopy attributes. Abstract: Digital cover photography (DCP) is an increasingly popular method to estimate canopy attributes of forest canopies. Compared with other canopy photographic methods, DCP is fast, simple, and less sensitive to image acquisition and processing. However, the image processing steps used by default in DCP have a large substantial subjective component, particularly regarding the separation of canopy gaps into large gaps and small gaps. In this study, we proposed an objective procedure to analyse DCP based on the statistical distribution of gaps occurring in any image. The new method was tested in 11 deciduous forest stands in central Italy, with different tree composition, stand density, and structure, which is representative of the natural variation of these forest types. Results indicated that the new method removed the subjectivity of manual and semi-automated gap size classifications performed so far in cover photography. A comparison with direct LAI measurements demonstrated that the new method outperformed the previous approaches and increased the precision of LAI estimates. Results have important implications in forestry, because the simplicity of the method allowed objective, reliable, and highly reproducible estimates of canopy attributes, which are largely suitable in forest monitoring, where measures are routinely repeated. In addition, the use of a restricted field of view enables implementation of this photographic method in many devices, including smartphones, downward-looking cameras, and unmanned aerial vehicles. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

The "Bosco Fontana" natural reserve includes the last remaining mixed floodplain forest in northern Italy and one of the most endangered ecosystems in Europe. Its effective management is hindered by the complexity of interactions of mixed-tree species and the influence of environmental factors on understory plant diversity. In this study we analyzed the patterns of natural evolution in semi-natural floodplain forest stands at Bosco Fontana with the aim of better understanding its current natural processes and dynamics. Stand structure, taxonomic and functional diversity, species composition, and leaf area index (LAI) of overstory and understory layers were surveyed in permanent plots over two inventory years (1995, 2005). The influence of environmental factors on understory plant diversity was assessed using Ellenberg’s indices for light, soil moisture, soil nutrient and soil reaction. Results indicated that overstory species composition varies according to the soil moisture, with hornbeam prevailing in xeric sites and deciduous oak species in mesic sites. Xeric sites showed high functional dispersion in both drought and shade tolerant traits, while it was significantly lower in both overstory and understory in the moist site. Functional dispersion of drought tolerance in the overstory and understory layers was positively correlated, while species richness was negatively correlated between the two layers. Diversity in the understory was mainly correlated with soil conditions. Understory LAI was positively correlated with overstory LAI in xeric and mesic plots, while no correlations were found in the moist plot. Overall, our results suggest that site conditions (soil conditions and water availability) are the major drivers of understory and overstory dynamics in the study forest. Hence, local site conditions and the understory should be carefully considered in the management of mixed floodplain forests. © SISEF.