Accurate estimates of canopy structure like canopy cover (CC), Leaf Area Index (LAI), crown volume (Vcr), as well as tree and stand structure like stem volume (V_st) and basal area (G), are considered essential measures to manage poplar plantations effectively as they are correlated with the growth rate and the detection of possible stress. This research exploits the possibility of developing a precision forestry application using an unmanned aerial vehicle (UAV), terrestrial digital camera and traditional field measurements to monitor poplar plantation variables. We set up the procedure using explanatory variables from the Grey Level Co-occurrence Matrix textural metrics (Entropy, Variance, Dissimilarity and Contrast) calculated based on UAV multispectral imagery. Our results show that the GCLM texture derived by multispectral ortomosaic provides adequate explanatory variables to predict poplar plantation characteristics related to plants' canopy and stand structure. The evaluation of the models targeting the different poplar plantation variables (i.e. Vcr, G_ha, Vst_ha, CC and LAI) with the four GLCM explanatory variables (i.e. Entropy, Variance, Dissimilarity and Contrast) consistently higher or equal resulted to R² ≥0.86. © 2024, Editura Silvica. All rights reserved.

Key message: coveR is an R package for estimating canopy attributes from digital cover photography (DCP) images. The simplicity of the method and the open-accessibility of coveR can effectively extend the accessibility and applicability of DCP to a wider audience. Abstract: Digital cover photography (DCP) is an increasingly popular tool for estimating canopy cover and leaf area index (LAI). However, existing solutions to process canopy images are predominantly tailored for hemispherical photography, whereas open-access tools for DCP are lacking. We developed an R package (coveR) to support the whole processing of DCP images in an automated, fast, and reproducible way. The package functions, which are designed for step-by-step single-image analysis, can be performed sequentially in a pipeline while ensuring quality-checking of each processing step. A wrapper function ‘coveR()’ is also created to perform all the image processing workflow in a single function. A case study is presented to demonstrate the reliability of canopy attributes derived from coveR in pure beech (Fagus sylvatica L.) stands with variable canopy density and structure. Estimates of gap fraction and effective LAI from DCP were validated against reference measurements obtained from terrestrial laser scanning. By providing a simple, transparent, and flexible image processing procedure, coveR supported the use of DCP for routine measurements and monitoring of forest canopy attributes. This, combined with the possibility to implement DCP in many devices, including smartphones, micro-cameras, and remote trail cameras, can greatly expand the accessibility of the method also by non-experts. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Accurate estimates of crown cover (CC) are central for a wide range of forestry studies. As direct measurements do not exist to retrieve this variable in the field, CC is conventionally determined from optical measurements as the complement of gap fraction close to the zenith. As an alternative to passive optical measurements, active sensors like terrestrial Light Detection And Ranging (LiDAR) allows for characterizing the 3D canopy structure with unprecedented detail. We evaluated the reliability of terrestrial LiDAR (TLS) to estimate CC using a voxel-based approach. Specifically, we tested how different voxel sizes (ranging from 5-20 cm) and voxel densities (1-9 points/dm³) influenced the retrieval of CC. Results were compared against benchmark values obtained from digital cover photography (DCP). The trial was performed in hybrid poplar plantations in Northern Italy. Results indicate that TLS can be used for obtaining accurate estimates of CC, but the choice of voxel size and point density is critical for achieving such accuracy. In hybrid poplars, the best performance was obtained using voxel size of 10 cm and point density of 8 points/dm³. The combined ability of measuring and mapping CC also holds great potential to use TLS for calibrating and upscaling results using coarser-scale remotely sensed products. © 2021 Centro di Ricerca per la Selvicoltura, Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria. All rights reserved.

Accurate estimates of canopy cover (CC) are central for a wide range of forestry studies. As direct measurements are impractical, indirect optical methods have often been used to estimate CC from the complement of gap fraction measurements obtained with restricted-view sensors. In this short note we evaluated the influence of the image pixel resolution (ground sampling distance; GSD) on CC estimation in poplar plantations obtained from field (cover photography; GSD < 1 cm), unmanned aerial (UAV; GSD <10 cm) and satellite (Sentinel-2; GSD = 10 m) imagery. The trial was conducted in poplar tree plantations in Northern Italy, with varying age and canopy cover. Results indicated that the coarser resolution available from satellite data is suitable to obtain estimates of canopy cover, as compared with field measurements obtained from cover photography; therefore, S2 is recommended for larger scale monitoring and routine assessment of canopy cover in poplar plantations. The higher resolution of UAV compared with Sentinel-2 allows finer assessment of canopy structure, which could also be used for calibrating metrics obtained from coarser-scale remote sensing products, avoiding the need of ground measurements. © 2021 Centro di Ricerca per la Selvicoltura, Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria. All rights reserved.