Annals of Forest Research

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.

Soil shear strength (SSS) is an important soil attribute that is influenced by vegetation. If aboveground biomass estimates can be used to predict soil shear strength, it would greatly enhance our ability to estimate SSS across large areas. Using data collected from 24 plots in Alaska, we analyzed the relationship between soil shear strength and ground-collected vegetation attributes and remotely sensed (RS) variables. We constructed both univariate and multivariate models to assess the predictive capabilities of the vegetation and RS variables. Total trees and total conifers were significant predictors of SSS, with a negative relationship existing between total trees/total conifers and SSS. Graminoid cover (%) was positively correlated with soil shear strength and was also a significant predictor of SSS. Of the RS variables, the bands B1 (0.443 μm), B2 (0.490 μm), and B3 (0.560 μm) from the Sentinel 2 satellite system were all significant predictors of SSS. A multivariate model improved model fit over the simple univariate models, with an R2 = 0.46. We have both demonstrated a connection between SSS and aboveground vegetation attributes for areas within interior Alaska and that it is possible to link SSS to RS variables using a multivariate model.

In recent years, the Asian chestnut gall wasp (ACGW) Dryocosmus kuriphilus has been reported to have a high incidence in Italy and other Mediterranean basin countries. In 2021-2022, a study was undertaken in the Basilicata Region (Southern Italy) to investigate the relationship between the galls produced by ACGW on sweet chestnut (Castanea sativa Mill.) and fungal pathogens. In particular, the fungal diversity from green and necrotic galls collected from two important sweet chestnut sites (Melfi and Rionero in Vulture) was investigated. Nineteen fungal taxa were identified based on their morphological and molecular traits. In both localities, the most frequent species isolated from green and necrotic galls were Gnomoniopsis castaneae, Colletotrichum acutatum, and Pestalotiopsis sp. It is essential to understand the role played by the galls as an inoculum source for sweet chestnut fungal pathogens, particularly for G. castaneae, an emerging pathogen of which biology is still poorly understood. Findings from the present study stressed that the complex relationship between host-insect-microbial community needs to be elucidated to be able to control the pathogenic fungi and consequently maintain sweet chestnut trees' health as they play a key role in the local agriculture (horticulture, forestry) and subsidiary economy

The research aimed to determine the actual distribution of visits in suburban forests in the temperate climate zone, using the Rzeszów metropolitan area as an example. The study also examined whether there is a correlation between the number of visitors to the forests and weather conditions: average daily air temperature, total daily precipitation, and the maximum sustained wind speed within a day. The distribution of visits was determined based on a 365-day monitoring of recreational traffic intensity using a sensor in the form of a pyroelectric detector. Weather data for each day of observation were obtained from a meteorological station. An average of 51 daily visitors was recorded (29 on weekdays and 101 on weekends and holidays). Most people visited the forest during the vacations, in August (14.7%) and July (14.1%), and least in winter: in February (2.7%) and December (3.4%). It was observed that the number of visits to the forest increased with the rise in average daily air temperature. In contrast, as the maximum sustained wind speed increased throughout the day, the number of visits decreased. There was no clear correlation between the number of visits and the total daily rainfall, except for weekends and holidays (number of visits decreased with the increase in rainfall). The number of visitors to suburban forests was more influenced by public holidays than weather conditions. Many forest visitors were significantly more frequently observed on holidays and weekends than weekdays. More than half of all visits occurred on weekends. Forests were most frequently visited on Sundays (38.2%). Suburban forests were visited from 5 AM to 10 PM, with shorter weekend hours (from 6 AM to 8 PM). The results obtained in the study can be valuable for managing recreational activities in suburban forests.

Terrestrial laser scanning (TLS) has emerged as a powerful tool for acquiring detailed three-dimensional information about tree species. This study focuses on the development of models for tree volume estimation using TLS data for even aged Fagus sylvatica L. stands located in the western part of the Southern Carpathians, Romania. Both parametric and non-parametric modeling approaches were explored, leveraging variables extracted from TLS point clouds such as diameter at breast height (DBH), height, crown radius, and other relevant crown and height parameters. Reference data were collected through high-precision field measurements across 76 circular Permanent Sample Areas (PSA) spanning 500 m2 each.  A multi-scan approach was implemented for TLS data collection, involving four scanning stations within each PSA. Concurrently, parametric (regression equations) and non-parametric (Random Forest - RF) models were applied, leveraging all TLS-derived variables to explore potential enhancements in volume estimation accuracy. Among the parametric models, the most effective performer was the one featuring solely DBH as an input variable. The RF non-parametric model yielded more accurate stem volume estimates (RMSE = 1.52 m3*0.1ha-1; RRMSE = 3.62%; MAE = 1.22m3*0.1ha-1) compared to the best-performing regression model (RMSE = 5.24 m3*0.1ha-1; RRMSE = 12.48%; MAE = 4.28 m3*0.1ha-1). Both types of models identified DBH as the most important predictive variable, while the RF model also included height and crown related parameters among the variables of importance. Results demonstrate the effectiveness of the non-parametric RF model in providing accurate and robust estimates of tree stem volume within even aged European beech stands. This highlights the significance of TLS data, increasingly employed in diverse forest inventory and management applications. Nevertheless, additional research and refinement of the proposed models are needed. This includes thorough validation across various forest ecosystems and continued efforts to enhance the accuracy of tree height determination from point cloud data.