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In European mountains most beech forest areas have been managed for timber production. This practice has reduced the availability of biomass for the whole forest-dwelling species assemblage and of deadwood for the saproxylic community. Despite most of Italy's beech stands having a long history of management, its effects on forest species remain poorly understood. To address this gap, we studied beetle abundance and diversity in five beech-dominated forests with increasing management intensity in central Italy's Apennines (Tuscany). We assessed if forests with similar management intensity exhibited comparable patterns in beetle diversity, abundance, and commonness versus rarity. Three forests were managed with even-aged shelterwood; one was managed with continuous cover forestry; and one was old-growth. We found 25 beetle families and 195 species across all sites with similar total abundance and richness. However, the representation of the most abundant families varied among sampling sites (ANOVA test: always significant for the total abundance of the most abundant families: F ≥ 2.77, d.f. = 4, p ≤ 0.038). The old-growth forest harbored more threatened species than managed sites. Saproxylic assemblages were similar between the recently cut site and the old-growth forest, and between shelterwood and continuous cover sites. While the similarity gradient among the whole species assemblages reflected geographical proximity, the similarity gradient among saproxylic assemblages reflected the successional proximity among forest management systems. Our research underscores the effects of management on beetle diversity, offering insights for sustainable forestry. [ABSTRACT FROM AUTHOR]

Remote sensing products are typically assessed using a single accuracy estimate for the entire map, despite significant variations in accuracy across different map areas or classes. Estimating per-pixel uncertainty is a major challenge for enhancing the usability and potential of remote sensing products. This paper introduces the dataDriven open access tool, a novel statistical design-based approach that specifically addresses this issue by estimating per-pixel uncertainty through a bootstrap resampling procedure. Leveraging Sentinel-2 remote sensing data as auxiliary information, the capabilities of the Google Earth Engine cloud computing platform, and the R programming language, dataDriven can be applied in any world region and variables of interest. In this study, the dataDriven tool was tested in the Rincine forest estate study area—eastern Tuscany, Italy—focusing on volume density as the variable of interest. The average volume density was 0.042, corresponding to 420 m3 per hectare. The estimated pixel errors ranged between 93 m3 and 979 m3 per hectare and were 285 m3 per hectare on average. The ability to produce error estimates for each pixel in the map is a novel aspect in the context of the current advances in remote sensing and forest monitoring and assessment. It constitutes a significant support in forest management applications and also a powerful communication tool since it informs users about areas where map estimates are unreliable, at the same time highlighting the areas where the information provided via the map is more trustworthy. In light of this, the dataDriven tool aims to support researchers and practitioners in the spatially exhaustive use of remote sensing-derived products and map validation. [ABSTRACT FROM AUTHOR]

The quantification of tree-related microhabitats (TreMs) and multi-taxon biodiversity is pivotal to the implementation of forest conservation policies, which are crucial under the current climate change scenarios. We assessed the capacity of Airborne Laser Scanning (ALS) data to quantify biodiversity indices related to both forest beetle and bird communities and TreMs, calculating the species richness and types of saproxylic and epixylic TreMs using the Shannon index. As biodiversity predictors, 240 ALS-derived metrics were calculated: 214 were point-cloud based, 14 were pixel-level from the canopy height model, and 12 were RGB spectral statistics. We used the random forests algorithm to predict species richness and the Shannon diversity index, using the field plot measures as dependent variables and the ALS-derived metrics as predictors for each taxon and TreMs type. The final models were used to produce wall-to-wall maps of biodiversity indices. The Shannon index produced the best performance for each group considered, with a mean difference of −6.7%. Likewise, the highest R2 was for the Shannon index (0.17, against 0.14 for richness). Our results confirm the importance of ALS data in assessing forest biodiversity indicators that are relevant for monitoring forest habitats. The proposed method supports the quantification and monitoring of the measures needed to implement better forest stands and multi-taxon biodiversity conservation. [ABSTRACT FROM AUTHOR]

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%). [ABSTRACT FROM AUTHOR]

Assessing forest biodiversity, naturalness and old-growth status (B-N-OG) is crucial for supporting sustainable forest planning, yet comprehensive monitoring networks specifically designed for such purposes are lacking in many countries. National Forest Inventories (NFIs) are the official source of statistics on status and trends of forests. While initially designed for wood production assessment, NFI data may be pivotal for ecological forest monitoring, thanks to their robust sampling protocols—enabling statistical inference—and regular field campaigns that ensure continuous information updates. As a consequence, in this study we explore the potential of NFIs for estimating B-N-OG indexes, aiming to establish compatible, scientifically relevant, and cost-effective indicators using existing NFI data at a European level. Based on data from the 2005 Italian NFI collected from 6563 plots, 18 indicators were selected following previous experiences and then used to estimate B-N-OG aggregated indexes. Relationships between the 18 indicators and the three indexes were investigated, along with comparisons of their relationships relative to forest type categories, management types and protected versus non-protected areas. Results confirm that NFI data offer valuable insights into specific B-N-OG indexes, especially concerning forest structure and deadwood. Moreover, the indexes contribute to developing meaningful relationships across geographic regions, forest categories and types of management. However, limitations in NFI field protocols are evident, as they are not explicitly designed for certain indicators. The study suggests the potential for NFIs to evolve into accessible, harmonized European reference networks for B-N-OG assessment for better supporting sustainable forest management, planning and conservation decisions related to forest ecosystems. [ABSTRACT FROM AUTHOR]