Mixed forests are particularly interesting for forest structure and diversity analyses, as higher complexity and diversity can be expected in these forests compared to pure ones. Integrating different approaches in the analyses of structure and diversity in these forests can provide complementary information on non-spatial, spatial and functional diversity patterns. The study aimed at evaluating the spatio-temporal dynamics in forest structure and diversity in a semi-natural mixed oak-hornbeam floodplain forest. All standing trees were mapped and inventoried in 1995, 2005 and 2016 in three 1-ha mixed forest stands, with different soil moisture regime (xeric, mesic, moist conditions). Traditional, non-spatial structure and diversity measures were coupled with spatially-explicit and functional diversity measures. Results indicated that the three stands showed limited variation in stand structure and similar non-spatial diversity attributes, despite the different species composition. Only the extension to spatial and functional analyses was able to reveal more pronounced differences of diversity patterns, as higher complexity, species mingling, and functional tree complementarity was observed in the moister stand. These findings support use of spatially-explicit measurements in traditional inventory measurement protocols to allow more refined analysis of diversity patterns. On the other hand, functional diversity can be easily implemented in diversity analyses, as it requires species abundance information (which is traditionally collected in forest inventory) and species-specific tree traits which can be inferred from literature. © 2019 Elsevier Ltd

Accurate inventory and mapping of olive (Olea europaea L.) tree attributes represents a central issue to support the olive production system. With reference to the cultivation, there is a high heterogeneity and complexity in the cultivation of olive trees, which is reflected in the large variability in olive grove surfaces. This poses some challenge in accurately estimating olive tree attributes via traditional inventory approaches, as commonly adopted in national forest inventory. From a methodological point of view, the complexity and heterogeneity of olive tree groves can be comparable to the problem of accurately estimating tree outside forests (TOF) attributes. In this study, we tested whether a plot sampling approach formerly developed for TOF is suitable for estimating olive tree attributes at large scale. We tested this approach in a case study where the census of the olive crop area and the number of olive groves was conducted from photo-interpretation of high resolution aerial orthoimagery, used as benchmark to test the effectiveness of the plot sampling approach. The main result of this study is that the plot sampling method can be applied for estimating olive tree attributes. Our obtained RSEs were below 20%, with a limited sampling effort of about 6% of the studied population; the obtained RSEs were below 6% when increasing sampling up to about 21% the studied population. Using robust statistical procedures among countries, should allow obtaining harmonized and comparable information, which can increase the knowledge of olive geographical distribution and structure at its relevant Mediterranean scale. © 2019, Italian Society of Remote Sensing. All rights reserved.

The value of wood for different timber assortments can vary by a factor of ten, optimization of stems assortment is hence a key element in the wood products supply chain, particularly for plantations. ‘Taper functions’ are commonly used in other countries to tackle this issue. In Italy, this approach has not yet entered operational use. These functions are developed based on measures of stem diameters taken at different distances from the base. Such measurements are commonly taken felling the tree and using a tape meter and the tree calliper, clearly assuming some approximations. This research assesses the advantages, in terms of assortments evaluation, that can be obtained if the diameters at different heights are extracted adequately processing Terrestrial Laser Scanning (TLS) output. TLS data have been collected, in a poplar plantation, on 36 trees distributed on three stands with different plantation densities in Padana Plane, Italy. The estimated profiles display a very high variability with an average of 1.8 cm of lateral compression. The results from this study demonstrate the potential and feasibility of estimating bole eccentricity by TLS, providing preliminary tools that will hopefully favour the diffusion of taper functions in operational environments. © 2019, Italian Society of Remote Sensing. All rights reserved.

The value of wood for different timber assortments can vary by a factor of ten. Optimization of stem assortments is, hence, a key element in the wood products supply chain, particularly for plantations. 'Taper functions' are commonly used in other countries to tackle this issue. In Italy, this approach has not yet entered operational use. These functions are developed based on measures of stem diameters taken at different distances from the base. Such measurements are commonly taken felling the tree and using a tape meter and tree caliper, clearly assuming some approximations. This research assesses the advantages, in terms of assortments evaluation, that can be obtained if the diameters at different heights are extracted adequately to process terrestrial laser scanning (TLS) output. TLS data have been collected, in a poplar plantation, on 36 trees distributed on three stands with different plantation densities in Padana Plane, Italy. The estimated profiles display high variability with an average of 1.6 cm of lateral compression. The results from this study demonstrate the potential and feasibility of estimating bole eccentricity by TLS, providing preliminary tools that will hopefully favor the diffusion of taper functions in operational environments. © 2019 by the authors.

Monitoring vegetation structure and functioning is critical for modelling terrestrial ecosystems and energy cycles. Leaf area index (LAI) is an important structural property of vegetation used in many land-surface, climate, and forest monitoring applications. Remote sensing provides a unique way to obtain estimates of leaf area index at spatially extensive areas. However, the analysis and extraction of quantitative information from remotely-sensed data require accurate cross-calibration with in situ forest measurements, which are generally spatially-and temporally-limited, thereby limiting the ability to compare the seasonal dynamic patterns between field and remotely-sensed time series. This is particularly relevant in temperate broadleaved forests, which are characterized by high level of complexity, which can complicate the retrieval of vegetation attributes from remotely-sensed data. In this study, we performed a long-term comparison of MODIS LAI products with continuous in situ leaf area index measurements collected monthly in temperate and Mediterranean forests from 2000 to 2016. Results indicated that LAI showed a good correlation between satellite and ground data for most of the stands, and the pattern in seasonal changes were highly overlapping between the time-series. We conclude that MODIS LAI data are suitable for phenological application and for up-scaling LAI from the stand level to larger scales. © 2019, Italian Society of Remote Sensing. All rights reserved.