Influence of climatic variations and competitive interactions on the productivity of mountain forests in Italy

Garfi, VittorioTognetti, RobertoFabbrocino, Giovanni1handle UniMol: http://hdl.handle.net/11695/97766openTree growth is influenced by multiple factors including, climate and competition processes. Climate change has a strong impact on growth of trees and can cause negative impacts on forests, especially in the Mediterranean basin. However, tree growth can also be influenced by competitive interactions, through the use and absorption of resources within tree communities. To quantify the level of competition between trees, competition indices are used, which are normally computed over small areas. Predicting competitive interactions over larger areas can be very important and light detection and ranging (lidar) data, could be the suitable tool. Based on these considerations, the main objective of the thesis was to identify and study the influence of climatic variations and competitive interactions on the growth of three important forest species, European beech (Fagus sylvatica L.), Norway spruce (Picea abies L.) and silver fir (Abies alba Mill.). The work is structured into three chapters, in which the first analyzes the influence of climate and extreme events on the radial growth of beech and silver fir in mixed and pure plots along a latitudinal gradient in Italy. In the second chapter the competitive interactions in mixed and pure populations of European beech and silver fir, located at the limits of their distribution range (southern Italy) are analyzed. In the third chapter, instead, was to estimate the competition dynamics for individual trees of Norway spruce and silver fir, located in the municipality of Lavarone (Trentino), and to identify the relationship between competitive interactions and tree aboveground biomass. Overall, results highlighted the response of trees under to climate and competition processes in mountain forests in Italy. In particular, the results of the first work showed a different response only at the regional level for the maximum temperatures. In Trentino the temperatures in winter, for silver fir, and summer, for both species, had a lesser negative impact on radial growth of trees compared to southern sites. Despite this, the results obtained from the correlations (radial growthdrought indices) and from principal component analysis have shown that no plot was sensitive to summer drought. Results are important to implement operational techniques that increase species adaptation to climate change. In the second work showed that the basal area increment, under the negative influence of high competition levels and slope terrains, varied between stands. In this sense, higher competitive interactions have been observed in Molise than in Calabria. Finally, in the third work showed that lidar metrics could be used to predict the competition indices of individual trees. In addition, biomass was observed to decrease as competition increased. The results of the three works showed that for the choice of sustainable forestry options it is necessary to consider the conditions of the site where these species are found and the structure of the forest stands, in terms of density and arrangement of the trees. Furthermore, it has been found that the use of remote sensing techniques (e.g. lidar) can be very useful in the forestry field, since they can provide information on larger areas.embargoed_20211115Versace, S

Puude konkurentsi- ja struktuuriindeksite analüüs arukase (Betula pendula Roth) puistute modelleerimise eesmärgil

A Thesis for applying for the degree of Doctor of Philosophy in ForestryThe thesis is designed to establish and assess different approaches of competition measurement, incorporated with various competitor selection methods, focusing on silver birch in Estonia. The effect of competition and some other tree and stand variables on the growth and the mortality of trees are explored. Moreover, stands are characterized employing several structural indices calculated for sample plots of different shapes and sizes. The findings indicate that the measures including the trees spatial information, have superiority over the measures ignoring the trees locations within a forest stand, when studying the dynamics of stands and their effects on the structure and functioning of forest ecosystems. Also, plots with different shapes showed almost equal estimation accuracy, thus circular plots are preferred due to their smaller ratio of perimeter to surface. Besides, the optimum plot size depends on the stand structure, with an attempt to keep sample plots as small as possible to reduce the assessment efforts and costs.Doktoritöö eesmärgiks on uurida erinevaid meetodeid puudevahelise konkurentsi hindamiseks ja konkurentpuude kindlakstegemiseks eelkõige Eestis kogutud arukase puistute andmetele tuginedes. Põhjalikumalt uuriti puudevahelise konkurentsi ning puude ja puistu takseertunnuste seoseid puude kasvu ja suremusega. Lisaks kirjeldati puistute seisundit mitmete struktuuriindeksite abil erineva suuruse ja kujuga proovialasid kasutades. Töö tulemused näitavad, et puistute dünaamika uurimisel on puude ruumilist paiknemist arvestavatel tunnustel eeliseid võrreldes tunnustega, mis ei arvesta puude ruumilist paiknemist. Erineva kujuga proovialad andsid takseertunnuste hinnangu küll sarnase täpsusega, kuid ringikujulisi proovialasid tuleks eelistada nende ümbermõõdu-pindala väiksema suhtarvu tõttu. Optimaalne prooviala suurus sõltub puistu struktuurist, kuid proovialad tuleks siiski valida võimalikult väiksena, et vähendada mõõtmistele kuluvat aega ja vahendeid.Publication of this dissertation is supported by the Estonian University of Life Sciences and by the Doctoral School of Earth Sciences and Ecology created under the auspices of the European Social Fund

Exploring the Potential to Improve the Estimation of Boreal Tree Structural Attributes with Simple Height- and Distance-Based Competition Index

In many cases, the traditional ground-based estimates of competition between trees are not directly applicable with modern aerial inventories, due to incompatible measurements. Moreover, many former studies of competition consider extreme stand densities, hence the effect of competition under the density range in managed stands remains less explored. Here we explored the utility of a simple tree height- and distance-based competition index that provides compatibility with data produced by modern inventory methods. The index was used for the prediction of structural tree attributes in three boreal tree species growing in low to moderate densities within mixed stands. In silver birch, allometric models predicting tree diameter, crown height, and branch length all showed improvement when the effect of between-tree competition was included. A similar but non-significant trend was also present in a proxy for branch biomass. In Siberian larch, only the prediction of branch length was affected. In Scots pine, there was no improvement. The results suggest that quantification of competitive interactions based on individual tree heights and locations alone has potential to improve the prediction of tree attributes, although the outcomes can be species-specific.Peer reviewe

Climate Change and Air Pollution Effects on Forest Ecosystems

Both climate change and air pollution have large negative impacts on physiological processes and functions at the individual tree level and on whole forest ecosystems. The objective of climate change and air quality monitoring is to make decisions, based on scientific knowledge, regarding how to best manage and improve the current state of the environment. Our ability to take urgent measures to combat climate change and its impact on forest ecosystems and conserve forest biodiversity depends upon our knowledge of the latest scientific results on the status of forest ecosystems. Unfortunately, there are a lot of gaps in our knowledge of the detection and monitoring of their effects on forest ecosystems. This book presents relevant results from scientific research in the fields of climate change, air pollution, forest conservation, protection and monitoring that can contribute to a better science–policy interaction and to the elaboration of specific strategies, in accordance with the areas of forest sciences from IUFRO RG 8.04.00 - Impacts of air pollution and climate change on forest ecosystems

Mapping a European spruce bark beetle outbreak using sentinel-2 remote sensing data

Insect outbreaks affect forests, causing the deaths of trees and high economic loss. In this study, we explored the detection of European spruce bark beetle (Ips typographus, L.) outbreaks at the individual tree crown level using multispectral satellite images. Moreover, we explored the possibility of tracking the progression of the outbreak over time using multitemporal data. Sentinel-2 data acquired during the summer of 2020 over a bark beetle–infested area in the Italian Alps were used for the mapping and tracking over time, while airborne lidar data were used to automatically detect the individual tree crowns and to classify tree species. Mapping and tracking of the outbreak were carried out using a support vector machine classifier with input vegetation indices extracted from the multispectral data. The results showed that it was possible to detect two stages of the outbreak (i.e., early, and late) with an overall accuracy of 83.4%. Moreover, we showed how it is technically possible to track the evolution of the outbreak in an almost bi-weekly period at the level of the individual tree crowns. The outcomes of this paper are useful from both a management and ecological perspective: it allows forest managers to map a bark beetle outbreak at different stages with a high spatial accuracy, and the maps describing the evolution of the outbreak could be used in further studies related to the behavior of bark beetle

Detection of heartwood rot in Norway spruce trees with lidar and multi-temporal satellite data

Norway spruce pathogenic fungi causing root, butt and stem rot represent a substantial problem for the forest sector in many countries. Early detection of rot presence is important for efficient management of the forest resources but due to its nature, which does not generate evident exterior signs, it is very difficult to detect without invasive measurements. Remote sensing has been widely used to monitor forest health status in relation to many pathogens and infestations. In particular, multi-temporal remotely sensed data have shown to be useful in detecting degenerative diseases. In this study, we explored the possibility of using multi-temporal and multi-spectral satellite data to detect rot presence in Norway spruce trees in Norway. Images with four bands were acquired by the Dove satellite constellation with a spatial resolution of 3 m, ranging over three years from June 2017 to September 2019. Field data were collected in 2019–2020 by a harvester during the logging: 16163 trees were recorded, classified in terms of species and presence of rot at the stump and automatically geo-located. The analysis was carried out at individual tree crown (ITC) level, and ITCs were delineated using lidar data. ITCs were classified as healthy, infested and other species using a weighted Support Vector Machine. The results showed an underestimation of the rot presence (balanced accuracy of 56.3%, producer’s accuracies of 64.3 and 48.4% and user’s accuracies of 81.0% and 32.7% respectively for healthy and rot ITCs). The method can be used to provide a tentative map of the rot presence to guide more detailed assessments in field and harvesting activitie

Climate-Smart Forestry in Mountain Regions

This open access book offers a cross-sectoral reference for both managers and scientists interested in climate-smart forestry, focusing on mountain regions. It provides a comprehensive analysis on forest issues, facilitating the implementation of climate objectives. This book includes structured summaries of each chapter. Funded by the EU’s Horizon 2020 programme, CLIMO has brought together scientists and experts in continental and regional focus assessments through a cross-sectoral approach, facilitating the implementation of climate objectives. CLIMO has provided scientific analysis on issues including criteria and indicators, growth dynamics, management prescriptions, long-term perspectives, monitoring technologies, economic impacts, and governance tools

Climate-Smart Forestry in Mountain Regions

This open access book offers a cross-sectoral reference for both managers and scientists interested in climate-smart forestry, focusing on mountain regions. It provides a comprehensive analysis on forest issues, facilitating the implementation of climate objectives. This book includes structured summaries of each chapter. Funded by the EU’s Horizon 2020 programme, CLIMO has brought together scientists and experts in continental and regional focus assessments through a cross-sectoral approach, facilitating the implementation of climate objectives. CLIMO has provided scientific analysis on issues including criteria and indicators, growth dynamics, management prescriptions, long-term perspectives, monitoring technologies, economic impacts, and governance tools

The Assessment of habitat condition and consevation status of lowland British woodlands using earth observation techniques.

The successful implementation of habitat preservation and management demands regular and spatially explicit monitoring of conservation status at a range of scales based on indicators. Woodland condition can be described in terms of compositional and structural attributes (e.g. overstorey, understorey, ground flora), evidence of natural turnover (e.g. deadwood and tree regeneration), andanthropogenic influences (e.g.disturbance, damage). Woodland condition assessments are currently conducted via fieldwork, which is hampered by cost, spatial coverage, objectiveness and repeatability.This projectevaluates the ability of airborne remote sensing (RS) techniques to assess woodland condition, utilising a sensor-fusion approach to survey a foreststudy site and develop condition indicators. Here condition is based on measures of structural and compositional diversity in the woodland vertical profile, with consideration of the presence of native species, deadwood, and tree regeneration. A 22 km2 study area was established in the New Forest, Hampshire, UK, which contained a variety of forest types, including managed plantation, semi-ancient coniferous and deciduous woodland. Fieldwork was conducted in 41 field plots located across this range of forest types, each with varying properties. The field plots were 30x30m in size and recorded a total of 39 forest metrics relating to individual elements of condition as identified in the literature. Airborne hyperspectral data (visible and near-infrared) and small footprint LiDAR capturing both discrete-return (DR) and full-waveform (FW) data were acquired simultaneously, under both leaf-on and leaf-off conditions in 2010. For the combined leaf-on and leaf-off datasets a total of 154 metrics were extracted from the hyperspectral data, 187 metrics from the DR LiDAR and 252 metrics from the FW LiDAR. This comprised both area-based and individual tree crown metrics. These metrics were entered into two statistical approaches, ordinary least squares and Akaike information criterion regression, in order to estimate each of the 39 field plot-level forest variables. These estimated variables were then used as inputs to six forest condition assessment approaches identified in the literature. In total, 35 of the 39 field plot-level forest variables could be estimated with a validated NRMSE value below 0.4 using RS data (23 of these models had NRMSE values below 0.3). Over half of these models involved the use of FW LiDAR data on its own or combined with hyperspectral data, demonstrating this to be single most able dataset. Due to the synoptic coverage of the RS data, each of these field plot variables could be estimated and mapped continuously over the entire study site at the 30x30m resolution (i.e. field plot-level scale). The RS estimated field variables were then used as inputs to six forest condition assessment approaches identified in the literature.Three of the derived condition indices were successful based on correspondence with field validation data and woodlandcompartment boundaries. The three successful condition assessment methods were driven primarily by tree size and tree size variation. The best technique for assessing woodland condition was a score-based method which combined seventeen inputs which relate to tree species composition, tree size and variability, deadwood, and understory components; all of whichwere shown to be derived successfully from the appropriate combination of airborne hyperspectral and LiDAR datasets. The approach demonstrated in this project therefore shows that conventional methods of assessing forest condition can be applied with RS derived inputs for woodland assessment purposes over landscape-scale areas