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Pubblicazioni Scientifiche
Filtri di ricerca 15 risultati
Pubblicazioni per anno
Nondestructive tree stem and crown volume allometry in hybrid poplar plantations derived from terrestrial laser scanning
Chianucci
,
Francesco
,
Puletti
,
Nicola
,
Grotti
,
Mirko
,
Ferrara
,
Carlotta
,
Giorcelli
,
Achille
,
Coaloa
,
Domenico
,
Tattoni
,
Clara
Mostra abstract
Accurate and frequently updated tree volume estimates are required for poplar plantations, which are characterized by fast growth rate and short rotation. In this study, we tested the potential of terrestrial laser scanning (TLS) as a reliable method for developing nondestructive tree volume allometries in poplar plantations. The trial was conducted in Italy, where 4- to 10-year-old hybrid plantations were sampled to develop tree crown volume allometry in leaf-on conditions, tree stem volume, and height-diameter allometries in leaf-off conditions. We tested one-entry models based on diameter and two-entry models based on both diameter and height. Model performance was assessed by residual analysis. Results indicate that TLS can provide accurate models of tree stem and crown volume, with percentage of root-mean-square error of about 20 percent and 15 percent, respectively. The inclusion of height does not bring relevant improvement in the models, so that only diameter can be used to predict tree stem and crown volume. The TLS-measured stem volume estimates agreed with an available formula derived from harvesting. We concluded that TLS is a reliable method for developing nondestructive volume allometries in poplar plantations and holds great potential to enhance conventional tree inventory and monitoring. © The Author(s) 2020. Published by Oxford University Press on behalf of the Society of American Foresters. All rights reserved.
Plant functional traits are correlated with species persistence in the herb layer of old-growth beech forests
Campetella
,
Giandiego
,
Chelli
,
Stefano
,
Simonetti
,
Enrico
,
Damiani
,
Claudia
,
Bartha
,
Sándor
,
Wellstein
,
Camilla
,
Giorgini
,
Daniele
,
Puletti
,
Nicola
,
Mucina
,
Ladislav
,
Cervellini
,
Marco
,
Canullo
,
R.
beech
forest
genetics
plant leaf
plant seed
quantitative trait
fagus
forests
plant leaves
heritable
seeds
Mostra abstract
This paper explores which traits are correlated with fine-scale (0.25 m<sup>2</sup>) species persistence patterns in the herb layer of old-growth forests. Four old-growth beech forests representing different climatic contexts (presence or absence of summer drought period) were selected along a north–south gradient in Italy. Eight surveys were conducted in each of the sites during the period spanning 1999–2011. We found that fine-scale species persistence was correlated with different sets of plant functional traits, depending on local ecological context. Seed mass was found to be as important for the fine-scale species persistence in the northern sites, while clonal and bud-bank traits were markedly correlated with the southern sites characterised by summer drought. Leaf traits appeared to correlate with species persistence in the drier and wetter sites. However, we found that different attributes, i.e. helomorphic vs scleromorphic leaves, were correlated to species persistence in the northernmost and southernmost sites, respectively. These differences appear to be dependent on local trait adaptation rather than plant phylogenetic history. Our findings suggest that the persistent species in the old-growth forests might adopt an acquisitive resource-use strategy (i.e. helomorphic leaves with high SLA) with higher seed mass in sites without summer drought, while under water-stressed conditions persistent species have a conservative resource-use strategy (i.e. scleromorphic leaves with low SLA) with an increased importance of clonal and resprouting ability. © 2020, The Author(s).
Probabilistic sampling and estimation for large-scale assessment of poplar plantations in Northern Italy
Corona
,
P.
,
Chianucci
,
Francesco
,
Marcelli
,
Agnese
,
Gianelle
,
Damiano
,
Fattorini
,
Lorenzo
,
Grotti
,
Mirko
,
Puletti
,
Nicola
,
Mattioli
,
Walter
Mostra abstract
In the recent decades, growing demand for wood products, combined with efforts to conserve natural forests, has supported a steady increase in the global extent of planted forests. In this paper, a two-phase sampling strategy for large-scale assessment of hybrid poplar plantations in Northern Italy was implemented. The first phase was performed by means of tessellation stratified sampling on high-resolution remotely sensed imagery, covering the survey area by a grid of regular polygons of equal size and randomly and independently selecting one point per quadrat. All the plantations spotted by at least one sample point were selected. In the second phase, we randomly chosen a subset of plantations by stratified sampling that were visited on the ground to collect qualitative and quantitative attributes. The resulting estimates were reliable, and the survey demonstrated relatively easy to be implemented and replicated. These considerations support the use of the proposed sampling strategy to frequently update information on fast-growing forest plantations within agricultural farms, like hybrid poplar crops. Moreover, the results of the case study here presented highlight the relevance of hybrid poplar plantations in Italy, in the context of sustainable development strategies under a green economy perspective. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
Species dominance and above ground biomass in the Białowieża Forest, Poland, described by airborne hyperspectral and lidar data
Vaglio Laurin
,
Gaia
,
Puletti
,
Nicola
,
Grotti
,
Mirko
,
Stereńczak
,
Krzysztof Jan
,
Modzelewska
,
Aneta
,
Lisiewicz
,
Maciej
,
Sadkowski
,
Rafał
,
Kuberski
,
Łukasz
,
Chirici
,
Gherardo
,
Papale
,
Dario
climate change
lidar
aboveground biomass
algorithm
data set
deciduous tree
species diversity
species richness
vegetation dynamics
bialowieza forest
scolytinae
Mostra abstract
The objective of this research is to test and evaluate hyperspectral and lidar data to derive information on tree species dominance and above ground biomass in the Białowieża Forest in Poland. This forest is threatened by climate change, fire, bark beetles attacks, and logging, with changes in species composition and dominance. In this conservation valuable area, the monitoring of forest resources is thus critical. Results indicate that vegetation indices from hyperspectral data can support species dominance detection: using a Classification and Regression Trees algorithm the three main plot types (dominated by Deciduous, Spruce, and Pines species) were classified with an Overall Accuracy > 0.9. The accuracy decreased when a ‘Mixed’ group was added to account for very heterogeneous plots, and plots dominated by Spruce were not correctly detected. Hyperspectral vegetation indices were also used to estimate the level of species dominance in the forest plots, using a Multivariate Multiple Linear Regression model; the obtained accuracy varied according to groups, being higher for Deciduous (R<sup>2</sup> = 0.87), compared to Pines (R<sup>2</sup> = 0.61), and to Spruce-dominated plots (R<sup>2</sup> = 0.37). Lidar data were employed to estimate above ground biomass, using an exponential regression model; overall the R<sup>2</sup> resulted equal to 0.66 but ranged from 0.57 to 0.78 when considering subgroups according to species dominance; the addition of hyperspectral vegetation indices improved the result only for Pines. The illustrated methods provide a reliable description of important forest characteristics and simplify resource monitoring, supporting local authorities to address the challenges imposed by climate change and other forest threats. © 2020 The Authors
Lidar-based estimates of aboveground biomass through ground, aerial, and satellite observation: A case study in a Mediterranean forest
aboveground biomass
global ecosystem dynamics investigation mission
light detection and ranging
mobile terrestrial laser scanner
Mostra abstract
Light detection and ranging (Lidar) is considered the most advanced technology to assess forest aboveground biomass (AGB). Currently, this technology is shared by different sensors ranging from ground [terrestrial laser scanning (TLS)], airborne [aerial laser scanning (ALS)] up to spaceborne ones, which entail different spatial scales. However, few studies tested the simultaneous and combined use of Lidar to estimate AGB, linking ground measurements up to satellite observations. To fill this gap, we performed a study in two Mediterranean forest types [i.e., mountainous beech (Fagus sylvatica) and black pine (Pinus nigra subsp. laricio)] with contrasting structures (i.e., broadleaf versus needleleaf forests), where field inventory, TLS, ALS, and the recent spaceborne Global Ecosystem Dynamics Investigation (GEDI) data were simultaneously acquired. A three-step procedure was followed, which involved (i) the validation of AGB estimates obtained from TLS against reference values obtained from conventional field inventory; (ii) the calibration and validation of AGB estimates derived from ALS against TLS measurements, and (iii) the calibration and validation of AGB estimates derived from GEDI against mapped AGB values obtained from ALS. Our main results indicated that TLS provides consistent measurements of AGB as compared with field measurements (R2 ranged between 0.6 and 0.9 and root-mean-square error ranged between 29% and 49%), indicating its potential as ground reference for airborne Lidar observations. The combined availability of ground, airborne, and spaceborne observations is suitable to link ground measurements up to satellite observations. Differences in Lidar performance between needleleaf and broadleaf forests are also considered and discussed. © 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).
Assessment of UAV photogrammetric DTM-independent variables for modelling and mapping forest structural indices in mixed temperate forests
Giannetti
,
Francesca
,
Puletti
,
Nicola
,
Puliti
,
Stefano
,
Travaglini
,
Davide
,
Chirici
,
Gherardo
biodiversity
precision forestry
forest structure
forest inventory
airborne laser scanning
drone
dtm-independent
structure from motion
Mostra abstract
In the EU 2020 biodiversity strategy, maintaining and enhancing forest biodiversity is essential. Forest managers and technicians should include biodiversity monitoring as support for sustainible forest management and conservation issues, through the adoption of forest biodiversity indices. The present study investigates the potential of a new type of Structure from Motion (SfM) photogrammetry derived variables for modelling forest structure indicies, which do not require the availability of a digital terrain model (DTM) such as those obtainable from Airborne Laser Scanning (ALS) surveys. The DTM-independent variables were calculated using raw 3D UAV photogrammetric data for modeling eight forest structure indices which are commonly used for forest biodiversity monitoring, namely: basal area (G); quadratic mean diameter (DBH<inf>mean</inf>); the standard deviation of Diameter at Breast Height (DBH<inf>σ</inf>); DBH Gini coefficient (Gini); the standard deviation of tree heights (H<inf>σ</inf>); dominant tree height (H<inf>dom</inf>); Lorey's height (H<inf>l</inf>); and growing stock volume (V). The study included two mixed temperate forests areas with a different type of management, with one area, left unmanaged for the past 50 years while the other being actively managed. A total of 30 field sample plots were measured in the unmanaged forest, and 50 field plots were measured in the actively managed forest. The accuracy of UAV DTM-independent predictions was compared with a benchmark approach based on traditional explanatory variables calculated from ALS data. Finally, DTM-independent variables were used to produce wall-to-wall maps of the forest structure indices in the two test areas and to estimate the mean value and its uncertainty according to a model-assisted regression estimators. DTM-independent variables led to similar predictive accuracy in terms of root mean square error compared to ALS in both study areas for the eight structure indices (DTM-independent average RMSE<inf>%</inf> = 20.5 and ALS average RMSE<inf>%</inf> = 19.8). Moreover, we found that the model-assisted estimation, with both DTM-independet and ALS, obtained lower standar errors (SE) compared to the one obtained by model-based estimation using only field plots. Relative efficiency coefficient (RE) revealed that ALS-based estimates were, on average, more efficient (average RE ALS = 3.7) than DTM-independent, (average RE DTM-independent = 3.3). However, the RE for the DTM-independent models was consistently larger than the one from the ALS models for the DBH-related variables (i.e. G, DBH<inf>mean</inf>, and DBH<inf>σ</inf>) and for V. This highlights the potential of DTM-independent variables, which not only can be used virtually on any forests (i.e., no need of a DTM), but also can produce as precise estimates as those from ALS data for key forest structural variables and substantially improve the efficiency of forest inventories. © 2020 Elsevier Ltd
Global airborne laser scanning data providers database (GlobALS)-A new tool for monitoring ecosystems and biodiversity
Stereńczak
,
Krzysztof Jan
,
Vaglio Laurin
,
Gaia
,
Chirici
,
Gherardo
,
Coomes
,
David Anthony
,
Dalponte
,
Michele
,
Latifi
,
Hooman
,
Puletti
,
Nicola
Mostra abstract
Protection and recovery of natural resource and biodiversity requires accurate monitoring at multiple scales. Airborne Laser Scanning (ALS) provides high-resolution imagery that is valuable for monitoring structural changes to vegetation, providing a reliable reference for ecological analyses and comparison purposes, especially if used in conjunction with other remote-sensing and field products. However, the potential of ALS data has not been fully exploited, due to limits in data availability and validation. To bridge this gap, the global network for airborne laser scanner data (GlobALS) has been established as a worldwide network of ALS data providers that aims at linking those interested in research and applications related to natural resources and biodiversity monitoring. The network does not collect data itself but collects metadata and facilitates networking and collaborative research amongst the end-users and data providers. This letter describes this facility, with the aim of broadening participation in GlobALS. © 2020 by the authors.
An intensity, image-based method to estimate gap fraction, canopy openness and effective leaf area index from phase-shift terrestrial laser scanning
Grotti
,
Mirko
,
Calders
,
Kim
,
Origo
,
Niall
,
Puletti
,
Nicola
,
Alivernini
,
Alessandro
,
Ferrara
,
Carlotta
,
Chianucci
,
Francesco
Mostra abstract
Accurate in situ estimates of leaf area index (LAI) are essential for a wide range of ecological studies and applications. Due to the destructiveness and impracticality of direct measurements, indirect optical methods have mostly been used in the field to derive estimates of LAI from gap fraction measurements. Terrestrial laser scanning (TLS) is strongly supporting use of this active technology, which possesses several advantages compared to passive sensors. However, edge effects and partial beam interceptions are significantly challenges for the accurate retrieval of gap fraction from 3D point cloud data available from TLS, particularly in phase-shift instruments, which in turns require point cloud filtering to correct erroneous point measurements. As the limitations above influences the point cloud, we proposed a new method which is based only on the laser return intensity (LRI) information derived from raw TLS data, which are used to generate 2D intensity images. The intensity image contains all the unfiltered LRI information captured by TLS, which is used to separate gap from non-gap pixels, using a procedure comparable to the standard image analysis processing of digital hemispherical images. This allows a theoretically consistent comparison between active and passive optical measurements of gap fraction across all the zenith angle range. The method was tested in real and simulated forests. Gap fraction, canopy openness and effective leaf area index derived from real and simulated intensity TLS images were compared with those obtained using digital hemispherical photography (DHP). Results indicated that the intensity, image-based method outperformed DHP, as the higher pixel resolution of the intensity images and the larger distance covered by TLS allowed detection of many small canopy elements, particularly at higher zenith angles (longer optical distance), which are not detected in DHP. The main findings support the reliability of the intensity, image-based method to standardize protocols for TLS phase-shift scan data processing and use of the produced canopy estimates as a benchmark for passive optical measurements. © 2019 Elsevier B.V.
Large-scale two-phase estimation of wood production by poplar plantations exploiting sentinel-2 data as auxiliary information
Marcelli
,
Agnese
,
Mattioli
,
Walter
,
Puletti
,
Nicola
,
Chianucci
,
Francesco
,
Gianelle
,
Damiano
,
Grotti
,
Mirko
,
Chirici
,
Gherardo
,
D'Amico
,
Giovanni
,
Francini
,
Saverio
,
Travaglini
,
Davide
,
Fattorini
,
Lorenzo
,
Corona
,
P.
national forest inventories
regression estimator
sentinel-2
design-based inference
first-phase tessellation stratified sampling
second-phase stratified sampling
simulation study
Mostra abstract
Growing demand for wood products, combined with efforts to conserve natural forests, have supported a steady increase in the global extent of planted forests. Here, a two-phase sampling strategy for large-scale assessment of the total area and the total wood volume of fast-growing forest tree crops within agricultural land is presented. The first phase is performed using tessellation stratified sampling on high-resolution remotely sensed imagery and is sufficient for estimating the total area of plantations by means of a Monte Carlo integration estimator. The second phase is performed using stratified sampling of the plantations selected in the first phase and is aimed at estimating total wood volume by means of an approximation of the first-phase Horvitz-Thompson estimator. Vegetation indices from Sentinel-2 are exploited as freely available auxiliary information in a linear regression estimator to improve the design-based precision of the estimator based on the sole sample data. Estimators of the totals and of the design-based variances of total estimators are presented. A simulation study is developed in order to check the design-based performance of the two alternative estimators under several artificial distributions supposed for poplar plantations (random, clustered, spatially trended). An application in Northern Italy is also reported. The regression estimator turns out to be invariably better than that based on the sole sample information. Possible integrations of the proposed sampling scheme with conventional national forest inventories adopting tessellation stratified sampling in the first phase are discussed. © 2020, Finnish Society of Forest Science. All rights reserved.
Monitoring spring phenology in Mediterranean beech populations through in situ observation and Synthetic Aperture Radar methods
Proietti
,
R.
,
Antonucci
,
Serena
,
Monteverdi
,
Maria Cristina
,
Garfì
,
Vittorio
,
Marchetti
,
Marco
,
Plutino
,
Manuela
,
Di Carlo
,
Marco
,
Germani
,
Andrea
,
Santopuoli
,
Giovanni
,
Castaldi
,
Cristiano
,
Chiavetta
,
U.
Mostra abstract
The interest in tree phenology monitoring is increasing because this trait is a robust indicator of the impacts of climate change on natural and managed ecosystems. Different approaches to monitor phenology at different spatial scales, from in situ monitoring to remote sensing, are used to investigate spring and/or autumn phenological changes. In Mediterranean area, most of phenological changes occur during cloudy periods (spring and autumn), leading to a loss of information also for very high temporal resolution satellites. Instead, cloud-uninfluenced sensors, such as radar sensors, can allow to bypass this problem and produce a temporally continuous coverage. In this paper, we analyzed the spring phenology of two European beech (Fagus sylvatica L.) populations, located at different latitudes in Mediterranean area. Weekly in situ monitoring of leaf-out has been correlated with data collected by Synthetic Aperture Radar. Spring phenological phases were monitored in situ following a modified BBCH-code with a 5-scores scale (from 1 - buds closed and covered by scales, to 5 - leaf completely unfolded). The score 3 (young leaves starting to emerge from the bud) was considered the bud break. Different site conditions based on aspect (northern and southern) and altitudinal gradient (high and low altitude) have been considered. The aim was to test and implement a new methodology able to decrease the frequency of the field sampling, using remote data, to extend more detailed information on geographical scale, and to reconstruct past phenology. Results showed a statistically significant different length of the vegetative spring period, spanning from dormant buds, up to leaves completely unfolded, between sites. Through Synthetic Aperture Radar estimation, this study demonstrates that leaf-out can be monitored with an extreme accuracy. The phenophase score 4 and 5 estimation showed the best performance (RMSE < of 4 days), phenophases score 2 and 3 showed promising performances (4 days < RMSE <5 days), while phenophases score 1 seems to be not easily detectable, although it can be extrapolated with an RMSE <6 days. This radar approach fixes the cloud problem typical of multispectral approach and very frequent in phenophase change periods in Mediterranean climate. This study promotes the proposed remote sensing approach as a very useful tool to monitor growing season starting in remote areas, helping to reduce in situ observations and allowing past phenology reconstruction. © 2020 Elsevier Inc.
Individual tree crown segmentation in two-layered dense mixed forests from uav lidar data
Torresan
,
C.
,
Carotenuto
,
Federico
,
Chiavetta
,
U.
,
Miglietta
,
F.
,
Zaldei
,
Alessandro
,
Gioli
,
Beniamino
forest inventory
detection rate
itc detection algorithms
itcsegment package
laser scanning
lidr package
parameter calibration
Mostra abstract
In forests with dense mixed canopies, laser scanning is often the only effective technique to acquire forest inventory attributes, rather than structure-from-motion optical methods. This study investigates the potential of laser scanner data collected with a low-cost unmanned aerial vehicle laser scanner (UAV-LS), for individual tree crown (ITC) delineation to derive forest biometric parameters, over two-layered dense mixed forest stands in central Italy. A raster-based local maxima region growing algorithm (itcLiDAR) and a point cloud-based algorithm (li2012) were applied to isolate individual tree crowns, compute height and crown area, estimate the diameter at breast height (DBH) and the above ground biomass (AGB) of individual trees. To maximize the level of detection rate, the ITC algorithm parameters were tuned varying 1350 setting combinations and matching the segmented trees with field measured trees. For each setting, the delineation accuracy was assessed by computing the detection rate, the omission and commission errors over three forest plots. Segmentation using itcLiDAR showed detection rates between 40% and 57%, while ITC delineation was successful at segmenting trees with DBH larger than 10 cm (detection rate ~78%), while failed to detect trees with smaller DBH (detection rate ~37%). The performance of li2012 was quite lower with the higher detection rate equal to 27%. Errors and goodness-of-fit between field-surveyed and flight-derived biometric parameters (AGB and tree height) were species-dependent, with higher error and lower r<sup>2</sup> for shorter species that constitute the lowermost layer of the forest. Overall, while the application of UAV-LS to delineate tree crowns and estimate biometric parameters is satisfactory, its accuracy is affected by the presence of a multilayered and multispecies canopy that will require specific approaches and algorithms to better deal with the added complexity. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Testing Removal of Carbon Dioxide, Ozone, and Atmospheric Particles by Urban Parks in Italy
Fares
,
Silvano
,
Conte
,
Adriano
,
Alivernini
,
Alessandro
,
Chianucci
,
Francesco
,
Grotti
,
Mirko
,
Zappitelli
,
Ilaria
,
Petrella
,
Fabio
,
Corona
,
P.
italy
forestry
carbon dioxide
carbon dioxide process
ecosystems
gas emissions
greenhouse gases
ozone
particles (particulate matter)
atmospheric concentration
atmospheric particles
ecosystem services
in-situ measurement
multilayer canopy model
particulate matter
tree characteristics
tropospheric ozone
air pollution
aerosol
greenspace
pollutant removal
testing method
urban area
air quality
article
canopy
dry deposition
particulate matter 10
recreational park
tree
air pollutant
city
ecosystem
air pollutants
cities
parks
recreational
trees
Mostra abstract
Cities are responsible for more than 80% of global greenhouse gas emissions. Sequestration of air pollutants is one of the main ecosystem services that urban forests provide to the citizens. The atmospheric concentration of several pollutants such as carbon dioxide (CO2), tropospheric ozone (O3), and particulate matter (PM) can be reduced by urban trees through processes of adsorption and deposition. We predict the quantity of CO2, O3, and PM removed by urban tree species with the multilayer canopy model AIRTREE in two representative urban parks in Italy: Park of Castel di Guido, a 3673 ha reforested area located northwest of Rome, and Park of Valentino, a 42 ha urban park in downtown Turin. We estimated a total annual removal of 1005 and 500 kg of carbon per hectare, 8.1 and 1.42 kg of ozone per hectare, and 8.4 and 8 kg of PM10 per hectare. We highlighted differences in pollutant sequestration between urban areas and between species, shedding light on the importance to perform extensive in situ measurements and modeling analysis of tree characteristics to provide realistic estimates of urban parks to deliver ecosystem services. ©
Epiphytic lichen diversity and sustainable forest management criteria and indicators: A multivariate and modelling approach in coppice forests of Italy
Brunialti
,
Giorgio
,
Frati
,
Luisa
,
Calderisi
,
Marco
,
Giorgolo
,
Francesca
,
Bagella
,
Simonetta
,
Bertini
,
Giada
,
Chianucci
,
Francesco
,
Fratini
,
Roberto
,
Gottardini
,
Elena
,
Cutini
,
Andrea
Mostra abstract
Epiphytic lichens represent one of the most suitable indicators of forest continuity and management, especially in the context of ancient and old-growth forests. Nevertheless, they have not yet been included among Sustainable Forest Management (SFM) indicators to which Pan-European forest policy and governance refer. In addition, currently adopted SFM indicators are mainly designed for high forests rather than coppice forests, despite the fact that today this management system covers more than 10% of the total European forests. In this study we investigated these two issues by examining epiphytic lichen diversity in three coppice forest stands, located in the two Italian regions of Tuscany and Sardinia. In particular, we addressed: i) the role of lichen diversity as SFM indicator and ii) its relationship with consolidated and new SFM indicators dealing with structural, health, biodiversity, protective and socioeconomic functions. Multivariate Factor Analysis and Generalised Linear Models were adopted for data analysis. We found that lichen diversity and the frequency of single sensitive species were mainly related to the biodiversity of plants and fungi (Criterion 4), the health and vitality of the forests (Criterion 2) and their protective functions (Criterion 5). Furthermore, our results show that the lichen species highlighted by the models may represent suitable indicators in long-term studies, especially in relation to complex and interconnected aspects of sustainable forest management. Although our findings represent a first contribute to this issue, more in-depth researches will be needed to clarify further aspects of the complex interactions among SFM indicators in the context of coppice forests. © 2020 Elsevier Ltd
An overview of in situ digital canopy photography in forestry
canopy cover
leaf area index
canopy photography
canopy openness
hemispherical sensors
restricted view angle sensors
Mostra abstract
Since the 1960s, canopy photography has been widely used in forestry. Hemispherical photography has been the most widely used technique, but a great drawback of this method is its perceived sensitivity to hemispherical image acquisition and processing. Over the last decade, several alternative photographic approaches using restricted view angle have been proposed. Cover photography acquired via a normal lens was the first of the recently introduced photographic techniques. Use of a restricted view (often fixed) lens has subsequently contributed to the extension of canopy photography to new sensors and platforms, which ultimately have provided answers to some previous challenges regarding within-crown clumping correction, isolated and urban tree measurements, understory assessment, operational leaf inclination angle measurements, and phenological monitoring. This study provides a comprehensive review of the use of canopy photography in forestry and describes the theory and definitions of the variables used to quantify canopy structure. A case study is presented to illustrate and compare the different features and performance of the existing overstory photographic techniques; the results make it possible to suggest sampling strategies for consistent overstory canopy photographic measurements. Emerging operational fields of canopy photography are also described and discussed. © 2020, Canadian Science Publishing. All rights reserved.
TRY plant trait database – enhanced coverage and open access
Kattge
,
Jens
,
Bönisch
,
Gerhard
,
Díaz
,
Sandra M.
,
Lavorel
,
Sandra
,
Prentice
,
Iain Colin
,
Leadley
,
Paul W.
,
Tautenhahn
,
Susanne
,
Werner
,
Gijsbert
,
Aakala
,
Tuomas
,
Abedi
,
Mehdi
,
Acosta
,
Alicia Teresa Rosario
,
Adamidis
,
George C.
,
Adamson
,
Kairi
,
Aiba
,
Masahiro
,
Albert
,
Cécile Hélène
,
Alcántara
,
Julio M.
,
Alcázar C
,
Carolina
,
Aleixo
,
Izabela
,
Ali
,
Hamada E.
,
Amiaud
,
Bernard
,
Ammer
,
Christian
,
Amoroso
,
Mariano Martín
,
Anand
,
Madhur
,
Anderson
,
Carolyn G.
,
Anten
,
Niels P.R.
,
Antos
,
Joseph A.
,
Apgaua
,
Deborah Mattos Guimarães
,
Ashman
,
Tia Lynn
,
Asmara
,
Degi Harja
,
Asner
,
Gregory P.
,
Aspinwall
,
Michael J.
,
Atkin
,
Owen K.
,
Aubin
,
Isabelle
,
Baastrup-Spohr
,
Lars
,
Bahalkeh
,
Khadijeh
,
Bahn
,
Michael
,
Baker
,
Timothy R.
,
Baker
,
William J.
,
Bakker
,
Jan P.
,
Baldocchi
,
Dennis D.
,
Baltzer
,
Jennifer L.
,
Banerjee
,
Arindam
,
Baranger
,
Anne
,
Barlow
,
Jos B.
,
Barneche
,
Diego R.
,
Baruch
,
Zdravko
,
Bastianelli
,
Denis
,
Battles
,
John J.
,
Bauerle
,
William L.
,
Bauters
,
Marijn
,
Bazzato
,
Erika
,
Beckmann
,
Michael
,
Beeckman
,
Hans
,
Beierkuhnlein
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Carl
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Bekker
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Renée M.
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Belfry
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Gavin
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Belluau
,
Michaël
,
Beloiu Schwenke
,
Mirela
,
Benavides
,
Raquel
,
Benomar
,
Lahcen
,
Berdugo-Lattke
,
Mary Lee
,
Berenguer
,
Erika
,
Bergamin
,
Rodrigo Scarton
,
Bergmann
,
Joana
,
Carlucci
,
Marcos B.
,
Berner
,
Logan T.
,
Bernhardt-Römermann
,
Markus
,
Bigler
,
Christof
,
Bjorkman
,
Anne D.
,
Blackman
,
Chris J.
,
Blanco
,
Carolina Casagrande
,
Blonder
,
Benjamin Wong
,
Blumenthal
,
Dana M.
,
Bocanegra-González
,
Kelly Tatiana
,
Boeckx
,
Pascal
,
Bohlman
,
Stephanie Ann
,
Böhning-Gaese
,
Katrin
,
Boisvert-Marsh
,
Laura
,
Bond
,
William J.
,
Bond-Lamberty
,
Ben P.
,
Boom
,
Arnoud
,
Boonman
,
Coline C.F.
,
Bordin
,
Kauane Maiara
,
Boughton
,
Elizabeth H.
,
Boukili
,
Vanessa K.S.
,
Bowman
,
David M.J.S.
,
Bravo
,
Sandra Josefina
,
Brendel
,
Marco R.
,
Broadley
,
Martin R.
,
Brown
,
Kerry A.
,
Bruelheide
,
Helge
,
Brumnich
,
Federico
,
Bruun
,
Hans Henrik
,
Bruy
,
David
,
Buchanan
,
Serra Willow
,
Bucher
,
Solveig Franziska
,
Buchmann
,
Nina
,
Buitenwerf
,
Robert
,
Bunker
,
Daniel E.
,
Bürger
,
Jana
functional diversity
data coverage
data integration
data representativeness
plant traits
try plant trait database
Mostra abstract
Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives. © 2019 The Authors. Global Change Biology published by John Wiley & Sons Ltd