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Auteur Marco Marchetti |
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Inference on forest attributes and ecological diversity of trees outside forest by a two-phase inventory / Marco Marchetti in Annals of Forest Science, vol 75 n° 2 (June 2018)
[article]
Titre : Inference on forest attributes and ecological diversity of trees outside forest by a two-phase inventory Type de document : Article/Communication Auteurs : Marco Marchetti, Auteur ; Vittorio Garfì, Auteur ; Caterina Pisani, Auteur ; Sara Franceschi, Auteur ; et al., Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] arbre hors forêt
[Termes IGN] biodiversité végétale
[Termes IGN] données de terrain
[Termes IGN] données dendrométriques
[Termes IGN] écosystème
[Termes IGN] inférence statistique
[Termes IGN] inventaire de la végétation
[Termes IGN] Molise (Italie)
[Termes IGN] puits de carbone
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (Auteur) Key message: Trees outside forests (TOF) have crucial ecological and social-economic roles in rural and urban contexts around the world. We demonstrate that a large-scale estimation strategy, based on a two-phase inventory approach, effectively supports the assessment of TOF’s diversity and related climate change mitigation potential.
Context: Although trees outside forest (TOF) affect the ecological quality and contribute to increase the social and economic developments at various scales, lack of data and difficulties to harmonize the known information currently limit their integration into national and global forest inventories.
Aims: This study aims to develop and test a large-scale estimation framework to assess ecological diversity and above-ground carbon stock of TOF.
Methods: This study adopts a two-phase inventory approach.
Results: In the surveyed territory (Molise region, Central Italy), all the attributes considered (tree abundance, basal area, wood volume, above-ground carbon stock) are concentrated in a few dominant species. Furthermore, carbon stock in TOF above-ground biomass is non-negligible (on average: 28.6 t ha−1). Compared with the low field sampling effort (0.08% out of 52,796 TOF elements), resulting uncertainty of the estimators are more than satisfactory, especially those regarding the diversity index estimators (relative standard errors Conclusion: The proposed approach can be suitably applied on vast territories to support landscape planning and maximize ecosystem services balance from TOF.Numéro de notice : A2018-326 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-018-0718-6 Date de publication en ligne : 16/03/2018 En ligne : https://doi.org/10.1007/s13595-018-0718-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90469
in Annals of Forest Science > vol 75 n° 2 (June 2018)[article]Statistical inference for forest structural diversity indices using airborne laser scanning data and the k-Nearest Neighbors technique / Matteo Mura in Remote sensing of environment, vol 186 (1 December 2016)
[article]
Titre : Statistical inference for forest structural diversity indices using airborne laser scanning data and the k-Nearest Neighbors technique Type de document : Article/Communication Auteurs : Matteo Mura, Auteur ; Ronald E. McRoberts, Auteur ; Gherardo Chirici, Auteur ; Marco Marchetti, Auteur Année de publication : 2016 Article en page(s) : pp 678 - 686 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification barycentrique
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] indice de diversité
[Termes IGN] inférence statistique
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Italie
[Termes IGN] optimisation (mathématiques)
[Termes IGN] structure d'un peuplement forestierRésumé : (auteur) Forest structural diversity plays a major role for forest management, conservation and restoration and is recognized as a fundamental aspect of forest biodiversity. The assessment, maintenance and restoration of a diversified forest structure have become major foci in the effort to preserve forest ecosystems from loss of biological diversity. However, the assessment of forest biodiversity is difficult because it involves multiple components and is characterized using multiple variables. The objective of the study was to develop a methodological approach for predicting, mapping, and constructing a statistical inference for a multiple-variable index of forest structural diversity. The method included three key components: (i) use of the k-Nearest Neighbors (k-NN) technique, field plot data, and airborne laser scanning metrics to predict multiple forest structural diversity variables simultaneously, (ii) incorporation of multiple diversity variable predictions into a single index, and (iii) construction of a statistically rigorous inference for the population mean of the index. Three structural diversity variables were selected to illustrate the method: growing stock volume and the standard deviations of tree diameter at breast-height and tree height. Optimization of the k-NN technique produced mean relative deviations less in absolute value than 0.04 for predictions for each of the three structural diversity variables, R2 values between 0.50 and 0.66 which were in the range of values reported in the literature, and a confidence interval for the population mean of the index whose half-width was approximately 5% of the mean. Finally, the spatial pattern depicted in the resulting map of forest structural diversity for the study area contributed to validating the proposed method. Numéro de notice : A2016-769 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2016.09.010 En ligne : http://dx.doi.org/10.1016/j.rse.2016.09.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82419
in Remote sensing of environment > vol 186 (1 December 2016) . - pp 678 - 686[article]Testing the applicability of BIOME-BGC to simulate beech gross primary production in Europe using a new continental weather dataset / Marta Chiesi in Annals of Forest Science, vol 73 n° 3 (September 2016)
[article]
Titre : Testing the applicability of BIOME-BGC to simulate beech gross primary production in Europe using a new continental weather dataset Type de document : Article/Communication Auteurs : Marta Chiesi, Auteur ; Gherardo Chirici, Auteur ; Marco Marchetti, Auteur Année de publication : 2016 Article en page(s) : pp 713 – 727 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biome
[Termes IGN] données météorologiques
[Termes IGN] Fagus (genre)
[Termes IGN] production primaire brute
[Termes IGN] teneur en carbone
[Termes IGN] teneur en eau liquide
[Termes IGN] variation saisonnière
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Key message : A daily 1-km Pan-European weather dataset can drive the BIOME-BGC model for the estimation of current and future beech gross primary production (GPP). Annual beech GPP is affected primarily by spring temperature and more irregularly by summer water stress.
Context : The spread of beech forests in Europe enhances the importance of modelling and monitoring their growth in view of ongoing climate changes.
Aims : The current paper assesses the capability of a biogeochemical model to simulate beech gross primary production (GPP) using a Pan-European 1-km weather dataset.
Methods : The model BIOME-BGC is applied in four European forest ecosystems having different climatic conditions where the eddy covariance technique is used to measure water and carbon fluxes. The experiment is in three main steps. First, the accuracy of BIOME-BGC GPP simulations is assessed through comparison with flux observations. Second, the influence of two major meteorological drivers (spring minimum temperature and growing season dryness) on observed and simulated inter-annual GPP variations is analysed. Lastly, the impacts of two climate change scenarios on beech GPP are evaluated through statistical analyses of the ground data and model simulations.
Results : The weather dataset can drive BIOME-BGC to simulate most of the beech GPP evolution in all four test areas. Both observed and simulated inter-annual GPP variations are mainly dependent on minimum temperature around the beginning of the growing season, while spring/summer dryness exerts a secondary role. BIOME-BGC can also reasonably predict the impacts of the examined climate change scenarios.
Conclusion : The proposed modelling approach is capable of approximately reproducing spatial and temporal beech GPP variations and impacts of expected climate changes in the examined European sites.Numéro de notice : A2016-713 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s13595-016-0560-7 Date de publication en ligne : 07/06/2016 En ligne : https://doi.org/10.1007/s13595-016-0560-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82091
in Annals of Forest Science > vol 73 n° 3 (September 2016) . - pp 713 – 727[article]