Pubblicazioni Scientifiche

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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. ©
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2020
Rivista
Environmental Science and Technology
Dettagli
Vol. 54 (23) pp. 14910 - 14922
DOI
Repeated mapped tree inventory in an oak-hornbeam planted forest in Po Valley (Foresta Carpaneta, Italy)
Minari , Emma , Toni , Ilaria , Nastasio , Paolo , Chianucci , Francesco
lowland artificial forest quercus robur l tree level forest inventory
Mostra abstract
Stand structure and tree spatial patterns are key elements to understand natural dynamics and competition processes in forest ecosystems. We performed repeated, mapped tree inventory measures (x, y, height, diameter, vitality, etc.) to allow analysis of the spatial and temporal structure and diversity in 1 ha oak-hornbeam planted forest with pedunculate oak (Quercus robur L.), established in 2003 also for the conservation of a variety of oak genotypes. Two inventories were carried out in 2009 and 2019. The use of repeated and mapped tree measures allows to investigate the changes in spatial pattern processes through time in this forest. © 2024 Istituto Sperimentale per la Selvicoltura. All rights reserved.
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2024
Rivista
Annals of Silvicultural Research
Dettagli
Vol. 49 (1) pp. 24 - 27
DOI
IN SITU (TREE TALKER) AND REMOTELY-SENSED MULTISPECTRAL IMAGERY (SENTINEL-2) INTEGRATION FOR CONTINUOUS FOREST MONITORING: THE FIRST STEP TOWARD WALL-TO-WALL MAPPING OF TREE FUNCTIONAL TRAITS
Francini , Saverio , Zorzi , Ilaria , Giannetti , Francesca , Chianucci , Francesco , Travaglini , Davide , Chirici , Gherardo , Cocozza , C.
remote sensing deep learning modelling sensors tree talker tree-functional traits
Mostra abstract
Monitoring tree functional traits is essential for understanding forest ecosystems' capability to respond to climate change. Advancements in continuous proximal sensors and IoT technologies hold great potential for monitoring forest and tree ecosystem processes at the finest spatial and temporal scale. An example is the TreeTalker (TT) technology, which features sensors for measurements of the radial growth, sap flow, multispectral light transmission, air temperature, and humidity at tree level with an hourly frequency rate. Such information can be linked with remote sensing data acquired by the Sentinel-2 (S2) mission, allowing for scaling results over more spatially extensive areas. Firstly, we compared six TT with four S2 spectral bands with similar wavelengths. No correlation was found for blue, green and red channels (R<sup>2</sup> ranged between 0.04 and 0.09) while higher values were found for the near-infrared channel (R<sup>2</sup> = 0.9). To obtain an accurate prediction of TTs bands, also for those TTs bands which wavelengths are not similar to that of S2 bands, we implemented a Sentinel-2 to TreeTalker model (S2TT) by using an 8-layers fully connected deep neural network. The model was tested by using 23 Sentinel-2 imagery and data acquired by 40 TreeTalkers located in two different sites in Tuscany (a beech and a silver fir forest stand) in the period between 2020-07-15 and 2020-11-15. The R<sup>2</sup> ranged between 0.61 (B7, blue) and 0.96 (B6, near-infrared band). The S2TT model represents the first link between remote sensing and TreeTalkers, which might allow predicting tree functional traits using Sentinel-2 imagery. © 2021, Italian Society of Remote Sensing. All rights reserved.
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2021
Rivista
Trends in Earth Observation
Dettagli
Vol. 2 pp. 108 - 111