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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
forest inventory hybrid poplar populus spp tessellation stratified sampling two-phase sampling
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.
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2020
Rivista
European Journal of Forest Research
Dettagli
Vol. 139 (6) pp. 981 - 988
DOI
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.
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2020
Rivista
Silva Fennica
Dettagli
Vol. 54 (2)
DOI
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